Núñez et al. (2019) argue that cognitive science has failed either “to transition to a mature inter‐disciplinary coherent field” (p. 782) or “to generate a successful [Lakatosian] research program” (p. 789). We argue that the former was never the intention of many early researchers within the field, while the latter is an inappropriate criterion by which to judge an entire discipline. However, we concur with Núñez et al. (2019) that the individual disciplinary balance within cognitive science has changed over time. (...) Of particular concern is the fact that the use of computational methods appears to be on the wane. (shrink)

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience to develop (...) stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. (shrink)

As the cognitive revolution was slow to come to the study of animal behavior, the vast majority of what we know about primate cognition has been discovered in the last 30 years. Building on the recognition that the physical and social worlds of humans and their living primate relatives pose many of the same evolutionary challenges, programs of research have established that the most basic cognitive skills and mental representations that humans use to navigate those worlds are already possessed by (...) other primates. There may be differences between humans and other primates, however, in more complex cognitive skills, such as reasoning about relations, causality, time, and other minds. Of special importance, the human primate seems to possess a species-unique set of adaptations for “cultural intelligence,” which are broad reaching in their effects on human cognition. (shrink)

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)