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

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