Preserved Metacognition for Undetected Visuomotor Deviations

Abstract

Humans can successfully correct deviations of movements without conscious detection of such deviations, suggesting limited awareness of movement details. We ask whether such limited awareness impairs confidence (metacognition). We recorded functional magnetic resonance imaging data while 31 human female and male participants detected cursor deviations during a visuomotor reaching task and rated their confidence retrospectively. We show that participants monitor a summary statistic of the unfolding visual feedback (the peak cursor error) to detect visuomotor deviations and adjust their confidence ratings, even when they report being unaware of a deviation. Crucially, confidence ratings were as metacognitively efficient for aware and unaware deviations. At the neural level, activity in the ventral striatum tracks high confidence, whereas a broad network encodes cursor error but not confidence. These findings challenge the notion of limited conscious action monitoring and uncover how humans monitor their movements as they unfold, even when unaware of ongoing deviations.SIGNIFICANCE STATEMENT We are unaware of the small corrections we apply to our movements as long as our goals are achieved. Here, although we replicate the finding that participants deny perceiving small deviations they correct, we show that their confidence reliably reflects the presence or absence of a deviation. This observation shows they can metacognitively monitor the presence of a deviation, even when they deny perceiving it. We also describe the hemodynamic correlates of confidence ratings. Our study questions the extent to which humans are unaware of the details of their movements; describes a plausible mechanism for metacognition in a visuomotor task, along with its neural correlates; and has important implications for the construction of the sense of self.

Keywords: awareness; confidence; detection; fMRI; metacognition; visuomotor.

Figure 1.

Experimental paradigm and detection responses. A, Participants started every trial by using the joystick to bring a visible cursor (red triangle) to a target (circle) on top of the screen. A deviation was applied to the cursor in 79% of the trials, titrated so as to reach 71% of detection accuracy. Titrated detection rates can be found in Extended Data Figure 1-1. After reaching the target, participants reported whether the cursor was deviated or not (detection) and how confident they were about their answer. B, Trajectories of the cursor for correct rejections (c. rej.; green), false alarms (f.a.; gray), hits (blue), and misses (red). Note that deviations could be toward the left or the right, but right deviation trials are mirrored and pooled with left trials for display purposes. C, Generated trajectories obtained by recomputing the cursor position had there been no deviation for hits (blue) and misses (red). D, Cross-correlation between cursor error and joystick lateral position. The vertical arrow indicates the time lag at which cross-correlation is strongest. E, Cursor error over time for different signal detection theory categories, hits (blue), misses (red), correct rejections (green), and false alarms (black). The significant (p < 0.05; false discovery rate corrected) main effect of cursor error over time is depicted by the purple line and the main effect of deviation by the gray line; and the significant interaction effects between deviated trials and cursor error is depicted by the cyan line. The dashed vertical line represents the onset of the experimental deviation (dev.). Model comparison, peak cursor error distributions and discriminability analyses can be found in Extended Data Figures 1-2, 1-3, 1-4, 1-5. F, Same as E for joystick correction over time. Shaded areas in A–F indicate 95% confidence intervals.

Confidence ratings and metacognition. A, Distribution of confidence ratings for hits (blue), misses (red) and correct rejections (green). Each black dot represents the data of one participant. B, Schematic depiction of the four regressors used for the four confidence regression models tested. C, Improvement in BIC (compared with a model with no cursor information). Note that the peak cursor error (peak err.) model shows the largest improvement (*). Additional model comparisons can be found in Extended Data Figure 2-1. D, Confidence for different percentiles of peak cursor error for hits (blue), misses (red), and correct rejections (green). E, Fixed effects predictions of confidence for comparable levels of peak error (normalized) for hits (blue), misses (red), correct rejections (green) and false-alarms (black). F, Hierarchical Bayesian estimation of response-specific metacognitive efficiency using the M-ratio. Left, Posterior probability for yes (blue) and no (red) responses Vertical lines show the mean M-ratio, and horizontal bars show the 95% confidence interval. Right, Single participant values of the M-ratio. Shaded areas and whiskers A–F indicate 95% confidence intervals.

Figure 3.

fMRI results. A–D, Statistical maps of parametric modulation contrast for explicit detection (yes responses; A), high peak cursor error (B), high confidence (C), and low confidence (D). Colors represent different parametric regressors and are independent from Figure 1. Results are displayed at p < 0.001, uncorrected, but all clusters displayed were significant after FWE correction (p < 0.05). IFG, Inferior frontal gyrus. Other brain activations can be found in Tables 1 and 2. Correlation between regressors can be found in Extended Data Figure 3-1. Extended Data Figure 3-2 shows beta values averaged across participants for each level of confidence for each ventral striatum using MarsBaR toolbox for SPM (Brett et al., 2002).