upcoming conferences pt. 4

4S (Society for the Social Study of Science) / ESOCITE | December 7–10 | Cholula, Mexico

Title: “Cognitive ‘Overfitting’ and Computational-Economic Metaphors for Thought”

Abstract: This paper interrogates the metaphoric use of “overfitting” in neuro- and psy- science research. I begin by defining “overfitting” as a common problem in machine learning. As I write, overfit algorithms too closely model the sample data on which they are trained. While highly effective on these particular data, they are prone to error on data not encountered during their training phase. From there, I address “overfitting” as a descriptor for cognitive patterns that appear to function likewise. In neuro- and psy- contexts, specific disorders (e.g., depression) may be attributed to a reduced capacity for generalization/cognition beyond ingrained mental “data.” As a scientific metaphor for this type of failure, “overfitting” is both descriptive and normative, indicating an empirically-informed perspective on its neurophysiological catalysts while suggesting treatment approaches inspired by machine learning.

I then explore two recent studies which portray certain mental conditions as a problem of “overfitting.” The first recommends the therapeutic use of psychedelics to “unbind” overfit cognitive processes. The second argues that dreaming safeguards against overfitting, as dreams introduce unfamiliar “data” to minds and thereby primes them to accept new information. At this point, I make my central argument: although “overfitting” has some scientific purchase, it does not account for — and implicitly rejects — the possibility of a priori social and economic determinants of cognition. Here, I cite research from critical socioeconomic theory, emphasizing the work of Alfred Sohn-Rethel. By way of conclusion, I claim that “cognitive overfitting” also reifies the dubious analogy between computers and brains.



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