Using sequential dependencies in neural activity and behavior to dissect choice related activity in V2

During perceptual decisions the activity of sensory neurons co-varies with choice. Previous findings suggest that this partially reflects “bottom-up” and “top-down” effects. However, the quantitative contributions of these effects are unclear. To address this question, we take advantage of the observation that past choices influence current behavior (sequential dependencies). Here, we use data from two macaque monkeys performing a disparity discrimination task during simultaneous extracellular recordings of disparity selective V2 neurons. We quantify the sequential dependencies using generalized linear models to predict choices or spiking activity of the V2 neurons. We find that past choices predict current choices substantially better than the spike counts on the current trial, i.e. have a higher “choice probability”. In addition, we observe that past choices have a significant predictive effect on the activity of sensory neurons on the current trial. This effect results from sequential dependencies of choices and neural activity alone, but also reflects a direct influence of past choices on the spike count on the current trial. We then use these sequential dependencies to dissect the neuronal co-variation with choice: We decomposed the choice co-variation of neural spike counts into components, which can be explained by behavior or neural activity on previous trials. We find that about 30 of the observed co-variation is already explained by the animals’ previous choice, suggesting a “top-down” contribution of at least 30. Additionally, our results exemplify how variability frequently regarded as noise reflects the systematic effect of ignored neural and behavioral co-variates, and that interpretation of co-variations between neural activity and observed behavior should take the temporal context within the experiment into account