Mental models accurately predict emotion transitions

Thornton, Mark Allen; Tamir, Diana

doi:10.1073/pnas.1616056114

Cited by 91 publications

(132 citation statements)

References 28 publications

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“…1b in the target article) include beliefs and desires but not the agent's affective states as such, yet affect may be a primary source of the information needed to explain oneself and others, or to predict or evaluate what might happen next (cf. Thornton & Tamir 2017). At the same time, seeking to bring an emotional reaction into a rationalizing frame can contribute to emotion regulation, focusing or changing felt affect, and dampening dysfunctional effects (Wilson & Gilbert 2003).…”

Section: Introductionmentioning

confidence: 99%

Rationalization is rational

Cushman¹

2019

Behav Brain Sci

130

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Rationalization occurs when a person has performed an action and then concocts the beliefs and desires that would have made it rational. Then, people often adjust their own beliefs and desires to match the concocted ones. While many studies demonstrate rationalization, and a few theories describe its underlying cognitive mechanisms, we have little understanding of its function. Why is the mind designed to construct post hoc rationalizations of its behavior, and then to adopt them? This may accomplish an important task: transferring information between the different kinds of processes and representations that influence our behavior. Human decision making does not rely on a single process; it is influenced by reason, habit, instinct, norms, and so on. Several of these influences are not organized according to rational choice (i.e., computing and maximizing expected value). Rationalization extracts implicit information – true beliefs and useful desires – from the influence of these non-rational systems on behavior. This is a useful fiction – fiction, because it imputes reason to non-rational psychological processes; useful, because it can improve subsequent reasoning. More generally, rationalization belongs to the broader class of representational exchange mechanisms, which transfer information between many different kinds of psychological representations that guide our behavior. Representational exchange enables us to represent any information in the manner best suited to the particular tasks that require it, balancing accuracy, efficiency, and flexibility in thought. The theory of representational exchange reveals connections between rationalization and theory of mind, inverse reinforcement learning, thought experiments, and reflective equilibrium.

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Section: Introductionmentioning

confidence: 99%

Rationalization is rational

Cushman¹

2019

Behav Brain Sci

130

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“…This Here, we employed a naturalistic continuous stimulation paradigm to foster emotional contagion and empathic reactions 22 . Indeed, we found that within a 60 seconds time window, emotion transitions represented in the Forrest Gump movie are similar to those experienced in real life 44 and are predicted by a mental model of emotion co-occurrence 45 (see Supplementary Materials). This supports the ecological validity of our stimulus and emphasizes that movies can be successfully adopted to nurture emotional resonance [46][47][48] , also in the fMRI setting.…”

Section: Discussionmentioning

confidence: 54%

“…Even though this component does not pertain to classical emotion dimension theories, complexity may be related to the involvement of Theory of Mind 28 in emotion perception 59 . In addition, a recent study on mental representation of emotions 45 We collected behavioral emotion ratings to explain brain activity in independent subjects. In line with previous studies 10,12 , results highlighted a set of regions located mainly in the right hemisphere (Figure 3 and Supplementary Table 1).…”

Section: Discussionmentioning

confidence: 99%

Emotionotopy in the Human Right Temporo-Parietal Cortex

Lettieri

Handjaras

Ricciardi

et al. 2018

Preprint

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Humans use emotions to decipher complex cascades of internal events. However, which mechanisms link descriptions of affective states to brain activity is unclear, with evidence supporting either local or distributed processing. A biologically favorable alternative is provided by the notion of gradient, which postulates the isomorphism between functional representations of stimulus features and cortical distance. Here, we use fMRI activity evoked by an emotionally charged movie and continuous ratings of the perceived emotion intensity to reveal the topographic organization of affective states. Results show that three orthogonal and spatially overlapping gradients encode the polarity, complexity and intensity of emotional experiences in right temporoparietal territories. The spatial arrangement of these gradients allows the brain to map a variety of affective states within a single patch of cortex. As this organization resembles how sensory regions represent psychophysical properties (e.g., retinotopy), we propose emotionotopy as a principle of emotion coding.

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“…Research has indeed found the structure of conceptual associations to substantially reflect that of actual personality (Lay & Jackson, 1969). As further evidence, recent work has found perceiver conceptual associations between emotion concepts allow accurate prediction of others' emotional states (Thornton & Tamir, 2017). For this reason, the application of conceptual associations has recently been proposed as central to mental state inference (Tamir & Thornton, 2018).…”

Section: Discussionmentioning

confidence: 95%

Conceptual associations guide social inference

Rm¹

2019

Preprint

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This is a preprint of a manuscript submitted as a doctoral dissertation to the Department of Psychology at New York University. The dissertation is in stapled form, where a broad introduction and discussion explain the theoretical trajectory of research published elsewhere (peer-reviewed journals, and preprint form). Each chapter references these external manuscripts for the reader. ABSTRACT: In order to efficiently navigate our social world, humans sort one another along dimensions and categories intended to reflect the structure of human behavior. Popular models of social perception generally theorize a relatively fixed detection process to identify functionally and adaptively significant social attributes (e.g., warmth, competence, anger, race). However, recent research suggests considerable malleability in social perception, which is not adequately accounted for by current models. Here I argue that a number of social perception phenomena may be parsimoniously explained not by a set of fixed detectors but by a domain-general account of spreading activation between social concepts through an associative network. Specifically, I propose that, similar to other forms of non-social inference, perceivers form a knowledge structure of what social concepts exist in the world (e.g., frequent speakers are ‘extroverted’) and how those concepts associate with one another (e.g., ‘extroverted’ people are often ‘kind’ and ‘male’), and they then use this structure to make inferences (e.g., 'this kind male is likely extroverted'). Although quite simple, this perspective provides rich predictions that may describe many facets of the social perception process, such as how perceptions vary in their initial formation from cues, automaticity and temporal dynamics, variance within and between perceivers and contexts, and dimensional and categorical structure. This perspective also helps integrate theory of social perception, bridging both perceptual classes (e.g., traits, social categories, and emotion) and their contexts (e.g., face impressions, person knowledge, and group stereotyping).To provide an initial test of this perspective, I examine how social perceptions (e.g., warmth, extroversion) correlate with one another along the lines of their conceptual associations (e.g., ‘are warm people likely to be extroverted?’). In Chapter 1, we demonstrate that face-based trait impressions color one another in to the extent they are conceptually associated. Faces perceived to possess one personality trait (e.g., trustworthiness) elicited additional trait impressions (e.g., creativity) to the extent perceivers conceptually associated the traits (e.g. ‘trustworthy people are often creative’). Chapter 2 extends the findings of Chapter 1 across contexts of social cognition, where the same conceptual structuring of trait impressions emerged across the domains of face impressions, familiar person knowledge, and group stereotype content. Lastly, in Chapter 3, I apply this perspective to the domains of emotion recognition and social categorization. Survey, mouse-tracking, and neuroimaging analyses showed categories apparent in a face (e.g., ‘male’) facilitated or impaired perceptions and neural representations of other categories (e.g., ‘black’) to be in accordance with their conceptual associations. Together, these findings provide evidence for a domain-general account of social perception, which assumes only basic semantic-processing principles, accounts for a number of social perception phenomena, and generates several new theoretical predictions. Overall, this research demonstrates that the perceptions and dimensions which emerge in social perception are bound to perceivers’ conceptual representations of the social world.

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Mental models accurately predict emotion transitions

Cited by 91 publications

References 28 publications

Rationalization is rational

Rationalization is rational

Emotionotopy in the Human Right Temporo-Parietal Cortex

Conceptual associations guide social inference

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