Extended Data Fig. 10: Interaction of trace correlation with influence regression model coefficients.

a, Further characterization of the effects of trace correlation on feature competition versus amplification (compare to Fig. 5d). Influence regression (as in Fig. 3d) was performed after including an interaction of each predictor with the magnitude of trace correlation. Coefficient estimates for each interaction plotted with uncertainty from bootstrap: grey line, median; box, 25–75% interval; whiskers, 1–99% interval. This analysis used no manually specified division between ‘strong’ and ‘weak’ correlations, and considered whether trace correlation changed the relationship between influence and any predictors in the influence regression. Signal correlation exhibited a highly significant positive interaction, indicating a transition from competition (negative slope) to amplification (positive slope) as the magnitude of trace correlation increased; n = 64,845 pairs, P = 0.0002 (bootstrap). Interactions with all other activity predictors were not significant (P > 0.444). Interactions with the slopes of distance predictors were not significant (P > 0.2716). There were weak interactions with offsets for near (P = 0.0486) and mid (P = 0.0076) distance bins, but not for far (P = 0.4738). These results indicate that the magnitude of trace correlation had a substantial effect on the relationship between signal correlation and influence.