Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence

Tamir, Diana; Thornton, Mark Allen; Contreras, Juan Manuel; Mitchell, James

doi:10.1073/pnas.1511905112

Cited by 148 publications

(192 citation statements)

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“…These dimensions each uniquely mediate the accuracy of participants' mental models. We previously established that the brain is particularly attuned to these same three dimensions when thinking about others' mental states (20). The fact that the dimensional space that the brain uses to encode emotions also facilitates prediction supports a predictive coding account of mental state representation (5), and the foundational role of these particular dimensions to that end.…”

Section: Discussionmentioning

confidence: 79%

“…These states were selected from previous work to representatively sample the conceptual space of states-both emotional and cognitive-that people regularly experience (20), thus affording us a fourth approach for testing the accuracy of participants' mental models. Using Markov modeling, we translated transition ratings into a prediction about the frequency with which people experience of each of the 60 states.…”

Section: Resultsmentioning

confidence: 99%

“…In study 4, we tested the extent to which valence informs people's mental models of emotion transition using the transitions ratings between 60 mental states described above. Each state's valence had been assessed in earlier research (20). If valence informs participants' transition models, then a pair of states that are highly similar on that dimension (e.g., two neutral states or two negative states) should be highly likely to transition from one to the other.…”

Section: Resultsmentioning

confidence: 99%

“…S3) when thinking about others' static mental experiences (20): "social impact" or whether a state is highly socially relevant or irrelevant (e.g., envy vs. sleepiness); "human mind," reflecting whether only humans can experience a state or whether other animals can experience it as well, particularly because it is more bodily in nature (e.g., self-consciousness vs. hunger); and "rationality," indicating whether a state is consider emotional or cognitive (e.g., worry vs. thought). Study 4 tested whether these dimensions likewise inform people's mental models of emotion transitions.…”

Section: Resultsmentioning

confidence: 99%

“…Study 4 also investigated the conceptual building blocks of participants' mental models, testing the extent to which transitional probability ratings were informed by four conceptual dimensions from previous research (20): valence (positive vs. negative), social impact (high arousal, social vs. low arousal, asocial), rationality (cognition vs. affect), and human mind (purely mental and human specific vs. bodily and shared with animals).…”

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confidence: 99%

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Mental models accurately predict emotion transitions

Thornton

Tamir

2017

Proc. Natl. Acad. Sci. U.S.A.

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Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.emotion | experience-sampling | social cognition | theory of mind H umans must navigate a wide variety of stimuli in everyday life, ranging from apples and oranges to automobiles and computer operating systems. However, other humans are perhaps the most consequential stimuli of all, potentially driving the very evolution of the human brain (1). Despite the dazzling array of actions and internal states of which humans are capable, people are remarkably good at understanding each other (2-4). Indeed, the social mind appears particularly attuned to the problem of predicting other people (5). Perceivers make use of a wide variety of perceptible cues-including social context, facial expression, and tone of voiceto infer what emotions others' are feeling (6-8), likely because emotions predict behavior (9, 10). However, these perceptual mechanisms only get us so far: we cannot see what expression our friend will wear next week, nor hear tomorrow's tone of voice. How might we make social predictions beyond the immediate future? Such foresight could convey significant strategic advantages: in the social domain, as in the game of chess (11), success may depend on the depth and breadth of a player's search through others' possible future moves. Here we propose that people use a powerful mechanism for gain...

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Section: Discussionmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Mental models accurately predict emotion transitions

Thornton

Tamir

2017

Proc. Natl. Acad. Sci. U.S.A.

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110

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Posttraumatic stress disorder and the social brain: Affect‐related disruption of the default and mirror networks

et al. 2019

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Background: Social cognitive impairments, specifically in mentalizing and emotion recognition, are common and debilitating symptoms of posttraumatic stress disorder (PTSD). Despite this, little is known about the neurobiology of these impairments, as there are currently no published neuroimaging investigations of social inference in PTSD.Methods: Trauma-exposed veterans with and without PTSD (n = 20 each) performed the Why/How social inference task during functional magnetic resonance imaging (fMRI). Patients with PTSD had two fMRI sessions, between which they underwent affect labeling training. We probed the primary networks of the "social brain"-the default mode network (DMN) and mirror neuron system (MNS)-by examining neural activity evoked by mentalizing and action identification prompts, which were paired with emotional and nonemotional targets.Results: Hyperactivation to emotional stimuli differentiated PTSD patients from controls, correlated with symptom severity, and predicted training outcomes.Critically, these effects were nonsignificant or marginal for nonemotional stimuli.Results were generally consistent throughout DMN and MNS. Unexpectedly, effects were nonsignificant in core affect regions, but robust in regions that overlap with the dorsal attention, ventral attention, and frontoparietal control networks. Conclusions:The array of social cognitive processes subserved by DMN and MNS appear to be inordinately selective for emotional stimuli in PTSD. However, core affective processes do not appear to be the primary instigators of such selectivity. Instead, we propose that affective attentional biases may instigate widespread affect-selectivity throughout the social brain. Affect labeling training may inhibit such biases. These accounts align with numerous reports of affect-biased attentional processes in PTSD.

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Overlapping representations of observed actions and action‐related features

Kabulska,

Zhuang,

Lingnau

2024

Human Brain Mapping

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The lateral occipitotemporal cortex (LOTC) has been shown to capture the representational structure of a smaller range of actions. In the current study, we carried out an fMRI experiment in which we presented human participants with images depicting 100 different actions and used representational similarity analysis (RSA) to determine which brain regions capture the semantic action space established using judgments of action similarity. Moreover, to determine the contribution of a wide range of action‐related features to the neural representation of the semantic action space we constructed an action feature model on the basis of ratings of 44 different features. We found that the semantic action space model and the action feature model are best captured by overlapping activation patterns in bilateral LOTC and ventral occipitotemporal cortex (VOTC). An RSA on eight dimensions resulting from principal component analysis carried out on the action feature model revealed partly overlapping representations within bilateral LOTC, VOTC, and the parietal lobe. Our results suggest spatially overlapping representations of the semantic action space of a wide range of actions and the corresponding action‐related features. Together, our results add to our understanding of the kind of representations along the LOTC that support action understanding.

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Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence

Cited by 148 publications

References 36 publications

Mental models accurately predict emotion transitions

Mental models accurately predict emotion transitions

Posttraumatic stress disorder and the social brain: Affect‐related disruption of the default and mirror networks

Overlapping representations of observed actions and action‐related features

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