Frontal Eye Field Neurons Assess Visual Stability Across Saccades

The image on the retina may move because the eyes move, or because something in the visual scene moves. The brain is not fooled by this ambiguity. Even as we make saccades, we are able to detect whether visual objects remain stable or move. Here we test whether this ability to assess visual stability across saccades is present at the single-neuron level in the frontal eye field (FEF), an area that receives both visual input and information about imminent saccades. Our hypothesis was that neurons in the FEF report whether a visual stimulus remains stable or moves as a saccade is made. Monkeys made saccades in the presence of a visual stimulus outside of the receptive field. In some trials, the stimulus remained stable, but in other trials, it moved during the saccade. In every trial, the stimulus occupied the center of the receptive field after the saccade, thus evoking a reafferent visual response. We found that many FEF neurons signaled, in the strength and timing of their reafferent response, whether the stimulus had remained stable or moved. Reafferent responses were tuned for the amount of stimulus translation, and, in accordance with human psychophysics, tuning was better (more prevalent, stronger, and quicker) for stimuli that moved perpendicular, rather than parallel, to the saccade. Tuning was sometimes present as well for nonspatial transaccadic changes (in color, size, or both). Our results indicate that FEF neurons evaluate visual stability during saccades and may be general purpose detectors of transaccadic visual change

Neuronal mechanisms for evaluating the visual scene across eye movements

As a foveate animal, the primate must redirect its gaze with saccadic eye movements to subject different objects to high resolution analysis. Though beneficial in extending the range of visual analysis, the saccade-and-fixate oculomotor strategy poses a problem to the visual system as it performs its analyses. Each saccade results in a whole-field displacement of the visual image across the retina. Nevertheless, we experience a stable visual percept, implying a brain mechanism for visuo-spatial correction. The experiments reported here examine the neural mechanisms underwriting this correction.In the first study, we sought to understand how the frontal eye field (FEF) gains access to information about ipsilateral space. Information about all of space, not just the contralateral hemifield, is a prerequisite for omnidirectional processes such as spatial remapping, a putative mechanism of visual stability. We found that one source of ipsilateral information is the superior colliculus (SC) on the opposite side of the brain. In the second study, we set out to test a major prediction of one theory of visual stability. This theory invokes the function of neurons with shifting receptive fields (RFs) as a mechanism for achieving transaccadic visual stability. Shifting RFs effectively sample the same region of space twice, presaccadically and postsaccadically, and a percept of stability may rely on how well the samples match. This theory has the salient prediction that neurons in areas where shifting RFs are found should be sensitive to changes that occur to stimuli during saccades. We tested this prediction by recording from FEF neurons while monkeys performed a task during which a probe changed along a particular dimension during a saccade. We found that FEF neurons are indeed sensitive to intrasaccadic alterations of visual stimuli. In a third and final study, we sought to bridge the neuron-behavior gap by recording from FEF neurons while monkeys performed a visual stability judgment task that probed their capacity to detect changes occurring during saccades. We found that monkeys are clearly able to discern whether a stimulus is stable or unstable during a saccade and moreover that FEF neural activity is predictive of monkey psychophysical performance

The effect of face patch microstimulation on perception of faces and objects

What is the range of stimuli encoded by face-selective regions of the brain? We asked how electrical microstimulation of face patches in macaque inferotemporal cortex affects perception of faces and objects. We found that microstimulation strongly distorted face percepts and that this effect depended on precise targeting to the center of face patches. While microstimulation had no effect on the percept of many non-face objects, it did affect the percept of some, including non-face objects whose shape is consistent with a face (for example, apples) as well as somewhat facelike abstract images (for example, cartoon houses). Microstimulation even perturbed the percept of certain objects that did not activate the stimulated face patch at all. Overall, these results indicate that representation of facial identity is localized to face patches, but activity in these patches can also affect perception of face-compatible non-face objects, including objects normally represented in other parts of inferotemporal cortex

Insights into decision making using choice probability.

A long-standing question in systems neuroscience is how the activity of single neurons gives rise to our perceptions and actions. Critical insights into this question occurred in the last part of the 20th century when scientists began linking modulations of neuronal activity directly to perceptual behavior. A significant conceptual advance was the application of signal detection theory to both neuronal activity and behavior, providing a quantitative assessment of the relationship between brain and behavior. One metric that emerged from these efforts was choice probability (CP), which provides information about how well an ideal observer can predict the choice an animal makes from a neuron's discharge rate distribution. In this review, we describe where CP has been studied, locational trends in the values found, and why CP values are typically so low. We discuss its dependence on correlated activity among neurons of a population, assess whether it arises from feedforward or feedback mechanisms, and investigate what CP tells us about how many neurons are required for a decision and how they are pooled to do so

Insights into decision making using choice probability.

A long-standing question in systems neuroscience is how the activity of single neurons gives rise to our perceptions and actions. Critical insights into this question occurred in the last part of the 20th century when scientists began linking modulations of neuronal activity directly to perceptual behavior. A significant conceptual advance was the application of signal detection theory to both neuronal activity and behavior, providing a quantitative assessment of the relationship between brain and behavior. One metric that emerged from these efforts was choice probability (CP), which provides information about how well an ideal observer can predict the choice an animal makes from a neuron's discharge rate distribution. In this review, we describe where CP has been studied, locational trends in the values found, and why CP values are typically so low. We discuss its dependence on correlated activity among neurons of a population, assess whether it arises from feedforward or feedback mechanisms, and investigate what CP tells us about how many neurons are required for a decision and how they are pooled to do so