A low-frequency oscillatory neural signal in humans encodes a developing decision variable

Abstract

We often make decisions based on sensory evidence that is accumulated over a period of time. How the evidence for such decisions is represented in the brain and how such a neural representation is used to guide a subsequent action are questions of considerable interest to decision sciences. The neural correlates of developing perceptual decisions have been thoroughly investigated in the oculomotor system of macaques who communicated their decisions using an eye movement. It has been found that the evidence informing a decision to make an eye movement is in part accumulated within the same oculomotor circuits that signal the upcoming eye movement. Recent evidence suggests that the somatomotor system may exhibit an analogous property for choices made using a hand movement. To investigate this possibility, we engaged humans in a decision task in which they integrated discrete quanta of sensory information over a period of time and signaled their decision using a hand movement or an eye movement. The discrete form of the sensory evidence allowed us to infer the decision variable on which subjects base their decision on each trial and to assess the neural processes related to each quantum of the incoming decision evidence. We found that a low-frequency electrophysiological signal recorded over centroparietal regions strongly encodes the decision variable inferred in this task, and that it does so specifically for hand movement choices. The signal ramps up with a rate that is proportional to the decision variable, remains graded by the decision variable throughout the delay period, reaches a common peak shortly before a hand movement, and falls off shortly after the hand movement. Furthermore, the signal encodes the polarity of each evidence quantum, with a short latency, and retains the response level over time. Thus, this neural signal shows properties of evidence accumulation. These findings suggest that the decision-related effects observed in the oculomotor system of the monkey during eye movement choices may share the same basic properties with the decision-related effects in the somatomotor system of humans during hand movement choices.

Figure 1

Decision task and behavioral model. (A) After acquiring a fixation cross, subjects listen to a binaurally presented auditory stimulus. Subjects decide whether they hear more click sounds in the right ear or in the left ear. The stimulus is followed by a variable delay period. After the delay, the fixation cross shrinks and changes color to green, thus cuing the subject to make a choice. If subjects heard more clicks in the right ear, they press two buttons of the joystick with their right index finger and the thumb. Otherwise, they make a saccade to the eye icon on the left side of the screen. (B) Mean±SEM percentage of subjects' correct choices as a function of the modeled evidence for that response. The dashed line represents an ideal match between the model's predictions and the probabilistic behavior. The ideal match explains 97.6% of the variance in the 5 data points. The brown histogram gives the number of trials in each bin. (C) Mean±SEM reaction time for four levels of decision evidence (see text), separately for button press choices (red), and saccade choices (blue).

Figure 2

Human centroparietal cortex reflects the dynamics of a perceptual decision process (A) Topography of the choice effect, i.e., the significance of the difference between desynchronization on button press versus saccade trials during the delay period. The bright colors represent locations at which neural activity is more desynchronized for button presses compared to saccades. The dark colors represent the converse. (B) Mean±SEM neural desynchronization at C3 and CP3 during the delay period as a function of decision evidence, separately for button press choices (red), and saccade choices (blue). (C) Mean±SEM desynchronization at C3 and CP3 as a function of time. Data are shown separately for button press choices (red) and saccade choices (blue), and choices for which evidence was either strong (dark) or weak (light)—see inset. Desynchronization was measured in 100 ms periods overlapping by 1 sample (3.9 ms). The bottom part of the plot shows the mean±SEM button press and eye gaze signals, respectively.

Figure 3

Signal dynamics during hand-movement choices. Mean±SEM button press desynchronization for four levels of evidence (HI, ME, LO, OP) relative to the average desynchronization for all saccade trials.

Figure 4

The steepness of the early ramping activity on each trial is proportional to decision evidence. Rate of rise (slope) of linear fits to the neural signal during the stimulus period, computed on each trial. These trial-wise slopes are binned according to increasing decision evidence. The error bars represent the SEM.

Figure 5

Line fit explains the early ramping activity during the stimulus period on each trial better than a step fit. (A) Variance explained on each trial in the neural signal during the stimulus period. The figure compares, for each trial, the variance explained by a line fit (abscissa) versus variance explained by a step fit (ordinate)—see text for details. The line fit explains substantially more variance (mean R² = 0.16) than the trial-wise step fit (mean R² = 0.08). (B) The Bayesian information criterion (BIC) computed for the same data as in A. The line fit model is associated with a significantly smaller BIC (see text), and thus represents a better fit to the data than the step fit model.

Figure 6

The neural signal encodes the polarity of each element of decision evidence. Each trace gives the mean±SEM change of the neural signal following a right click (red) and a left click (blue), relative to average response to all clicks, for all trials that result in a button-press. The top panels give the responses in the left centroparietal regions considered previously in the paper (channels C3 and CP3). The bottom panels give the responses in left temporal regions (channel T7). The left panels include all clicks during the stimulus period. The right panels include clicks that occur within the first 400 ms of the stimulus period. The thick vertical line at time 0 marks the occurrence of a click. The thin vertical line with the two stars indicates the time when the red and blue curves start to significantly differ (p < 0.01, two-tailed t-test). As elsewhere in the paper, the desynchronization of the neural signal was measured in 100 ms windows, 1 sample (3.9 ms) overlap.

Figure 7

The neural signal encodes the instantaneous value of decision evidence during evidence integration. Correlation (ρ) of the neural signal and the decision variable at each time point during the stimulus period (starting at 0 ms and ending at 1000 ms), for all trials that result in a button press. The thick segments give the time points at which the correlation is significant (p < 0.05). When computing the correlation, the neural signal has been shifted in time by 74 ms to account for the lag of the neural signal behind decision evidence (74 ms (Fig. 6); a particular value of the lag does not substantially change the result).

Figure 8

Comparison of the encoding of the input quanta and the cumulated evidence. The figure gives the t-statistic associated with the weights in a linear model in which the instantaneous cumulated evidence (solid) and a signal that represents the onset and the polarity of each click (dashed; see text) is regressed on the neural signal. The regression is performed separately at each time point following a click, for every click, and for all button press choices.

Figure 9

Whole-brain analysis of the representation of decision evidence. The significance of the effect of decision evidence is rendered in color for each channel, separately for saccade choices (left) and button press choices (right). The bright (dark) hues of the color bar indicate cases for which the effects are positive (negative), that is, where stronger evidence is significantly associated with more (less) desynchronization. For both saccades (left) and button presses (right), the effect is not negative for any channel. For saccades, the effect of decision evidence is strongest at parietal channels P4, Pz. For button presses, the effect is strongest at motor channels CP3. The significance values are corrected for the number of channels (i.e., 16) and for the number of observations (i.e., 2)—by a factor of 32.

Figure 10

Dynamics of the low-frequency neural signal in right parietal cortex. Same format as in Fig. 2C. Activity was averaged over channels P4 and Pz.

Figure 11

Cortical representation of decision evidence as a function of frequency of neural signals. The effect of decision evidence (slope) outlined by its 95% confidence intervals as a function of frequency, separately for button press (red) and saccade (blue) choices. The signals at each frequency were measured in the delay interval and the effects were averaged over all channels.