Neural arbitration between social and individual learning systems

Diaconescu, Andreea O.; Stecy, Madeline; Kasper, Lars; Burke, Christopher; Nagy, Zoltán; Mathys, Christoph; Tobler, Philippe N.

doi:10.7554/elife.54051

Cited by 25 publications

(17 citation statements)

References 79 publications

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“…2) In line with prior work examining the influence of cannabis on paranoia [ 44 ] and the specific role of dopamine modulation on attributions of harmful intent [ 45 ], we predict dopamine potentiation will increase uncertainty over partner policies (uπ) and the strength of priors over harmful intent (pHI 0 ), but not the strength of priors over self-interest (pSI 0 ). 3) On a neural there is evidence that social context may be biologically realised through the engagement of different structures [ 46 ], including the dorsomedial prefrontal cortex where social computations may be implemented [ 9 ]. We predict that dopaminergic changes that underlying learning in multiple contexts may lead to context specific effects (e.g., social vs non-social learning) such as a participant’s uncertainty over their partner (uπ).…”

Section: Discussionmentioning

confidence: 99%

“…These studies used probabilistic learning tasks with changing reward probabilities over time, in the absence of a discernible agent controlling the contingency shifts (e.g., [ 5 – 6 ]). To examine reinforcement learning observations within social contexts relevant to paranoia, experimenters have also framed probabilistic tasks in terms of interaction with social agents, demonstrating that those with higher paranoia are slower learners and more sensitive to changes in the social environment [ 7 ], more rigid in their beliefs about partners [ 8 ], and less likely to take advice from partners [ 9 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

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The computational relationship between reinforcement learning, social inference, and paranoia

Barnby

Mehta²,

Moutoussis³

2022

PLoS Comput Biol

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Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent’s uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n = 693) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified repeated reversal Dictator game, and the impact of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals and the uncertainty about the environment/partner. Consistent with prior work we show that paranoia was associated with uncertainty around a partner’s behavioural policy and rigidity in harmful intent attributions in the social task. In the non-social task we found that pre-existing paranoia was associated with larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow the representation of sociocognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The computational relationship between reinforcement learning, social inference, and paranoia

Barnby

Mehta²,

Moutoussis³

2022

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…These studies used probabilistic learning tasks with changing reward probabilities over time, in the absence of a discernible agent controlling the contingency shifts (e.g., Behrens et al, 2007;Mathys et al, 2014). To examine reinforcement learning observations within social contexts relevant to paranoia, experimenters have also framed probabilistic tasks in terms of interaction with social agents, demonstrating that those with higher paranoia are slower learners and more sensitive to changes in the social environment (Henco et al, 2020), more rigid in their beliefs about partners, and less likely to take advice from partners (Diaconescu et al, 2020;Hertz et al, 2021;Wellstein et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The computational relationship between reinforcement learning, social inference, and paranoia

Barnby¹,

Mehta²,

Moutoussis³

2022

Preprint

View full text Add to dashboard Cite

Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent’s uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n=750) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified serial Dictator game, and the influence of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals, and the uncertainty of individuals about the environment/their partner. Consistent with prior work we show that, after reversals, pre-existing paranoia was independently associated with uncertainty around a partner’s behavioural policy and rigidity in harmful intent attributions in the social task, and in the probabilistic task, larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow clarification of cognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.

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“…So far, existing research presents mixed evidence. Some studies find that in dual learning tasks – when a participant gathers information through both social and individual learning – the two types of knowledge acquisition elicit distinct brain activity ( Behrens et al, 2008 ; Diaconescu et al, 2020 ; Zhang and Gläscher, 2020 ). Other experiments, however, suggest that there is no neural mechanism specific to social learning.…”

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confidence: 99%