A recruitment theory of simple behavior

LaBerge, David

doi:10.1007/bf02289645

Cited by 189 publications

(153 citation statements)

References 16 publications

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“…Instead, a decision maker accumulates evidence in favor of each response and makes a decision as soon as one of the response accumulators reaches a criterial value. Although accumulator models have a long history in psychology (LaBerge, 1962;Vickers, 1970;Pike, 1973;Townsend & Ashby, 1983;Smith & Vickers, 1988;Smith & Van Zandt, 2000)-including in the study of recognition memory (Van Zandt, 2000)-they typically assume that the evidence accumulated within a trial is noisy. A ballistic accumulator model assumes no noise within a trialhence the term "ballistic".…”

Section: Theories Of Response Dynamicsmentioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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Section: Theories Of Response Dynamicsmentioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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“…The accuracy of all methods depended critically on the size of the approximating time step. The large (∼10 ms) step sizes often used by psychological researchers resulted in large and systematic errors in evaluating RT distributions.Over the past 40 years, models of response time (RT) for simple decision making have become very successful at capturing the details of observed data (Audley & Pike, 1965;Brown & Heathcote, 2005;Busemeyer & Townsend, 1993;Diederich, 1997;Heath, 1981;LaBerge, 1962;Lacouture and Marley, 1991;Laming, 1966;Link & Heath, 1975;Ratcliff, 1978; Ratcliff & Smith, 2004, Appendix;Ratcliff, Van Zandt, & McKoon, 1999;Smith, 1995;Vickers, 1970;Vickers & Lee, 2000). More recently, the same models have also become quite successful at explaining decision making at a neural level (Carpenter & Reddi, 2001;Cook & Maunsell, 2002;Glimcher 2003;Gold & Shadlen, 2001;Ratcliff, Cherian & Seagraves, 2003;Reddi & Carpenter, 2000;Roitman & Shadlen, 2002;Sato, Murthy, Thompson & Schall, 2001;Sato & Schall, 2003;Shadlen, Britten, Newsome & Movshon, 1996;Wang, 2002).…”

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confidence: 99%

Evaluating methods for approximating stochastic differential equations

Brown

Ratcliff

Smith

2006

Journal of Mathematical Psychology

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Models of decision making and response time (RT) are often formulated using stochastic differential equations (SDEs). Researchers often investigate these models using a simple Monte Carlo method based on Euler's method for solving ordinary differential equations. The accuracy of Euler's method is investigated and compared to the performance of more complex simulation methods. The more complex methods for solving SDEs yielded no improvement in accuracy over the Euler method. However, the matrix method proposed by Diederich and Busemeyer (2003) yielded significant improvements. The accuracy of all methods depended critically on the size of the approximating time step. The large (∼10 ms) step sizes often used by psychological researchers resulted in large and systematic errors in evaluating RT distributions.Over the past 40 years, models of response time (RT) for simple decision making have become very successful at capturing the details of observed data (Audley & Pike, 1965;Brown & Heathcote, 2005;Busemeyer & Townsend, 1993;Diederich, 1997;Heath, 1981;LaBerge, 1962;Lacouture and Marley, 1991;Laming, 1966;Link & Heath, 1975;Ratcliff, 1978; Ratcliff & Smith, 2004, Appendix;Ratcliff, Van Zandt, & McKoon, 1999;Smith, 1995;Vickers, 1970;Vickers & Lee, 2000). More recently, the same models have also become quite successful at explaining decision making at a neural level (Carpenter & Reddi, 2001;Cook & Maunsell, 2002;Glimcher 2003;Gold & Shadlen, 2001;Ratcliff, Cherian & Seagraves, 2003;Reddi & Carpenter, 2000;Roitman & Shadlen, 2002;Sato, Murthy, Thompson & Schall, 2001;Sato & Schall, 2003;Shadlen, Britten, Newsome & Movshon, 1996;Wang, 2002). The most successful models of decision making in both cognitive and neural domains are the sequential sampling models. These models are based on the idea that noisy stimulus information is accumulated progressively over time until sufficient information for one of the response alternatives has been obtained. The predicted decision time in such models is obtained mathematically by solving a first-passage-time (FPT) problem, that is, the time taken for the accumulated information to reach a criterion and trigger a response. For some models, there exist explicit analytic methods or highly accurate numerical methods for solving the FPT problem (see Ratcliff & Smith, Appendix, for a survey). For other models, this problem may be complex or intractable. In such situations, researchers must resort to Monte Carlo simulation techniques to obtain predicted RT distributions and choice probabilities. We investigate the properties of such simulation techniques in this article.One of the best-known sequential sampling models, and one that has been applied to a wide range of experimental data, is the diffusion model of Ratcliff (1978;Ratcliff & Rouder, 1998). This model assumes that evidence accumulation begins at some initial value (z) and

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“…The question arises concerning the applicability of the model to other related situations, particularly those of easy discrimination, choice reaction, and signal detection. As envisaged by Laberge (1962), the model would be appropriate to different latency situations, including simple RT. In this respect, Pachella and Pew (1968) have obtained results supporting a generalized random walk model for simple RT.…”

Section: Discussionmentioning

confidence: 99%

“…Atkinson et Audley & Pike, 1965;Laberge, 1962) will be examined for its ability to account for latency features in difficult two-choice discrimination, and a preliminary comparison will be made with Experiment 1 was originally performed to assess the validity of different kinds of stochastic model for discrimination behavior (Pike, 1967). The accumulator model appeared to fit the data best, and the results from other work (e.g., Kellogg, 1931) also supported the model.…”

Section: The Accumulatormentioning

confidence: 99%

“…Each incorrect mean latency has a P value of from 0 to 0.5 and each correct latency of from 0.5 to I. For earlier uses of the graph, see Luce (1960) or Laberge (1962) where the P axis is referred to as response probability. The mean latencies are best expressed as proportional to the latency at P = 0.5 since the graphs are then standardized for comparison between Ss and models (e.g., see Fig.…”

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The latencies of correct and incorrect responses in discrimination and detection tasks: Their interpretation in terms of a model based on simple counting

Pike

1971

Perception & Psychophysics

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A model for two-choice discrimination based on a process of simple counting is described, and two experiments are performed to test the predictions of the model concerning the graph of latency as a function of response proportion. Two main forms of this graph are identified and predicted to arise in different circumstances. The experimental results support the model, and its possible extension to other psychophysical situations, especially signal detection, are then discussed. It is compared with a model derived directly from the detection situation, and the usefulness of testing these models is pointed out.

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A recruitment theory of simple behavior

Cited by 189 publications

References 16 publications

A Dynamic Approach to Recognition Memory

A Dynamic Approach to Recognition Memory

Evaluating methods for approximating stochastic differential equations

The latencies of correct and incorrect responses in discrimination and detection tasks: Their interpretation in terms of a model based on simple counting

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