A computational foundation for cognitive development: comment on Griffths et al. and McLelland et al

The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model

Evaluating individuals based on their pro- and anti-social behaviors is fundamental to successful human interaction. Recent research suggests that even preverbal infants engage in social evaluation; however, it remains an open question whether infants' judgments are driven uniquely by an analysis of the mental states that motivate others' helpful and unhelpful actions, or whether non-mentalistic inferences are at play. Here we present evidence from 10-month-olds, motivated and supported by a Bayesian computational model, for mentalistic social evaluation in the first year of life.A video abstract of this article can be viewed at http://youtu.be/rD_Ry5oqCYE.

Why won't you do what I want? The informative failures of children and models

Computational models are powerful tools - too powerful, according to some. We argue that the idea that models can "do anything" is wrong, and describe how their failures have been informative. We present new work showing surprising diversity in the effects of feedback on children's task-switching, such that some children perseverate despite this feedback, other children switch as instructed, and yet others play an "opposites" game without truly switching to the newly-instructed task. We present simulations that demonstrate the failure of an otherwise-successful neural network model to capture this failure of children. Simulating this pattern motivates the inclusion of updating mechanisms that make contact with a growing literature on frontostriatal function, despite their absence in extant theories of the development of cognitive flexibility. We argue from this and other examples that computational models are more constrained than is typically acknowledged, and that their resulting failures can be theoretically illuminating.