Statistical Learning Signals for Complex Visual Images in Macaque Early Visual Cortex

Abstract

Animals of several species, including primates, learn the statistical regularities of their environment. In particular, they learn the temporal regularities that occur in streams of visual images. Previous human neuroimaging studies reported discrepant effects of such statistical learning, ranging from stronger occipito-temporal activations for sequences in which image order was fixed, compared with sequences of randomly ordered images, to weaker activations for fixed-order sequences compared with sequences that violated the learned order. Several single-unit studies in macaque monkeys reported that after statistical learning of temporal regularities, inferior temporal (IT) neurons show reduced responses to learned fixed-order sequences of visual images compared with random or mispredicted sequences. However, it is unknown how other macaque brain areas respond to such temporal statistical regularities. To address this gap, we exposed rhesus monkeys (Macaca mulatta) to two types of sequences of complex images. The "regular" sequences consisted of a continuous stream of quartets, and within each quartet, the image order was fixed. The quartets themselves were displayed, uninterrupted, in a random order. The same monkeys were exposed to sequences of other images having a pseudorandomized order ("random" sequence). After exposure, both monkeys were scanned with functional MRI (fMRI) using a block design with three conditions: regular sequence, random sequence, and fixation-only blocks. A wholebrain analysis showed a reduced activation in mainly the occipito-temporal cortex for the regular compared to the random sequences. Marked response reductions for the regular sequence were observed in early extrastriate visual cortical areas, including area V2, despite the use of rather complex images of animals. These data suggest that statistical learning signals are already present in early visual areas of monkeys, even for complex visual images. These monkey fMRI data are in line with recent human fMRI studies that showed a reduced activation in early visual areas for predicted compared with mispredicted complex images.

Prediction, Suppression of Visual Response, and Modulation of Visual Perception: Insights From Visual Evoked Potentials and Representational Momentum

“…Based on these previous findings, prediction suppression of central P2 (Kimura and Takeda, 2015) is best assumed to represent reduced delayed reactivation of lower visual areas around the striate and prestriate cortices. This assumption is consistent with human neuroimaging findings that automatic prediction based on sequential regularities resulted in suppressed activation in lower visual areas including the striate cortex, whereas activation in higher visual areas including the dorsal extrastriate cortex was not affected (Alink et al, 2010) and non-human neuroimaging findings that automatic prediction based on sequential regularities resulted in markedly suppressed activation in lower visual areas (i.e., monkey V2) rather than higher visual areas (Vergnieux and Vogels, 2020; see also Kaposvari et al, 2018).…”

The contribution of dynamics to macaque body and face patch responses

“…Note that although there are numerical differences among the stimulus conditions in the percent of kept data for the subsequent eye movement analysis, these are small and inconsistent across monkeys. We employed the method of Vergnieux and Vogels (2020) , based on the algorithm of Engbert and Kliegl (2003) , to detect saccades. After filtering the eye movement trace using a 40 Hz low-pass filter (5th order Butterworth), horizontal and vertical eye velocities ( Engbert and Kliegl, 2003 ;Vergnieux and Vogels, 2020 ) were computed.…”

“…We employed the method of Vergnieux and Vogels (2020) , based on the algorithm of Engbert and Kliegl (2003) , to detect saccades. After filtering the eye movement trace using a 40 Hz low-pass filter (5th order Butterworth), horizontal and vertical eye velocities ( Engbert and Kliegl, 2003 ;Vergnieux and Vogels, 2020 ) were computed. Saccades were detected using an elliptic threshold with a linear factor lambda ( Engbert and Mergenthaler, 2006 ) of 4 in velocity space.…”

Inferior temporal cortex leads prefrontal cortex in response to a violation of a learned sequence

“…The choice of this area as a recording target was based on multiple reasons. First, monkey fMRI studies in our laboratory (Vergnieux and Vogels 2020) showed that VLPFC is one of few areas outside the ventral visual stream that are activated by visual image sequences during passive fixation following the long exposure to image sequences during statistical learning, as employed in previous IT studies. An area that is activated by a visual stimulus is a candidate to learn temporal regularities of the visual sequences.…”