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The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.New & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

Trial-to-trial variability is a reflection of the circuitry and cellular physiology that makeup a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. Previous model cortical networks are at loss to explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons internally generate low dimensional shared variability that captures the properties ofin vivopopulation recordings along the visual pathway. Shifting spatial attention into the receptive field of visual neurons has been shown to reduce low dimensional shared variability within a brain area, yet increase the variability shared between areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation, providing support for our theory of cortical variability. Our work provides a critical and previously missing mechanistic link between observed cortical circuit structure and realistic population-wide shared neuronal variability and its modulation.

The trial-to-trial response variability that is shared between pairs of neurons (termed spike count correlations1 or rSC) has been the subject of many recent studies largely because it might limit the amount of information that can be encoded by neuronal populations. Spike count correlations are flexible and change depending on task demands2-7. However, the relationship between correlated variability and information coding is a matter of current debate2-14. This debate has been difficult to resolve because testing the theoretical predictions would require simultaneous recordings from an experimentally unfeasible number of neurons. We hypothesized that if correlated variability limits population coding, then spike count correlations in visual cortex should a) covary with subjects’ performance on visually guided tasks and b) lie along the dimensions in neuronal population space that contain information that is used to guide behavior. We focused on two processes that are known to improve visual performance: visual attention, which allows observers to focus on important parts of a visual scene15-17, and perceptual learning, which slowly improves observers’ ability to discriminate specific, well-practiced stimuli18-20. Both attention and learning improve performance on visually guided tasks, but the two processes operate on very different timescales and are typically studied using different perceptual tasks. Here, by manipulating attention and learning in the same task, subjects, trials, and neuronal populations, we show that there is a single, robust relationship between correlated variability in populations of visual neurons and performance on a change-detection task. We also propose an explanation for the mystery of how correlated variability might affect performance: it is oriented along the dimensions of population space used by the animal to make perceptual decisions. Our results suggest that attention and learning affect the same aspects of the neuronal population activity in visual cortex, which may be responsible for learning- and attention-related improvements in behavioral performance. More generally, our study provides a framework for leveraging the activity of simultaneously recorded populations of neurons, cognitive factors, and perceptual decisions to understand the neuronal underpinnings of behavior.

Face recognition is a complex cognitive process that requires distinguishable neuronal representations of individual faces. Previous functional magnetic resonance imaging (fMRI) studies using the “fMRI-adaptation” technique have suggested the existence of face-identity representations in face-selective regions, including the fusiform face area (FFA). Here, we present face-identity adaptation findings that are not well explained in terms of face-identity representations. We performed blood-oxygen level–dependent (BOLD) fMRI measurements, while participants viewed familiar faces that were shown repeatedly throughout the experiment. We found decreased activation for repeated faces in face-selective regions, as expected based on previous studies. However, we found similar effects in regions that are not face-selective, including the parahippocampal place area (PPA) and early visual cortex (EVC). These effects were present for exact-image (same view and lighting) as well as different-image (different view and/or lighting) repetition, but more widespread for exact-image repetition. Given the known functional properties of PPA and EVC, it appears unlikely that they contain domain-specific face-identity representations. Alternative interpretations include general attentional effects and carryover of activation from connected regions. These results remind us that fMRI stimulus-change effects can have a range of causes and do not provide conclusive evidence for a neuronal representation of the changed stimulus property.

Neurons in sensory cortical areas are tuned to multiple dimensions, or features, of their sensory space. Understanding how single neurons represent multiple features is of great interest for determining the informative dimensions of the neurons' response, the decoding algorithms appropriate for extracting this information from the neuronal population, and for determining where specific transformations occur along the visual hierarchy. Despite the established role of cortical area MT in judgments of motion and depth, it is not known how individual neurons jointly encode the two dimensions. We investigated the joint tuning of individual MT neurons for two visual features: direction of motion and binocular disparity, an important depth cue. We found that a separable, multiplicative combination of tuning for the two features can account for more than 90% of the variance in the joint tuning function for over 91% of MT neurons. These results suggest 1) that each feature can be read out independently from MT by simply averaging across the population without regard to the other feature and 2) that the inseparable representations seen in subsequent areas, such as MST, must be computed beyond MT. Intriguingly, we found that the remaining nonseparable component of the joint tuning function often manifested as small but systematic changes in the neurons' preferences for one feature as the other one was varied. We believe this reflects the local columnar organization of tuning for direction and binocular disparity in MT, indicating that joint tuning may provide a new tool with which to probe functional architecture.

Human inferior temporal cortex contains category-selective visual regions, including the fusiform face area (FFA) and the parahippocampal place area (PPA). These regions are defined by their greater category-average activation to the preferred category (faces and places, respectively) relative to nonpreferred categories. The approach of investigating category-average activation has left unclear to what extent category selectivity holds for individual object images. Here we investigate single-image activation profiles to address (1) whether each image from the preferred category elicits greater activation than any image outside the preferred category (categorical ranking), (2) whether there are activation differences within and outside the preferred category (gradedness), and (3) whether the activation profile falls off continuously across the category boundary or exhibits a discontinuity at the boundary (category step). We used functional magnetic resonance imaging to measure the activation elicited in the FFA and PPA by each of 96 object images from a wide range of categories, including faces and places, but also humans and animals, and natural and manmade objects. Results suggest that responses in FFA and PPA exhibit almost perfect categorical ranking, are graded within and outside the preferred category, and exhibit a category step. The gradedness within the preferred category was more pronounced in FFA; the category step was more pronounced in PPA. These findings support the idea that these regions have category-specific functions, but are also consistent with a distributed object representation emphasizing categories while still distinguishing individual images.

Voxel-Based Morphometry (VBM) has been used for several years to study differences in brain structure between populations. Recently, a longitudinal version of VBM has been used to show changes in gray matter associated with relatively short periods of training. In the present study we use fMRI and three different standard implementations of longitudinal VBM: SPM2, FSL, and SPM5 to assess functional and structural changes associated with a simple learning task. Behavioral and fMRI data clearly showed a significant learning effect. However, initially positive VBM results were found to be inconsistent across minor perturbations of the analysis technique and ultimately proved to be artifactual. When alignment biases were controlled for and recommended statistical procedures were used, no significant changes in grey matter density were found. This work, initially intended to show structural and functional changes with learning, rather demonstrates some of the potential pitfalls of existing longitudinal VBM methods and prescribes that these tools be applied and interpreted with extreme caution.

Perceptual decision making typically entails the processing of sensory signals, the formation of a decision, and the planning and execution of a motor response. Although recent studies in monkeys and humans have revealed possible neural mechanisms for perceptual decision making, much less is known about how the decision is subsequently transformed into a motor action and whether or not the decision is represented at an abstract level, i.e., independently of the specific motor response. To address this issue, we used functional MRI to monitor changes in brain activity while human subjects discriminated the direction of motion in random-dot visual stimuli that varied in coherence and responded with either button presses or saccadic eye movements. We hypothesized that areas representing decision variables should respond more to high- than to low-coherence stimuli independent of the motor system used to express a decision. Four areas were found that fulfilled this condition: left posterior dorsolateral prefrontal cortex (DLPFC), left posterior cingulate cortex, left inferior parietal lobule, and left fusifom/parahippocampal gyrus. We previously found that, when subjects made categorical decisions about degraded face and house stimuli, left posterior DLPFC showed a greater response to high- relative to low-coherence stimuli. Furthermore, the left posterior DLPFC appears to perform a comparison of signals from sensory processing areas during perceptual decision making. These data suggest that the involvement of left posterior DLPFC in perceptual decision making transcends both task and response specificity, thereby enabling a flexible link among sensory evidence, decision, and action.