The Role of Intelligence in Social Learning

Vostroknutov, Alexander; Polonio, Luca; Coricelli, Giorgio

doi:10.1038/s41598-018-25289-9

Cited by 19 publications

(19 citation statements)

References 66 publications

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“…In the present study we assessed reinforcement learning with a behavioral paradigm involving both private and social contexts, while concomitantly assessing depressive and anxiety symptoms in the general population. First, we replicate previous findings showing that participants integrate the demonstrator’s choices and outcomes, which is consistent with the idea that social learning processes (both in terms of imitation and vicarious trial-and-error) play a role in human reinforcement learning [ 14 , 15 , 26 – 28 ]. Second, we show that the severity of depressive symptoms is associated with a learning impairment that is specific to the learning context where participants are informed about the demonstrator’s choices (social context).…”

Section: Discussionsupporting

confidence: 89%

Depressive symptoms are associated with blunted reward learning in social contexts

2019

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Depression is characterized by a marked decrease in social interactions and blunted sensitivity to rewards. Surprisingly, despite the importance of social deficits in depression, non-social aspects have been disproportionally investigated. As a consequence, the cognitive mechanisms underlying atypical decision-making in social contexts in depression are poorly understood. In the present study, we investigate whether deficits in reward processing interact with the social context and how this interaction is affected by self-reported depression and anxiety symptoms in the general population. Two cohorts of subjects (discovery and replication sample: N = 50 each) took part in an experiment involving reward learning in contexts with different levels of social information (absent, partial and complete). Behavioral analyses revealed a specific detrimental effect of depressive symptoms–but not anxiety–on behavioral performance in the presence of social information, i.e. when participants were informed about the choices of another player. Model-based analyses further characterized the computational nature of this deficit as a negative audience effect, rather than a deficit in the way others’ choices and rewards are integrated in decision making. To conclude, our results shed light on the cognitive and computational mechanisms underlying the interaction between social cognition, reward learning and decision-making in depressive disorders.

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Section: Discussionsupporting

confidence: 89%

Depressive symptoms are associated with blunted reward learning in social contexts

2019

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“…Here, we focused on the Demonstrator's skills and found that when the Demonstrator was not skilled, the imitation learning rate was down-regulated. In other terms, consistent with previous studies, we show that the adaptive regulation of imitation can be achieved by monitoring endogenous signals (likely an estimate of the Demonstrator's performance) and does not necessarily require explicit cues and instructions [7,8,14]. Interestingly, participants in Exp 1 were more successful in modulating their imitation of the UD compared to participants in Exp 2 (cf.…”

Section: Adaptive Modulation Of Imitationsupporting

confidence: 90%

“…The debriefing of the experiment was done with each pair of participants, and all participants were debriefed about the cover story after the experiment. The third dataset presented in this paper comes from a previously published study [14]. The main differences between our design and theirs are summarized in Table 2 The Demonstrator's skills are defined by the correct choice rate.…”

Section: Experimental Task and Proceduresmentioning

confidence: 99%

The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning

et al. 2020

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While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented. To address this issue, we compared three psychologically plausible hypotheses about the algorithmic implementation of imitation in reinforcement learning. The first hypothesis, decision biasing (DB), postulates that imitation consists in transiently biasing the learner’s action selection without affecting their value function. According to the second hypothesis, model-based imitation (MB), the learner infers the demonstrator’s value function through inverse reinforcement learning and uses it to bias action selection. Finally, according to the third hypothesis, value shaping (VS), the demonstrator’s actions directly affect the learner’s value function. We tested these three hypotheses in 2 experiments (N = 24 and N = 44) featuring a new variant of a social reinforcement learning task. We show through model comparison and model simulation that VS provides the best explanation of learner’s behavior. Results replicated in a third independent experiment featuring a larger cohort and a different design (N = 302). In our experiments, we also manipulated the quality of the demonstrators’ choices and found that learners were able to adapt their imitation rate, so that only skilled demonstrators were imitated. We proposed and tested an efficient meta-learning process to account for this effect, where imitation is regulated by the agreement between the learner and the demonstrator. In sum, our findings provide new insights and perspectives on the computational mechanisms underlying adaptive imitation in human reinforcement learning.

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“…In the present study we assessed reinforcement learning with a behavioral paradigm involving both private and social contexts, while concomitantly assessing depressive and anxiety symptoms. First, we replicate previous findings showing that subjects integrate the demonstrator’s choices and outcomes, which is consistent with the idea that social learning processes (both in terms of imitation and vicarious trial-and-error) play a role in human reinforcement learning (14, 15, 25–27) . Second, we show that the severity of depressive symptoms is associated with a learning impairment that is specific to the learning context where participants are informed about the demonstrator’s choices (social context).…”

Section: Resultssupporting

confidence: 90%

Social information impairs reward learning in depressive subjects: behavioral and computational characterization

Safra

Chevallier

Palminteri

2018

Preprint

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Social information impairs reward learning in depressive subjects: behavioral and 2 computational characterization 3 4 5 Abstract (max 218/250) 16 Depression is characterized by a marked decrease in social interactions and blunted sensitivity 17 to rewards. Surprisingly, despite the importance of social deficits in depression, non-social 18 aspects have been disproportionally investigated. As a consequence, the cognitive 19 mechanisms underlying atypical decision-making in social contexts in depression are poorly 20 understood. In the present study, we investigate whether deficits in reward processing interact 21 with the social context and how this interaction is affected by self-reported depression and 22 anxiety symptoms. Two cohorts of subjects (discovery and replication sample: N = 50 each) 23 took part in a task involving reward learning in a social context with different levels of social 24 information (absent, partial and complete). Behavioral analyses revealed a specific detrimental 25 effect of depressive symptoms -but not anxiety -on behavioral performance in the presence 26 of social information, i.e. when participants were informed about the choices of another player. 27Model-based analyses further characterized the computational nature of this deficit as a 28 negative audience effect, rather than a deficit in the way others' choices and rewards are 29 integrated in decision making. To conclude, our results shed light on the cognitive and 30 computational mechanisms underlying the interaction between social cognition, reward 31 learning and decision-making in depressive disorders. 32 33 Page 3 of 29 MAIN TEXT 34 35 36 37One of the core clinical symptoms of depression is anhedonia, which refers to a reduced 38 motivation to engage in daily life activities (motivational anhedonia) and a reduced enjoyment 39 of usually enjoyable activities (consummatory anhedonia) (1, 2). In principle, this clinical 40 manifestation could be explained by reduced reward sensitivity, both in terms of incentive 41 motivation and in terms of reinforcement processes (3-5). A direct prediction of this hypothesis 42 is that depressive symptoms should be associated with reduced reward sensitivity in learning 43 contexts both at the behavioral and neural level. However, while some studies do find 44 evidence that depressive symptoms in the general population and in clinical depression are 45 associated with blunted reward learning and reward-related signals in the brain (6, 7), others 46 indicate no (8, 9) or mixed effects (5). As a consequence, there is no strong consensus about 47 which components of reward processing are most predictive of depressive symptoms in both 48 the general population and clinical depression (5). 50Another striking clinical manifestation of depressed symptoms is a marked decrease in social 51 interactions. Depression is indeed associated with social risk factors, social impairments and 52 poor social functioning (10). Surprisingly, despite the importance of the socio-cognitive 53 impairments that ...

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The Role of Intelligence in Social Learning

Cited by 19 publications

References 66 publications

Depressive symptoms are associated with blunted reward learning in social contexts

Depressive symptoms are associated with blunted reward learning in social contexts

The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning

Social information impairs reward learning in depressive subjects: behavioral and computational characterization

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