Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History

Abstract

Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice- and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.SIGNIFICANCE STATEMENT Decades of research has provided a rich description of how visual information is represented in the visual cortex. Yet how cortical responses relate to visual perception remains poorly understood. Here we relate fluctuations in small neuronal population responses, recorded simultaneously in primary visual cortex (V1) and area V4 of monkeys, to perceptual reports in an orientation discrimination task. Choice-related signals were robust in V4, particularly late in the behavioral trial, but not in V1. Models that include both neuronal responses and choice-history information were able to predict a substantial portion of decisions. Our work shows the power of integrating information across neurons and including decision history in relating neuronal responses to perceptual decisions.

Keywords: choice signals; perceptual decision making; visual cortex.

Figure 1.

Behavioral task and psychophysical performance. A, Task design. B, Example psychometric function from one session in M1L. C–E, Psychometric thresholds (C), biases (D), and lapse rates (E) for all three cases. Triangles in C–E indicate mean. Prop., Proportion.

Figure 2.

V1 and V4 physiology. A, Population spatial RFs for all three cases. Lines indicate the 75% contour line of a 2-dimensional Gaussian fit to the aggregate RF. Blue, V1; green, V4. Stimulus position and size is indicated by yellow shading. B, PSTHs for all three cases. PSTHs were calculated from responses to all behavioral stimuli, using all neurons included in choice-related analyses. Zero milliseconds indicates stimulus onset.

Figure 3.

Distribution of choice probabilities for V1 (top row, blue) and V4 (bottom row, green). Dark filled bars indicate units whose CP was significantly different from 0.5 (see Materials and Methods). Triangle indicates the mean CP. CP is based on responses measured 0–250 ms after stimulus onset. Prop., Proportion.

Figure 4.

CP dynamics. Average CP, measured using a time window of 100 ms with an overlap of 50%. Top row, V1 neurons; bottom row, V4 neurons. Green and blue shaded areas denote SEM. Shaded gray areas denote the boundaries (95%) of the null distribution. Time of each bin is defined by the starting time of the time window. Zero milliseconds indicates stimulus onset.

Figure 5.

Relationship between neuronal threshold and choice probability. A, Definition of the neurometric function. Left, Spike count distributions for a 45° stimulus (dark brown) and a 55° stimulus (light brown). Middle, ROC curve for the two given spike count distributions in the left figure. Right, Stimulus discriminability for all orientations (dots) and fitted cumulative Gaussian (line). This neuron had a threshold of 20.9°. Arrow indicated the stimulus orientation that is discriminated from the 45° stimulus. B, absCP as a function of neuronal threshold for all V1 (top row, blue) and V4 (bottom row, green) neurons. Neuronal thresholds are based on responses measured 0–250 ms after stimulus onset; CP on responses 200–400 ms after stimulus onset.

Figure 6.

Choice signals in neuronal populations. A, Histogram of predictive (Pred) performance for each session in V1 (top row, blue) and V4 (bottom row, green). Dark bars indicate decoding performance, which is statistically significantly above chance performance. B, Performance of the neuronal population compared with that of the best unit within that population, for V1 (left, blue) and V4 (right, green). Dark dots indicate performance that is significantly above chance. C, Number of units included in the population analysis as a function of the available population size, for V1 (left, blue) and V4 (right, green). Size of the dots indicate the number of sessions. Decoding performance is based on responses measured 200–400 ms after stimulus onset.

Figure 7.

Influence of choice and reward history on the decision. A, Example weights for one session in M1L. B, Bias estimated from the psychometric function (ordinate) compared with the bias estimated from the choice-history model (abscissa). Small dots are individual sessions; large dots with red circle indicate the mean over sessions for each dataset. C, Weights for the history term variables of each session. Small dots represent individual sessions; large dots with red circle indicate the mean over all sessions for each dataset (indicated by shading).

Figure 8.

Predicting choices using neuronal responses and choice-history information. A, Predictive (Pred) performance (perf) of a model based on single neuron responses alone (abscissa), compared with the performance of a model that also includes history terms for V1 (left) and V4 (right). B, Performance of models using the neuronal population (pop) responses alone, compared with the performance of models that include choice-history terms for V1 (left) and V4 (right). Performance is based on responses measured 200–400 ms after stimulus onset.

Figure 9.

Testing for an influence of microsaccades. A, Detecting microsaccades. Left, Eye position during an example trial in M1R, from the moment fixation was established (−200 ms) to the appearance of the choice targets (400 ms). Red line indicates a detected microsaccade; gray square is the fixation window. Middle, Velocity profile for the example trial. Red indicates the detected microsaccade; gray dashed line indicates 10°/s criterion. Right, Peak velocity of detected microsaccades as a function of their amplitude. B, CP for trials containing microsaccades compared with CP for trials without microsaccades. C, Performance of the neuronal population (pop) on trials with microsaccades compared with trials without. D, Performance of models using neuronal population responses and trial history, using trials with microsaccades compared with the performance using trials without. Top (blue), V1; bottom (green), V4. All analyses based on responses measured 200–400 ms after stimulus onset.