The Poisson shot noise model of visual short-term memory and choice response time: Normalized coding by neural population size

Smith, Philip L.

doi:10.1016/j.jmp.2015.03.007

Cited by 22 publications

(20 citation statements)

References 81 publications

(91 reference statements)

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“…If we assume that the evidence in the accumulator maps onto the firing rate of a neuron, then the translation from rate to spikes will introduce an additional source of noise in any measure of neural dynamics (Figure 1C; Churchland et al, 2011; Nawrot et al, 2008; Smith, 2010, 2015). This additional source of variability can be added to the model dynamics by assuming that the neural activity is a doubly-stochastic process reflecting both rate variability (trial-by-trial variation in accumulated evidence) and point-process variability (Poisson-like spiking noise).…”

Section: General Simulation Methodsmentioning

confidence: 99%

Relating accumulator model parameters and neural dynamics

Purcell

Palmeri

2017

Journal of Mathematical Psychology

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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.

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Section: General Simulation Methodsmentioning

confidence: 99%

Relating accumulator model parameters and neural dynamics

Purcell

Palmeri

2017

Journal of Mathematical Psychology

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“…Such an information limitation can be reformulated as the result of aggregating a fixed-size pool of opponent-coded Poisson neurons (Smith, 2015) or, more generally, as the normalization of the strength of information within memory based upon stimulus energy (Smith & Sewell, 2013;Smith, Sewell, & Lilburn, 2015). This constraint makes clear predictions for performance across different memory array size conditions, as additional items place additional demands (potentially equal demands) on a fixed information limit.…”

Section: A Sample-size Information Constraintmentioning

confidence: 99%

The separable effects of feature precision and item load in visual short-term memory

2019

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# Visual short-term memory (VSTM) has been described as being limited by the number of discrete visual objects, the aggregate quantity of information across multiple visual objects, or some combination of the two. Many recent studies examining these capacity limitations have shown that increasing the number of items in VSTM increases the frequency and magnitude of errors in a participant's recall of the stimulus. This increase in response dispersion has been interpreted as a loss of precision in an item's representation as the number of items in memory increases, possibly due to a change in the tuning of the underlying representation. However, increased response dispersion can also be caused by a reduction in the total memory strength available for decision making as a consequence of a reduction in the total amount of a fixed resource representing a stimulus. We investigated the effects of load on the precision of memory representations in a fine orientation discrimination task. Accuracy was well captured by extending a simple sample-size model of VSTM, using a tuning function to account for the effect of orientation precision on performance. The best model of the data was one in which the item strength decreased progressively with memory load at all stimulus exposure durations but in which tuning bandwidth was invariant. Our results imply that memory strength and feature precision are experimentally dissociable attributes of VSTM.

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“…When brief nearthreshold stimuli are used, however, like those used by Corbett and Smith (2017), there is a possibility that drift rate means and variances might covary with stimulus difficulty. This possibility arises from the idea that the drift rate mean and variance might both depend on Poisson-like neural coding of stimulus information in perception and visual working memory (Bays, 2014;Smith, 2010Smith, , 2015Smith & McKenzie, 2011). We considered this possibility by allowing mean drift rate and variance to both vary with stimulus contrast: Specifically, we allowed η to vary with contrast and stimulus condition (single-versus double-target trials).…”

Section: Parameterizing the 4d Modelmentioning

confidence: 99%

Speeded multielement decision-making as diffusion in a hypersphere: Theory and application to double-target detection

Smith

Corbett

2018

Psychon Bull Rev

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We generalize the circular 2D diffusion model of Smith (Psychological Review, 123, 425-451: 2016) to provide a new model of speeded decision-making in multielement visual displays. We model decision-making in tasks with multielement displays as evidence accumulation by a vector-valued diffusion process in a hypersphere, whose radius represents the decision criterion for the task. We show that the methods used to derive response time and accuracy predictions for the 2D model can be applied, with only minor changes, to predict performance in higher-dimensional spaces as well. We apply the model to the double-target deficit paradigm of Duncan (Psychological Review, 87, 272-300: 1980) in which participants judge whether briefly presented four-element displays contain one- or two-digit targets among letter distractors. A 4D version of the hyperspherical diffusion model correctly predicted distributions of response times and response accuracy as a function of task difficulty in single-target and double-target versions of the task. The estimated drift rate parameters from the model imply that the mental representation of the decision alternatives, which we term the "decision template" for the task, encodes configural stimulus properties that reflect the number of targets in the display. Along with its application to multielement decision-making, the model has the potential to characterize the speed and accuracy of multiattribute decisions in studies of cognitive categorization, visual attention, and other areas.

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The Poisson shot noise model of visual short-term memory and choice response time: Normalized coding by neural population size

Cited by 22 publications

References 81 publications

Relating accumulator model parameters and neural dynamics

Relating accumulator model parameters and neural dynamics

The separable effects of feature precision and item load in visual short-term memory

Speeded multielement decision-making as diffusion in a hypersphere: Theory and application to double-target detection

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