Adaptation can explain evidence for encoding of probabilistic information in macaque inferior temporal cortex

In predictive coding theory, the brain is conceptualized as a prediction machine that constantly constructs and updates expectations of the sensory environment [1]. In the context of this theory, Bell et al.[2] recently studied the effect of the probability of task-relevant stimuli on the activity of macaque inferior temporal (IT) neurons and observed a reduced population response to expected faces in face-selective neurons. They concluded that "IT neurons encode long-term, latent probabilistic information about stimulus occurrence", supporting predictive coding. They manipulated expectation by the frequency of face versus fruit stimuli in blocks of trials. With such a design, stimulus repetition is confounded with expectation. As previous studies showed that IT neurons decrease their response with repetition [3], such adaptation (or repetition suppression), instead of expectation suppression as assumed by the authors, could explain their effects. The authors attempted to control for this alternative interpretation with a multiple regression approach. Here we show by using simulation that adaptation can still masquerade as expectation effects reported in [2]. Further, the results from the regression model used for most analyses cannot be trusted, because the model is not uniquely defined.