Neurocognitive Modeling of Perceptual Decision Making

2015 ◽  
Author(s):  
Thomas J. Palmeri ◽  
Jeffrey D. Schall ◽  
Gordon D. Logan
Keyword(s):  
Decision Making ◽  
Drift Rate ◽  
Response Times ◽  
Perceptual Processing ◽  
Mathematical Psychology ◽  
Frontal Eye Field ◽  
Perceptual Decision Making ◽  
Systems Neuroscience ◽  
Eye Field ◽  
Accumulator Models

Mathematical psychology and systems neuroscience have converged on stochastic accumulator models to explain decision making. We examined saccade decisions in monkeys while neurophysiological recordings were made within their frontal eye field. Accumulator models were tested on how well they fit response probabilities and distributions of response times to make saccades. We connected these models with neurophysiology. To test the hypothesis that visually responsive neurons represented perceptual evidence driving accumulation, we replaced perceptual processing time and drift rate parameters with recorded neurophysiology from those neurons. To test the hypothesis that movement related neurons instantiated the accumulator, we compared measures of neural dynamics with predicted measures of accumulator dynamics. Thus, neurophysiology both provides a constraint on model assumptions and data for model selection. We highlight a gated accumulator model that accounts for saccade behavior during visual search, predicts neurophysiology during search, and provides insights into the locus of cognitive control over decisions.

scholarly journals Salience by Competitive and Recurrent Interactions: Bridging Neural Spiking and Computation

2021 ◽  
Author(s):  
Gregory Edward Cox ◽  
Thomas Palmeri ◽  
Gordon D. Logan ◽  
Philip L. Smith ◽  
Jeffrey Schall
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Target Selection ◽  
Interaction Model ◽  
Saccade Target ◽  
Frontal Eye Field ◽  
Visual Neurons ◽  
Discharge Rates ◽  
Neural Spiking ◽  
Eye Field

Decisions about where to move the eyes depend on neurons in Frontal Eye Field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called Salience by Competitive and Recurrent Interactions (SCRI), based on the Competitive Interaction model of attentional selection and decision making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identification information to yield a dynamically evolving representation of salience across the visual field. SCRI accounts for neural spiking of individual FEF visual neurons, explaining idiosyncratic differences in neural dynamics with specific parameters. Many visual neurons resolve the competition between search items through feedforward inhibition between signals representing different search items, some also require lateral inhibition, and many act as recurrent gates to modulate the incoming flow of information about stimulus identity. SCRI was tested further by using simulated spiking representations of visual salience as input to the Gated Accumulator Model of FEF movement neurons (Purcell et al., 2010; Purcell, Schall, Logan, & Palmeri, 2012). Predicted saccade response times fit those observed for search arrays of different set size and different target-distractor similarity, and accumulator trajectories replicated movement neuron discharge rates. These findings offer new insights into visual decision making through converging neuro-computational constraints and provide a novel computational account of the diversity of FEF visual neurons.

scholarly journals Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

2019 ◽  
Vol 121 (4) ◽  
pp. 1300-1314 ◽  
Author(s):  
Mathieu Servant ◽  
Gabriel Tillman ◽  
Jeffrey D. Schall ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri
Keyword(s):  
Decision Making ◽  
Visual Search ◽  
Response Times ◽  
Frontal Eye Field ◽  
Search Performance ◽  
Model Framework ◽  
Accumulator Model ◽  
Eye Field ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.

Dissociating neural variability related to stimulus quality and response times in perceptual decision-making

2018 ◽  
Vol 111 ◽  
pp. 190-200 ◽  
Author(s):  
Stefan Bode ◽  
Daniel Bennett ◽  
David K. Sewell ◽  
Bryan Paton ◽  
Gary F. Egan ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Perceptual Decision Making ◽  
Stimulus Quality ◽  
Perceptual Decision ◽  
Neural Variability

scholarly journals Transient neuronal suppression for exploitation of new sensory evidence

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Maxwell Shinn ◽  
Daeyeol Lee ◽  
John D. Murray ◽  
Hyojung Seo
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Temporal Integration ◽  
Motor Preparation ◽  
Stimulus Onset ◽  
Frontal Eye Field ◽  
Perceptual Decision Making ◽  
Behavioral Studies ◽  
The Face ◽  
Sensory Evidence

AbstractIn noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

scholarly journals Perceptual decision-making in children: Age-related differences and EEG correlates

2020 ◽  
Author(s):  
Catherine Manning ◽  
Eric-Jan Wagenmakers ◽  
Anthony Norcia ◽  
Gaia Scerif ◽  
Udo Boehm
Keyword(s):  
Decision Making ◽  
Drift Rate ◽  
Decision Time ◽  
Diffusion Models ◽  
Perceptual Task ◽  
Model Parameters ◽  
Older Children ◽  
Perceptual Decision Making ◽  
Boundary Separation ◽  
Age Related

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.

Higher level influences on saccade generation in normals and patients with visual hemineglect

1999 ◽  
Vol 22 (4) ◽  
pp. 688-689
Author(s):  
Wolfgang Heide ◽  
Andreas Sprenger ◽  
Detlef Kömpf
Keyword(s):  
Human Subjects ◽  
Perceptual Processing ◽  
Visual Exploration ◽  
Frontal Eye Field ◽  
Automatic Processes ◽  
Eye Field ◽  
Level 3 ◽  
Normal Human ◽  
Reflexive Saccades ◽  
Saccade Generation

In this commentary we describe findings in normal human subjects and in patients with visual hemineglect that support the importance of higher-level influences on saccade generation during visual exploration. As the duration of fixations increases with increases in the cognitive demand of the task, the timing of exploratory saccades is controlled more by centers of cognitive and perceptual processing at levels 4 and 5 than by reflex-like automatic processes at level 3. In line with this, unilateral frontal eye field lesions impair systematic, intentional saccadic exploration of visual scenes, causing prolonged fixations and contralesional hemineglect, but leave visually triggered reflexive saccades largely intact.

scholarly journals Brain Areas Consistently Linked to Individual Differences in Perceptual Decision-making in Younger as well as Older Adults before and after Training

2011 ◽  
Vol 23 (9) ◽  
pp. 2147-2158 ◽  
Author(s):  
Simone Kühn ◽  
Florian Schmiedek ◽  
Björn Schott ◽  
Roger Ratcliff ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Individual Differences ◽  
Drift Rate ◽  
Parietal Lobe ◽  
Perceptual Decision Making ◽  
Inferior Parietal Lobe ◽  
Perceptual Decision ◽  
Boundary Separation ◽  
Before And After

Perceptual decision-making performance depends on several cognitive and neural processes. Here, we fit Ratcliff's diffusion model to accuracy data and reaction-time distributions from one numerical and one verbal two-choice perceptual-decision task to deconstruct these performance measures into the rate of evidence accumulation (i.e., drift rate), response criterion setting (i.e., boundary separation), and peripheral aspects of performance (i.e., nondecision time). These theoretical processes are then related to individual differences in brain activation by means of multiple regression. The sample consisted of 24 younger and 15 older adults performing the task in fMRI before and after 100 daily 1-hr behavioral training sessions in a multitude of cognitive tasks. Results showed that individual differences in boundary separation were related to striatal activity, whereas differences in drift rate were related to activity in the inferior parietal lobe. These associations were not significantly modified by adult age or perceptual expertise. We conclude that the striatum is involved in regulating response thresholds, whereas the inferior parietal lobe might represent decision-making evidence related to letters and numbers.

scholarly journals CHaRTr: An R toolbox for modeling Choices and Response Times in decision-making tasks

2019 ◽  
Author(s):  
Chandramouli Chandrasekaran ◽  
Guy E. Hawkins
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Response Times ◽  
Research Effort ◽  
Perceptual Decision Making ◽  
Low Pass ◽  
Decision Making Models ◽  
Collapsing Boundaries ◽  
Decision Making Model ◽  
Diffusion Decision Model

AbstractDecision-making is the process of choosing and performing actions in response to sensory cues so as to achieve behavioral goals. A sophisticated research effort has led to the development of many mathematical models to describe the response time (RT) distributions and choice behavior of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. Although some of these problems have been surmounted in existing software packages, the packages have often focused on the classical decision-making model, the diffusion decision model. Recent theoretical advances in decision-making that posit roles for “urgency”, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence, have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluations among these competing classes of decision-making models. Here, we present a toolbox —Choices and Response Times in R, orCHaRTr— that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. Earlier versions ofCHaRTrhave been instrumental in a number of recent studies of humans and monkeys performing perceptual decision-making tasks. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

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