Modeling Variation in Empathic Sensitivity Using Go/No-Go Social Reinforcement Learning

O’Connell, Katherine; Walsh, Marissa; Padgett, Brandon; Connell, Sarah; Marsh, Abigail A.

doi:10.1007/s42761-022-00119-4

Cited by 4 publications

(4 citation statements)

References 68 publications

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“…We found a link between trait psychopathy and learning rates, a finding consistent with prior studies suggesting atypical learning for others with increased psychopathy (Cutler et al, 2021) and antisocial behavior (O'Connell et al, 2022). Our study goes beyond these prior findings by demonstrating that when an action simultaneously affects the agent and someone else, people with higher psychopathy are less sensitive to unexpectedly helping or harming others (i.e., they do not reliably update the cached value of their choice as strongly, and thus their future behavior remains relatively unaffected by these outcomes for others).…”

Section: Discussionsupporting

confidence: 90%

“…All also completed a battery of self-report questionnaires. These included individual differences measures of the following: A 31-item measure of cognitive and affective empathy called the Questionnaire of Cognitive and Affective Empathy (QCAE;Reniers et al, 2011), following previous work demonstrating the relationship between trait empathy and (1) learning rates when learning to gain rewards for others (Lockwood et al, 2016) and (2) sensitivity to subjective values of choice alternatives (e.g., temperature) when learning to avoid punishments for others (Lengersdorff et al, 2020) and a 58-item measure psychopathy, including its three component factors, using the Triarchic Psychopathy Measure (TriPM; Patrick et al, 2009), following previous work demonstrating the relationship between antisociality and sensitivity to others' losses and prediction errors when learning for others (Cutler et al, 2021;O'Connell et al, 2022). This psychopathy measure is consistent, reliable, and valid in comparison to other clinically-assessed psychopathy measures (Drislane et al, 2014;van Dongen et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…More prosocial individuals may be those who are more sensitive to information about outcomes for others, whereas antisocial individuals may be less sensitive to information about outcomes for others. For example, prior research indicates that those who are higher in trait cognitive empathy and lower in psychopathy exhibit increased learning rates when learning to benefit others (i.e., they place greater weight on other-relevant prediction errors to guide future choices) Lockwood et al, 2016) and people who are less sensitive to others' losses during learning exhibit greater antisocial behavioral tendencies (O'Connell et al, 2022). In addition, those who place greater weight on the learned values of available actions for others (i.e., exhibit higher inverse temperatures) display increased valuation-related ventromedial prefrontal cortex activation when learning to avoid harmful outcomes for others (Lengersdorff et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Rhoads¹,

Gan²,

Berluti³

et al. 2023

Preprint

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Most prosocial and antisocial behaviors affect ourselves and others simultaneously. To know whether to repeat choices that help or harm, we must learn from their outcomes. But the neurocomputational processes supporting such simultaneous learning remain poorly understood. In this pre-registered study, two independent samples (N=89) learned to make choices that simultaneously affected themselves and another person. Detailed model comparison showed that people integrate self- and other-relevant information into a single cached value per choice, but update this value asymmetrically based on different types of prediction errors related to the target (e.g., self, other) and valence (e.g., positive, negative). People who acquire more prosocial patterns are more sensitive to information about how their choices affect others. However, those with higher levels of subclinical psychopathic traits are relatively insensitive to unexpected outcomes for others and more sensitive for themselves. Model-based neuroimaging revealed distinct brain regions tracking prediction errors guided by the asymmetric value update. These results demonstrate that the way people distinctly encode self- and other-relevant outcomes resulting from a particular behavior guides how desirable the same behavior will be in the future, regardless of whether it is mutually beneficial or costly, instrumentally harmful, or altruistic.

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Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Rhoads¹,

Gan²,

Berluti³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In real-world social interactions, however, the basic RL model does not take into consideration the complex social contexts or associated emotional reactions. Sophisticated studies have begun to explore the impact of emotional experiences on RL-based learning in social contexts, demonstrating that empathy influences the prosocial learning rate 14,15 and acquiring knowledge about others’ emotions facilitates inferences regarding the informational value of social cues 16 . A recent study by Heffner and colleagues utilized a modified version of the UG that required participants to report their anticipated and actual emotional state for (rather unfair) monetary offers and could demonstrate that emotional, rather than reward PEs are critical determinants for punishing unfair offers in an UG 17 .…”

Section: Introductionmentioning

confidence: 99%

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

Xu,

Zhang,

Zhou

et al. 2024

Preprint

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Traditional decision-making models conceptualize humans as optimal learners aiming to maximize outcomes by leveraging reward prediction errors (PE). While violated emotional expectations(emotional PEs) have recently been formalized, the underlying neurofunctional basis and whether it differs from reward PEs remain unclear. Using a modified fMRI Ultimatum Game on n=43 participants we modelled reward and emotional PEs in response to unfair offers and subsequent punishment decisions. Computational modelling revealed distinct contributions of reward and emotional PEs to punishment decisions, with reward PE exerting a stronger impact. This process was neurofunctionally dissociable such that (1) reward engaged the dorsomedial prefrontal cortex while emotional experience recruited the anterior insula, (2) multivariate decoding accurately separated reward and emotional PEs. Predictive neural expressions of reward but not emotional PEs in fronto-insular systems predicted neurofunctional and behavioral punishment decisions. Overall, these findings suggest distinct neurocomputational processes underlie reward and emotional PEs which uniquely impact social decisions.

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Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness

Saulin,

Ting,

Engelmann

et al. 2024

J. Neurosci.

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Humans need social closeness to prosper. There is evidence that empathy can induce social closeness. However, it remains unclear how empathy-related social closeness is formed and how stable it is as time passes. We applied an acquisition-extinction paradigm combined with computational modelling and fMRI, to investigate the formation and stability of empathy-related social closeness. Female participants observed painful stimulation of another person with high probability (acquisition) and low probability (extinction), and rated their closeness to that person. The results of two independent studies showed increased social closeness in the acquisition block that resisted extinction in the extinction block. Providing insights into underlying mechanisms, reinforcement learning modelling revealed that the formation of social closeness is based on a learning signal (prediction error) generated from observing another's pain, whereas maintaining social closeness is based on a learning signal generated from observing another's pain relief. The results of a reciprocity control study indicate that this feedback recalibration is specific to learning of empathy-related social closeness. On the neural level, the recalibration of the feedback signal was associated with neural responses in anterior insula and adjacent inferior frontal gyrus and the bilateral superior temporal sulcus/temporo-parietal junction. Together, these findings show that empathy-related social closeness generated in bad times, i.e., empathy with the misfortune of another person, transfers to good times, and thus may form one important basis for stable social relationships.Significance StatementHumans feel close to others if they empathize with them. Here we test whether this feeling of social closeness remains if empathy is no longer elicited. Combining mathematical learning models and functional magnetic-resonance imaging, we find that empathy with others’ pain establishes stable social closeness that is maintained even if the other person is feeling well again. Explaining the mechanism, we show that the stability of empathy-induced social closeness is based on the recalibration of an empathy-related learning signal in the anterior insula/inferior frontal gyrus and the temporo-parietal junction. These findings reveal how empathy maintains social closeness and thus contributes to the formation of stable social relationships.

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Modeling Variation in Empathic Sensitivity Using Go/No-Go Social Reinforcement Learning

Cited by 4 publications

References 68 publications

Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness

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