Evidence accumulation detected in BOLD signal using slow perceptual decision making

Krueger, P; Vugt, Marieke K. van; Simen, Patrick; Nystrom, Leigh E.; Holmes, Philip; Cohen, Jonathan D.

doi:10.1016/j.jneumeth.2017.01.012

Cited by 33 publications

(43 citation statements)

References 63 publications

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“…Might evidence integration engage a long-term memory system? As this question comes into focus, we note that prolonged yet controlled deliberation would make the integration process amenable to study with functional MRI, complementing previous investigations of gradual perceptual judgments [42, 43]. …”

Section: Discussionmentioning

confidence: 72%

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

Waskom

Kiani

2018

Current Biology

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Section: Discussionmentioning

confidence: 72%

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

Waskom

Kiani

2018

Current Biology

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“…Can evidence integration at long timescales be implemented through interactions between multiple memory systems? As this question comes into focus, we note that the prolonged yet controlled timescale of evidence in our task would make the integration process amenable to study with functional MRI, complementing previous efforts that have leveraged gradual yet continuous perceptual judgments [48,49].…”

Section: Discussionmentioning

confidence: 96%

Decision-making through integration of sensory evidence at prolonged timescales

Waskom

Kiani

2018

Preprint

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When multiple pieces of information bear on a decision, the best approach is to combine the evidence provided by each one. Evidence integration models formalize the computations underlying this process [1-3], explain human perceptual discrimination behavior [4][5][6][7][8][9][10][11], and correspond to neuronal responses elicited by discrimination tasks [12][13][14][15][16][17]. These findings indicate that evidence integration is key to understanding the neural basis of decision-making [18][19][20][21]. Evidence integration has most often been studied with simple tasks that limit the timescale of deliberation to hundreds of milliseconds, but many natural decisions unfold over much longer durations. Because neural network models imply acute limitations on the timescale of evidence integration [22][23][24][25][26], it is unknown whether current computational insights can generalize beyond rapid judgments. Here, we introduce a new psychophysical task and report model-based analyses of human behavior that demonstrate evidence integration at long timescales. Our task requires probabilistic inference using brief samples of visual evidence that are separated in time by long and unpredictable gaps. We show through several quantitative assays how decision-making can approximate a normative integration process that extends over tens of seconds without accruing significant memory leak or noise. These results support the generalization of evidence integration models to a broader class of behaviors while posing new challenges for models of how these computations are implemented in biological networks.

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“…Together, this suggests that momentary motion evidence in the RDM task is computed within MT, and the accumulation of this evidence occurs downstream within LIP. Studies in humans using magnetoencephalography, electroencephalography, and functional magnetic resonance imaging (fMRI) have also shown support for bounded evidence accumulation. While these experiments might seem to support choice selection at the level of saccades tuned to specific directions (action‐based), others have argued that these patterns might instead simply reflect the motion direction of the random dot stimulus .…”

Section: Good‐based Models: From Stimulus To Reaction Timementioning

confidence: 97%

Models, movements, and minds: bridging the gap between decision making and action

Wispinski

Gallivan

Chapman

2018

Annals of the New York Academy of Sciences

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Decision making is a fundamental cognitive function, which not only determines our day‐to‐day choices but also shapes the trajectories of our movements, our lives, and our societies. While immense progress has been made in recent years on our understanding of the mechanisms underlying decision making, research on this topic is still largely split into two halves. Good‐based models largely state that decisions are made between representations of abstract value associated with available options; while action‐based models largely state that decisions are made at the level of action representations. These models are further divided between those that state that a decision is made before an action is specified, and those that regard decision making as an evolving process that continues until movement completion. Here, we review computational models, behavioral findings, and results from neural recordings associated with these frameworks. In synthesizing this literature, we submit that decision making is best understood as a continuous, graded, and distributed process that traverses a landscape of behaviorally relevant options, from their presentation until movement completion. Identifying and understanding the intimate links between decision making and action processing has important implications for the study of complex, goal‐directed behaviors such as social communication, and for elucidating the underlying mechanisms by which decisions are formed.

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Evidence accumulation detected in BOLD signal using slow perceptual decision making

Cited by 33 publications

References 63 publications

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

Decision Making through Integration of Sensory Evidence at Prolonged Timescales

Decision-making through integration of sensory evidence at prolonged timescales

Models, movements, and minds: bridging the gap between decision making and action

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