The Urge to Decide and Act: Implications for Brain Function and Dysfunction

Carland, Matthew A.; Thura, David; Cisek, Paul

doi:10.1177/1073858419841553

Cited by 75 publications

(88 citation statements)

References 150 publications

(216 reference statements)

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“…Urgency may underpin the personality trait of impulsivity (Carland et al, 2019). The relation between caudate activation and inverse urgency found here may provide an explanation for a recent metaanalysis reporting hypoactivation of the striatum during reward among individuals with substance use or gambling disorders -two groups often associated with higher impulsivity traits (Luijten et al, 2017).…”

Section: Discussionmentioning

confidence: 52%

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Yau

Dadar

Taylor

et al. 2019

Preprint

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Current models of decision-making assume that the brain gradually accumulates evidence and drifts towards a threshold which, once crossed, results in a choice selection. These models have been especially successful in primate research, however transposing them to human fMRI paradigms has proved challenging. Here, we exploit the face-selective visual system and test whether decoded emotional facial features from multivariate fMRI signals during a dynamic perceptual decision-making task are related to the parameters of computational models of decision-making. We show that trial-bytrial variations in the pattern of neural activity in the fusiform gyrus reflect facial emotional information and modulate drift rates during deliberation. We also observed an inverse-urgency signal based in the caudate nucleus that was independent of sensory information but appeared to slow decisions, particularly when information in the task was ambiguous. Taken together, our results characterize how decision parameters from a computational model (i.e., drift rate and urgency signal) are involved in perceptual decision-making and reflected in the activity of the human brain.YY and AD designed research; YY and MT collected the data; YY, MD, and YZ analyzed the data and contributed methods; and YY, AD, LF, and PC contributed to result interpretation. YY drafted the initial manuscript; all authors contributed to writing of this manuscript.

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Section: Discussionmentioning

confidence: 52%

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Yau

Dadar

Taylor

et al. 2019

Preprint

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“…Altogether, these observations indicate that the level of urgency at which a decision is made directly 594 influences movement vigor, suggesting that decision and movement durations are determined by 595 a global decision urgency/movement vigor signal that invigorates behavior in order to control 596 reward rate Carland et al, 2019). However, a missing test of the shared 597 regulation hypothesis required to vary the motor context in which a decision is made and assess 598 whether or not a motor context permitting execution of vigorous movements to express choices 599 leads to faster decisions compared to the same difficult decisions made in a demanding motor 600 context, imposing slow and accurate movements.…”

Section: A Flexible Mechanism For Regulating Decision and Movement Dumentioning

confidence: 92%

Humans sacrifice decision-making for action execution when a demanding control of movement is required

Reynaud

Lunazzi

Thura

2020

Preprint

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ABSTRACTA growing body of evidence suggests that decision-making and action execution are governed by partly overlapping operating principles. Especially, previous work proposed that a shared decision urgency/movement vigor signal, possibly computed in the basal ganglia, coordinates both deliberation and movement durations in a way that maximizes the reward rate. Recent data support one aspect of this hypothesis, indicating that the urgency level at which a decision is made influences the vigor of the movement produced to express this choice. Here we investigated whether conversely, the motor context in which a movement is executed determines decision speed and accuracy. Twenty human subjects performed a probabilistic decision task in which perceptual choices were expressed by reaching movements toward targets whose size and distance from a starting position varied in distinct blocks of trials. We found strong evidence for an influence of the motor context on most of the subjects’ decision policy but contrary to the predictions of the “shared regulation” hypothesis, we observed that slow movements executed in the most demanding motor blocks in terms of accuracy were often preceded by faster and less accurate decisions compared to blocks of trials in which big targets allowed expression of choices with fast and inaccurate movements. These results suggest that decision-making and motor control are not regulated by one unique “invigoration” signal determining both decision urgency and action vigor, but more likely by independent, yet interacting, decision urgency and movement vigor signals.NEW & NOTEWORTHYRecent hypotheses propose that choices and movements share optimization principles derived from economy, possibly implemented by one unique context-dependent regulation signal determining both processes speed. In the present behavioral study conducted on human subjects, we demonstrate that action properties indeed influence perceptual decision-making, but that decision duration and action vigor are actually independently set depending on the difficulty of the movement executed to report a choice.

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“…A current challenge is to learn suitable action selection policies. Although explorationexploitation is known to be central in motor learning , it is yet unclear what process drives action selection in the brain (Carland et al, 2019;Sugiyama et al, 2020). These approaches still need to be experimentally assessed in a motor learning context.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches for Motor Learning: A Short Review

Caramiaux

Françoise

Liu

et al. 2020

Front. Comput. Sci.

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Machine learning approaches have seen a considerable number of applications in human movement modeling but remain limited for motor learning. Motor learning requires that motor variability be taken into account and poses new challenges because the algorithms need to be able to differentiate between new movements and variation in known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.

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The Urge to Decide and Act: Implications for Brain Function and Dysfunction

Cited by 75 publications

References 150 publications

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Neural correlates of evidence and urgency during human perceptual decision-making in dynamically changing conditions

Humans sacrifice decision-making for action execution when a demanding control of movement is required

Machine Learning Approaches for Motor Learning: A Short Review

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