Decoding the social mind in depression: A computational dissociation between explicit trust and implicit belief...

Area of Science:

• Computational psychiatry
• Social cognition
• Neuroscience

Background:

• Depression is linked to altered social cognition and decision-making.
• Understanding belief updating deficits in depression is crucial for targeted interventions.

Purpose of the Study:

• To investigate how depressive symptoms affect belief updating about trustworthiness in different social contexts.
• To identify computational mechanisms underlying social cognitive impairments in depression.

Main Methods:

• Employed a computational psychiatry approach using a modified Trust Game.
• Assessed explicit belief reports and implicit computational processes in 169 participants across trustworthy and untrustworthy partner conditions.

Main Results:

• Higher depression scores predicted lower posterior trust beliefs, indicating a generalized negative updating bias.
• Computational modeling revealed context-dependent likelihood bias, with depressive symptoms linked to reduced bias in the untrustworthy condition.
• Depression scores correlated with reduced cooperation solely in the untrustworthy condition.

Conclusions:

• Findings reveal a dissociation in depression: pervasive negative explicit beliefs alongside context-specific implicit biases.
• Identified computational markers of impaired belief updating in depression, highlighting cognitive inflexibility in processing negative social evidence.
• Supports process dissociation frameworks for understanding mood disorders and bridges social cognition with computational psychiatry.