The Experts below are selected from a list of 3906 Experts worldwide ranked by ideXlab platform

Thomas J. Palmeri - One of the best experts on this subject based on the ideXlab platform.

  • Neurally constrained Modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence.
    Journal of neurophysiology, 2019
    Co-Authors: Mathieu Servant, Jeffrey D Schall, Gordon D. Logan, Gabriel Tillman, Thomas J. Palmeri
    Abstract:

    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 accum...

  • RELATING Accumulator Model PARAMETERS AND NEURAL DYNAMICS
    Journal of mathematical psychology, 2016
    Co-Authors: Braden A. Purcell, Thomas J. Palmeri
    Abstract:

    Accumulator Models explain decision-making as an accumulation of evidence to a response threshold. Specific Model parameters are associated with specific Model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive Modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a Model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between Accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated Accumulator Model dynamics while systematically varying Model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between Models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve Model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous Modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception.

  • Neurocognitive Modeling of Perceptual Decision Making
    Oxford Handbooks Online, 2015
    Co-Authors: Thomas J. Palmeri, Jeffrey D Schall, Gordon D. Logan
    Abstract:

    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.

  • from salience to saccades multiple alternative gated stochastic Accumulator Model of visual search
    The Journal of Neuroscience, 2012
    Co-Authors: Braden A. Purcell, Jeffrey D Schall, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We describe a stochastic Accumulator Model demonstrating that visual search performance can be understood as a gated feedforward cascade from a salience map to multiple competing Accumulators. The Model quantitatively accounts for behavior and predicts neural dynamics of macaque monkeys performing visual search for a target stimulus among different numbers of distractors. The salience accumulated in the Model is equated with the spike trains recorded from visually responsive neurons in the frontal eye field. Accumulated variability in the firing rates of these neurons explains choice probabilities and the distributions of correct and error response times with search arrays of different set sizes if the Accumulators are mutually inhibitory. The dynamics of the stochastic Accumulators quantitatively predict the activity of presaccadic movement neurons that initiate eye movements if gating inhibition prevents accumulation before the representation of stimulus salience emerges. Adjustments in the level of gating inhibition can control trade-offs in speed and accuracy that optimize visual search performance.

  • neural mechanisms of saccade target selection gated Accumulator Model of the visual motor cascade
    European Journal of Neuroscience, 2011
    Co-Authors: Jeffrey D Schall, Richard P Heitz, Braden A. Purcell, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We review a new computational Model developed to understand how evidence about stimulus salience in visual search is translated into a saccade command. The Model uses the activity of visually responsive neurons in the frontal eye field as evidence for stimulus salience that is accumulated in a network of stochastic Accumulators to produce accurate and timely saccades. We discovered that only when the input to the accumulation process was gated could the Model account for the variability in search performance and predict the dynamics of movement neuron discharge rates. This union of cognitive Modeling and neurophysiology indicates how the visual–motor transformation can occur, and provides a concrete mapping between neuron function and specific cognitive processes.

Jeffrey D Schall - One of the best experts on this subject based on the ideXlab platform.

  • Neurally constrained Modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence.
    Journal of neurophysiology, 2019
    Co-Authors: Mathieu Servant, Jeffrey D Schall, Gordon D. Logan, Gabriel Tillman, Thomas J. Palmeri
    Abstract:

    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 accum...

  • Neurocognitive Modeling of Perceptual Decision Making
    Oxford Handbooks Online, 2015
    Co-Authors: Thomas J. Palmeri, Jeffrey D Schall, Gordon D. Logan
    Abstract:

    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.

  • neural mechanisms of speed accuracy tradeoff
    Neuron, 2012
    Co-Authors: Richard P Heitz, Jeffrey D Schall
    Abstract:

    Summary Intelligent agents balance speed of responding with accuracy of deciding. Stochastic Accumulator Models commonly explain this speed-accuracy tradeoff by strategic adjustment of response threshold. Several laboratories identify specific neurons in prefrontal and parietal cortex with this accumulation process, yet no neurophysiological correlates of speed-accuracy tradeoff have been described. We trained macaque monkeys to trade speed for accuracy on cue during visual search and recorded the activity of neurons in the frontal eye field. Unpredicted by any Model, we discovered that speed-accuracy tradeoff is accomplished through several distinct adjustments. Visually responsive neurons modulated baseline firing rate, sensory gain, and the duration of perceptual processing. Movement neurons triggered responses with activity modulated in a direction opposite of Model predictions. Thus, current stochastic Accumulator Models provide an incomplete description of the neural processes accomplishing speed-accuracy tradeoffs. The diversity of neural mechanisms was reconciled with the Accumulator framework through an integrated Accumulator Model constrained by requirements of the motor system.

  • from salience to saccades multiple alternative gated stochastic Accumulator Model of visual search
    The Journal of Neuroscience, 2012
    Co-Authors: Braden A. Purcell, Jeffrey D Schall, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We describe a stochastic Accumulator Model demonstrating that visual search performance can be understood as a gated feedforward cascade from a salience map to multiple competing Accumulators. The Model quantitatively accounts for behavior and predicts neural dynamics of macaque monkeys performing visual search for a target stimulus among different numbers of distractors. The salience accumulated in the Model is equated with the spike trains recorded from visually responsive neurons in the frontal eye field. Accumulated variability in the firing rates of these neurons explains choice probabilities and the distributions of correct and error response times with search arrays of different set sizes if the Accumulators are mutually inhibitory. The dynamics of the stochastic Accumulators quantitatively predict the activity of presaccadic movement neurons that initiate eye movements if gating inhibition prevents accumulation before the representation of stimulus salience emerges. Adjustments in the level of gating inhibition can control trade-offs in speed and accuracy that optimize visual search performance.

  • neural mechanisms of saccade target selection gated Accumulator Model of the visual motor cascade
    European Journal of Neuroscience, 2011
    Co-Authors: Jeffrey D Schall, Richard P Heitz, Braden A. Purcell, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We review a new computational Model developed to understand how evidence about stimulus salience in visual search is translated into a saccade command. The Model uses the activity of visually responsive neurons in the frontal eye field as evidence for stimulus salience that is accumulated in a network of stochastic Accumulators to produce accurate and timely saccades. We discovered that only when the input to the accumulation process was gated could the Model account for the variability in search performance and predict the dynamics of movement neuron discharge rates. This union of cognitive Modeling and neurophysiology indicates how the visual–motor transformation can occur, and provides a concrete mapping between neuron function and specific cognitive processes.

Richard P Heitz - One of the best experts on this subject based on the ideXlab platform.

  • neural mechanisms of speed accuracy tradeoff
    Neuron, 2012
    Co-Authors: Richard P Heitz, Jeffrey D Schall
    Abstract:

    Summary Intelligent agents balance speed of responding with accuracy of deciding. Stochastic Accumulator Models commonly explain this speed-accuracy tradeoff by strategic adjustment of response threshold. Several laboratories identify specific neurons in prefrontal and parietal cortex with this accumulation process, yet no neurophysiological correlates of speed-accuracy tradeoff have been described. We trained macaque monkeys to trade speed for accuracy on cue during visual search and recorded the activity of neurons in the frontal eye field. Unpredicted by any Model, we discovered that speed-accuracy tradeoff is accomplished through several distinct adjustments. Visually responsive neurons modulated baseline firing rate, sensory gain, and the duration of perceptual processing. Movement neurons triggered responses with activity modulated in a direction opposite of Model predictions. Thus, current stochastic Accumulator Models provide an incomplete description of the neural processes accomplishing speed-accuracy tradeoffs. The diversity of neural mechanisms was reconciled with the Accumulator framework through an integrated Accumulator Model constrained by requirements of the motor system.

  • neural mechanisms of saccade target selection gated Accumulator Model of the visual motor cascade
    European Journal of Neuroscience, 2011
    Co-Authors: Jeffrey D Schall, Richard P Heitz, Braden A. Purcell, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We review a new computational Model developed to understand how evidence about stimulus salience in visual search is translated into a saccade command. The Model uses the activity of visually responsive neurons in the frontal eye field as evidence for stimulus salience that is accumulated in a network of stochastic Accumulators to produce accurate and timely saccades. We discovered that only when the input to the accumulation process was gated could the Model account for the variability in search performance and predict the dynamics of movement neuron discharge rates. This union of cognitive Modeling and neurophysiology indicates how the visual–motor transformation can occur, and provides a concrete mapping between neuron function and specific cognitive processes.

Gordon D. Logan - One of the best experts on this subject based on the ideXlab platform.

  • Neurally constrained Modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence.
    Journal of neurophysiology, 2019
    Co-Authors: Mathieu Servant, Jeffrey D Schall, Gordon D. Logan, Gabriel Tillman, Thomas J. Palmeri
    Abstract:

    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 accum...

  • Neurocognitive Modeling of Perceptual Decision Making
    Oxford Handbooks Online, 2015
    Co-Authors: Thomas J. Palmeri, Jeffrey D Schall, Gordon D. Logan
    Abstract:

    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.

  • from salience to saccades multiple alternative gated stochastic Accumulator Model of visual search
    The Journal of Neuroscience, 2012
    Co-Authors: Braden A. Purcell, Jeffrey D Schall, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We describe a stochastic Accumulator Model demonstrating that visual search performance can be understood as a gated feedforward cascade from a salience map to multiple competing Accumulators. The Model quantitatively accounts for behavior and predicts neural dynamics of macaque monkeys performing visual search for a target stimulus among different numbers of distractors. The salience accumulated in the Model is equated with the spike trains recorded from visually responsive neurons in the frontal eye field. Accumulated variability in the firing rates of these neurons explains choice probabilities and the distributions of correct and error response times with search arrays of different set sizes if the Accumulators are mutually inhibitory. The dynamics of the stochastic Accumulators quantitatively predict the activity of presaccadic movement neurons that initiate eye movements if gating inhibition prevents accumulation before the representation of stimulus salience emerges. Adjustments in the level of gating inhibition can control trade-offs in speed and accuracy that optimize visual search performance.

  • neural mechanisms of saccade target selection gated Accumulator Model of the visual motor cascade
    European Journal of Neuroscience, 2011
    Co-Authors: Jeffrey D Schall, Richard P Heitz, Braden A. Purcell, Gordon D. Logan, Thomas J. Palmeri
    Abstract:

    We review a new computational Model developed to understand how evidence about stimulus salience in visual search is translated into a saccade command. The Model uses the activity of visually responsive neurons in the frontal eye field as evidence for stimulus salience that is accumulated in a network of stochastic Accumulators to produce accurate and timely saccades. We discovered that only when the input to the accumulation process was gated could the Model account for the variability in search performance and predict the dynamics of movement neuron discharge rates. This union of cognitive Modeling and neurophysiology indicates how the visual–motor transformation can occur, and provides a concrete mapping between neuron function and specific cognitive processes.

Scott D. Brown - One of the best experts on this subject based on the ideXlab platform.

  • Identifying relationships between cognitive processes across tasks, contexts, and time
    Behavior Research Methods, 2020
    Co-Authors: Laura Wall, Scott D. Brown, David Gunawan, Minh-ngoc Tran, Robert Kohn, Guy E. Hawkins
    Abstract:

    It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalize beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive Model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to “borrow” strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic Accumulator Model. We conclude by highlighting the potential of the parameter-correlation method to provide an “assumption-light” tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.

  • New estimation approaches for the hierarchical Linear Ballistic Accumulator Model
    Journal of Mathematical Psychology, 2020
    Co-Authors: David Gunawan, Guy E. Hawkins, Minh-ngoc Tran, Robert Kohn, Scott D. Brown
    Abstract:

    Abstract The Linear Ballistic Accumulator (LBA: Brown and Heathcote, 2008) Model is used as a measurement tool to answer questions about applied psychology. The analyses based on this Model depend upon the Model selected and its estimated parameters. Modern approaches use hierarchical Bayesian Models and Markov chain Monte-Carlo (MCMC) methods to estimate the posterior distribution of the parameters. Although there are several approaches available for Model selection, they are all based on the posterior samples produced via MCMC, which means that the Model selection inference inherits the properties of the MCMC sampler. To improve on current approaches to LBA inference we propose two methods that are based on recent advances in particle MCMC methodology; they are qualitatively different from existing approaches as well as from each other. The first approach is particle Metropolis-within-Gibbs; the second approach is density tempered sequential Monte Carlo. Both new approaches provide very efficient sampling and can be applied to estimate the marginal likelihood, which provides Bayes factors for Model selection. The first approach is usually faster. The second approach provides a direct estimate of the marginal likelihood, uses the first approach in its Markov move step and is very efficient to parallelise on high performance computers. The new methods are illustrated by applying them to simulated and real data, and through pseudo code. The code implementing the methods is freely available.

  • New Estimation Approaches For The Linear Ballistic Accumulator Model
    2019
    Co-Authors: David Gunawan, Guy E. Hawkins, Minh-ngoc Tran, Robert Kohn, Scott D. Brown
    Abstract:

    The Linear Ballistic Accumulator (Brown & Heathcote, 2008) Model is used as a measurement tool to answer questions about applied psychology. The analyses based on this Model depend upon the Model selected and its estimated parameters. Modern approaches use hierarchical Bayesian Models and Markov chain Monte-Carlo (MCMC) methods to estimate the posterior distribution of the parameters. Although there are several approaches available for Model selection, they are all based on the posterior samples produced via MCMC, which means that the Model selection inference inherits the properties of the MCMC sampler. To improve on current approaches to LBA inference we propose two methods that are based on recent advances in particle MCMC methodology; they are qualitatively different from existing approaches as well as from each other. The first approach is particle Metropolis-within-Gibbs; the second approach is density tempered sequential Monte Carlo. Both new approaches provide very efficient sampling and can be applied to estimate the marginal likelihood, which provides Bayes factors for Model selection. The first approach is usually faster. The second approach provides a direct estimate of the marginal likelihood, uses the first approach in its Markov move step and is very efficient to parallelize on high performance computers. The new methods are illustrated by applying them to simulated and real data, and through pseudo code. The code implementing the methods is freely available.

  • Generalising the drift rate distribution for linear ballistic Accumulators
    Journal of Mathematical Psychology, 2015
    Co-Authors: Andrew Terry, Eric-jan Wagenmakers, A. A. J. Marley, Avinash Barnwal, Andrew Heathcote, Scott D. Brown
    Abstract:

    The linear ballistic Accumulator Model is a theory of decision-making that has been used to analyse data from human and animal experiments. It represents decisions as a race between independent evidence Accumulators, and has proven successful in a form assuming a normal distribution for accumulation ("drift") rates. However, this assumption has some limitations, including the corollary that some decision times are negative or undefined. We show that various drift rate distributions with strictly positive support can be substituted for the normal distribution without loss of analytic tractability, provided the candidate distribution has a closed-form expression for its mean when truncated to a closed interval. We illustrate the approach by developing three new linear ballistic accumulation variants, in which the normal distribution for drift rates is replaced by either the lognormal, Frechet, or gamma distribution. We compare some properties of these new variants to the original normal-rate Model.

  • Generalising the drift rate distribution for linear ballistic Accumulators
    Journal of Mathematical Psychology, 2015
    Co-Authors: Andrew Terry, Eric-jan Wagenmakers, A. A. J. Marley, Avinash Barnwal, Andrew Heathcote, Scott D. Brown
    Abstract:

    The linear ballistic Accumulator Model is a theory of decision-making that has been used to analyse data from human and animal experiments. It represents decisions as a race between independent evidence Accumulators, and has proven successful in a form assuming a normal distribution for accumulation ("drift") rates. However, this assumption has some limitations, including the corollary that some decision times are negative or undefined. We show that various drift rate distributions with strictly positive support can be substituted for the normal distribution without loss of analytic tractability, provided the candidate distribution has a closed-form expression for its mean when truncated to a closed interval. We illustrate the approach by developing three new linear ballistic accumulation variants, in which the normal distribution for drift rates is replaced by either the lognormal, Fréchet, or gamma distribution. We compare some properties of these new variants to the original normal-rate Model