Subjective Beliefs In, Out, and About Control: A Quantitative Analysis
Mancinelli, F.; Roiser, J.; Dayan, P.
Show abstract
A critical facet of adjusting one’s behaviour after succeeding or failing at a task is assigning responsibility for the ultimate outcome. Humans have trait- and state-like tendencies to implicate aspects of their own behaviour (called ‘internal’ ascriptions) or facets of the particular task or Lady Luck (‘chance’). However, how these tendencies interact with actual performance is unclear. We designed a novel task in which subjects had to learn the likelihood of achieving their goals, and the extent to which this depended on their efforts. High internality (Levenson I-score) was associated with decision making patterns that are less vulnerable to failure, and at the same time less oriented to more rewarding achievements. Our computational analyses suggested that this depended heavily on the adjustment in the perceived achievability of riskier goals following failure. We found beliefs about chance not to be explanatory of choice behaviour in our task. Beliefs about powerful others were strong predictors of behaviour, but only when subjects lacked substantial influence over the outcome. Our results provide an evidentiary basis for heuristics and learning differences that underlie the formation and maintenance of control expectations by the self.Competing Interest StatementThe authors have declared no competing interest.View Full Text
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Cognitive Computational Model Reveals Repetition Bias in a Sequential Decision-Making Task 98%
- Asymmetric learning and adaptability to changes in relational structure during transitive inference 95%
- The Temporal Dynamics of Metacognitive Experiences Track Rational Adaptations in Task Performance 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.