The Evidence Accumulation Process

Author(s):  
Russell G. Almond ◽  
Robert J. Mislevy ◽  
Linda S. Steinberg ◽  
Duanli Yan ◽  
David M. Williamson
eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Kobe Desender ◽  
K Richard Ridderinkhof ◽  
Peter R Murphy

Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behavior. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity –a signal with thus far unique properties in cognitive neuroscience – can be leveraged to furnish key new insights into the inputs to, adaptation, and consequences of the post-decisional accumulation process.


2019 ◽  
Vol 15 (7) ◽  
pp. e1007060 ◽  
Author(s):  
Jacob D. Davidson ◽  
Ahmed El Hady

2002 ◽  
Vol 02 (04) ◽  
pp. 603-616 ◽  
Author(s):  
R. SOODAMANI ◽  
Z. Q. LIU

To use the Hough transform to detect shapes we need to accumulate votes for the edge passing a specific bin. Most existing Hough transform techniques use a sharp (crisp) cutoff to determine whether the bin has received a vote or not. This results in considerable errors. In this paper, we propose a new line Hough transform (LHT) using evidence accumulation and fuzzy aggregation function. The resulting voting process is dependent on the distance ρ from the grid centers. This effectively handles uncertainty in the accumulation process and achieves a better performance. To show the effectiveness this approach, we present our experimental results for a set of 2D parametric and 3D nonparametric objects.


2018 ◽  
Author(s):  
Arkady Zgonnikov ◽  
Gustav Markkula

Human operators often employ intermittent, discontinuous control strategies in a variety of tasks. A typical intermittent controller monitors control error and generates corrective action when the deviation of the controlled system from the desired state becomes too large to ignore. Most contemporary models of human intermittent control employ simple, threshold-based trigger mechanism to model the process of control activation. However, recent experimental studies demonstrate that the control activation patterns produced by human operators do not support threshold-based models, and provide evidence for more complex activation mechanisms. In this paper, we investigate whether intermittent control activation in humans can be modeled as a decision-making process. We utilize an established drift-diffusion model, which treats decision making as an evidence accumulation process, and study it in simple numerical simulations. We demonstrate that this model robustly replicates the control activation patterns (distributions of control error at movement onset) produced by human operators in previously conducted experiments on virtual inverted pendulum balancing. Our results provide support to the hypothesis that intermittent control activation in human operators can be treated as an evidence accumulation process.


2018 ◽  
Author(s):  
Kitty K. Lui ◽  
Michael D. Nunez ◽  
Jessica M. Cassidy ◽  
Joachim Vandekerckhove ◽  
Steven C. Cramer ◽  
...  

AbstractDecision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.


2019 ◽  
Author(s):  
Ben Deverett ◽  
Mikhail Kislin ◽  
David W. Tank ◽  
Samuel S.-H. Wang

AbstractTo select actions based on sensory evidence, animals must create and manipulate representations of stimulus information in memory. We found that during accumulation of somatosensory evidence, optogenetic manipulation of cerebellar Purkinje cells reduced the accuracy of subsequent memory-guided decisions and caused mice to downweight prior information. Behavioral deficits were consistent with the addition of noise and leak to the evidence accumulation process, suggesting the cerebellum can influence the maintenance of working memory contents.


2021 ◽  
Author(s):  
Kobe Desender ◽  
K. Richard Ridderinkhof ◽  
Peter Murphy

Performance monitoring is a key cognitive function, allowing to detect mistakes and adapt future behaviour. Post-decisional neural signals have been identified that are sensitive to decision accuracy, decision confidence and subsequent adaptation. Here, we review recent work that supports an understanding of late error/confidence signals in terms of the computational process of post-decisional evidence accumulation. We argue that the error positivity, a positive-going centro-parietal potential measured through scalp electrophysiology, reflects the post-decisional evidence accumulation process itself, which follows a boundary crossing event corresponding to initial decision commitment. This proposal provides a powerful explanation for both the morphological characteristics of the signal and its relation to various expressions of performance monitoring. Moreover, it suggests that the error positivity –a signal with thus far unique properties in cognitive neuroscience – can be leveraged to furnish key new insights into the inputs to, adaptation, and sequelae of the post-decisional accumulation process.


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