Measures of repetition suppression in the Fusiform Face Area are inflated by co-occurring effects of statistically learned visual associations

Abstract

Repeated presentation of a stimulus leads to reductions in measures of neural responses. This phenomenon, termed repetition suppression (RS), has recently been conceptualized using models based on predictive coding, which describe RS as due to expectations that are weighted toward recently-seen stimuli. To evaluate these models, researchers have manipulated the likelihood of stimulus repetition within experiments. They have reported findings that are inconsistent across hemodynamic and electrophysiological measures, and difficult to interpret as clear support or refutation of predictive coding models. We instead investigated a different type of expectation effect that is apparent in stimulus repetition experiments: the difference in ones ability to predict the identity of repeated, compared to unrepeated, stimuli. In previous experiments that presented pairs of repeated or alternating images, once participants had seen the first stimulus image in a pair, they could form specific expectations about the repeated stimulus image. However they could not form such expectations for the alternating image, which was often randomly chosen from a large stimulus set. To assess the contribution of stimulus predictability effects to previously observed RS, we measured BOLD signals while presenting pairs of repeated and alternating faces. This was done in contexts whereby stimuli in alternating trials were either i.) predictable through statistically learned associations between pairs of stimuli or ii.) chosen randomly and therefore unpredictable. We found that RS in the right FFA was much larger in trials with unpredictable compared to predictable alternating faces. This was primarily due to unpredictable alternating stimuli evoking larger BOLD signals than predictable alternating stimuli. We show that imbalances in stimulus predictability across repeated and alternating trials can greatly inflate measures of RS, or even mimic RS effects. Our findings also indicate that stimulus-specific expectations, as described by predictive coding models, may account for a sizeable portion of observed RS effects.