An image-computable characterization of the non-conditioned linkage of visual drive and valence in the primate amygdala

The amygdala is a key node in linking high-level representations of visual stimuli to affective state, and much research has focused on its role in learning artificial stimulus-value associations. By comparison, the primate amygdala’s encoding of natural, non-conditioned visual stimuli is less well understood. Here, we report that some amygdala neurons have visual selectivity that is systematically linked to their valence tuning, without laboratory conditioning. First, electrophysiological recordings revealed that the firing rate responses of many amygdala recording sites are selective across arbitrary non-conditioned natural visual stimuli. Second, this selectivity was well-predicted by contemporary, image-computable models of the visually-driven selectivity of high-level ventral stream neurons that provide major afferents to the amygdala. Third, a subpopulation of visually selective amygdala units also coded valence, as defined in prior work. Fourth, these valence preferences were correlated with their visual tuning, in that the image-computable models predicted which visual stimuli tended to drive positive-valence sites and which tended to drive negative-valence sites. Taken together, these results suggest that the visual drive provided from the ventral visual stream into the amygdala is strong, and that it is not unlinked or randomly linked to valence coding. Furthermore, the results establish baseline image-computable models of visual encoding in the primate amygdala.