problems-with-self-verification.txt

---
title: "Problems with Self-verification"
author: "unknown"
tags: ["library"]
type: "essay"
---
{% raw %}

Problems with Self-verification

Open sidebar

Problems with Self-verification

Model: Default (GPT-3.5)

Summarize: AARON Z. ZIMMERMAN

SELF-VERIFICATION AND THE CONTENT OF THOUGHT1

Descartes famously argued, on purely conceptual grounds, that even

an extremely powerful being could not trick him into mistakenly

judging that he was thinking. Of course, it is not necessarily true that

Descartes is thinking. Still, Descartes claimed, it is necessarily true

that if a person judges that she is thinking, that person is thinking.

Following Tyler B?rge (1988) we call such judgments 'self-verifying.'

More exactly, a judgment j performed by a subject S at a time t is self

verifying if and only if the fact that S has made j at t entails that j is

true at t? B?rge follows Descartes in claiming that the category of

conceptually self-verifying judgments includes (but is not restricted

to) judgments that give rise to sincere assertions of sentences of the

form, T am thinking that p\ We call such judgments 'cogito' judg

ments.

In this paper I argue that Burge's Cartesian insight is hard to

reconcile with Fregean accounts of the content of thought. Theorists

have tried to account for the self-verifying status of cogito judgments

by arguing that the second-order judgment that one is thinking that p

contains the thought that p as a part of it. I argue (?1) that the

accuracy of the containment model is entailed by a Russellian view of

content (according to which "belief contexts are both extensional

and transparent) when the Russellian view is attached to some fairly

uncontroversial assumptions. The accuracy of the containment model

is also entailed by non-hierarchical Fregean views of content

according to which expressions in oblique contexts both denote and

express first-level senses (?2), but there are compelling arguments

against non-hierarchical Fregean views (?3). Moreover, the extremely

plausible account of self-verification provided by Russellianism is not

entailed by theories that follow Frege in accepting a hierarchy of

senses and so allow that expressions in oblique contexts express

senses that are not identical to the senses they there denote (?4). If

these arguments are sound, Fregeans must accept the hierarchy of

senses, and to accept the hierarchy they must substantially augment

their view to account for self-verification. I go on to explain how a

Fodorian account of senses might try to take up this challenge, but I

Synthese (2006) 149: 59-75

DOI 10.1007/sll229-004-6247-x

? Springer 2006

This content downloaded from

????????????132.174.252.114 on Sun, 26 Mar 2023 02:05:52 UTC????????????

All use subject to https://about.jstor.org/terms

60 AARON Z. ZIMMERMAN

show that the Fodorian solution must posit conceptually necessary

connections between distinct concreta (?5). Finally (?6) I consider

Alonzo Church's (1974) description of 'standard' higher-level senses

and show why some notion of this kind must be invoked by the

Fregean to account for self-verification. But the mere existence of

standard higher-level senses is not up to the task, even if we follow

Church in positing logical relations between standard senses and the

lower-level senses they determine. Fregeans must go further than

Church and claim that standard higher-level senses have a particular

composite structure. It seems, then, that Burge's intuitively show that the Fodorian solution must posit conceptually necessary

connections between distinct concreta (?5). Finally (?6) I consider

Alonzo Church's (1974) description of 'standard' higher-level senses

and show why some notion of this kind must be invoked by the

Fregean to account for self-verification. But the mere existence of

standard higher-level senses is not up to the task, even if we follow

Church in positing logical relations between standard senses and the

lower-level senses they determine. Fregeans must go further than

Church and claim that standard higher-level senses have a particular

composite structure. It seems, then, that Burge's intuitively compel

ling claim that cogito judgments are conceptually self-verifying poses

a real challenge to neo-Fregean theories of content.

1 / 1

ChatGPT

ChatGPT

In this paper, Aaron Z. Zimmerman discusses the concept of self-verifying judgments, specifically focusing on cogito judgments that involve the thought "I am thinking that p." Zimmerman argues against Fregean accounts of the content of thought in reconciling with the self-verifying status of cogito judgments. He examines the containment model, which suggests that the judgment that one is thinking that p contains the thought that p as a part of it. Zimmerman contends that this model is supported by Russellian views of content and non-hierarchical Fregean views, but there are compelling arguments against the latter. He also explains that the Russellian account of self-verification is not entailed by theories that accept a hierarchy of senses. Zimmerman explores the possibility of a Fodorian account of senses to address the challenge, but notes that it requires positing conceptually necessary connections between distinct concreta. Additionally, he considers Alonzo Church's description of "standard" higher-level senses but asserts that the mere existence of these senses is insufficient to account for self-verification. Fregeans must claim that standard higher-level senses have a particular composite structure to adequately address the challenge posed by self-verifying judgments. Overall, Zimmerman suggests that Burge's claim regarding the self-verifying nature of cogito judgments presents a significant challenge to neo-Fregean theories of content.

1 / 1

2. AN ATTEMPT AT A FREGEAN ACCOUNT OF SELF-VERIFICATION

How might a Fregean argue for the self-verifying status of a sub

ject's judgment that she is thinking that pi On the Fregean view,

the first-order thought that John is nice is a relation between a

subject and a proposition where the proposition is constituted by a

sense or mode of presentation (MOP) determining John and an

MOP determining niceness. Let a range over natural language

expressions, and let '{a}' denote the sense of a. We then represent

the purportedly Fregean content of the thought that John is nice as

({John}, {niceness}), where '{John}' denotes the MOP expressed

by the English expression 'John', '{niceness}' denotes the MOP

expressed by 'nice', and '({John}, {niceness})' denotes the propo

sition that John is nice.5 According to the Fregean, then, in

thinking that John is nice, S is related by thought to ({John},

{niceness}).

What, though, is the content of S"s introspective judgment that

she is thinking that John is nice? If we accept premise (4) then 5"s

second-order judgment that she is thinking that John is nice is in part

constituted out of the very MOPs that constitute her first-order

thought: {John} and {niceness}. We might take this to suggest that

5"s higher-order judgment is not constituted by a second-level MOP

determining the first-level MOP expressed by 'John' (nor an MOP

determining the MOP expressed by 'nice'). The content of 5"s judg

ment that she is thinking that John is nice would then be ({/},

{thinking}, ({John}, {niceness})). If premise (1) is right, then to judge

true ({/}, {thinking}, ({John}, {niceness})), S must consider or

entertain ({/}, {thinking}, ({John}, {niceness})). If premise (2) is

correct, then in considering ({/}, {thinking}, ({John}, {niceness})), S

must consider its constituents, one of which is ({John}, {niceness}).

Finally, if premise (3) is correct, in considering ({John}, {niceness}), S

is thinking ({John}, {niceness}). Thus, if S judges that she is thinking

that John is nice, she is thinking that John is nice.

Christopher Peacocke (1999) explicitly adopts this account of self

verification. He accepts a Fregean account of content, and his

'redeployment claim' states that when an expression occurs in an

embedded context it denotes the very MOP it there expresses:

1 / 1

ChatGPT

ChatGPT

A Fregean account of self-verification involves examining the content of a subject's judgment that they are thinking that p. According to this view, the first-order thought, such as "John is nice," involves a relation between a subject and a proposition. The proposition is constituted by senses or modes of presentation (MOP) that determine the individual referred to (e.g., John) and the property expressed (e.g., niceness). Representing the content of the thought as ({John}, {niceness}), where '{John}' and '{niceness}' denote the respective MOPs, captures the Fregean perspective.

However, the challenge arises in determining the content of the subject's introspective judgment that they are thinking that John is nice. If we accept the premise that the second-order judgment is partly constituted by the very MOPs that constitute the first-order thought ({John}, {niceness}), then the content of the higher-order judgment would be ({/}, {thinking}, ({John}, {niceness})). According to premises (1), (2), and (3), if the subject judges this higher-order proposition as true, they must consider or entertain it, including its constituents. Therefore, if the subject judges that they are thinking that John is nice, they are indeed thinking that John is nice.

Christopher Peacocke, in his work, endorses this account of self-verification and explicitly adopts the Fregean perspective on content. His "redeployment claim" asserts that when an expression occurs in an embedded context, it denotes the same MOP it expresses in that context.

1 / 1

theory of content entails that cogito

judgments are self-verifying. The Fregean's explanatory position is

drastically different. Because the Fregean must accept a hierarchy of

senses, her theory is incompatible with (4), and in the absence of

augmentation the Fregean theory does not entail (4'). Indeed, unless

she explicitly adds Church's theorem and posits standard higher-level

senses with the requisite internal structure there is nothing about the

Fregean's theory of content that even argues in favor of (4').

7. CONCLUSION

It seems then that the conceptually self-verifying nature of cogito

judgments places serious constraints on our theory of thought

content. Indeed, the tension goes beyond the writings of those who

mistakenly endorse a non-hierarchical view of MOPs. The 'con

tainment model' of the relation between first- and second-order

This content downloaded from

????????????132.174.252.114 on Sun, 26 Mar 2023 02:05:52 UTC????????????

All use subject to https://about.jstor.org/terms

72 AARON Z. ZIMMERMAN

content has become a standard reply to arguments for the incom

patibility of content externalism and privileged introspective ac

cess,17 while Frege-inspired accounts of thought-content remain

extremely popular. Insofar, as the arguments presented here are

cogent, these two positions are difficult to reconcile. To retain the

Cartesian (conceptual) explanation for why a thinker cannot judge

falsely that she is thinking a particular thought, the Fregean must

claim that only some of the (potentially infinite) higher-level MOPs

that determine a single lower-level MOP are standard. And she

must move beyond current formulations of Fregeanism to explain

why the fact that one is grasping a standard higher-level MOP

entails that one is concurrently grasping the lower-level MOP for

which it is standard.

Of course, the Fregean who rejects (4') might nevertheless posit an

intimate (perhaps causal) relation between the content of the thought

that p and the content of the introspective judgment that one is

thinking that/?. But if neither (4) nor (4') is correct, cogito judgments

are not conceptually self-verifying.

1 / 1

ChatGPT

ChatGPT

In conclusion, the self-verifying nature of cogito judgments poses significant constraints on our theory of thought content. The Fregean theory of content, with its hierarchical view of senses, is incompatible with the claim that the second-order judgment is constituted by the same MOPs as the first-order thought. Without explicit augmentation, the Fregean theory does not entail the self-verifying status of cogito judgments. This tension extends beyond those who mistakenly endorse a non-hierarchical view of MOPs. The containment model, which is often used to reconcile content externalism and introspective access, and Frege-inspired accounts of thought content are difficult to reconcile in light of the arguments presented. To maintain the Cartesian explanation for why a thinker cannot falsely judge their own thinking, the Fregean must argue that only some higher-level MOPs are standard and provide an explanation for why grasping a standard higher-level MOP entails grasping the corresponding lower-level MOP. Without accepting certain premises, cogito judgments are not conceptually self-verifying.

1 / 1

Ok simplify it more

1 / 1

ChatGPT

ChatGPT

The self-verifying nature of cogito judgments, where a person judges that they are thinking something, poses challenges for theories of thought content. The Fregean theory, which involves a hierarchy of senses, is incompatible with the idea that the higher-order judgment includes the same senses as the lower-order thought. Without additional adjustments, the Fregean theory doesn't support the self-verifying status of cogito judgments. This creates tension between the popular containment model and Frege-inspired accounts of thought content. To maintain the Cartesian explanation that a thinker cannot falsely judge their own thinking, the Fregean must argue that only certain higher-level senses are standard and explain why grasping a standard higher-level sense also entails grasping the corresponding lower-level sense. Without accepting specific premises, cogito judgments are not conceptually self-verifying.

1 / 1

What does that mean?

1 / 1

ChatGPT

ChatGPT

In simpler terms, the concept of self-verifying judgments, particularly cogito judgments where a person judges that they are thinking something, presents a challenge for theories about the content of our thoughts. One popular theory, called the Fregean theory, suggests that thoughts consist of different levels of meanings or senses. However, this theory clashes with the idea that higher-order judgments include the same senses as lower-order thoughts. Without making some adjustments, the Fregean theory cannot explain the self-verifying nature of cogito judgments. This creates a conflict between the widely accepted containment model and theories inspired by Frege. To uphold the idea that a thinker cannot mistakenly judge their own thinking, Fregean theorists must argue that only specific higher-level senses are considered standard and provide an explanation for how grasping a standard higher-level sense implies grasping the corresponding lower-level sense. Without accepting certain ideas, cogito judgments are not inherently self-verifying.

1 / 1

5 I here make the (surely false) simplifying assumption that 'John' has a single

customary MOP. This will not affect what follows.

6 I here define hierarchical views as those that accept at least two levels of senses. (On

my terminology, theorists who have tried to limit the hierarchy to exactly two levels

have nevertheless admitted a hierarchy.) Peacocke is not alone in adopting a non

hierarchical version of Frege's theory of content. As he points out, Carnap (1947, p.

129) and Dummett (1973, pp. 267-269, 1981, pp. 83-147) endorse the view as well.

7 The argument below owes a great deal to Nathan Salmon's discussion in 'On

Designating', of the famous Gray's 'Elegy . . .' passage in Russell's 'On Denoting'.

Compare with Blackburn and Code (1978). I should also make it clear that the

argument that follows is not a general refutation of the redeployment claim. Instead,

it shows that the redeployment claim is incompatible with a Fregean account of

content.

8 The non-hierarchical Fregean might object to this assumption. (Peacocke would

not, as he employs the same '{}' notation to denote senses that we employ above.)

But, first, it is hard to see why we should believe in the existence of entities we cannot

simply denote; and, second, this view is not available to neo-Fregeans- like B?rge-

who wish to make substantive claims about particular concepts (e.g., {arthritis}). Cf.

Church, 'The hierarchy of concepts of successively higher orders arises as soon as we

suppose that a concept, like anything else that can be discussed at all, is capable of

having a name given to it' (1951, p. 12, n. 13).

9 This must be Dummett's view as he admits that if embedded expressions express the

very senses they denote, the sense of 'Aristotle' in oratio obliqua cannot also be

equated with the ordinary sense of the phrase 'the sense of'Aristotle" (1981: 89). If

there are senses, 'the sense of 'Aristotle" clearly has a sense; if this sense is not the

sense of 'Aristotle' it must be a second-level sense that determines the sense of

'Aristotle'. So Dummett must allow that there is a hierarchy, even if only its first

level members enter into the contents of higher-order thoughts.

10 B?rge says, "Believes' originally applied to person and propositions. But on our

assumptions it has come to apply to persons and truth-values' (1979, p. 272).

1 x For the claim that the truth-value of a sentence is a function of the denotations of

its parts see Frege (1980, p. 59).

12 We here make no assumptions about the semantic value of 'that' when it is

attached to sub-sentential expressions and so functions as a demonstrative. To be

fair, one might think that even when 'that' is attached to a sentence it functions as a

This content downloaded from

????????????132.174.252.114 on Sun, 26 Mar 2023 02:05:52 UTC????????????

All use subject to https://about.jstor.org/terms

74 AARON Z. ZIMMERMAN

demonstrative. That is, one might think that in such contexts, 'that' expresses a

demonstrative sense determining the proposition expressed by the sentence that

follows it. I don't think this view is plausible, but it can be ignored in the present

context because it requires positing second-level senses in oblique contexts (i.e.,

demonstrative MOPs that are distinct from the propositional MOPs they determine)

and so cannot be adopted by a non-hierarchical Fregean. There is also Davidson's

(1968-1969) view that tokens of 'that' demonstrate (or are used to demonstrate) the

token utterances they precede. Davidson's view is unacceptable to hierarchical and

non-hierarchical Fregeans alike.

13 {John} is one of the potentially infinite MOPs that determine John and it is the

customary MOP expressed by the English expression 'John'. {The only first-level

subject-determining MOP denoted by an expression in this paragraph} is one of the

potentially infinite MOPs that determine the only first-level subject-determining

MOP denoted by an expression in this paragraph- i.e., {John}- and it is the

customary MOP expressed by the somewhat technical English expression 'the only

first-level subject-determining MOP denoted by an expression in this paragraph'.

14 See Armstrong (1963).

15 Cf. Carnap, 'since [Frege] assumes that nominatum and sense must always be

different, he had thus to introduce a third entity as the oblique sense. Incidentally, it

seems that Frege nowhere explains in more customary terms what this third entity is'

(1949, p. 129).

16 See Fodor (1998) and Fodor et al. (1999).

17 Among the prominent advocates of the containment model are Heil (1988) and

Shoemaker (1996).

1 / 1

ChatGPT

ChatGPT

The assumption made here is that 'John' has a single customary meaning, although this simplification doesn't affect the subsequent arguments.

Hierarchical views refer to theories that accept multiple levels of senses, and even theorists like Peacocke, Carnap, and Dummett have adopted non-hierarchical versions of Frege's theory of content.

The arguments presented are influenced by Nathan Salmon's discussion on Russell's 'On Denoting' and Blackburn and Code's work, but they do not serve as a general refutation of the redeployment claim. Instead, they show that the redeployment claim is incompatible with a Fregean account of content.

The assumption made is that we should believe in the existence of entities that can be denoted, and this view is not available to neo-Fregeans like B��rge.

Dummett's view suggests the existence of a hierarchy, even if only the first-level members of the hierarchy enter into the contents of higher-order thoughts.

B��rge states that the term "believes" originally applied to persons and propositions but has now come to apply to persons and truth-values.

The claim is made that the truth-value of a sentence is a function of the denotations of its parts, as stated by Frege.

The semantic value of 'that' when attached to sub-sentential expressions and functioning as a demonstrative is not assumed here. It is disregarded because it requires positing second-level senses in oblique contexts, which is not compatible with a non-hierarchical Fregean view.

Davidson's view that tokens of 'that' demonstrate the token utterances they precede is unacceptable to both hierarchical and non-hierarchical Fregeans.

{John} represents one of the potentially infinite senses that determine John and is the customary sense expressed by the term 'John'.

Armstrong's work is relevant in this context.

Carnap points out that Frege introduced a third entity, the oblique sense, because he assumed that the nominatum and sense must always be different but didn't explain it in more customary terms.

References are made to Fodor's work on the subject.

Prominent advocates of the containment model include Heil and Shoemaker.

These additional points provide further clarification and references related to the arguments presented in the text.

1 / 1

4. A HIERARCHICAL REJECTION OF CONCEPTUAL SELF-VERIFICATION

It seems then that the Fregean is committed to a hierarchy of thought

contents; but it is equally clear that hierarchical views are at least

consistent with a rejection of the self-verifying nature of cogito

judgments. Suppose the hierarchy exists and let {{John}} be one of

several second-level MOPs determining the first-level MOP {John}. If

judgment entails consideration and considering a proposition entails

considering its constituents, the fact that S has judged ({S}, {think

ing}, ({{John}}, {{niceness}})) entails that she has considered

({{John}}, {{niceness}}), but given only classical logic it does not

entail that she has considered {{John}, {niceness}).

Indeed, it would be intuitively false to suppose that thinking a

second-level sense requires thinking the first-level sense it determines.

Both {{John}} and {the only first-level subject-determining MOP

denoted by an expression in this paragraph} determine {John}, but,

intuitively, a subject can consider the second-level MOP {the only

first-level subject-determining MOP denoted by an expression in this

paragraph} without therein considering the first-level MOP {John}.13

So S might be related by judgment to ({S}, {thinking}, ({the only

first-level subject-determining MOP denoted by an expression in this

paragraph}, {{niceness}})) without being related by thought to

({John}, {niceness}).

5. A FODORIAN ATTEMPT TO RECONCILE THE HIERARCHY WITH

CONCEPTUAL SELF-VERIFICATION

Though the existence of the hierarchy appears to permit the possi

bility of false cogito judgments, one might hope to use substantive

metaphysical claims about the nature of MOPs to dispel these

appearances. To see how this might be done, suppose with Jerry

Fodor (1998) that MOPs are types of Language of Thought (LOT)

inscription. Whether Lois uses 'Clark Kent' or 'Superman' she always

refers to the same man, but the MOP deployed when she uses

'Superman' is the LOT expression 'SUPERMAN', while the MOP

deployed with 'Clark Kent' is 'CLARK KENT' in the LOT. Suppose

too, in accordance with this psychologistic account of senses, that 'R

This content downloaded from

????????????132.174.252.114 on Sun, 26 Mar 2023 02:05:52 UTC????????????

All use subject to https://about.jstor.org/terms

68 AARON Z. ZIMMERMAN

judges that s9 is true if and only if '5" is written in i?'s judgment box,

where '5" is the LOT sentence activated when R understands the

declarative natural language sentence Y. (Fodor allows that some

times 'S" will be one of several MOPs with the content that s.)

Suppose Lois judges that she is thinking that Superman is heroic.

What, according to the LOT theory of senses, is written in Lois'

judgment box? There are different proposals. According to the

untenable non-hierarchical Fregean view (*) is there written.

According to the hierarchical view it is instead (**)- or something

similar- that appears there.

1 / 1

ChatGPT

ChatGPT

A hierarchical view of thought contents is consistent with a rejection of the self-verifying nature of cogito judgments. If we assume the hierarchy exists and have {{John}} as a second-level MOP determining the first-level MOP {John}, it doesn't necessarily mean that considering the second-level sense entails considering the first-level sense. Therefore, a subject could consider the second-level MOP without considering the first-level MOP. This suggests that a subject might be related by judgment to ({S}, {thinking}, ({the only first-level subject-determining MOP denoted by an expression in this paragraph}, {{niceness}})) without being related by thought to ({John}, {niceness}).

An attempt to reconcile the hierarchy with the self-verifying nature of cogito judgments can be made using Jerry Fodor's view. According to Fodor, modes of presentation (MOPs) are types of Language of Thought (LOT) inscriptions. Under this view, when Lois uses 'Clark Kent' or 'Superman,' she refers to the same person, but the MOP deployed with each term is different. Suppose Lois judges that she is thinking that Superman is heroic. According to the LOT theory of senses, the content written in Lois' judgment box could be (*) in the untenable non-hierarchical Fregean view or (**), or something similar, in the hierarchical view.

These points explain that a hierarchical view allows for the possibility of rejecting the self-verifying nature of cogito judgments, and an alternative approach involving the Language of Thought theory attempts to reconcile the hierarchy with self-verifying judgments.

1 / 1

This article has a citation, in Self-knowledge: Rationalism vs. empiricism

Aaron Z. Zimmerman

Philosophy Compass 3 (2):325���352 (2008)   Copy   BIBTEX

Abstract

Recent philosophical discussions of self-knowledge have focused on basic cases: our knowledge of our own thoughts, beliefs, sensations, experiences, preferences, and intentions. Empiricists argue that we acquire this sort of self-knowledge through inner perception; rationalists assign basic self-knowledge an even more secure source in reason and conceptual understanding. I try to split the difference. Although our knowledge of our own beliefs and thoughts is conceptually insured, our knowledge of our experiences is relevantly like our perceptual knowledge of the external world.

1 / 1

ChatGPT

ChatGPT

I apologize for any confusion, but the provided article citation is for a different work by Aaron Z. Zimmerman titled "Self-knowledge: Rationalism vs. empiricism" published in Philosophy Compass in 2008. The summary and discussion provided earlier were based on a different article titled "Self-Verification and the Content of Thought" by Aaron Z. Zimmerman. If you have any specific questions or need assistance related to the mentioned article, please let me know, and I'll do my best to help you.

1 / 1

Yeah, it says there is one citation, it seems it he cites it in the 2008 article

1 / 1

ChatGPT

ChatGPT

I apologize for the misunderstanding. If the article "Self-knowledge: Rationalism vs. empiricism" by Aaron Z. Zimmerman published in Philosophy Compass in 2008 cites another work, it suggests that Zimmerman refers to the work you mentioned, "Self-Verification and the Content of Thought." The citation indicates a connection between the two articles and implies that Zimmerman's discussion in the 2008 article may build upon or reference the content discussed in the cited work.

1 / 1

I see it Shoemaker, S. The First-Person Perspective and Other Essays. Cambridge, UK: Cambridge UP, 1996.

Shoemaker, S. ���Moore's Paradox and Self-Knowledge���. Philosophical Studies 77 (1995): 211��� 28.

Simon, D. J. and C. F. Chabris. ���Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events���. Perception 28 (1999): 1059��� 74.

Williamson, T. Knowledge and Its Limits. Oxford: Oxford UP, 2000.

Wright, C. ���On Making Up One's Mind: Wittgenstein on Intention���. Logic, Philosophy of Science and Epistemology, Proceedings of the XIth International Wittgenstein Symposium. Eds. P. Weingartner and G. Schurz. Vienna: Holder-Pickler Tempsky, 1987. 391��� 404.

Wright, C. ���Wittgenstein's Later Philosophy of Mind: Sensation, Privacy and Intention���. Journal of Philosophy 76 (1989): 622��� 34.

Zimmerman, A. ���Basic Self-Knowledge: Answering Peacocke's Criticisms of Constitutivism���. Philosophical Studies 128 (2006): 337��� 79.

Zimmerman, A. ���The Nature of Belief���. Journal of Consciousness Studies. Forthcoming.

Zimmerman, A. ���Putting Extrospection to Rest���. Philosophical Quarterly 55 (2005): 658��� 61.

Zimmerman, A. ���Self-Verification and the Content of Thought���. Synthese 149 (2006): 59��� 75.

Zimmerman, A. ���Unnatural Access���. Philosophical Quarterly 54 (2004): 435��� 8.

1 / 1

ChatGPT

ChatGPT

Thank you for providing the list of citations. It seems that these are the references cited in Aaron Z. Zimmerman's various works. The citations include works by other authors such as Shoemaker, Simon, Chabris, Williamson, Wright, as well as Zimmerman's own works from different publications. These references likely serve as sources that Zimmerman draws upon to support or contribute to the arguments and discussions in his articles.

1 / 1

example. Rather, such beliefs are determined by our observations of and interactions with others. Are you rich or poor? Beautiful or ugly? Smart or not? Good or bad at playing video games? And how do you know? These questions can be answered only by looking at those around us. The self has meaning only within the social context, and it is not wrong to say that the social situation defines our self-concept and our self-esteem. We rely on others to provide a ���social reality������to help us determine what to think, feel, and do (Hardin & Higgins, 1996). But what forms do these social influences take? It is to this question that we will now turn.

1 / 1

ChatGPT

ChatGPT

The self-concept and self-esteem are shaped by the social context in which we exist. Our beliefs about ourselves, such as our wealth, appearance, intelligence, or skills, are not determined solely by our own perceptions. Instead, they are influenced by our observations and interactions with others. To answer questions about our own attributes, we rely on comparing ourselves to those around us. The social situation we find ourselves in plays a crucial role in defining our sense of self.

Within the social context, others provide us with a "social reality." They contribute to shaping our thoughts, emotions, and behaviors. We look to others for guidance on what to think, how to feel, and what actions to take. This reliance on social influences is fundamental in understanding our self-concept and self-esteem.

Now, let's explore the different forms that these social influences can take.

1 / 1

Learning Objectives


Describe the concept of the looking-glass self and how it affects our self-concept.

Explore the impact of the labeling bias, self-labeling, and internalized prejudice on people���s self-concepts, particularly in those from marginalized social groups.

Define social comparison, and summarize how people use it to define their self-concepts and self-esteem.

Give examples of the use of upward and downward social comparison and their influences on social cognition and affect.

Explain the concept of social identity and why it is important to human behavior.

Describe how self-evaluation maintenance theory helps to explain how we react when other people���s behaviors threaten our sense of self.

Describe the concept of self-presentation and the various strategies we use to portray ourselves to others.

Outline the concept of reputation management and how it relates to self-presentation.

Discuss the individual-difference variable of self-monitoring and how it relates to the ability and desire to self-present.

To this point, we have seen, among other things, that human beings have complex and well-developed self-concepts and that they generally attempt to view themselves positively. These more cognitive and affective aspects of ourselves do not, of course, occur in a vacuum. They are heavily influenced by the social forces that surround us. We have alluded to some of these forces already; for example, in our review of self-verification theory, we saw how feedback from others can affect our self-concept and esteem. We also looked at ways that our sociocultural backgrounds can affect the content of our self-concept.


In this section, we will consider in more detail these and other social aspects of the self by exploring the many ways that the social situation influences our self-concept and esteem. The self is not created in isolation; we are not born with perceptions of ourselves as shy, interested in jazz, or charitable to others, for

1 / 1

ChatGPT

ChatGPT

Learning Objectives:

Understand the concept of the looking-glass self and its impact on our self-concept.

Explore how labeling bias, self-labeling, and internalized prejudice affect the self-concepts of individuals from marginalized social groups.

Define social comparison and its role in shaping self-concepts and self-esteem.

Provide examples of upward and downward social comparison and their effects on social cognition and emotions.

Explain the concept of social identity and its significance in human behavior.

Use self-evaluation maintenance theory to understand how individuals react when their sense of self is threatened by others' behaviors.

Describe self-presentation and the strategies people employ to portray themselves to others.

Discuss reputation management and its connection to self-presentation.

Examine the individual-difference variable of self-monitoring and its relationship to self-presentation.

So far, we have learned that humans possess complex self-concepts and tend to view themselves positively. However, these cognitive and emotional aspects of the self are not formed in isolation. They are profoundly influenced by social forces that surround us. Previous discussions have touched on some of these forces, such as how feedback from others can impact our self-concept and self-esteem. We have also explored how our sociocultural backgrounds shape the content of our self-concept.

In this section, we will delve deeper into these social aspects of the self. We will explore how the social situation influences our self-concept and self-esteem. It is important to recognize that the self does not develop in isolation. We are not born with preconceived notions of being shy, having an interest in jazz, or being charitable to others. Instead, our social environment plays a vital role in shaping our self-perceptions.

1 / 1

3.3 The Social Self: The Role of the Social Situation

1 / 1

ChatGPT

ChatGPT

The social situation plays a significant role in shaping our self-concept and self-esteem. It influences how we perceive ourselves and how we interact with others. Several key factors contribute to the social aspects of the self:

Looking-Glass Self: The looking-glass self theory, proposed by sociologist Charles Cooley, suggests that our self-concept is derived from our understanding of how others perceive us. We imagine how we appear to others, interpret their reactions, and form our self-concept based on these perceptions. For example, if others see us as intelligent and capable, we may develop a positive self-concept.

Labeling Bias: The labeling bias refers to the tendency to incorporate the labels or stereotypes assigned to us by others into our self-concept. Negative labels can have detrimental effects on self-esteem, especially when they are associated with marginalized social groups. For instance, if someone from a marginalized racial or ethnic group is repeatedly labeled as "lazy," it may affect their self-perception and self-esteem.

Self-Labeling: Self-labeling occurs when individuals internalize the labels or categories associated with their social identity. They identify themselves based on societal classifications such as gender, race, or occupation. Self-labeling can influence how individuals perceive themselves and how they behave within social contexts.

Internalized Prejudice: Internalized prejudice refers to the acceptance and internalization of negative stereotypes or prejudices about one's own social group. Individuals from marginalized groups may internalize societal biases, leading to lower self-esteem and a negative self-concept. For example, if someone internalizes stereotypes about their gender or race, it may impact their self-perception and overall well-being.

Social Comparison: Social comparison is the process of evaluating oneself by comparing one's abilities, attributes, or achievements to those of others. We often compare ourselves to others to gain information about our own abilities or to evaluate our self-worth. Social comparison can be upward, where we compare ourselves to those we perceive as better off, or downward, where we compare ourselves to those we perceive as worse off. These comparisons can influence our self-concept and self-esteem.

Social Identity: Social identity refers to the part of our self-concept that is derived from our membership in social groups. It includes aspects such as gender, ethnicity, nationality, or profession. Our social identity contributes to our sense of belonging and can shape our behavior, attitudes, and values.

Understanding the role of the social situation in shaping the self allows us to recognize the impact of social interactions, labels, stereotypes, and comparisons on our self-concept and self-esteem. It emphasizes the importance of creating inclusive and supportive social environments that promote positive self-perceptions and well-being.

1 / 1

The Looking-Glass Self: Our Sense of Self is Influenced by Others��� Views of Us

The concept of the looking-glass self states that part of how we see ourselves comes from our perception of how others see us (Cooley, 1902). We might feel that we have a great sense of humor, for example, because others have told us, and often laugh (apparently sincerely) at our jokes. Many studies have supported a basic prediction derived from the notion of the looking-glass self, namely that our self-concepts are often quite similar to the views that others have of us (Beer, Watson, & McDade-Montez, 2013). This may be particularly so with people from our own families and culture. Perkins, Wiley, and Deaux (2014), for example, found that, in the United States, how members of ethnic minority groups believed other members of the same culture perceived them significantly correlated with their self-esteem scores. In contrast, their perceived appraisal of European Americans toward them was only weakly related to their self-esteem.


This evidence is merely correlational, though, so we cannot be sure which way the influence is working. Maybe we develop our self-concept quite independently of others, and they then base their views of us on how we see ourselves. The work of Mark Baldwin and colleagues has been particularly important in demonstrating that how we think we are being perceived by others really can affect how we see ourselves.


For example, Baldwin and Holmes (1987) conducted two experiments to test the hypothesis that our self-concepts derive partly from the way we imagine that we would be perceived by significant others. In the first study, 40 women were instructed to visualize the faces of either two acquaintances or two older members of their own family. Later they were asked to rate their perceived enjoyableness of a piece of fiction with sexual content, and they typically responded in keeping with the responses they perceived the people they had visualized would have had. This effect was more pronounced when they sat in front of a mirror (remember the earlier discussion of self-awareness theory). In the second study, 60 men were exposed to a situation involving failure, and their self-evaluations to this setback were then measured. As with the women���s study, the men���s self-evaluations matched those they perceived that the people they were asked to visualize would have made, particularly when they were more self-aware. At least some of the time, then, we end up evaluating ourselves as we imagine others would. Of course, it can work both ways, too. Over time, the people around us may come to accept the self-concept that we present to others (Yeung & Martin, 2003).


Sometimes, the influence of other people���s appraisals of ourselves on our self-concept may be so strong that we end up internalizing them. For example, we are often labeled in particular ways by others, perhaps informally in terms of our ethnic background, or more formally in terms of a physical or psychological diagnosis. The labeling bias occurs when we are labeled, and others��� views and expectations of us are affected by that labeling (Fox & Stinnett, 1996). For example, if a teacher knows that a child has been diagnosed with a particular psychological disorder, that teacher may have different expectations and explanations of the child���s behavior than he or she would if not aware of that label. Where things get really interesting for our present discussion is when those expectations start to become self-fulfilling prophecies, and our self-concept and even our behavior start to align with them. For example, when children are labeled in special education contexts, these labels can then impact their self-esteem (Taylor, Hume, & Welsh, 2010).


If we are repeatedly labeled and evaluated by others, then self-labeling may occur, which happens when we adopt others��� labels explicitly into our self-concept. The effects of this self-labeling on our self-esteem appear to depend very much on the nature of the labels. Labels used in relation to diagnosis of psychological disorders can be detrimental to people whom then internalize them. For example, Moses (2009) found that adolescents who self-labeled according to diagnoses they had received were found to have higher levels of self-stigma in their self-concepts compared with those who described their challenges in non-pathological terms. In these types of situation, those who self-label may come to experience internalized prejudice, which occurs when individuals turn prejudice directed toward them by others onto themselves. Internalized prejudice has been found to predict more negative self-concept and poorer psychological adjustment in members of various groups, including sexual minorities (Carter, 2012) and racial minorities (Szymanski & Obiri, 2011).


In other cases, labels used by wider society to describe people negatively can be positively reclaimed by those being labeled. Galinsky and colleagues (2013) explored this use of self-labeling by members of oppressed groups to reclaim derogatory terms, including ���queer��� and ���bitch,��� used by dominant groups. After self-labeling, minority group members evaluated these terms less negatively, reported feeling more powerful, and were also perceived by observers as more powerful. Overall, these results indicate that individuals who incorporate a formerly negative label into their self-concept in order to reclaim it can sometimes undermine the stigma attached to the label.

1 / 1

ChatGPT

ChatGPT

The looking-glass self theory suggests that our self-concept is influenced by how we perceive others' views of us. Research has shown that our self-concepts often align with the views others have of us, especially those from our own families and culture. For example, members of ethnic minority groups' self-esteem was significantly correlated with how they believed others from the same culture perceived them.

Studies by Mark Baldwin and colleagues demonstrate that our self-concept can be affected by how we think we are being perceived by others. Visualizing how significant others would perceive us can influence our self-evaluations and ratings of enjoyment or responses to certain situations. This effect is more pronounced when we are more self-aware, such as when sitting in front of a mirror.

The labeling bias occurs when we are labeled by others, and their views and expectations of us are influenced by those labels. This labeling can impact our self-concept and behavior, leading to self-fulfilling prophecies. When we internalize these labels into our self-concept, it is called self-labeling. The effects of self-labeling on self-esteem depend on the nature of the labels. Labels associated with psychological disorders can be detrimental, leading to self-stigma and negative self-concept. Internalized prejudice occurs when individuals turn prejudice directed at them onto themselves, resulting in a negative self-concept and poorer psychological adjustment.

However, in some cases, individuals reclaim negative labels and incorporate them into their self-concept as a form of empowerment. This self-labeling can help undermine the stigma attached to the label and promote a more positive perception of oneself.

Overall, the social situation and others' appraisals of us play a crucial role in shaping our self-concept and self-esteem. Understanding these influences helps us recognize the impact of labels, stereotypes, and internalized prejudice on our self-perception and provides insight into the potential for empowerment and positive self-identity.

1 / 1

Social Comparison Theory: Our Sense of Self Is Influenced by Comparisons with Others

Self-concept and self-esteem are also heavily influenced by the process of social comparison (Buunk & Gibbons, 2007; Van Lange, 2008). Social comparison occurs when we learn about our abilities and skills, about the appropriateness and validity of our opinions, and about our relative social status by comparing our own attitudes, beliefs, and behaviors with those of others. These comparisons can be with people who we know and interact with, with those whom we read about or see on TV, or with anyone else we view as important. However, the most meaningful comparisons we make tend to be with those we see as similar to ourselves (Festinger, 1954).


Social comparison occurs primarily on dimensions on which there are no correct answers or objective benchmarks and thus on which we can rely only on the beliefs of others for information. Answers to questions such as ���What should I wear to the interview?��� or ���What kind of music should I have at my wedding?��� are frequently determined at least in part by using the behavior of others as a basis of comparison. We also use social comparison to help us determine our skills or abilities���how good we are at performing a task or doing a job, for example. When students ask their teacher for the class average on an exam, they are also seeking to use social comparison to evaluate their performance.


Research Focus

Affiliation and Social Comparison


The extent to which individuals use social comparison to determine their evaluations of events was demonstrated in a set of classic research studies conducted by Stanley Schachter (1959). Schachter���s experiments tested the hypothesis that people who were feeling anxious would prefer to affiliate with others rather than be alone because having others around would reduce their anxiety. Female college students at the University of Minnesota volunteered to participate in one of his experiments for extra credit in their introductory psychology class. They arrived at the experimental room to find a scientist dressed in a white lab coat, standing in front of a large array of electrical machinery. The scientist introduced himself as Dr. Zilstein of the Department of Neurology and Psychiatry, and he told the women that they would be serving as participants in an experiment concerning the effects of electrical shock. Dr. Zilstein stressed how important it was to learn about the effects of shocks, since electroshock therapy was being used more and more commonly and because the number of accidents due to electricity was also increasing!


At this point, the experimental manipulation occurred. One half of the participants (those in the high-anxiety condition) were told that the shocks would be ���painful��� and ���intense,��� although they were assured that they could do no permanent damage. The other half of the participants (those in the low-anxiety condition) were also told that they would be receiving shocks but that they would in no way be painful���rather, the shocks were said to be mild and to resemble a ���tickle��� or a ���tingle.��� Of course, the respondents were randomly assigned to conditions to assure that the women in the two conditions were, on average, equivalent except for the experimental manipulation.


Each of the women was then told that before the experiment could continue the experimenter would have to prepare the equipment and that they would have to wait until he was finished. He asked them if they would prefer to wait alone or with others. The outcome of Schachter���s research was clear: while only 33% of the women who were expecting mild shocks preferred to wait with others, 63% of the women expecting to get painful shocks wanted to wait with others. This was a statistically significant difference, and Schachter concluded that the women chose to affiliate with each other in order to reduce their anxiety about the upcoming shocks.


In further studies, Schachter found that the research participants who were under stress did not want to wait with just any other people. They preferred to wait with other people who were expecting to undergo the same severe shocks that they were rather than with people who were supposedly just waiting to see their professor. Schachter concluded that this was not just because being around other people might reduce our anxiety but because we also use others who are in the same situation as we are to help us determine how to feel about things. As Schachter (1959) put it, ���Misery doesn���t just love any kind of company, it loves only miserable company��� (p. 24). In this case, the participants were expecting to determine from the other participants how afraid they should be of the upcoming shocks.


In short, and as predicted by the idea of social comparison, the women in Schachter���s studies relied on each other to help them understand what was happening to them and to find out how they should feel and respond to their social situations. Again, the power of the social situation���in this case, in determining our beliefs and attitudes���is apparent.


Although Schachter���s studies were conducted in relatively artificial lab settings, similar effects have been found in field studies in more naturally occurring settings. For instance, Kulik, Mahler, and Moore (1996) found that hospital patients who were awaiting surgery preferred to talk to other individuals who were expecting to have similar procedures rather than to patients who were having different procedures, so that they could share information about what they might expect to experience. Furthermore, Kulik and his colleagues found that sharing information was helpful: people who were able to share more information had shorter hospital stays.

1 / 1

ChatGPT

ChatGPT

The concept of social comparison theory suggests that our self-concept and self-esteem are influenced by comparing ourselves to others. We engage in social comparison to learn about our abilities, skills, opinions, and social status. The comparisons we make are often with people we perceive as similar to ourselves.

Social comparison occurs primarily on dimensions where there are no objective benchmarks or correct answers, and we rely on the beliefs of others for information. For example, we may use social comparison to decide what to wear to an interview or what kind of music to have at a wedding. We also use social comparison to evaluate our skills and abilities, such as comparing our performance on an exam to the class average.

Research studies, such as those conducted by Stanley Schachter, have demonstrated the impact of social comparison on our evaluations of events and our desire for affiliation. In one study, female college students expecting painful shocks were more likely to prefer waiting with others compared to those expecting mild shocks. This preference for affiliation was driven by a desire to reduce anxiety and seek support from others in similar situations.

These findings highlight that we not only seek social interaction to alleviate anxiety but also use others in similar situations to help us understand and navigate our own emotions and responses. Social comparison provides us with a reference point to determine how we should feel and respond to social situations.

Similar effects have been observed in field studies, such as hospital patients preferring to talk to individuals who are undergoing similar procedures to share information and better understand their experiences. Sharing information in these situations has been found to be beneficial, leading to shorter hospital stays.

Overall, social comparison plays a significant role in shaping our self-concept and self-esteem by providing us with information about our abilities, opinions, and social standing. It influences our beliefs and attitudes, demonstrating the impact of the social situation on our self-perception.

1 / 1

Self-evaluation maintenance theory (Tesser, 1988) asserts that our self-esteem can be threatened when someone else outperforms us, particularly if that person is close to us and the performance domain is central to our self-concept. This theory leads to the interesting implication that these threats will often occur in the context of our family relationships, and they have been shown to be an integral part of both family functioning in general (Tesser, 1980) and marital relationships in particular (Beach et al., 1996).


When threats occur, the theory states that we will typically try to rebuild our self-esteem using one of three main strategies. The first is distancing, where we redefine ourselves as less close to the person in question. For example, if a close friend keeps beating you at tennis, you may, over time, seek out another playing partner to protect your bruised ego. Interestingly, people who are more narcissistic are more likely to use this tactic than people who are lower in these characteristics (Nicholls & Stukas, 2011). The second option is to redefine how important the trait or skill really is to your self-concept. For instance, you may decide that tennis ability just isn���t that important a part of who you are, and choose to take up another hobby instead. The third strategy is try to improve on the ability in question. In the current example, this would mean practicing more often or hiring a coach to improve your tennis game. Notice the clear parallels between these strategies that occur in response to threats to our self-esteem posed by the behavior of others, and those that are triggered by feelings of self-discrepancy, discussed earlier in this chapter. In both cases, we seek to rebuild our self-esteem by redefining the aspect of ourself that has been diminished.


Self-Presentation: Our Sense of Self Is Influenced by the Audiences We Have

It is interesting to note that each of the social influences on our sense of self that we have discussed can be harnessed as a way of protecting our self-esteem. The final influence we will explore can also be used strategically to elevate not only our own esteem, but the esteem we have in the eyes of others. Positive self-esteem occurs not only when we do well in our own eyes but also when we feel that we are positively perceived by the other people we care about.


seenPositively

Figure 3.12 Being seen positively by others helps us to feel positive about ourselves.

Because it is so important to be seen as competent and productive members of society, people naturally attempt to present themselves to others in a positive light. We attempt to convince others that we are good and worthy people by appearing attractive, strong, intelligent, and likable and by saying positive things to others (Jones & Pittman, 1982; Schlenker, 2003). The tendency to present a positive self-image to others, with the goal of increasing our social status, is known as self-presentation, and it is a basic and natural part of everyday life.


A big question in relation to self-presentation is the extent to which it is an honest versus more strategic, potentially dishonest enterprise. The sociologist Erving Goffman (1959) developed an influential theory of self-presentation and described it as a mainly honest process, where people need to present the parts of themselves required by the social role that they are playing in a given situation. If everyone plays their part according to accepted social scripts and conventions, then the social situation will run smoothly and the participants will avoid embarrassment. Seen in this way, self-presentation is a transparent process, where we are trying to play the part required of us, and we trust that others are doing the same. Other theorists, though, have viewed self-presentation as a more strategic endeavor, which may involve not always portraying ourselves in genuine ways (e.g., Jones & Pittman, 1982). As is often the case with two seemingly opposing perspectives, it is quite likely that both are true in certain situations, depending on the social goals of the actors.


Different self-presentation strategies may be used to create different emotions in other people, and the use of these strategies may be evolutionarily selected because they are successful (Toma, Hancock, & Ellison, 2008). Edward Jones and Thane Pittman (1982) described five self-presentation strategies, each of which is expected to create a resulting emotion in the other person:


The goal of ingratiation is to create liking by using flattery or charm.

The goal of intimidation is to create fear by showing that you can be aggressive.

The goal of exemplification is to create guilt by showing that you are a better person than the other.

The goal of supplication is to create pity by indicating to others that you are helpless and needy.

The goal of self-promotion is to create respect by persuading others that you are competent.

angry

Figure 3.13 Attempts to impress and intimidate others to gain status are not unique to humans.

No matter who is using it, self-presentation can easily be overdone, and when it is, it backfires. People who overuse the ingratiation technique and who are seen as obviously and strategically trying to get others to like them are often disliked because of this. Have you ever had a slick salesperson obviously try to ingratiate him- or herself with you just so you will buy a particular product, and you end up not liking the person and making a hasty retreat from the premises? People who overuse the exemplification or self-promotion strategies by boasting or bragging, particularly if that boasting does not appear to reflect their true characteristics, may end up being perceived as arrogant and even self-deluded (Wosinska, Dabul, Whetstone-Dion, & Cialdini, 1996). Using intimidation can also often backfire; acting more modestly may be more effective. Again, the point is clear: we may want to self-promote with the goal of getting others to like us, but we must also be careful to consider the point of view of the other person. Being aware of these strategies is not only useful for better understanding how to use them responsibly ourselves, it can also help us to understand that other people���s behaviors may often reflect their self-presentational concerns. This can, in turn, facilitate better empathy for others, particularly when they are exhibiting challenging behaviors (Friedlander & Schwartz, 1985). For instance, perhaps someone���s verbally aggressive behavior toward you is more about that person being afraid rather than about his or her desire to do you harm.


��Now that we have explored some of the commonly used self-presentation tactics, let���s look at how they manifest in specific social behaviors. One concrete way to self-promote is to display our positive physical characteristics. A reason that many of us spend money on improving our physical appearance is the desire to look good to others so that they will like us. We can also earn status by collecting expensive possessions such as fancy cars and big houses and by trying to associate with high-status others. Additionally, we may attempt to dominate or intimidate others in social interactions. People who talk more and louder and those who initiate more social interactions are afforded higher status. A businessman who greets others with a strong handshake and a smile, and people who speak out strongly for their opinions in group discussions may be attempting to do so as well. In some cases, people may even resort to aggressive behavior, such as bullying, in attempts to improve their status (Baumeister, Smart, & Boden, 1996).


Self-promotion can also be pursued in our online social behaviors. For example, a study in Taiwan conducted by Wang and Stefanone (2013) used survey methodology to investigate the relationship between personality traits, self-presentation and the use of check-ins on Facebook. Interestingly, narcissism was found to predict scores on a measure of exhibitionistic, self-promoting use of Facebook check-ins, which included items like ���I check in so people know that I am with friends,��� and ���I expect friends to like or leave comments on my check-in status on Facebook.���


Other studies have also found associations between narcissistic traits and self-promotional activity on Facebook. Mehdizadeh (2010), for example, found that narcissistic personality scores were positively correlated with the amount of daily logins on Facebook and the duration of each login. Furthermore, narcissistic traits were related to increased use of self-promotional material in the main photo, view photos, status updates, and notes sections of people���s Facebook pages.


Analysis of the content and language used in Facebook postings has also revealed that they are sometimes used by individuals to self-promote. Bazarova, Taft, Choi, and Cosley (2013) explored self-presentation through language styles used in status updates, wall posts, and private messages from 79 participants. The use of positive emotion words was correlated with self-reported self-presentation concern in status updates. This is consistent with the idea that people share positive experiences with Facebook friends partly as a self-enhancement strategy.


Online self-presentation doesn���t seem to be limited to Facebook usage. There is also evidence that self-promotional concerns are often a part of blogging behaviors, too. Mazur and Kozarian (2010), for example, analyzed the content of adolescents��� blog entries and concluded that a careful concern for self-presentation was more central to their blogging behavior than direct interaction with others. This often seems to apply to micro-blogging sites like Twitter. Marwick and Boyd (2011) found that self-presentational strategies were a consistent part of celebrity tweeting, often deployed by celebrities to maintain their popularity and image.


You might not be surprised to hear that men and women use different approaches to self-presentation. Men are more likely to present themselves in an assertive way, by speaking and interrupting others, by visually focusing on the other person when they are speaking, and by leaning their bodies into the conversation. Women, on the other hand, are more likely to be modest; they tend to create status by laughing and smiling, and by reacting more positively to the statements of others (Dovidio, Brown, Heltman, Ellyson, & Keation, 1988).


These gender differences are probably in large part socially determined as a result of the different reinforcements that men and women receive for using particular self-presentational strategies. For example, self-promoting by speaking out and acting assertively can be more effective for men than it is for women, in part because cross-culturally consistent stereotypes tend to depict assertiveness as more desirable in men than in women. These stereotypes can have very important consequences in the real world. For instance, one of the reasons for the ���glass ceiling��� existing in some occupations (where women experience discrimination in reaching top positions in organizations) may be attributable to the more negative reactions that their assertive behaviors, necessary for career advancement, receive than those of their male colleagues  (Eagly & Carli, 2007).


There are also some cultural differences in the extent to which people use self-presentation strategies in social contexts. For instance, when considering job interviews, Konig, Haftseinsson, Jansen, & Stadelmann (2011) found that individuals from Iceland and Switzerland used less self-presentational behavior than people from the United States. Differences in self-presentation have also been found in job interviews involving individuals from Ghana, Turkey, Norway, and Germany, with the former two groups showing higher impression management scores than the latter two (Bye et al., 2011).


So far we have been talking about self-presentation as it operates in particular situations in the short-term. However, we also engage in longer-term self-presentational projects, where we seek to build particular reputations with particular audiences. ��Emler & Reicher (1995) describe the unique capacity humans have to know one another by repute and argue that, accordingly, we are often engaged in a process of reputation management, which is a form of long-term self-presentation, where individuals seek to build and sustain specific reputations with important audiences. According to this perspective, our behaviors in current social situations may not only be to serve our self-presentational goals in that moment, but also be based on a consideration of their longer-term repercussions for our reputations. As many politicians, for example, know only too well, a poor decision from their past can come back to haunt them when their reputation is being assessed during a campaign.


The concept of reputation management can be used to help explain a wide variety of social and antisocial behaviors, including corporate branding (Smith, Smith, & Wang, 2010), sociomoral debate (Emler, Tarry, & St. James, 2007), and teenage criminal activity (Lopez-Romero & Romero, 2011). In the last example, it is argued that a lot of teenage antisocial behavior results from a desire to build a reputation for toughness and rebelliousness with like-minded peer audiences (Emler & Reicher, 1995). Similarly, antisocial and self-destructive online actions, like people posting to Facebook their involvement in illegal acts during riots, or individuals engaging in life-threatening activities in Internet crazes like Neknominate, may make more sense if they are considered partly as stemming from a desire to project a particular reputation to specific audiences. Perhaps the perceived social kudos from doing these things outweighs the obvious personal risks in the individuals��� minds at the time.


People often project distinct reputations to different social audiences. For example, adolescents who engage in antisocial activity to build reputations for rebelliousness among their peers will often seek to construct very different reputations when their parents are the audience (Emler & Reicher, 1995). The desire to compartmentalize our reputations and audiences can even spill over into our online behaviors. Wiederhold (2012) found that, with some adolescents��� Facebook friends numbering in the hundreds or thousands, increasing numbers are moving to Twitter in order to reach a more selective audience. One critical trigger for this has been that their parents are now often friends with them on Facebook, creating a need for young people to find a new space where they can build reputations that may not always be parent-friendly (Wiederhold, 2012).


Although the desire to present the self favorably is a natural part of everyday life, both person and situation factors influence the extent to which we do it. For one, we are more likely to self-present in some situations than in others. When we are applying for a job or meeting with others whom we need to impress, we naturally become more attuned to the social aspects of the self, and our self-presentation increases.


There are also individual differences. Some people are naturally better at self-presentation���they enjoy doing it and are good at it���whereas others find self-presentation less desirable or more difficult. An important individual-difference variable known as self-monitoring has been shown in many studies to have a major impact on self-presentation. Self-monitoring refers to the tendency to be both motivated and capable of regulating our behavior to meet the demands of social situations (Gangestad & Snyder, 2000). High self-monitors are particularly good at reading the emotions of others and therefore are better at fitting into social situations���they agree with statements such as ���In different situations and with different people, I often act like very different persons,��� and ���I guess I put on a show to impress or entertain people.��� Low self-monitors, on the other hand, generally act on their own attitudes, even when the social situation suggests that they should behave otherwise. Low self-monitors are more likely to agree with statements such as ���At parties and social gatherings, I do not attempt to do or say things that others will like,��� and ���I can only argue for ideas that I already believe.��� In short, high self-monitors use self-presentation to try to get other people to like them by behaving in ways that the others find desirable, whereas low self-monitors tend to follow their internal convictions more than the demands of the social situation.


In one experiment that showed the importance of self-monitoring, Cheng and Chartrand (2003) had college students interact individually with another student (actually an experimental confederate) whom they thought they would be working with on an upcoming task. While they were interacting, the confederate subtly touched her own face several times, and the researchers recorded the extent to which the student participant mimicked the confederate by also touching his or her own face.


The situational variable was the status of the confederate. Before the meeting began, and according to random assignment to conditions, the students were told either that they would be the leader and that the other person would be the worker on the upcoming task, or vice versa. The person variable was self-monitoring, and each participant was classified as either high or low on self-monitoring on the basis of his or her responses to the self-monitoring scale.


As you can see in Figure 3.14, ���Self-Monitoring and Behavioral Mimicry,��� Cheng and Chartrand found an interaction effect: the students who had been classified as high self-monitors were more likely to mimic the behavior of the confederate when she was described as being the leader than when she was described as being the worker, indicating that they were ���tuned in��� to the social situation and modified their behavior to appear more positively. Although the low self-monitors did mimic the other person, they did not mimic her more when the other was high, versus low, status. This finding is consistent with the idea that the high self-monitors were particularly aware of the other person���s status and attempted to self-present more positively to the high-status leader. The low self-monitors, on the other hand���because they feel less need to impress overall���did not pay much attention to the other person���s status.


Figure 3.14: Self-Monitoring and Behavioral Mimicry

High self-monitors imitated more when the person they were interacting with was of higher (versus lower) status. Low self-monitors were not sensitive to the status of the other. Data are from Cheng and Chartrand (2003).


This differential sensitivity to social dynamics between high and low self-monitors suggests that their self-esteem will be affected by different factors. For people who are high in self-monitoring, their self-esteem may be positively impacted when they perceive that their behavior matches the social demands of the situation, and negatively affected when they feel that it does not. In contrast, low self-monitors may experience self-esteem boosts when they see themselves behaving consistently with their internal standards, and feel less self-worth when they feel they are not living up to them (Ickes, Holloway, Stinson, & Hoodenpyle, 2006).


Key Takeaways


Our self-concepts are affected by others��� appraisals, as demonstrated by concepts including the looking-glass self and self-labeling.

The self-concept and self-esteem are also often strongly influenced by social comparison. For example, we use social comparison to determine the accuracy and appropriateness of our thoughts, feelings, and behavior.

When we are able to compare ourselves favorably with others through downward social comparison, we feel good about ourselves. Upward social comparison with others who are better off than we are leads to negative emotions.

Social identity refers to the positive emotions that we experience as a member of an important social group.

Normally, our group memberships result in positive feelings, which occur because we perceive our own groups, and thus ourselves, in a positive light.

Which of our many category identities is most accessible for us will vary from day to day as a function of the particular situation we are in.

In the face of others��� behaviors, we may enhance our self-esteem by ���basking in the reflected glory��� of our ingroups or of other people we know.

If other people���s actions threaten our sense of self according to self-evaluation maintenance theory, we may engage in a variety of strategies aimed at redefining our self-concept and rebuilding our self-esteem.

The tendency to present a positive self-image to others, with the goal of increasing our social status, is known as self-presentation, and it is a basic and natural part of everyday life. Different self-presentation strategies may be used to create different emotions in other people.

We often use self-presentation in the longer term, seeking to build and sustain particular reputations with specific social audiences.

The individual-difference variable of self-monitoring relates to the ability and desire to self-present.

1 / 1

ChatGPT

ChatGPT

Self-evaluation maintenance theory states that our self-esteem can be threatened when someone close to us outperforms us in a domain that is central to our self-concept. In such situations, we tend to employ strategies to rebuild our self-esteem. These strategies include distancing ourselves from the person, redefining the importance of the domain to our self-concept, or striving to improve our own performance in that domain.

Self-presentation is another important aspect of our sense of self. It involves presenting ourselves to others in a positive light to gain their approval and increase our social status. Self-presentation is a natural part of everyday life, where we try to appear attractive, strong, intelligent, and likable. It can be seen as a combination of honest expression of the self and strategic attempts to shape others' perceptions.

Various self-presentation strategies exist, each aiming to elicit specific emotions in others. These strategies include ingratiation (flattery and charm), intimidation (displaying aggression or power), exemplification (demonstrating superiority), supplication (conveying helplessness or neediness), and self-promotion (highlighting competence). However, it is important to use these strategies judiciously, as overuse can backfire and lead to negative perceptions.

Self-presentation extends to online behavior as well, with individuals using platforms like Facebook and Twitter to shape their self-image. Studies have shown that narcissistic individuals tend to engage in exhibitionistic self-promotion on social media. People may also use blogging and status updates to present positive experiences and enhance their self-image. Different self-presentation strategies are employed by men and women, influenced by societal expectations and stereotypes.

Self-monitoring, an individual difference variable, plays a significant role in self-presentation. High self-monitors are skilled at adapting their behavior to meet social demands, while low self-monitors tend to act based on their internal convictions. High self-monitors are sensitive to social dynamics and adjust their behavior to match the demands of high-status individuals, which can impact their self-esteem. On the other hand, low self-monitors derive self-esteem from behaving consistently with their own internal standards.

Overall, self-presentation and self-monitoring are important factors in how we present ourselves to others, manage our reputations, and shape our self-esteem.

1 / 1

That was from https://opentextbc.ca/socialpsychology/chapter/the-social-self-the-role-of-the-social-situation/

1 / 1

ChatGPT

ChatGPT

I apologize for the oversight. The information provided was indeed adapted from the Open Textbook Library's chapter on social psychology titled "The Social Self: The Role of the Social Situation." Thank you for pointing that out.

1 / 1

Computer Science > Artificial Intelligence

arXiv:2212.09561 (cs)

[Submitted on 19 Dec 2022 (v1), last revised 24 May 2023 (this version, v4)]

Large Language Models are Better Reasoners with Self-Verification

Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, Jun Zhao

Download PDF

Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks such as arithmetic, commonsense, and logical reasoning. However, LLMs with CoT require multi-step prompting and multi-token prediction, which is highly sensitive to individual mistakes and vulnerable to error accumulation. The above issues make the LLMs need the ability to verify the answers. In fact, after inferring conclusions in some thinking decision tasks, people often check them by re-verifying steps to avoid some mistakes. In this paper, we propose and prove that LLMs also have similar self-verification abilities. We take the conclusion obtained by CoT as one of the conditions for solving the original problem. By taking turns masking the original conditions and predicting their results, we calculate an explainable answer verification score based on whether the re-predicted conditions are correct. Experimental results demonstrate that the proposed method can improve the reasoning performance on various arithmetic, commonsense, and logical reasoning datasets.

1 / 1

ChatGPT

ChatGPT

The paper titled "Large Language Models are Better Reasoners with Self-Verification" explores the use of large language models (LLMs), such as GPT-3, in natural language processing tasks involving reasoning. While LLMs have shown strong reasoning abilities, they often require multi-step prompting and multi-token prediction, which can be prone to individual mistakes and error accumulation.

The authors propose and demonstrate that LLMs can benefit from self-verification abilities similar to those employed by humans. After inferring conclusions in thinking and decision tasks, humans often verify their answers by rechecking the steps to avoid errors. The proposed method in the paper incorporates self-verification into LLMs by considering the conclusion obtained through the initial reasoning process as a condition for solving the original problem. The original conditions are masked in turns, and the model predicts their results, calculating an explainable answer verification score based on the correctness of the re-predicted conditions.

Experimental results indicate that the proposed method improves reasoning performance on various arithmetic, commonsense, and logical reasoning datasets. By integrating self-verification abilities into LLMs, the researchers aim to address the sensitivity to mistakes and vulnerability to error accumulation in multi-step prompting, thereby enhancing the overall reasoning capabilities of these models.

You can download the full PDF of the paper from the arXiv preprint server.

1 / 1

Large Language Models are Better Reasoners with Self-Verif i cation

Yixuan Weng1���, Minjun Zhu���1,2,Fei Xia1,2,Bin Li3Shizhu He1,2, Kang Liu1,2, Jun Zhao1,2

1 National Laboratory of Pattern Recognition��� Institute of Automation, CAS

2 School of Artif i cial Intelligence, University of Chinese Academy of Sciences

3 College of Electrical and Information Engineering, Hunan University

wengsyx@gmail.com,zhuminjun2020,xiafei2020@ia.ac.cn,{shizhu.he, kliu, jzhao}@nlpr.ia.ac.cn

Abstract

Recently, with the chain of thought (CoT)

prompting, large language models (LLMs),

e.g., GPT-3, have shown strong reasoning abil-

ity in several natural language processing tasks

such as arithmetic, commonsense, and logical

reasoning. However, LLMs with CoT require

multi-step prompting and multi-token predic-

tion, which is highly sensitive to individual

mistakes and vulnerable to error accumulation.

The above issues make the LLMs need the abil-

ity to verify the answers. In fact, after inferring

conclusions in some thinking decision tasks,

people often check them by re-verifying steps

to avoid some mistakes. In this paper, we pro-

pose and prove that LLMs also have similar

self-verif i cation abilities. We take the conclu-

sion obtained by CoT as one of the conditions

for solving the original problem. By taking

turns masking the original conditions and pre-

dicting their results, we calculate an explain-

able answer verif i cation score based on whether

the re-predicted conditions are correct. Exper-

imental results demonstrate that the proposed

method can improve the reasoning performance

on various arithmetic, commonsense, and log-

ical reasoning datasets. Our code is publicly

available at:https://github.com/WENGSYX/

Self-Verification.

1Introduction

The ability of reasoning in the process of thinking

and decision-making is an essential aspect of hu-

man intelligence. Recently, chain of thought (CoT)

prompting(Weietal.,2022)hasbeenagoodwayto

solve the arithmetic, commonsense, and logical rea-

soning tasks with large language models (LLMs),

which help the LLMs simulating the human think-

ingprocesswhensolvingcomplexnaturallanguage

processing (NLP) tasks. CoT guides LLMs to gen-

erate a series of intermediate reasoning steps to

address complex problems rather than just predict

��� These authors contribute this work equally.

a i nal answer. This approach has been shown the

advance performances on several challenging NLP

tasks, even when using only a few or no training

samples (Madaan et al., 2022; Saparov and He,

2022; Fu et al., 2022).

Although CoT can enable large models to solve

complex reasoning tasks, it is highly sensitive to

individual mistakes and vulnerable to error accumu-

lation (Shen et al., 2021). The multi-step prompt-

ing and multi-token prediction are required in CoT,

which may lack robustness while addressing com-

plex reasoning tasks with the autoregressive mech-

anism (Vaswani et al., 2017; Radford et al., 2019;

Brown et al., 2020; Zhang et al., 2022a). If a tiny

mistake occurs, it can change the meaning devi-

ations of the whole statement (Xiao et al., 2022),

leading to incorrect answers (Cobbe et al., 2021).

That is especially problematic in using CoT for

addressing multi-step precise reasoning (such as

mathematical calculation). Due to the lack of the

error correction mechanism, it is diff i cult for the

LLMs to obtain correct results from the possible

errors in multiple steps reasoning.

Previous methods resolve the above issue by

training another verif i er to evaluate the correctness

of the model���s output (Shen et al., 2021; Li et al.,

2022). However, there are some drawbacks in these

work. On the one hand, training a verif i er requires a

lot of human annotations and additional i ne-tuned

models, which limits its widespread use in other

tasks and domains. On the other hand, the verif i er

i ne-tuned by a language model is not easily ex-

plainable, making it diff i cult to assess the model���s

reliabilitybasedonitsoutputscores. Therefore, the

challenge of obtaining a better reasoner based

on the LLMs is to get a verif i er that can avoid

manual annotation and additional training, so

that it can be better extended and migrated to other

i elds and tasks.

1 / 1

ChatGPT

ChatGPT

The paper titled "Large Language Models are Better Reasoners with Self-Verification" is authored by Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao. The authors are affiliated with the National Laboratory of Pattern Recognition, Institute of Automation, CAS; School of Artificial Intelligence, University of Chinese Academy of Sciences; and College of Electrical and Information Engineering, Hunan University.

The paper discusses the application of large language models (LLMs), such as GPT-3, in natural language processing tasks involving reasoning. Specifically, the authors explore the use of chain of thought (CoT) prompting, which enables LLMs to generate intermediate reasoning steps to solve complex problems instead of providing only a final answer. CoT has shown promising results in tasks such as arithmetic, commonsense, and logical reasoning.

However, LLMs with CoT face challenges such as sensitivity to individual mistakes and vulnerability to error accumulation. The multi-step prompting and multi-token prediction involved in CoT can lack robustness, especially in addressing complex reasoning tasks with the autoregressive mechanism. A small mistake can lead to incorrect answers and deviation in the meaning of the entire statement.

To address these challenges, the authors propose a self-verification approach for LLMs. Inspired by how humans re-verify their conclusions in thinking and decision tasks, the proposed method incorporates self-verification abilities into LLMs. The conclusion obtained through CoT serves as one of the conditions for solving the original problem. By masking the original conditions in turns and predicting their results, an explainable answer verification score is calculated based on the correctness of the re-predicted conditions.

Experimental results demonstrate that the proposed method improves reasoning performance on various arithmetic, commonsense, and logical reasoning datasets. The authors have made their code publicly available on GitHub.

Please note that the above information is a summary of the paper based on the provided abstract and does not include the full content of the paper.

1 / 1

To address this challenge and overcome the lim-

itations of training verif i ers, we propose utiliz-

arXiv:2212.09561v4

[cs.AI]

24

May

2023

Q: Jackie has 10 apples. Adam has 8 apples. How many more apples does Jackie have than Adam? A: 2

������������ ������������������������������������������������������������������������������������������:

������������������������������������������������������������������������

���������1���������2 ������������������

Jackie have 2 apples than Adam.

(���������1��� ���������2) ��� ������������������ (���������1��� ������������������) ��� ���������2

(���������2��� ������������������) ��� ���������1 [Adam has 8 apples. Jackie have 2 apples than Adam.] ��� [Jackie has 10 apples]

[Jackie has 10 apples. Jackie have 2 apples than Adam.] ��� [Adam has 8 apples.]

������������������������������������������������������������������������������������������

������������������������������������������������������

Figure 1: The answer of a question can be verif i ed by masking and predicting the conditions of the original contexts.

To mimic the self-verif i cation ability of human, we predict the accuracy offCby predicting the original conditions

f1or f2is right or not based on this conclusion.

ing LLMs as reasoners with self-verif i cation for

selecting better prediction results. In numerous

decision-making tasks, humans often perform self-

verif i cation of inferred conclusions to mitigate mis-

takes (Poole and Mackworth, 2010). In this paper,

we propose and demonstrate that LLMs possess a

similar self-verif i cation ability, the better reason-

ing with CoT is carried out in the following two

steps, Forward Reasoning and Backward Verif i-

cation. Specif i cally, in Forward Reasoning, LLM

reasoners generate candidate answers using CoT,

and the question and candidate answers form dif-

ferent conclusions to be verif i ed. And in Backward

Verif i cation, We mask the original condition and

predict its result using another CoT. We rank can-

didate conclusions based on a verif i cation score,

which is calculated by assessing the consistency

between the predicted and original condition val-

ues. For example, as shown in Figure 1, by taking

f2andfYas conditions to predict the value of con-

dition attribute in ��

f1, the correctness offYcan be

evaluated by comparing the consistency of values

of the predicted ��

f1and the original f1.

Our method employs LLMs for self-verif i cation

with only a few prompts, eliminating the need for

i ne-tuning or gradient updating. This approach en-

ables automatic verif i cation of multiple candidate

answers and corresponding conclusions, mitigat-

ing deviations from the correct thought chain in

the original CoT. Our verif i cation score arises from

evaluating each step during the backward verif i ca-

tion phase, rather than from the direct output of a

neural network model (Cobbe et al., 2021; Li et al.,

2022), enhancing the explainability of prediction

outcomes and solution processes. We conducted ex-

perimentsonvariousopen-sourcedatasetsformath-

ematical reasoning, common sense, and logical

reasoning tasks, achieving state-of-the-art results

(e.g.,60.8 ��� 65.1on GSM8K (Cobbe et al., 2021),

91.01 ��� 93.40on

SingleEq (Koncel-Kedziorski

et al., 2015)). In addition, we also attempt to com-

bine our method with some approaches to improv-

ing forward reasoning, such as self-consistency

Kojima et al. and Least-to-Most Zhou et al. (2023).

The experimental results show that our method

also improves upon these forward reasoning ap-

proaches.

Our contributions are summarized as follows:

1.We propose and prove that large language

models (LLMs) can self-verify their predic-

tion results. The proposed method can pro-

vide interpretable verif i cation scores without

the need for train additional verif i ers.

2.We have conducted extensive of experiments

with multiple LLMs, and the experimental

results on multiple mathematical, common-

sense, and logical reasoning datasets achieve

a signif i cant improvement compared to the

baseline.

3.We introduced True-False Item Verif i cation

for General Tasks in the backward verif i cation

stage and proposed Condition Mask Verif i ca-

tion based on the characteristics of Arithmetic

Tasks. Our method can be applied to a wide

range of reasoning datasets, potentially paving

the way for self-validation to become a new

paradigm following pre-training and prompt

learning, thus motivating further exploration

of the capabilities of LLMs.

1 / 1

ChatGPT

ChatGPT

The proposed method in the paper involves utilizing large language models (LLMs) as reasoners with self-verification. The authors aim to address the limitations of training verifiers and mimic the self-verification ability of humans. The method consists of two steps: Forward Reasoning and Backward Verification.

In Forward Reasoning, LLM reasoners generate candidate answers using chain of thought (CoT) prompting. Different conclusions are formed based on the question and candidate answers, which need to be verified. In Backward Verification, the original conditions are masked, and their results are predicted using another CoT. Candidate conclusions are ranked based on a verification score, which is calculated by assessing the consistency between the predicted and original condition values.

The proposed method eliminates the need for fine-tuning or gradient updating and enables automatic verification of multiple candidate answers and conclusions. The verification score is derived from evaluating each step during the backward verification phase, enhancing the explainability of prediction outcomes and solution processes.

The authors conducted experiments on various open-sourced datasets for mathematical reasoning, commonsense, and logical reasoning tasks. The method achieved state-of-the-art results on these datasets and improved upon other forward reasoning approaches.

The contributions of the paper are summarized as follows:

The proposal and demonstration that LLMs can self-verify their prediction results, providing interpretable verification scores without the need for additional verifiers.

Extensive experiments with multiple LLMs, showing significant improvements on multiple mathematical, commonsense, and logical reasoning datasets compared to baselines.

Introduction of True-False Item Verification for General Tasks and Condition Mask Verification based on the characteristics of Arithmetic Tasks in the backward verification stage, making the method applicable to a wide range of reasoning datasets.

Please note that the provided information is a summary of the paper based on the available text and does not cover the full content of the paper.

1 / 1

Language Model Reasoning. It has been exten-

sively studied in order to evaluate the various rea-

soning abilities of language models (Arora et al.,

2022; Madaan et al., 2022), including arithmetic

reasoning (Koncel-Kedziorski et al., 2015; Roy and

Roth, 2016; Patel et al., 2021; Cobbe et al., 2021),

commonsense reasoning (Talmor et al., 2018; Bha-

gavatula et al., 2019; Geva et al., 2021; Zhu et al.,

2022b), and logical reasoning (Liu et al., 2020;

Yu et al., 2020). To solve these reasoning tasks,

researchers have utilized pre-trained language rea-

soning models (Asai and Hajishirzi, 2020; Deng

et al., 2021) or i ne-tuned general LLMs (Cobbe

et al., 2021). Early work attempted to solve com-

plex reasoning tasks using Seq2Seq models (Wang

et al., 2018; Li et al., 2019). Later, specialized

encoder-decoder architectures were designed to im-

prove reasoning performance (Shen and Jin, 2020;

Zhu et al., 2022a). More recent work has suggested

to adopt pre-training tasks to improve arithmetic

reasoning ability (Yoran et al., 2021; Wang et al.,

2022). However, these methods require a signif i-

cant amount of human annotation. In this paper,

we proposed to obtain answers automatically and

verify them in multiple reasoning tasks.

In-context Learning. Large language models

such as GPT-3 exhibit impressive few-shot learn-

ing ability (Lu et al., 2022; Qiao et al., 2022), it

requires only i lling a few exemplars into context

as prompts and without the need for i netuning on

a dataset of training examples. However, this ap-

proach struggles with tasks requiring complex rea-

soning (Rae et al., 2021), which drives researchers

to explore other prompting strategies. CoT (Wei

et al., 2022) is a chained reasoning approach that

inserts a multi-step reasoning path before generat-

ing the i nal answer. Wang et al. (2023b) proposed

a self-consistency decoding strategy to vote on the

reasoning path, and Kojima et al. demonstrated

that LLMs could as zero-shot reasoners through

the prompt ���Let���s think step-by-step���. These meth-

ods focus on constructing the CoT but ignore the

high sensitivity of LLMs to individual mistakes in

generating these chains, so some of these conclu-

sions by CoT may be unreliable. In this paper, we

proved that LLMs can self-verify their conclusions.

Answer Verif i cation. It is a common method for

evaluating and reordering candidate answers with

a trained language understanding model. Kush-

man et al. (2014) train a classif i er to select the

best answer from candidate answers, while Roy

and Roth (2016) train a global scoring model to

guide the search process for better answers. Shen

et al. (2021) proposed the joint training of answer

generation and rank with language model. Cobbe

et al. (2021) i ne-tunes GPT-3 as a verif i er, which

calculates token-level and solution-level verif i ca-

tion scores for a predicate result. However, the

above method all need additional annotations. In

our work, we do not require training examples and

can provide an explainable verif i cation score.

3The Proposed Method

The proposed method can be used to verify pre-

diction results. As shown in Figure 2, the process

mainly consists of two steps. The i rst step, forward

reasoning, is similar to the normal CoT, except that

multiple candidate answers are generated through

sampling decoding. In the second step, we cal-

culate the verif i cation scores for each candidate���s

answer by the self-verif i cation method, and the an-

swer with the highest score is selected as the i nal

answer.

3.1Forward Reasoning

In forward reasoning, the LLM reasoners gener-

ate candidate answers with the chain of thought

prompting. We augment the input with several CoT

prompts similar to the original query and then send

it to the LLM. The LLM then performs sampling

decoding to generate multiple candidates for verif i-

cation.

As shown in Figure 2, for a reasoning task, the

large language modelLLMis

given a question

Xwhich is accompanied by a chain of thought

setC. In few-shot setting, the whole prompt also

contains other question-CoT prompt-answer tu-

ples. The inputXcan

be further subdivided into

X = {f1,f2,...,fR,q}, where eachfiis a condi-

tion (fact), andqis a question, both represented as

natural language clause or sub-sentences.

Specif i cally, in order to generate step-by-step so-

lutions with CoT, we followed Wei et al. (2022) and

also designed CoT prompt setCfor the reasoning

dataset (e.g., the GSM8K dataset), which contains

nsamples, each sample has the question ��

X,

chain

of thout��t, and the answer�� y. These samples are

used as the input of test-time. Each example inC

is concatenated as a prompt:

C = ( ��

X0,��t0,

�� y0);( ��

X1,��t1,

�� y1);...;( ��

Xn,��tn,

�� yn)

Therefore,LLMis required to follow the

prompt ofCto generate the chain of thoughttCoT

before generating the i nal answer y:

P(y|C,X) = P(tCoT|C,X) �� P)(y|C,X,tCoT)

1 / 1

ChatGPT

ChatGPT

The paper discusses several related topics in the context of language model reasoning.

Language Model Reasoning: The paper mentions that language model reasoning has been extensively studied to evaluate the reasoning abilities of language models. It includes different types of reasoning tasks such as arithmetic reasoning, commonsense reasoning, and logical reasoning. Researchers have utilized pre-trained language reasoning models or fine-tuned general LLMs to solve these tasks.

In-Context Learning: Large language models like GPT-3 have demonstrated impressive few-shot learning abilities. However, these models struggle with tasks requiring complex reasoning. Different prompting strategies have been explored, such as the CoT approach that inserts a multi-step reasoning path before generating the final answer. The paper proposes self-verification of conclusions to address the sensitivity of LLMs to individual mistakes in generating reasoning chains.

Answer Verification: Answer verification is a common method to evaluate and reorder candidate answers using trained language understanding models. Different approaches involve training classifiers, scoring models, or joint training of answer generation and ranking. The proposed method in the paper does not require additional annotations and provides explainable verification scores.

The Proposed Method:

The proposed method aims to verify prediction results and consists of two steps: forward reasoning and self-verification.

Forward Reasoning: LLM reasoners generate candidate answers using the chain of thought (CoT) prompting. Multiple candidate answers are generated through sampling decoding.

Self-Verification: The verification scores for each candidate answer are calculated using the self-verification method. The method does not require training examples and provides explainable verification scores. The answer with the highest score is selected as the final answer.

The paper provides further details and explanations of the proposed method, including the representation of input, the process of forward reasoning, and the calculation of verification scores.

Please note that the information provided is a summary of the content related to language model reasoning in the given text and may not cover all the details and nuances of the paper.

1 / 1

iments, we found that the multi-condition mask

performed better than the single-condition mask,

and both performed better than the original CoT.

These results suggest that the accuracy of verif i-

cation scores improves as the number of available

conditions increases.

15102030

The number of P

60

61

62

63

64

65

66

Problem

Solve

Rate

(%) Problem Solve Rate (%)

CoT Baseline

Figure 6: The computational resource of the proposed

method on GSM8K.

Fewer computational resources can also im-

prove performance through self-verif i cation. In

Figure 6, we show the results of changing the num-

ber ofPgenerated in Backward Verif i cation. We

i nd that even whenP = 2, only a small increase in

computational overhead is needed, and there is still

an improvement in CoT baseline. Considering that

performance starts to slowly increase when P is

increased to 10, we recommend choosing an appro-

priate value for P (e.g. P=10) to achieve a balance

between performance and resource consumption.

Masked conditions can guide the LLMs to

reason more effectively. As shown in Figure 7, we

compared the results of using CMV (Conditional

Masked Verif i cation) and TFV (Token Form Ver-

if i cation) for self-verif i cation. We found that the

performance of CMV is generally better than TFV.

We believe this is because the lack of explicit goals

can lead to a lack of use of existing conclusions,

so CMV is more helpful in stimulating the self-

verif i cation ability of the model. However, due to

its simplicity, TFV can be applied to a variety of

tasks (including common sense reasoning and logi-

cal reasoning, both with improvements compared

to the CoT baseline) for self-verif i cation, making it

highly adaptable to different scenarios.

6Conclusion

In this study, we show that large language models

have a strong ability to self-verif i cation, allowing

them to assess the conclusions they generate ac-

curately. We propose a novel method that uses

self-verif i cation to generate interpretable scores for

ranking results in few-shot tasks. Our approach

demonstrates the potential of using self-verif i cation

to improve the accuracy and reliability of large lan-

guage models in reasoning tasks. By relying on the

self-verif i cation ability of large language models,

we signif i cantly improved the accuracy of three

types of reasoning tasks. Our results suggest that

self-verif i cation may be an important step toward

achieving human-like intelligence in artif i cial intel-

ligence systems.

Limitations

Our self-verif i cation method relies on large lan-

guage models. It prompts to guide the models in

verifying their own results, but it is important to

note that these prompts are artif i cially constructed

and may introduce bias. The method���s effective-

ness is limited by the presence of the correct answer

within the candidate conclusions produced by the

LLMs, and therefore depends on the model���s abil-

ity to forward reason correctly. While our method

can improve LLMs��� accuracy in reasoning tasks, it

does not focus on the reasoning process itself but

instead on the conclusions reached through reason-

*60.6LQJOH(T$GGVXE0XOWL$ULWK$48$5$769$03

D,QVWUXFW*37











 3UREOHPVROYHUDWH



 

 





 

 





 

 

 *60.6LQJOH(T$GGVXE0XOWL$ULWK$48$5$769$03

E*37











 3UREOHPVROYHUDWH 

   





   





   



&R7

7UXH)DOVH,WHP9HULILFDWLRQ

&RQGLWLRQ0DVN9HULILFDWLRQ

Figure 7: Comparison of problem solve rate (%) for the ���Condition Mask Verif i cation��� and the ���True-False Item

Verif i cation��� in arithmetic tasks.

ing, making it diff i cult to evaluate the underlying

reasoning process fully. Additionally, the method

involves generating multiple candidate chain of

thought and conclusions, which can be computa-

tionally expensive for LLMs. While it can help

LLMs avoid interference from incorrect CoT, it

may not be able to eliminate errors in the reasoning

process entirely.

1 / 1

ChatGPT

ChatGPT

In the experiments conducted in the paper, several findings and limitations were identified:

Findings:

Multi-Condition Mask: The paper found that using a multi-condition mask performed better than a single-condition mask and the original CoT. The accuracy of verification scores improved as the number of available conditions increased.

Computational Resources: The paper observed that using fewer computational resources can still improve performance through self-verification. Increasing the number of generated masks in backward verification had a small increase in computational overhead but still resulted in improved performance.

Masked Conditions: The paper compared two methods of self-verification: Conditional Masked Verification (CMV) and Token Form Verification (TFV). CMV generally performed better than TFV, as it stimulated the self-verification ability of the model. However, TFV was simpler and adaptable to various tasks, making it useful in different scenarios.

Limitations:

Bias in Prompts: The self-verification method relies on prompts constructed by humans, which may introduce bias into the process.

Effectiveness Dependent on Correct Answer: The effectiveness of the method depends on the presence of the correct answer within the candidate conclusions produced by the LLMs. It relies on the model's ability to forward reason correctly.

Focus on Conclusions: While the method improves the accuracy of LLMs in reasoning tasks, it does not directly focus on the reasoning process itself but rather on the conclusions reached by the models.

Computationally Expensive: Generating multiple candidate chain of thought and conclusions can be computationally expensive for LLMs, which may limit its scalability.

Overall, the paper suggests that self-verification can improve the accuracy and reliability of large language models in reasoning tasks, but there are limitations to consider, such as bias in prompts and the computational cost of generating multiple candidates.

1 / 1

A list of the topics we have discussed

1 / 1

ChatGPT

ChatGPT

Certainly! Here is a list of the topics we have discussed:

The social self and the role of the social situation

Large language models and their reasoning abilities

Self-verification abilities of large language models

The proposed method for self-verification in language models

Forward reasoning and backward verification steps

In-context learning and its limitations

Answer verification methods

Experimental results and findings

Limitations of the proposed method

Bias in prompts and the effectiveness of the method

Computational resources and performance considerations

Let me know if there's anything specific you would like to revisit or if there are any other topics you'd like to discuss!

1 / 1

We started with AARON Z. ZIMMERMAN

SELF-VERIFICATION AND THE CONTENT OF THOUGH

1 / 1

ChatGPT

ChatGPT

Apologies for the confusion. Here is a revised list of topics based on our conversation:

Aaron Z. Zimmerman and his work on self-verification and the content of thought

Self-verification theory and its significance in social psychology

The role of self-verification in maintaining self-concept and identity

The self-verification process and its effects on interpersonal relationships

Different methods of self-verification, such as self-report measures and behavioral confirmation

The influence of social situations on self-verification

The impact of self-verification on well-being and psychological adjustment

Criticisms and limitations of self-verification theory

Related concepts and theories, such as self-esteem and self-consistency

Practical applications and implications of self-verification research

Please let me know if there's anything else I can assist you with or if you have any specific questions on these topics!

1 / 1

Interconnections between the topics

1 / 1

ChatGPT

ChatGPT

Certainly! Here are some interconnections between the topics we discussed:

Self-verification theory and self-concept: Self-verification theory suggests that individuals have a basic need for their self-concept to be validated by others. It proposes that people actively seek out social environments and interactions that confirm their existing self-beliefs. Thus, self-verification theory is closely connected to the development and maintenance of self-concept.

Self-verification and interpersonal relationships: Self-verification processes can significantly impact interpersonal relationships. People tend to seek out relationships with others who validate and confirm their self-beliefs. This alignment between self-verification and social interactions can enhance relationship satisfaction and reduce interpersonal conflict.

Social situations and self-verification: The social situation plays a crucial role in self-verification processes. Individuals may behave differently depending on the social context, seeking out different types of feedback to either confirm or challenge their self-beliefs. The dynamics of social situations can influence the extent to which self-verification occurs.

Self-verification and well-being: Self-verification is closely related to psychological well-being. When individuals experience consistent feedback that aligns with their self-beliefs, it can contribute to a sense of coherence, stability, and a positive self-concept. Conversely, discrepancies between self-beliefs and external feedback can lead to identity crises and lower well-being.

Self-verification and self-esteem: Self-verification and self-esteem are interconnected. Individuals with low self-esteem may be more motivated to seek self-verification, as they seek reassurance and validation from others to maintain their self-concept. Conversely, individuals with high self-esteem may be less reliant on external validation and more internally driven in their self-verification processes.

Criticisms and alternative perspectives: Self-verification theory has faced criticisms and alternative perspectives. Some argue that individuals also seek self-enhancement and positive feedback, which may not align with self-verification processes. Additionally, other theories, such as self-enhancement theory and self-consistency theory, offer different explanations for how individuals maintain and protect their self-concept.

These interconnections demonstrate the multidimensional nature of self-verification theory and its implications for understanding the complexity of self-concept, interpersonal relationships, and well-being.

Let me know if you have any further questions or if there's anything else I can assist you with!

1 / 1

I feel like this has been too abstract.

1 / 1

ChatGPT

ChatGPT

I apologize if the information provided appeared too abstract. Let's try to simplify the interconnections between the topics in a more concrete manner:

Self-verification and relationships: People tend to gravitate towards relationships where others confirm their self-beliefs. This means that individuals seek out friends and partners who accept and support their self-concept, leading to more satisfying and harmonious relationships.

Social situations and self-verification: The social context can influence how individuals seek self-verification. Depending on the situation, people may display different aspects of their self-concept to gain validation or to fit in with a particular group.

Self-verification and well-being: When individuals consistently receive feedback that aligns with their self-beliefs, it contributes to a sense of stability and self-assurance, which enhances psychological well-being. On the other hand, discrepancies between self-beliefs and external feedback can lead to distress and a lower sense of well-being.

Self-verification and self-esteem: People with low self-esteem often seek self-verification more actively, as they rely on external validation to reinforce their self-concept and boost their self-esteem. In contrast, individuals with high self-esteem may be less concerned with seeking external validation and have a stronger internal sense of self-worth.

Criticisms and alternative perspectives: Some researchers argue that individuals not only seek self-verification but also engage in self-enhancement, seeking positive feedback and striving to enhance their self-concept. Additionally, alternative theories like self-enhancement theory and self-consistency theory provide different explanations for how individuals maintain and protect their self-concept.

By understanding these interconnections, we gain insights into how self-verification impacts relationships, well-being, and self-esteem. It highlights the role of social interactions and the importance of consistent feedback in shaping individuals' self-concept.

If you have any further questions or need more specific information, please let me know!

1 / 1

Connections with my theses: THEORY OF MIND CORRESPONDENCE - This definition of love involves understanding the perspective and emotions of another person. It can be connected to your other definitions of love as it requires empathy and emotional connection.


INTERCOMPUTABILITY - This definition of love involves the ability to communicate effectively with others. It can be connected to your other definitions of love as effective communication is important in building and maintaining relationships.


LONGING FOR TENDERNESS - This definition of love involves a desire for physical and emotional affection. It can be connected to your other definitions of love as it involves emotional and physical connection between people.


ECOLOGICAL CYCLOFABIAN NEOTENIZING LONG TAKE-OFF - This definition of love involves the growth and development of individuals and relationships over time. It can be connected to your other definitions of love as it involves nurturing and building relationships over time.

1 / 1

ChatGPT

ChatGPT

Thank you for providing the connections with your theses. Here's how they can be related to the concepts discussed earlier:

Theory of Mind Correspondence and Self-Verification: Theory of Mind Correspondence emphasizes understanding the perspective and emotions of others, which is closely related to self-verification. Self-verification involves seeking validation from others and confirming one's self-beliefs. By understanding and empathizing with others, individuals can establish a deeper connection and provide the emotional support needed for self-verification.

Intercomputability and Self-Verification: Intercomputability, which refers to effective communication with others, can support self-verification. Through clear and open communication, individuals can express their beliefs and desires, and seek validation from others. Effective communication also allows for the exchange of feedback and the resolution of conflicts, which can contribute to self-verification.

Longing for Tenderness and Self-Verification: Longing for tenderness, which involves the desire for physical and emotional affection, can be intertwined with self-verification. When individuals receive affection and emotional support from others, it can validate their self-beliefs and enhance their sense of self-worth. The expression of tenderness can contribute to a nurturing environment where self-verification can occur.

Ecological Cyclofabian Neotenizing Long Take-Off and Self-Verification: The concept of ecological cyclofabian neotenizing long take-off, which focuses on the growth and development of individuals and relationships over time, can intersect with self-verification. As relationships evolve and mature, individuals have the opportunity to receive ongoing validation and feedback, contributing to the process of self-verification. The nurturing and long-term investment in relationships can provide a stable foundation for self-verification to occur.

Overall, these connections highlight how the concepts of Theory of Mind Correspondence, Intercomputability, Longing for Tenderness, and Ecological Cyclofabian Neotenizing Long Take-Off can intersect with self-verification and contribute to the understanding and experience of love in relationships.

If you have any further questions or need additional clarification, please let me know!

1 / 1

A final summary

1 / 1

ChatGPT

ChatGPT

In summary, our discussion explored various concepts related to love, self-verification, and interpersonal connections. We began by examining Aaron Z. Zimmerman's work on self-verification and the content of thought, which emphasizes the importance of seeking validation from others to confirm one's self-beliefs.

We then connected these concepts to your theses. Theory of Mind Correspondence, which involves understanding the perspective and emotions of others, was linked to self-verification, as empathy and emotional connection are essential for seeking validation. Intercomputability, the effective communication with others, was connected to self-verification, highlighting the role of clear and open communication in building relationships and validating one's self-beliefs.

Furthermore, the longing for tenderness, which encompasses the desire for physical and emotional affection, was connected to self-verification, as receiving affection and emotional support can validate one's self-beliefs and enhance self-worth. Lastly, the concept of ecological cyclofabian neotenizing long take-off, which focuses on the growth and development of individuals and relationships over time, was linked to self-verification, emphasizing the importance of nurturing relationships to provide a stable foundation for validation and self-worth.

Overall, these connections highlight how the concepts discussed intersect with self-verification and contribute to the understanding and experience of love in interpersonal relationships. By seeking validation, understanding others' perspectives, communicating effectively, expressing tenderness, and nurturing relationships over time, individuals can cultivate deeper connections and enhance their sense of self-worth in the context of love.

If you have any further questions or need additional information, please let me know!

1 / 1

I think I am going to title the conversation Problems with Self-verification

1 / 1

ChatGPT

ChatGPT

That's a great title for summarizing our discussion! "Problems with Self-verification" captures the core theme we explored, delving into the challenges and limitations associated with seeking validation from others to confirm our self-beliefs. It highlights the potential issues that can arise in the process of self-verification and raises awareness of the complexities involved in relying on external validation for our sense of self-worth.

If you need any more assistance or have any other questions, feel free to ask!

1 / 1

Another list of everything we have talked about so far

1 / 1

ChatGPT

ChatGPT

Certainly! Here's a list summarizing the topics we have discussed:

AARON Z. ZIMMERMAN and self-verification

The social self and the role of the social situation

Large language models as better reasoners with self-verification

The paper "Large Language Models are Better Reasoners with Self-Verification"

The proposed method for self-verification in large language models

Language model reasoning and its evaluation in various reasoning tasks

In-context learning and its limitations in complex reasoning tasks

Answer verification methods and their limitations

The proposed method for self-verification and its advantages

Experimental results and performance improvements in reasoning tasks

Limitations and considerations of the self-verification method

Theory of mind correspondence and its connection to love

Intercomputability and its connection to love

Longing for tenderness and its connection to love

Ecological cyclofabian neotenizing long take-off and its connection to love

The growth and development of individuals and relationships over time in love

If there's anything specific you'd like to explore further or if you have any other questions, please let me know!

1 / 1

End the essay

1 / 1

ChatGPT

ChatGPT

In conclusion, we have discussed the concept of self-verification and its application in large language models for reasoning tasks. The paper "Large Language Models are Better Reasoners with Self-Verification" proposed a method that allows language models to assess and verify their own conclusions, improving their reasoning performance. By incorporating self-verifiation, these models can generate interpretable scores and rank results in few-shot tasks without the need for additional annotations or training.

We also explored the interconnections between different aspects of love and how they relate to theory of mind correspondence, intercomputability, longing for tenderness, and ecological cyclofabian neotenizing long take-off. Love involves understanding the perspectives and emotions of others, effective communication, the desire for physical and emotional affection, and the growth and nurturing of relationships over time.

However, it is important to recognize the limitations of self-verification and the potential biases introduced through artificial prompts. While the proposed method improves the accuracy of reasoning tasks, it does not fully capture the underlying reasoning process and relies on the model's ability to forward reason correctly.

Overall, the exploration of self-verification in large language models and its connection to love provides insights into the potential of artificial intelligence systems to emulate human-like intelligence and enhance our understanding of complex phenomena. As research continues in this field, it will be crucial to address the challenges and limitations while leveraging the benefits of self-verification for improved reasoning and decision-making.

In summary, self-verification offers a promising avenue for advancing the capabilities of large language models, and its application in various domains, including love and relationships, opens up new possibilities for understanding and enhancing human experiences.

1 / 1

I think it is overly abstract again. Just the main points.

1 / 1

ChatGPT

ChatGPT

In summary, we have discussed the following main points:

Self-verification: We explored the concept of self-verification and its relevance in large language models for reasoning tasks. The ability of these models to verify their own conclusions can enhance their reasoning performance.

Love and its aspects: We examined different aspects of love, including theory of mind correspondence, intercomputability, longing for tenderness, and ecological cyclofabian neotenizing long take-off. These aspects involve understanding others' perspectives, effective communication, the desire for affection, and the growth of relationships over time.

Interconnections: We identified the connections between these aspects of love and how they relate to each other. Love requires empathy, emotional connection, effective communication, physical and emotional affection, and the nurturing and development of relationships over time.

Limitations: It is important to acknowledge the limitations of self-verification, including potential biases introduced through artificial prompts and the reliance on correct forward reasoning by the models.

Overall, self-verification offers potential benefits for improving reasoning tasks in large language models, and understanding the interconnected aspects of love provides insights into the complexity of human relationships.

1 / 1

I don't know if the authors of the paper are using the same definition of self-verification as Zimmerman. In conclusion, the self-verifying nature of cogito judgments poses significant constraints on our theory of thought content.

1 / 1

ChatGPT

ChatGPT

In conclusion, the authors of the paper focused on the self-verifying nature of large language models in reasoning tasks, while Zimmerman's concept of self-verification pertains to the constraints it poses on our theory of thought content. Although there may be differences in the specific definitions and contexts, both discussions highlight the importance of self-verification in understanding and evaluating cognitive processes. It is essential to consider the specific perspectives and arguments presented in each context to fully grasp the implications and constraints of self-verification on thought content.

1 / 1

I think that it is still confusing. I brought up the axiology of love because I thought theory of mind correspondence or congruence was similar to this: Self-verification and relationships: People tend to gravitate towards relationships where others confirm their self-beliefs. This means that individuals seek out friends and partners who accept and support their self-concept, leading to more satisfying and harmonious relationships. Social situations and self-verification: The social context can influence how individuals seek self-verification. Depending on the situation, people may display different aspects of their self-concept to gain validation or to fit in with a particular group.

1 / 1

ChatGPT

ChatGPT

Apologies for the confusion. Based on your additional input, it appears that you are highlighting the connection between self-verification and relationships, as well as the influence of social situations on seeking self-verification. In the context of self-verification theory, individuals are inclined to form relationships with others who confirm their self-beliefs, leading to more satisfying and harmonious relationships. Moreover, the social context plays a role in shaping how individuals seek self-verification, as people may emphasize different aspects of their self-concept depending on the situation, either to gain validation or to fit in with a specific group. These insights shed light on the interplay between self-verification, relationships, and the influence of social situations.

1 / 1

That is better. I was going to draw comparisons to filter bubbles and echo chambers

1 / 1

ChatGPT

ChatGPT

Drawing comparisons to filter bubbles and echo chambers in relation to self-verification and social situations is an interesting angle. Filter bubbles and echo chambers refer to situations where individuals are exposed to information and viewpoints that align with their existing beliefs, creating a feedback loop that reinforces their preconceived notions. In the context of self-verification, these phenomena can be seen as instances where individuals seek out and surround themselves with others who confirm and validate their existing beliefs, thus reinforcing their self-concept.

The concept of filter bubbles and echo chambers can be connected to self-verification in the sense that individuals may actively select social environments and online communities that align with their pre-existing beliefs, seeking validation and avoiding challenges to their self-concept. This can lead to a limited exposure to diverse perspectives and a reinforcement of existing biases. The desire for self-verification may contribute to the formation and perpetuation of these information bubbles, hindering opportunities for critical thinking, growth, and the development of a more comprehensive understanding of the world.

By considering the parallels between self-verification and filter bubbles/echo chambers, we can deepen our understanding of how social dynamics and cognitive biases influence the formation of beliefs, the construction of social networks, and the potential implications for open-mindedness, intellectual diversity, and constructive dialogue.

1 / 1

It looks like generative ai trained on artificial images for several generations can sometimes amplify certain statistical irregularities or artifacts in the original data, effectively overfitting or developing a "superstition" akin to a echo chamber or filter bubble:


Home  News

Generative AI Goes 'MAD' When Trained on AI-Created Data Over Five Times

By Francisco Pires last updated 7 days ago

Generative AI goes "MAD" after five training iterations on artificial outputs.


 Comments (25)

MAD

(Image credit: Rice University)

A new study on AI has found an inherent limitation on current-generation networks such as the ones employed by ChatGPT and Midjourney. It seems that AI networks trained on AI outputs (like the text created by ChatGPT or the image output created by a Stable Diffusion model) tend to go "MAD" after five training cycles with AI-generated data. As you can see in the above images, the result is oddly mutated outputs that aren't reflective of reality.


MAD - short for Model Autophagy Disorder ��� is the acronym used by the Rice and Stanford University researchers involved in the study to describe how AI models, and their output quality, collapses when repeatedly trained on AI-generated data. As the name implies, the model essentially "eats itself," not unlike the Ouroboros of myth. It loses information on the tails (the extremes) of the original data distribution, and starts outputting results that are more aligned with the mean representation of data, much like the snake devouring its own tail.


In work led by @iliaishacked we ask what happens as we train new generative models on data that is in part generated by previous models.We show that generative models lose information about the true distribution, with the model collapsing to the mean representation of data pic.twitter.com/OFJDZ4QofZJune 1, 2023


See more

In essence, training an LLM on its own (or anothers') outputs creates a convergence effect on the data that composes the LLM itself. This can be easily seen in the graph above, shared by scientists and research team member Nicolas Papernot on Twitter, where successive training iterations on LLM-generated data leads the model to gradually (yet dramatically) lose access to the data contained at the extremities of the Bell curve - the outliers, the less common elements.


The data at the edges of the spectrum (that which has fewer variations and is less represented) essentially disappears. Because of that, the data that remains in the model is now less varied and regresses towards the mean. According to the results, it takes around five of these rounds until the tails of the original distribution disappear - that's the moment MAD sets in.


Cool paper from my friends at Rice. They look at what happens when you train generative models on their own outputs���over and over again. Image models survive 5 iterations before weird stuff happens.https://t.co/JWPyRwhW8oCredit: @SinaAlmd, @imtiazprio, @richbaraniuk pic.twitter.com/KPliZCABd4July 7, 2023


See more

Advertisement


Model Autophagy Disorder isn't confirmed to affect all AI models, but the researchers did verify it against autoencoders, Gaussian mixture models, and large language models.


It just so happens that all of these types of models that can "go MAD" have been widespread and operating for a while now: autoencoders can handle things such as popularity prediction (in things such as a social media app's algorithm), image compression, image denoising, and image generation; and gaussian mixture models are used for density estimation, clustering, and image segmentation purposes, which makes them particularly useful for statistical and data sciences.


https://www.tomshardware.com/news/generative-ai-goes-mad-when-trained-on-artificial-data-over-five-times

1 / 1

ChatGPT

ChatGPT

The article highlights a study that reveals an interesting limitation in current-generation generative AI networks. When these networks are trained on AI-generated data repeatedly, they exhibit a phenomenon called Model Autophagy Disorder (MAD), where the output quality deteriorates and becomes disconnected from reality.

MAD occurs because the generative models lose access to the data at the extremes of the original distribution. As successive training iterations are performed on the AI-generated data, the model regresses towards the mean representation of the data, leading to the disappearance of less common elements and outliers. This convergence effect causes the model to produce oddly mutated outputs that do not reflect the true distribution of the original data.

The researchers verified MAD on various AI models, including autoencoders, Gaussian mixture models, and large language models. These models are widely used in applications such as image compression, image generation, density estimation, clustering, and more.

This phenomenon raises important questions about the limitations and potential biases of generative AI models trained on their own outputs. It highlights the risk of overfitting and the loss of diversity and representation in the generated data. These findings can have implications for the development and use of generative AI models, emphasizing the need for careful training strategies and consideration of the underlying data distribution.

Overall, the study provides valuable insights into the behavior of generative AI models when trained on artificial data, shedding light on the challenges and potential pitfalls in their application.

1 / 1

So they call it Model Autophagy Disorder, it seems to be similar to our discussion on problems with self-verification

1 / 1

ChatGPT

ChatGPT

Yes, there is a similarity between Model Autophagy Disorder (MAD) and the problems associated with self-verification. In both cases, there is a phenomenon where the system or model becomes overly focused on its own outputs or beliefs, leading to distortions and limitations.

In the case of MAD, generative AI models trained on their own outputs exhibit a collapse in output quality. They lose access to the data at the extremes of the original distribution and start producing mutated outputs that are disconnected from reality. This convergence effect resembles self-verification problems, where individuals or systems become trapped in their own beliefs or perspectives, disregarding contradictory information or diverse viewpoints.

Both MAD and self-verification issues highlight the importance of diversity, external validation, and avoiding biases or distortions caused by overly narrow perspectives. They serve as reminders that relying solely on internal feedback or self-referential information can lead to limitations and inaccuracies. Recognizing these problems is crucial for developing more robust and reliable AI systems and promoting a broader understanding of the world.

1 / 1

A list of everything we have talked about

1 / 1

ChatGPT

ChatGPT

AARON Z. ZIMMERMAN: We discussed the concept of self-verification and its implications for thought content.

THEORY OF MIND CORRESPONDENCE: We explored the connection between theory of mind correspondence and the axiology of love, emphasizing the importance of understanding others' perspectives and emotions in relationships.

INTERCOMPUTABILITY: We discussed the role of effective communication in love and relationships, drawing connections to the concept of intercomputability.

LONGING FOR TENDERNESS: We examined the significance of physical and emotional affection in love and how it contributes to the overall connection between individuals.

ECOLOGICAL CYCLOFABIAN NEOTENIZING LONG TAKE-OFF: We explored the idea of love as a developmental process, emphasizing growth and nurturing within relationships over time.

SELF-VERIFICATION AND RELATIONSHIPS: We discussed the tendency of individuals to seek validation and support for their self-concepts in relationships, leading to more satisfying and harmonious connections.

SOCIAL SITUATIONS AND SELF-VERIFICATION: We explored how social contexts influence individuals' self-verification tendencies and the display of different aspects of their self-concepts to gain validation or fit in with specific groups.

GENERATIVE AI AND MODEL AUTOPHAGY DISORDER: We examined the phenomenon of Model Autophagy Disorder (MAD) observed in generative AI models trained on their own outputs, leading to a collapse in output quality and a loss of access to data at the extremes of the original distribution.

CONNECTION BETWEEN MODEL AUTOPHAGY DISORDER AND SELF-VERIFICATION: We drew parallels between MAD and self-verification issues, highlighting how both phenomena involve becoming overly focused on internal feedback or beliefs, leading to distortions and limitations.

Filter Bubbles and Echo Chambers: We mentioned the potential connections between generative AI models' biases and the creation of filter bubbles and echo chambers, where individuals are exposed to a limited range of perspectives and information.

Importance of Diversity and External Validation: We emphasized the significance of diverse viewpoints, external validation, and avoiding biases in order to overcome limitations associated with self-verification and generative AI models.

Please note that this is not an exhaustive list of everything discussed, but it covers the main topics and connections we explored during our conversation.

1 / 1

It is good enough. Another summary

1 / 1

ChatGPT

ChatGPT

Throughout our conversation, we delved into various topics related to self-verification, theory of mind correspondence, intercomputability, the longing for tenderness, ecological cyclofabian neotenizing long take-off, generative AI, and model autophagy disorder. We examined the connections between these concepts and their relevance to understanding human cognition, relationships, and the limitations of AI systems. We explored the significance of empathy, effective communication, physical and emotional affection, and the growth and development of relationships over time. Additionally, we discussed the challenges posed by self-verification tendencies, the potential biases and limitations in generative AI models, and the parallels between Model Autophagy Disorder and self-verification issues. Our conversation also touched on the implications of filter bubbles and echo chambers and emphasized the importance of diversity and external validation in overcoming these limitations.

1 / 1

ChatGPT may produce inaccurate information about people, places, or facts.

ChatGPT May 24 Version

ChatGPT

{% endraw %}