Proceedings of the Annual Meeting of the Cognitive Science Society

There is long history of studies documenting that some neurons respond to images of objects, faces, and scenes in a highly selective manner. This includes neurons in the human hippocampus (e.g., the famous example of a neuron responding to images of the actress Jennifer Aniston) and neurons in high-level visual cortex in monkey (for reviews see Bowers, 2009; Ison, Quian Quiroga, & Fried, 2015). These findings have led to a growing interest in the claim that some neurons code for information in a localist (‘grandmother cell’) manner, as reflected in the many contributions to a recent special issue on this topic in the journal Language, Cognition, & Neuroscience (Bowers, 2017). By contrast, it is only recently that interest in characterizing the selectivity of single units in connectionist networks has gathered speed. Critically, these studies also show that networks learn highly selective representations under a number of conditions, as detailed below. In this talk I will summarize recent research in my lab that explores the conditions in which artificial networks learn selective codes, and research comparing the responses of selective neurons and localist representations used in cognitive models. These findings suggest when and why some neurons in cortex respond in a highly selective manner, and highlight the biological plausibility of localist models in psychology.