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PLoS Comput Biol. 2017 Mar 30;13(3):e1005422. doi: 10.1371/journal.pcbi.1005422. eCollection 2017 Mar.

Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment.

Author information

1
Brain and Spine Institute (ICM), Paris, France.
2
ETH, Zurich, Switzerland.

Abstract

Peoples' subjective attitude towards costs such as, e.g., risk, delay or effort are key determinants of inter-individual differences in goal-directed behaviour. Thus, the ability to learn about others' prudent, impatient or lazy attitudes is likely to be critical for social interactions. Conversely, how adaptive such attitudes are in a given environment is highly uncertain. Thus, the brain may be tuned to garner information about how such costs ought to be arbitrated. In particular, observing others' attitude may change one's uncertain belief about how to best behave in related difficult decision contexts. In turn, learning from others' attitudes is determined by one's ability to learn about others' attitudes. We first derive, from basic optimality principles, the computational properties of such a learning mechanism. In particular, we predict two apparent cognitive biases that would arise when individuals are learning about others' attitudes: (i) people should overestimate the degree to which they resemble others (false-consensus bias), and (ii) they should align their own attitudes with others' (social influence bias). We show how these two biases non-trivially interact with each other. We then validate these predictions experimentally by profiling people's attitudes both before and after guessing a series of cost-benefit arbitrages performed by calibrated artificial agents (which are impersonating human individuals).

PMID:
28358869
PMCID:
PMC5373523
DOI:
10.1371/journal.pcbi.1005422
[Indexed for MEDLINE]
Free PMC Article

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