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

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Location-specific deviant responses to object sequences in macaque inferior temporal cortex

Many species learn temporal regularities in their visual environment, demonstrating visual statistical learning. In this study, we explored the sensitivity of macaque inferior temporal (IT) cortical neurons to transition probabilities of sequentially presented visual images, presented at different locations in the visual field. We exposed monkeys to sequences of two images, where the first image was presented either foveally or peripherally, and the second image was consistently presented foveally. Following several weeks of exposure, we recorded IT responses to assess differences between the exposed (Fixed) and new, Deviant sequences, where the identity of the first image in a sequence differed from the exposure phase. While enhanced responses to Deviant sequences were observed when both images of a pair were foveally presented during exposure, no such deviant responses were present when the first image was presented peripherally. This finding challenges the notion that mere exposure to image sequences always leads to deviant responses in macaque IT. The results highlight the complexity of the mechanisms underlying statistical learning in primates, particularly in the context of peripheral image presentations, emphasizing the need for further investigation into the origins of these responses in the IT cortex.

Monkey Dorsolateral Prefrontal Cortex Represents Abstract Visual Sequences during a No-Report Task

Monitoring sequential information is an essential component of our daily lives. Many of these sequences are abstract, in that they do not depend on the individual stimuli, but do depend on an ordered set of rules (e.g., chop then stir when cooking). Despite the ubiquity and utility of abstract sequential monitoring, little is known about its neural mechanisms. Human rostrolateral prefrontal cortex (RLPFC) exhibits specific increases in neural activity (i.e., "ramping") during abstract sequences. Monkey dorsolateral prefrontal cortex (DLPFC) has been shown to represent sequential information in motor (not abstract) sequence tasks, and contains a subregion, area 46, with homologous functional connectivity to human RLPFC. To test the prediction that area 46 may represent abstract sequence information, and do so with parallel dynamics to those found in humans, we conducted functional magnetic resonance imaging (fMRI) in three male monkeys. When monkeys performed no-report abstract sequence viewing, we found that left and right area 46 responded to abstract sequential changes. Interestingly, responses to rule and number changes overlapped in right area 46 and left area 46 exhibited responses to abstract sequence rules with changes in ramping activation, similar to that observed in humans. Together, these results indicate that monkey DLPFC monitors abstract visual sequential information, potentially with a preference for different dynamics in the two hemispheres. More generally, these results show that abstract sequences are represented in functionally homologous regions across monkeys and humans.SIGNIFICANCE STATEMENT Daily, we complete sequences that are "abstract" because they depend on an ordered set of rules (e.g., chop then stir when cooking) rather than the identity of individual items. Little is known about how the brain tracks, or monitors, this abstract sequential information. Based on previous human work showing abstract sequence related dynamics in an analogous area, we tested whether monkey dorsolateral prefrontal cortex (DLPFC), specifically area 46, represents abstract sequential information using awake monkey functional magnetic resonance imaging (fMRI). We found that area 46 responded to abstract sequence changes, with a preference for more general responses on the right and dynamics similar to humans on the left. These results suggest that abstract sequences are represented in functionally homologous regions across monkeys and humans.

The contribution of dynamics to macaque body and face patch responses

Previous functional imaging studies demonstrated body-selective patches in the primate visual temporal cortex, comparing activations to static bodies and static images of other categories. However, the use of static instead of dynamic displays of moving bodies may have underestimated the extent of the body patch network. Indeed, body dynamics provide information about action and emotion and may be processed in patches not activated by static images. Thus, to map with fMRI the full extent of the macaque body patch system in the visual temporal cortex, we employed dynamic displays of natural-acting monkey bodies, dynamic monkey faces, objects, and scrambled versions of these videos, all presented during fixation. We found nine body patches in the visual temporal cortex, starting posteriorly in the superior temporal sulcus (STS) and ending anteriorly in the temporal pole. Unlike for static images, body patches were present consistently in both the lower and upper banks of the STS. Overall, body patches showed a higher activation by dynamic displays than by matched static images, which, for identical stimulus displays, was less the case for the neighboring face patches. These data provide the groundwork for future single-unit recording studies to reveal the spatiotemporal features the neurons of these body patches encode. These fMRI findings suggest that dynamics have a stronger contribution to population responses in body than face patches.

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

Primates learn statistical regularities that are embedded in visual sequences, a form of statistical learning. Single-unit recordings in macaques showed that inferior temporal (IT) neurons are sensitive to statistical regularities in visual sequences. Here, we asked whether ventrolateral prefrontal cortex (VLPFC), which is connected to IT, is also sensitive to the transition probabilities in visual sequences and whether the statistical learning signal in IT originates in VLPFC. We recorded simultaneously multiunit activity (MUA) and local field potentials (LFPs) in IT and VLPFC after monkeys were exposed to triplets of images with a fixed presentation order. In both areas, the MUA was stronger to images that violated the learned sequence (deviants) compared to the same images presented in the learned triplets. The high-gamma and beta LFP power showed an enhanced and suppressed response, respectively, to the deviants in both areas. The enhanced response was present also for the image following the deviant, suggesting a sensitivity for temporal adjacent dependencies in IT and VLPFC. The increased response to the deviant occurred later in VLPFC than in IT, suggesting that the deviant response in IT was not inherited from VLPFC. These data support predictive coding theories that propose a feedforward flow of prediction errors.

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

Caught in the ACTS: Defining Abstract Cognitive Task Sequences as an Independent Process

Cognitive neuroscience currently conflates the study of serial responses (e.g., delay match to sample/nonsample, n-back) with the study of sequential operations. In this essay, our goal is to define and disentangle the latter, termed abstract cognitive task sequences (ACTS). Existing literatures address tasks requiring serial events, including procedural learning of implicit motor responses, statistical learning of predictive relationships, and judgments of attributes. These findings do not describe the behavior and underlying mechanism required to succeed at remembering to evaluate color, then shape; or to multiply, then add. A new literature is needed to characterize these sorts of second-order cognitive demands of studying a sequence of operations. Our second goal is to characterize gaps in knowledge related to ACTS that merit further investigation. In the following sections, we define more precisely what we mean by ACTS and suggest research questions' further investigation would be positioned to address.

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

When a visual object changes its position along with certain sequential regularities, the visual system rapidly and automatically forms a prediction regarding the future position of the object based on the regularities. Such prediction can drastically alter visual perception. A phenomenon called representational momentum (RM: a predictive displacement of the perceived final position of a visual object along its recent regular pattern) has provided extensive evidence for the predictive modulation of visual perception. The purpose of the present study was to identify neural effects that could explain individual differences in the strength of the predictive modulation of visual perception as measured by RM. For this purpose, in two experiments with a conventional RM paradigm where a bar was discretely presented in a regular rotation manner (with a step of 18° in Experiment 1 and a step of 20° in Experiment 2), visual evoked potentials (VEPs) in response to the regularly rotated bar were measured, and correlations between the magnitudes of RM and VEPs were examined. The results showed that the magnitudes of RM and central P2 were negatively correlated, consistently in both experiments; participants who showed a smaller central P2 tended to exhibit greater RM. Together with a previous proposal that central P2 would represent delayed reactivation of lower visual areas around the striate and prestriate cortices via reentrant feedback projections from higher areas, the present results suggest that greater suppression of delayed reactivation of lower visual areas (as indicated by smaller central P2) may underlie stronger predictive modulation of visual perception (as indicated by greater RM).

Evaluating the evidence for expectation suppression in the visual system

Reports of expectation suppression have shaped the development of influential predictive coding-based theories of visual perception. However recent work has highlighted confounding factors that may mimic or inflate expectation suppression effects. In this review, we describe four confounds that are prevalent across experiments that tested for expectation suppression: effects of surprise, attention, stimulus repetition and adaptation, and stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We found evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. Across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was inconsistent evidence for genuine expectation suppression. We discuss how an absence of expectation suppression could inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for expectation suppression in future work.