Sex mechanisms as nonbinary influences on cognitive diversity

Abstract

Essentially all neuropsychiatric diagnoses show some degree of sex and/or gender differences in their etiology, diagnosis, or prognosis. As a result, the roles of sex-related variables in behavior and cognition are of strong interest to many, with several lines of research showing effects on executive functions and value-based decision making in particular. These findings are often framed within a sex binary, with behavior of females described as less optimal than male "defaults"-- a framing that pits males and females against each other and deemphasizes the enormous overlap in fundamental neural mechanisms across sexes. Here, we propose an alternative framework in which sex-related factors encompass just one subset of many sources of valuable diversity in cognition. First, we review literature establishing multidimensional, nonbinary impacts of factors related to sex chromosomes and endocrine mechanisms on cognition, focusing on value- based decision-making tasks. Next, we present two suggestions for nonbinary interpretations and analyses of sex-related data that can be implemented by behavioral neuroscientists without devoting laboratory resources to delving into mechanisms underlying sex differences. We recommend (1) shifting interpretations of behavior away from performance metrics and towards strategy assessments to avoid the fallacy that the performance of one sex is worse than another; and (2) asking how much variance sex explains in measures and whether any differences are mosaic rather than binary, to avoid assuming that sex differences in separate measures are inextricably correlated. Nonbinary frameworks in research on cognition will allow neuroscience to represent the full spectrum of brains and behaviors.

Keywords: Decision making; Executive function; Reward; Sex differences; Strategies.

Fig. 1.

Two suggestions for experimenters on how to interpret potential sex influences in their data while reducing the risk of binary or sexist bias. The suggestions are illustrated with yellow and purple dots representing individuals to avoid any color associations with male or female sexes. Suggestion One is to consider using strategy assessments instead of performance metrics to describe differences in behavior. We suggest avoiding framing central findings as statements of better or worse performance between sexes, such as “yellow made more correct responses than purple”. Instead, consider interpreting behavioral differences across individuals as reflecting different prioritizations or tradeoffs. In the framework on the right, yellow dots display a strategy that prioritizes outcome X over other possible outcomes, while purple dots display a pattern that prioritizes outcome Y. This has the potential to avoid sexist interpretations of behavior based on a narrow focus on a specific outcome measure, and has the benefit of supporting potential neural circuits hypotheses that would support selection of different strategies across individuals and environments. Suggestion Two is to consider asking how much variance in behavior is contributed by sex, and whether sex impacts are mosaic. When considering sex differences in multiple outcome measures, such as Behavior A and Behavior B, we suggest avoiding conclusions that these outcome measures are correlated and regulated by the same sex mechanism without evidence. Instead, consider asking whether sex differences are a major dimension by which your measures differ, and whether differences in one measure are correlated with sex differences in another (or not). In the framework on the right, yellow dots are both on average different in Behavior A and Behavior B than purple dots are, but Behaviors A and B are not strongly correlated. It is likely that the reasons an individual shows some level of Behavior A have little to do with the reasons for showing Behavior B, even though there are average differences between purple and yellow. This has the potential to avoid sex binary interpretations of neural and behavioral measures, and has the benefit of supporting potential hypotheses disentangling different biological mechanisms contributing to Behaviors A versus B.