Implicit mechanisms of intention

Abstract

High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.

Keywords: Libet; awareness of intent; brain-machine interface; decision; motor planning; posterior parietal cortex; readiness potential; self-initiated action; volition.

Figure 1:. Neural dynamics demonstrate early encoding of movement intent.

A) Task paradigm. B) Population activity from a representative session (participant NS) summarized as 1^st principal component of population response (42% variance explained, mean ± sem with single-trial examples of the population response in grey; 200 ms boxcar smoothing.) C) Single unit examples illustrating diverse temporal responses (500 ms boxcar smoothing). Colors identify four basic temporal profiles found within the population (Cluster analysis, Bayesian information criteria to determine the number of clusters.) Percent of total population falling into each cluster shown in parenthesis. D) Proportion of neurons whose temporal response is best explained relative to reported urge to move (W aligned), EMG onset (M aligned), or neither (No preference), broken up by cluster identity. Percentages in legend (bottom) refer to the total percentage of population collapsing across neural classes identified in panel C. E) Sample neural responses illustrating effector specific and effector general dynamics beginning with trial onset (mean ± sem). Each panel illustrates a separate unit. Panels from left to right are aligned to cue onset, clock onset, and time of reported urge (W). F) Percent of the population demonstrating significant modulation (p<0.05 uncorrected, linear regression) from baseline (black) and significant differences between effectors (grey) through trial progression. G) Population-level latent dimensions demonstrating effector independent and specific network dynamics (cross-validated mean ± 95% ci). The dashed line represents temporal discontinuity from concatenating cue-aligned and movement-aligned signals. We adopt the concatenated visualization for supervised learning techniques to emphasize dependencies between time points. See also Figures S1-3.

Figure 2:. Volitional neural dynamics connect trial-onset to movement production.

A) Task paradigm. B) Single unit examples illustrate how neural behavior depends on the participant’s choice (N trials = 20±4, mean ± sem). E) Percent of the population (p<0.05 uncorrected, linear regression) exhibiting differential modulation to effector cue contingent on high-level response. F) Population decoding of the participant’s choice to participate on a trial-to-trial basis (cross-validated mean ± 95% CI). Asterix indicates significant decoding (shuffle test.)

Figure 3:. Early coding of motor intentions in a simplified choice task.

A) Task. The subject was free to move the shoulder or attempt movement of the thumb or index whenever she felt the urge to do so. There was no task structure outside the brief (250ms) change of annulus color from grey to green immediately after the movement had been detected following movement onset. B) Histogram of intervals between voluntarily initiated movements. C) Behavior during the free-choice task. Top: Transition probabilities between actions. Bottom: Percent of trials each action was performed (mean ± sem computed across sessions.) D) Single unit examples aligned to the verbal report of current movement onset (mean ± sem). E) Accuracy decoding current and next action in the voluntary movement sequence split by interval duration (mean ± sem across six sessions.)

Figure 4:. Early network dynamics can explain the “pre-conscious” triggering of the neural decoder.

A) Schematic. Through the course of a trial neural activity transitions from resting levels (ITI) to the response measured during execution (GO) (thin black-line). This activity can be quantified by the geometric distance to the ITI and Go activity patterns (D_GO, D_ITI). B) Schematic. D_GO and D_ITI can be used to calculate a continuous measure of similarity to ITI and Go. C) Distance analysis applied to actual data for shrug (red) and squeeze (green) trials (mean ± 95% CI). The dashed line represents temporal discontinuity from concatenating cue-aligned and movement-aligned signals. D) Decode analysis using LDA applied to the same data as C. Each column shows the percentage of trials each class was decoded for a single time bin (500 ms non-overlapping bins.) Populations dynamics observed in C are sufficient to generate early decodes of shrug actions (e.g., following cue & before reported urge). E) Decode analysis using a modified algorithm and training protocol applied to the same data as D can restrict decoding actions to the time of intended execution. See also Figure S4, Video S3.