Cognitive Science

Comparative psychology came into its own as a science of animal minds, so a standard story goes, when it abandoned anecdotal methods in favor of experimental methods. Even so, pragmatic constraints significantly constrain sample sizes in laboratories experiments. Studies are often published with sample sizes in the single digits, and sometimes samples of one animal. With such small samples, comparative psychology has arguably not actually moved on from its anecdotal roots. Replication failures in other branches of psychology have received substantial attention, but the potential implications of small sample sizes have only recently received attention in comparative psychology, and have received no serious attention in the attending philosophical literature. As a first step in grappling with the problem of interpreting studies like this, I argue that we should view studies with extreme small sample sizes as anecdotal experiments, lying somewhere between traditional experiments and traditional anecdotes in evidential weight.

According to "radical enactivists", cognitive sciences should abandon the representational framework. Radical enactivists often take perceptuomotor cognition and action control as paradigmatic examples of non-representational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that utilizes Reinforcement Learning (RL) algorithms. In Reinforcement Learning, action and motor control are understood as choosing the right action selection policy for a given environment, so as to maximize future reward in light of a predictive world model. Crucially, this approach is standardly given a thoroughly representational interpretation. Hence, RL provides a way to explicate ''action-oriented views'' of cognitive systems in a way that is overlooked by recent enactivists (and many other antirepresentationalists).

g-a statistical factor capturing strong intercorrelations between individuals' scores on different IQ tests-is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and idealizes away from those aspects of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge model of the relationship between its two targets, and thereby helps demystify the relationship between folk and scientific psychology.