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This study demonstrates how keystroke dynamics can isolate specific stages of Parkinson's motor control—distinguishing error detection from post-error recovery—with high statistical significance. By applying continuous accelerated failure time models to backspace events, researchers successfully dissociated these functions and validated the findings against independent clinical finger-tapping tests. The work highlights a novel application of machine learning survival analysis for quantifying subtle neural circuit impairments in neurodegenerative conditions.
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Selective Impairment of Motor Recovery from Typing Errors in Parkinson's Disease: A Survival Analysis

arXiv3 min read

arXiv:2607.24796v1 Announce Type: cross Abstract: Parkinson's disease (PD) affects multiple, dissociable stages of motor and cognitive control. We ask whether passively-collected keystroke dynamics can distinguish two of these stages: noticing a self-generated error (error monitoring) versus recovering normal motor rhythm afterward (motor restart). Using backspace events as naturally-occurring error-correction episodes in the public neuroQWERTY MIT-CSXPD dataset (57 subjects with sufficient backspace data, 27 with PD and UPDRS-III scores), we find no evidence that pre-error keystroke instability differs by disease severity ($r=0.164$, $p=0.413$), but strong evidence that post-error recovery time does, modeled as a continuous accelerated failure time (AFT) survival outcome ($p<10^{-9}$ in each of two independent sub-cohorts; permutation $p<0.0033$; bootstrap 95% CI excluding zero). The two measures are uncorrelated with each other ($r=-0.065$), and in a joint model only post-error recovery remains significant, confirming a genuine dissociation rather than two redundant signals. The effect survives controlling for raw typing speed and replicates against an independent clinical motor test (alternating finger-tapping, $p=0.011$). An initial attempt to model recovery as a discretized time-to-event outcome (analogous to Kaplan-Meier survival curves) destroyed the signal regardless of threshold choice; switching to a continuous AFT formulation, which also fits the data's right-skewed distribution far better than ordinary regression, recovered it. We relate this behavioral dissociation to existing electrophysiological evidence that error detection and post-error motor adjustment in PD are mediated by distinct neural circuits, the former largely spared, the latter linked to subthalamic nucleus activity, and argue that this dissociation is detectable through everyday typing alone.

Abstract:Parkinson's disease (PD) affects multiple, dissociable stages of motor and cognitive control. We ask whether passively-collected keystroke dynamics can distinguish two of these stages: noticing a self-generated error (error monitoring) versus recovering normal motor rhythm afterward (motor restart). Using backspace events as naturally-occurring error-correction episodes in the public neuroQWERTY MIT-CSXPD dataset (57 subjects with sufficient backspace data, 27 with PD and UPDRS-III scores), we find no evidence that pre-error keystroke instability differs by disease severity ($r=0.164$, $p=0.413$), but strong evidence that post-error recovery time does, modeled as a continuous accelerated failure time (AFT) survival outcome ($p<10^{-9}$ in each of two independent sub-cohorts; permutation $p<0.0033$; bootstrap 95% CI excluding zero). The two measures are uncorrelated with each other ($r=-0.065$), and in a joint model only post-error recovery remains significant, confirming a genuine dissociation rather than two redundant signals. The effect survives controlling for raw typing speed and replicates against an independent clinical motor test (alternating finger-tapping, $p=0.011$). An initial attempt to model recovery as a discretized time-to-event outcome (analogous to Kaplan-Meier survival curves) destroyed the signal regardless of threshold choice; switching to a continuous AFT formulation, which also fits the data's right-skewed distribution far better than ordinary regression, recovered it. We relate this behavioral dissociation to existing electrophysiological evidence that error detection and post-error motor adjustment in PD are mediated by distinct neural circuits, the former largely spared, the latter linked to subthalamic nucleus activity, and argue that this dissociation is detectable through everyday typing alone.

Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2607.24796 [q-bio.NC]

(or arXiv:2607.24796v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2607.24796 Focus to learn more arXiv-issued DOI via DataCite

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