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Influence of uncertainty and surprise on human corticospinal excitability during preparation for action.

Bestmann S, Harrison LM, Blankenburg F, Mars RB, Haggard P, Friston KJ, Rothwell JC - Curr. Biol. (2008)

Bottom Line: Actions are guided by prior sensory information [1-10], which is inherently uncertain.We systematically varied the uncertainty about an impending action by changing the validity of the instructive visual cue.We suggest that motor output is biased according to contextual probabilities that are represented dynamically in the brain.

View Article: PubMed Central - PubMed

Affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK. s.bestmann@fil.ion.ucl.ac.uk

ABSTRACT
Actions are guided by prior sensory information [1-10], which is inherently uncertain. However, how the motor system is sculpted by trial-by-trial content of current sensory information remains largely unexplored. Previous work suggests that conditional probabilities, learned under a particular context, can be used preemptively to influence the output of the motor system [11-14]. To test this we used transcranial magnetic stimulation (TMS) to read out corticospinal excitability (CSE) during preparation for action in an instructed delay task [15, 16]. We systematically varied the uncertainty about an impending action by changing the validity of the instructive visual cue. We used two information-theoretic quantities to predict changes in CSE, prior to action, on a trial-by-trial basis: entropy (average uncertainty) and surprise (the stimulus-bound information conveyed by a visual cue) [17-19]. Our data show that during preparation for action, human CSE varies according to the entropy and surprise conveyed by visual events guiding action. CSE increases on trials with low entropy about the impending action and low surprise conveyed by an event. Commensurate effects were observed in reaction times. We suggest that motor output is biased according to contextual probabilities that are represented dynamically in the brain.

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Related in: MedlinePlus

Experimental Task and Explanatory Variables(A) Schematic of the task. On valid trials, a preparatory CS predicted the identity of a subsequent IS, cueing a button press with the right thumb or little finger. On invalid trials the CS-IS mapping was invalid as the CS was followed by the alternative IS. The validity of the CS varied across blocks of 105 trials between 85:15%, 70:30%, and 55:45%, respectively, creating blocks with, low, medium, and high uncertainty about imperative stimuli. A single TMS pulse was applied during every trial, 200 ms before IS appearance.(B) Information theoretic and categorical quantities for two experimental blocks. Examples are shown of entropy and surprise during blocks with valid-invalid CS distributions of 85:15% (left panel) and 55:45% (right panel), respectively. Top panel, entropy; middle panel, surprise; and lower panel, regressors for a categorical model containing valid and invalid trial types. The ensuing time series were used as predictors for modeling CSE and RTs across the entire series of trials of each participant.
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fig1: Experimental Task and Explanatory Variables(A) Schematic of the task. On valid trials, a preparatory CS predicted the identity of a subsequent IS, cueing a button press with the right thumb or little finger. On invalid trials the CS-IS mapping was invalid as the CS was followed by the alternative IS. The validity of the CS varied across blocks of 105 trials between 85:15%, 70:30%, and 55:45%, respectively, creating blocks with, low, medium, and high uncertainty about imperative stimuli. A single TMS pulse was applied during every trial, 200 ms before IS appearance.(B) Information theoretic and categorical quantities for two experimental blocks. Examples are shown of entropy and surprise during blocks with valid-invalid CS distributions of 85:15% (left panel) and 55:45% (right panel), respectively. Top panel, entropy; middle panel, surprise; and lower panel, regressors for a categorical model containing valid and invalid trial types. The ensuing time series were used as predictors for modeling CSE and RTs across the entire series of trials of each participant.

Mentions: In the current study we asked how corticospinal excitability (CSE) changes when subjects prepare an action based on visual cues (FigureĀ 1A) under changing degrees of uncertainty associated with an impending action. Understanding how the brain uses the predictability of events to inform preparation for action requires models of how this predictability is learned and represented over time rather than how they change on average. We, therefore, measured CSE prior to overt action by measuring muscular responses to stimulation of the motor cortex using transcranial magnetic stimulation (TMS) (see the Experimental Procedures and the Supplemental Experimental Procedures available online). We used established computational models to examine how the motor system might encode the probability of future events for action preparation.


Influence of uncertainty and surprise on human corticospinal excitability during preparation for action.

Bestmann S, Harrison LM, Blankenburg F, Mars RB, Haggard P, Friston KJ, Rothwell JC - Curr. Biol. (2008)

Experimental Task and Explanatory Variables(A) Schematic of the task. On valid trials, a preparatory CS predicted the identity of a subsequent IS, cueing a button press with the right thumb or little finger. On invalid trials the CS-IS mapping was invalid as the CS was followed by the alternative IS. The validity of the CS varied across blocks of 105 trials between 85:15%, 70:30%, and 55:45%, respectively, creating blocks with, low, medium, and high uncertainty about imperative stimuli. A single TMS pulse was applied during every trial, 200 ms before IS appearance.(B) Information theoretic and categorical quantities for two experimental blocks. Examples are shown of entropy and surprise during blocks with valid-invalid CS distributions of 85:15% (left panel) and 55:45% (right panel), respectively. Top panel, entropy; middle panel, surprise; and lower panel, regressors for a categorical model containing valid and invalid trial types. The ensuing time series were used as predictors for modeling CSE and RTs across the entire series of trials of each participant.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2387198&req=5

fig1: Experimental Task and Explanatory Variables(A) Schematic of the task. On valid trials, a preparatory CS predicted the identity of a subsequent IS, cueing a button press with the right thumb or little finger. On invalid trials the CS-IS mapping was invalid as the CS was followed by the alternative IS. The validity of the CS varied across blocks of 105 trials between 85:15%, 70:30%, and 55:45%, respectively, creating blocks with, low, medium, and high uncertainty about imperative stimuli. A single TMS pulse was applied during every trial, 200 ms before IS appearance.(B) Information theoretic and categorical quantities for two experimental blocks. Examples are shown of entropy and surprise during blocks with valid-invalid CS distributions of 85:15% (left panel) and 55:45% (right panel), respectively. Top panel, entropy; middle panel, surprise; and lower panel, regressors for a categorical model containing valid and invalid trial types. The ensuing time series were used as predictors for modeling CSE and RTs across the entire series of trials of each participant.
Mentions: In the current study we asked how corticospinal excitability (CSE) changes when subjects prepare an action based on visual cues (FigureĀ 1A) under changing degrees of uncertainty associated with an impending action. Understanding how the brain uses the predictability of events to inform preparation for action requires models of how this predictability is learned and represented over time rather than how they change on average. We, therefore, measured CSE prior to overt action by measuring muscular responses to stimulation of the motor cortex using transcranial magnetic stimulation (TMS) (see the Experimental Procedures and the Supplemental Experimental Procedures available online). We used established computational models to examine how the motor system might encode the probability of future events for action preparation.

Bottom Line: Actions are guided by prior sensory information [1-10], which is inherently uncertain.We systematically varied the uncertainty about an impending action by changing the validity of the instructive visual cue.We suggest that motor output is biased according to contextual probabilities that are represented dynamically in the brain.

View Article: PubMed Central - PubMed

Affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK. s.bestmann@fil.ion.ucl.ac.uk

ABSTRACT
Actions are guided by prior sensory information [1-10], which is inherently uncertain. However, how the motor system is sculpted by trial-by-trial content of current sensory information remains largely unexplored. Previous work suggests that conditional probabilities, learned under a particular context, can be used preemptively to influence the output of the motor system [11-14]. To test this we used transcranial magnetic stimulation (TMS) to read out corticospinal excitability (CSE) during preparation for action in an instructed delay task [15, 16]. We systematically varied the uncertainty about an impending action by changing the validity of the instructive visual cue. We used two information-theoretic quantities to predict changes in CSE, prior to action, on a trial-by-trial basis: entropy (average uncertainty) and surprise (the stimulus-bound information conveyed by a visual cue) [17-19]. Our data show that during preparation for action, human CSE varies according to the entropy and surprise conveyed by visual events guiding action. CSE increases on trials with low entropy about the impending action and low surprise conveyed by an event. Commensurate effects were observed in reaction times. We suggest that motor output is biased according to contextual probabilities that are represented dynamically in the brain.

Show MeSH
Related in: MedlinePlus