SOME ALTERNATIVE STOCHASTIC MODELS OF CHOICE<sup>1</sup>

Audley, R. J.; Pike, A. R.

doi:10.1111/j.2044-8317.1965.tb00342.x

Cited by 147 publications

(151 citation statements)

References 12 publications

(20 reference statements)

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“…The class of random-walk models was rejected because of the failure of these models to handle the increase in latency for error responses observed in the present data (Audley & Pike, 1965). A simple accumulator model discussed by Audley and Pike (1965) seemed promising because it predicted an increase in latency for error responses. The predicted increase, however, was very small relative to the present data, and the model was rejected for that reason.…”

Section: Predicting Response Latenciesmentioning

confidence: 99%

An analysis of interference in recognition memory

Bowles

Glanzer

1983

Memory & Cognition

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A stimulus-sampling model of recognition memory is presented that predicts both proactive and retroactive interference. To test the predictions of the model, a recognition memory experiment was carried out using a standard proactive-retroactive design with a forced-choice task. Both accuracy and latency were measured. The data showed, as predicted, equal proactive and retroactive effects on accuracy, providing solid support for the model. The interference effects are interpreted in terms of the model as arising from an increase in indirect marking, the marking of shared stimulus elements in words other than the study word. The model has two parameters representing the rate of indirect marking for high-frequency and low-frequency words and two parameters reflecting the efficiency of direct marking. The latency results completely paralleled the accuracy findings, showing proactive and retroactive effects. A possible extension of the model to handle latencies is considered.On an intuitive level, there is considerable agreement among recognition theorists on the source of interference effects in recognition memory. While it is couched in different language, the fundamental concept proposed is a single one: Interference arises ou t of overlapping elements in study-list and test-list items. Underwood and his associates (e.g., Underwood & Freund, 1970) propose that study items elicit implicit associational responses. Such responses result from elements of the study items shared with other items. Shepard (1961), in his trace model of recognition, postulates that there is some probability of diffusion of trace elements from presented stimuli to other related stimuli. Bower's (1972) stimulus-sampling model of the recognition process has a parameter that represents the amount of overlap between stimulus elements of old and new items. Glanzer and Bowles (1976) use an indirect marking parameter to refer to the tagging of overlapping elements in unpresented stimuli in their stimulussampling model of recognition.Models such as these provide the means to identify the locus of interference effects by offering mechanisms of the underlying memory processes. In particular, the This article is based on a dissertation submitted to New York University in partial fulfillment of the requirements for the PhD degree. The authors wish to thank Doris Aaronson, Jean-Claude Falmagne, Richard Koppenaal, and Joan Gay Snodgrass for their helpful comments and criticism of the original manuscript. During the time the research was carried out, Nancy L. Bowles received support from NIMH Training Grant IT32 MHI4267-02. Additional support for the research was from NIMH Grant R MH32799-01 to Murray Glanzer. Requests for reprints should be submitted to Nancy L. Bowles, who is now at the Geriatric Research, Educational and Clinical Center, VA Outpatient Clinic, 17 Court Street, Boston, Massachusetts 02108.Glanzer and Bowles (1976) model makes strong predictions concerning interference effects. It will be presented here in detail. A MODEL FOR INTERFERENCE...

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Section: Predicting Response Latenciesmentioning

confidence: 99%

An analysis of interference in recognition memory

Bowles

Glanzer

1983

Memory & Cognition

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“…To evaluate the fit of the 2DSD interrogation model we adapted the latency-probability function plots first used by Audley and Pike (1965) to include confidence as well. We call the plots latency-confidence-choice probability (LCC) functions.…”

Section: Fit Of 2dsd Interrogation Model To Choices Decision Times mentioning

confidence: 99%

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

Pleskac¹,

Busemeyer²

2010

Psychological Review

638

947

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The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

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“…models also permit either discrete or continuous activation states. A special case of accumulator models is the set of counter models (i.e., discrete state with the states being positive integers; e.g., Townsend & Nozawa, 1995), and a special case of these is Poisson counter models (e.g., Audley & Pike, 1965;Diederich & Colonius, 1991;Pike, 1973;Smith & Zandt, 2000;Townsend & Ashby, 1983;Van Zandt et al, 2000). The other major class of state-space models is that of random walk and diffusion processes, the first being based on discrete and the second on continuous state spaces (e.g., Ashby, 1983;Busemeyer & Townsend, 1993;Link & Heath, 1975;Ratcliff, 1978;Ratcliff, Van Zandt, & McKoon, 1999).…”

Section: Theoretical Foundations: the Dynamics Of Parallel Processingmentioning

confidence: 99%

A Theory of Interactive Parallel Processing: New Capacity Measures and Predictions for a Response Time Inequality Series.

Townsend

Wenger

2004

Psychological Review

252

512

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The authors present a theory of stochastic interactive parallel processing with special emphasis on channel interactions and their relation to system capacity. The approach is based both on linear systems theory augmented with stochastic elements and decisional operators and on a metatheory of parallel channels' dependencies that incorporates standard independent and coactive parallel models as special cases. The metatheory is applied to OR and AND experimental paradigms, and the authors establish new theorems relating response time performance in these designs to earlier and novel issues. One notable outcome is the remarkable processing efficiency associated with linear parallel-channel systems that include mutually positive interactions. The results may offer insight into perceptual and cognitive configural-holistic processing systems.When a person views a work of art in all its complexity, it seems as though all dimensions-color, form, arrangement, perspective, and sharpness of edges-are acting in league. But are they? The antithetical notions of independence and dependence have long played a role in the philosophy and science of human perception and cognition. Whether the focus is on the internal representations that support psychological experience or on the channels or systems that work with those representations, eventually the implications of assumptions about independence or dependence need to be considered. 1It is natural to contrast the general notion of independence with ideas such as a holism or a gestalt. However, there have been very few attempts to develop explicit, systematic, quantitative definitions, taxonomies, and explanations of holistic cognitive processes. The work presented here is an attempt to provide these definitions, taxonomies, and explanations, by way of (a) an emphasis on the general characteristics of information processing and (b) a concern with real-time interactions in specific (parallel) informationprocessing architectures.It is the case that certain interactive architectures have been shown to be capable of simulating specific gestaltlike phenomena (e.g., Biederman & Kalocsai, 1998;Cottrell, Dailey, Padgett, & Adolphs, 2001;Grossberg, 1991b;Mordkoff & Yantis, 1991;Rumelhart & McClelland, 1981. Although such studies provide sufficiency arguments, it is not clear what highly simple (in a sense to be defined precisely) and interactive systems can do when compared, within a common framework, with noninteractive systems.Nevertheless, the great preponderance of quantitativecomputational accounts of processing multidimensional stimuli are based on the assumption of independence among channels or items (e.g

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SOME ALTERNATIVE STOCHASTIC MODELS OF CHOICE¹

Cited by 147 publications

References 12 publications

An analysis of interference in recognition memory

An analysis of interference in recognition memory

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

A Theory of Interactive Parallel Processing: New Capacity Measures and Predictions for a Response Time Inequality Series.

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SOME ALTERNATIVE STOCHASTIC MODELS OF CHOICE1

Cited by 147 publications

References 12 publications

An analysis of interference in recognition memory

An analysis of interference in recognition memory

Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

A Theory of Interactive Parallel Processing: New Capacity Measures and Predictions for a Response Time Inequality Series.

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SOME ALTERNATIVE STOCHASTIC MODELS OF CHOICE¹