Neural correlate of subjective sensory experience gradually builds up across cortical areas

Abstract

When a sensory stimulus is presented, many cortical areas are activated, but how does the representation of a sensory stimulus evolve in time and across cortical areas during a perceptual judgment? We investigated this question by analyzing the responses from single neurons, recorded in several cortical areas of parietal and frontal lobes, while trained monkeys reported the presence or absence of a mechanical vibration of varying amplitude applied to the skin of one fingertip. Here we show that the strength of the covariations between neuronal activity and perceptual judgments progressively increases across cortical areas as the activity is transmitted from the primary somatosensory cortex to the premotor areas of the frontal lobe. This finding suggests that the neuronal correlates of subjective sensory experience gradually build up across somatosensory areas of the parietal lobe and premotor cortices of the frontal lobe.

Fig. 1.

Detection task. (a) Trials began when the stimulator probe indented the skin of one fingertip of the restrained right hand (probe down, PD). The monkey then placed its left hand on an immovable key (key down, KD). After a variable prestimulus period (uniformly distributed from 1.5 to 3.5 s), on half of the randomly selected trials, a vibratory stimulus (20 Hz, 0.5 s) was presented. Then, after a fixed delay period (3 s), the stimulator probe moved up (probe up, PU), indicating to the monkey that it could make the response movement (MT) to one of two response push buttons. The button pressed indicated whether or not the monkey felt the stimulus (“yes” and “no” responses, respectively). (b) Depending on whether the stimulus was present or absent and on the behavioral response, the trial outcome was classified as a hit, miss, correct rejection (CR), or false alarm (FA). Trials were pseudorandomly chosen; 90 trials were stimulus-absent (amplitude 0), and 90 trials were stimulus-present with varying amplitudes (nine amplitudes with 10 repetitions each; 2.3–34.6 μm). (c) Psychometric detection curve obtained by plotting the proportion of “yes” responses as a function of stimulus amplitude in logarithmic abscissa (n = number of runs; a run consists of 180 trials, 90 stimulus-absent and 90 stimulus-present trials). (d) Recorded cortical areas include 1/3b, 2, 5, secondary somatosensory cortex (S2), and ventral premotor cortex (VPc) on the left hemisphere; dorsal premotor cortex (DPc) and MPc bilaterally; and primary motor cortex (M1) on the right hemisphere.

Fig. 2.

Mean firing rate in stimulus-present trials across the recorded cortical areas. (a) Each row plots mean firing rates to a suprathreshold stimulus in a given cortical area, and each column groups the neuronal responses with similar dynamics across cortical areas (n = number of neurons). Neurons from each cortical area were sorted into three possible categories (ordered into three columns). (Left) Neurons with transient responses to the stimulus (sensory neurons). The continuous line indicates rapidly adapting responses (area 3b and area 1 panels). Dashed lines indicate slowly adapting responses (area 3b and area 1 panels). Solid red lines in the remaining panels show neurons that transiently decreased their firing rate in response to the stimulus. Red dashed line in the area M1 panel shows mean activity of neurons that responded only during movement time. (Center) Activity of neurons that responded during the stimulus period and continued during the delay period (delay neurons). (Right) Mean activity of neurons with ramping changes in firing rate during the delay period. (b) Mean normalized firing rates as a function of stimulus amplitude. Colored lines are linear fits to the firing rate as a function of the logarithm of the amplitude (see Materials and Methods).

Fig. 3.

Proportion of behavioral responses that were predicted from the neuronal activity. Mean choice-probability indices across all neuronal types are plotted as a function of time for each of the recorded cortical areas during stimulus-present trials (Left) and stimulus-absent trials (Right). Note how choice-probability values increase from the primary sensory areas to the premotor areas (black lines, mean value; red area, ±SEM). Black lines at the top of each panel mark the times where choice-probability values significantly depart from 0.5 (t test, P < 0.01).

Fig. 4.

Timing and strength of perceptual decision signals across cortical areas. (a) Choice-probability indices for individual neurons (mean value: hits vs. misses and correct rejections vs. false alarms) plotted as a function of the response latency for each cortical area (colors are as in Fig. 1d). Neurons from each area were fitted with two-dimensional Gaussians. Color markings at the abscissa indicate the mean response latency for each cortical area. (b) Mean choice-probability index for each area plotted as a function of the mean response latency. A linear fit shows how the choice-probability index increasingly grows as a function of latency (M1 neurons were excluded from the fit; red dot and dotted circle). (c) Recorded areas grouped into five processing stages by analysis of variance of response latencies. Each rectangle groups the areas with latencies that were statistically indistinguishable from each other.

Fig. 5.

Sensory vs. motor responses during the detection task. Neurons for each area were tested in a control condition in which detection trials were presented as usual, but a light cue indicated to the monkeys which button to press at the beginning of each trial (n = number of neurons). In the control task, however, the response buttons were reversed relative to the detection task. (Left) Responses of the neurons during the stimulus-present trials (black continuous lines) and during the stimulus-present and reversed lights (red dashed lines). (Right) Stimulus-absent trials (black continuous lines) and stimulus-absent trials and reversed lights (red dashed lines).