The Functional Link between Area MT Neural Fluctuations and Detection of a Brief Motion Stimulus

Abstract

Fluctuations of neural firing rates in visual cortex are known to be correlated with variations in perceptual performance. It is important to know whether these fluctuations are functionally linked to perception in a causal manner or instead reflect non-causal processes that arise after the perceptual decision is made. We recorded from middle temporal (MT) neurons from monkey subjects while they detected the random occurrence of a brief 50 ms motion pulse that occurred in either of two (or simultaneously in both) random dot patches located in the same hemisphere. The receptive field parameters of the motion pulse were matched to that preferred by each MT neuron under study. This task contained uncertainty in both space and time because, on any given trial, the subjects did not know which patch would contain the motion pulse or when the motion pulse would occur. Covariations between MT activity and behavior began just before the motion pulse onset and peaked at the maximum neural response. These neural-behavioral covariations were strongest when only one patch contained the motion pulse and were still weakly present when a patch did not contain a motion pulse. A feedforward temporal integration model with two independent detector channels captured both the detection performance and evolution of the neuralbehavior covariations over time and stimulus condition. The results suggest that, when detecting a brief visual stimulus, there is a causal relationship between fluctuations in neural activity and variations in behavior across trials.

Linking Neural Activity to Visual Perception: Separating Sensory and Attentional Contributions

“…Attention [56] can modulate the activity of select neurons as well as affect the subject's behavior; thus it is a good candidate for the source of top-down behavioral sensitivity. Trial-to-trial fluctuations in attention could equally as well have been the source of behavioral sensitivity in studies that otherwise supported a bottom-up model [15,21,35]. Furthermore, two recent multielectrode studies were able to estimate the level of attention on each trial, from the collective responses of many simultaneously recorded neurons [33,34]; it was found that fluctuations in attention could account for the behavioral sensitivity of V4 neurons.…”

“…These neurons were recorded from two experiments, while monkeys performed either a motion detection [35] or a speed detection task [31]. These two experiments were combined because they were both detection tasks that used short, transient stimuli (~50 ms), as illustrated in Figure 3A.…”

“…The best-fit, linear regression line is y = 0.46x + 0, where x is normalized stimulus sensitivity and y is normalized behavioral sensitivity. Data are combined from two experiments using four monkeys [31,35].…”

“…An important point to note is that behavioral sensitivity may rise even while the stimulus parameters remain constant. However, some results suggest that the duration of behavioral sensitivity is confined to the time in each trial when the neuron receives useful stimulus information [15,21,35].…”

“…A further attraction of the bottom-up hypothesis is that computational models with a bottom-up structure are able to emulate the subject's behavior, neural responses and neural behavioral sensitivity, for a variety of tasks [13,[35][36][37]. Unfortunately, bottom-up models have difficulty explaining other properties of behavioral sensitivity without resorting to complex mechanisms.…”