Picking up the Pieces: Sex Differences in Mechanisms of Curve Tracing

This study examined potential sex differences in the application of models of curve tracing, namely the pixel-by-pixel model, the bipartite model, and the zoom lens model. The purpose of this study was therefore to determine whether sex differences existed in terms of reliance on a particular model or whether the results of each sex could be best explained by one model. This was done by examining the combined data obtained by Voyer and MacPherson (2020), consisting of 420 participants, with 194 men and 226 women. We examined only the curve-tracing task data from that study and compared the fit of the different models as well as a possible interaction with sex of participants on the proportion of correct responses and response time. Overall, sex was a significant factor, with men showing better average accuracy and faster performance than women. On accuracy, we found that the pixel-by-pixel model provided the best fit for women, whereas the zoom lens model produced the best fit for men. On response time, the zoom model was the best predictor of response time for both sexes. The discussion elaborates on an account of these findings and on how our results might generalize to other visual-spatial tasks where a performance advantage for men is found. Public Significance Statement This study presents a reanalysis of previously published research suggesting that women and men both use a piecemeal, part-by-part strategy when mentally tracing a curve despite an overall performance advantage for men. The results presented here suggest that men are using a larger attentional window than women when processing the curves. This work provides evidence that it might be possible to promote an optimal strategy when processing visual-spatial tasks.