The effect of stimulus strength on the speed and accuracy of a perceptual decision.

By John Palmer, Alexander C. Huk, Michael N. Shadlen. Journal of Vision, 2005

708 citations recorded by the source corpus.

Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piéron's Law, a power function dependence of response time on stimulus strength. The theory's analytic chronometric function allows one to extend theories of accuracy to response time.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/abf73168fa849aa554fea630060ed3f593512493

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In Cognitive Neuroscience

Citations in the corpus

An EZ-diffusion model for response time and accuracy (2007, methodology): “The close conceptual link between sequential sampling models (e.g., the diffusion model) and signal detection theory has also been a motivating factor in the work of Pew (1969), Emerson (1970), Balakrishnan et al. (2002), and Palmer et al. (2005).”
Diffusion Decision Model: Current Issues and History (2016, methodology): “Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44”
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions (2006, methodology): “The ramp-to-threshold dynamics is reminiscent of the " diffusion " model (Ratcliff, 1978; Luce, 1986; Smith and Ratcliff, 2004; Palmer et al., 2005), a popular mathematical model used in the study of reaction time tasks.”
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks (2008, background): “Experiments 1 and 2 were replications of, and experiment 3 was similar to, experiments with human subjects by Palmer et al. (2005).”
Changing your mind: a computational mechanism of vacillation (2009, positive): “2, black curves) consistent with previous studies in humans and monkeys1,9,14.”
Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks (2006, background): “We thus ask the following questions: (i) what is the neural substrate of a decision threshold, (ii) how is the threshold-crossing event read out, and (iii) can the threshold be tuned by biologically plausible mechanisms to optimize a decision-making proces”
Decision-making with multiple alternatives (2008, background): “Decisions between two choices in a perceptual-motion tas”
Improved probabilistic inference as a general learning mechanism with action video games (2010, methodology): “DDMmodels generate psychometric and chronometric curves that are constrained by three main variables [14]: (1) the rate at which information is accumulated over time, (2) the height of the decision bound at which the system stops accumulating evidence and a decision is made, and (3)”
Perceptual decision making in less than 30 milliseconds (2010, neutral): “…time to a specific color-sensitive area, such as the retina, or area V4, and that it also remains agnostic regarding the biophysical origin of this time scale, i.e., whether it results from slow sensory transduction or from internal noise in more central circuits25,26,36,38, for instance.”
How the brain integrates costs and benefits during decision making (2010, methodology): “We modeled behavioral data with a power-rate diffusion model (22), with the aim of quantitatively relating the drift parameter μ of the decision process to neural mechanisms measured with fMRI.”