Nathan Tardiff¹ (), Xingyu Ding¹, Xiao-Jing Wang¹, Clayton Curtis¹; ¹New York University

Working memory (WM) performance demonstrates substantial individual variation. This variability is an important predictor of cognitive functioning and real-world outcomes, including intelligence and psychiatric disorders. Despite its importance, the neurobiological substrates of individual differences in WM are unknown. One possibility is that individual differences stem from basic properties of the neural populations supporting WM. In perception, for example, differences in psychophysical performance correlate with differences in the surface area of early visual cortex (e.g., Song et al., 2013; Himmelberg et al., 2022). In this study, we used functional neuroimaging (fMRI) to test the hypothesis that the size of visual field maps in frontal and parietal cortex predict individual differences in WM precision. To bridge behavioral and neural variability, we implemented neural network models to identify mechanisms by which neural population size could affect WM performance. Human subjects (male and female) underwent population receptive field mapping to identify retinotopically-organized regions of visual, parietal, and frontal cortex, using fMRI. Separately, we assayed subjects’ WM using a memory-guided saccade task. We then correlated the size of subjects’ visual field maps with their average WM error. Confirming our hypothesis, we found significant negative correlations between the size of visual field maps and WM error in regions along the precentral and intraparietal sulci. We used a neural network model of WM to explore how size improves WM precision. We asked whether larger size: 1) makes WM representations more resilient to noise; 2) allows greater averaging over noise during readout; 3) increases encoding precision via finer tuning of units across stimulus space. We found that both resilience to noise and improved readout contributed to size effects. In sum, our findings identify a mechanistic basis for individual differences in WM and demonstrate the power of combining individual differences with computational modeling for uncovering basic cognitive mechanisms.

Acknowledgements: NIH R01 EY-016407; NIH R01 EY-033925; NIH 5T32EY007136-30