Isaac Davis

An increasingly prevalent approach to studying human cognition is to construe the mind as optimally allocating limited cognitive resources among cognitive processes. Under this bounded rationality approach (Icard in Philos Sci 85(1):79–101, 2018; Simon in Utility and probability, Palgrave Macmillan, 1980), it is common to assume that resource-bounded cognitive agents approximate normative solutions to statistical inference problems, and that much of the bias and variability in human performance can be explained in terms of the approximation strategies we employ. In this paper, we argue that this approach restricts itself to an unnecessarily narrow scope of cognitive models, which limits its ability to explain how humans flexibly adapt their representations to novel environments. We argue that more attention should be paid to how we form our cognitive representations in the first place, and advocate for pluralistic framework which jointly optimizes over both representations and algorithms for manipulating them. We identify several fundamental trade-offs that manifest in this joint optimization, and draw on recent work to motivate a unified formal framework for this analysis. We illustrate a simplified version of this analysis with a case study in social cognition, and outline several new directions for research that this approach suggests.

Most infant cognitive studies use visual fixation time as the measure of interest. There are, however, some serious methodological and theoretical concerns regarding what these studies reveal about infant cognition and how their results ought to be interpreted. We propose a Bayesian modeling framework which helps address these concerns. This framework allows us to more precisely formulate hypotheses about infants’ cognitive representations, formalize “linking hypotheses” that relate infants’ visual fixation behavior with stimulus complexity, and better determine what questions a given experiment can and cannot answer.