Using Covert Response Activation to Test Latent Assumptions of Formal Decision-Making Models in Humans

Abstract

Most decisions that we make build upon multiple streams of sensory evidence and control mechanisms are needed to filter out irrelevant information. Sequential sampling models of perceptual decision making have recently been enriched by attentional mechanisms that weight sensory evidence in a dynamic and goal-directed way. However, the framework retains the longstanding hypothesis that motor activity is engaged only once a decision threshold is reached. To probe latent assumptions of these models, neurophysiological indices are needed. Therefore, we collected behavioral and EMG data in the flanker task, a standard paradigm to investigate decisions about relevance. Although the models captured response time distributions and accuracy data, EMG analyses of response agonist muscles challenged the assumption of independence between decision and motor processes. Those analyses revealed covert incorrect EMG activity ("partial error") in a fraction of trials in which the correct response was finally given, providing intermediate states of evidence accumulation and response activation at the single-trial level. We extended the models by allowing motor activity to occur before a commitment to a choice and demonstrated that the proposed framework captured the rate, latency, and EMG surface of partial errors, along with the speed of the correction process. In return, EMG data provided strong constraints to discriminate between competing models that made similar behavioral predictions. Our study opens new theoretical and methodological avenues for understanding the links among decision making, cognitive control, and motor execution in humans.

Significance statement: Sequential sampling models of perceptual decision making assume that sensory information is accumulated until a criterion quantity of evidence is obtained, from where the decision terminates in a choice and motor activity is engaged. The very existence of covert incorrect EMG activity ("partial error") during the evidence accumulation process challenges this longstanding assumption. In the present work, we use partial errors to better constrain sequential sampling models at the single-trial level.

Keywords: accumulation models; decision making; electromyography; electrophysiology; partial errors.

Figure 1.

EMG partial errors and the sequential sampling model framework. A, EMG activity (in μV) of the muscles involved (Correct EMG, top) and not involved (Incorrect EMG, bottom) in the required response as a function of time (in ms) from stimulus onset. Partial motor activity in the incorrect EMG channel precedes the correct response. RT, Reaction time (from stimulus onset to the mechanical response); PE, partial error; CT, correction time (from the incorrect EMG activation to the correct one); MT, motor time (from the correct EMG activation to the mechanical response). B, Extended DDM. EMG bounds were incorporated within the response selection accumulator at locations m (incorrect EMG activation) and a-m (correct EMG activation). EMG bounds do not correspond to an actual choice. Evidence continues to accumulate until standard decision termination bounds 0 and a are reached. Therefore, part of the MT overlaps with the decision time. The decision sample path represents a partial error trial. Arrows correspond to EMG events: 1 = onset of the partial error, 2 = onset of the correct EMG burst. S, Stimulus; R, mechanical response. C, Extended LCA model. The two competing response accumulators are superimposed for convenience. An EMG bound was incorporated within each response accumulator at location m. The two decision sample paths represent a partial error trial. The sample path favoring the incorrect response (dashed sample path) hits the EMG bound m, but the sample path favoring the correct response (solid sample path) finally reaches the decision termination bound a and wins the competition. Arrows correspond to EMG events: 1 = onset of the partial error, 2 = onset of the correct EMG burst.

Figure 2.

Diffusion models incorporating selective attention mechanisms. An application to an incongruent flanker condition is provided as a working example. Left, DSTP model. Dashed lines joining the two accumulators highlight the effect of three target identification sample paths on response selection. Right, SSP model. S, Stimulus; R, mechanical response. See text for details.

Figure 3.

Observed (points) versus predicted (crosses) QPFs for each congruency condition averaged over the 12 participants. Each data point is accompanied by a 95% confidence interval assuming a Student's t distribution and between-subject conventional SEs.

Figure 4.

Observed (points) versus predicted (crosses) QPFs for each congruency condition and participant.

Figure 5.

A, Grand averages of rectified electromyographic activities time locked to EMG onset for partial errors (green lines), correct (black), and incorrect (red) overt responses. Plain lines represent congruent trials; dashed lines represent incongruent trials. Because errors are scarce in the congruent condition, the corresponding noisy data were removed for more clarity. B, Averaged sampled paths for partial error trials (green lines) in each congruency condition time locked to the starting point of the accumulation process z. Sample paths were generated by the SSP diffusion model using best-fitting parameters averaged over participants and model framework shown in Figure 1B. Alternative models made a similar prediction (data not shown here for sake of brevity). Averaged sample paths for overt responses have a necessarily higher amplitude because they reach a decision termination bound.

Figure 6.

Predicted versus observed mean partial error latency (both in ms) for each participant and congruency condition. Model predictions are not a fit to data and incorporate an estimate of sensory encoding time (see text for details). Also shown are lines of best fit for each congruency condition (dashed lines, equation provided in the insets) and the ideal y = x line (plain line).

Figure 7.

Magnitude (in ms) of observed and predicted congruency effect on partial error latencies for each participant. Model predictions incorporate an estimate of sensory encoding time.

Figure 8.

Cumulative distribution functions of observed (diamonds) and predicted (crosses) correction times (in ms) for each congruency condition averaged over the 12 participants. Predicted correction times are not a fit to data.

Figure 9.

Left, Observed EMG MT (in ms) for correct trials versus observed partial error rate for each congruency condition and participant. Also shown are lines of best fit (dashed lines) and Pearson's r coefficient correlations. Right, Predicted decision-related MT for correct trials (in ms) versus predicted partial error rate from each model, congruency condition, and participant.

Figure 10.

Cumulative distribution functions of the delta latency difference (in ms) between the corrective EMG burst and the end of the partial error for each congruency condition averaged over the 12 participants.