Merging race models and adaptive networks: A parallel race network

Cousineau, Denis

doi:10.3758/bf03196707

Cited by 14 publications

(10 citation statements)

References 42 publications

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“…Similar, although less impressive, results are found with the sampling models (including the signal detection theory), a class of models generally distinct from the neural networks models (but see Cousineau, 2004): Their use in psychology started in the 60s; they really exploded in the 80s.…”

Section: Quantitative Methods and Psychologysupporting

confidence: 60%

The rise of quantitative methods in psychology

Cousineau¹

2005

TQMP

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Quantitative methods have a long history in some scientific fields. Indeed, no one today would consider a qualitative data set in physics or a qualitative theory in chemistry. Quantitative methods are so central in these fields that they are often labelled "hard sciences". Here, we examine the question whether psychology is ready to enter the "hard science club" like biology did in the forties. The facts that a) over half of the statistical techniques used in psychology are less than 40 years old and that b) the number of simulations in empirical papers has followed an exponential growth since the eighties, both suggests that the answer is yes. The purpose of Tutorials in Quantitative Methods for Psychology is to provide a concise and easy access to the currents methods.

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Section: Quantitative Methods and Psychologysupporting

confidence: 60%

The rise of quantitative methods in psychology

Cousineau¹

2005

TQMP

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“…The model could also be used in order to account for the motivation of an individual when the second accumulator is assumed to represent the increasing level of demotivation (Cosineau, 2004;Dufau, Grainger, & Ziegler, 2012). This feature makes the actual model applicable to open questions, where individuals usually stop working after some time even though they have not found the right solution.…”

Section: Discussionmentioning

confidence: 99%

A Race Model for Responses and Response Times in Tests

2014

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Latent trait models for responses and response times in tests are often pure statistical models without a close connection to features of the assumed response process. In the present paper, a new model is presented that is more closely related to assumptions about the response process. The model is based on two increasing stochastic processes. Each stochastic process represents the accumulation of knowledge with respect to one of two response options, the correct and incorrect response. Both accumulators compete and the accumulator that first exceeds a critical level determines the response. General assumptions about the accumulators result in a race between two response times that follow a bivariate Birnbaum Saunders distribution. The model can be calibrated with marginal maximum likelihood estimation. Feasibility of the estimation approach is demonstrated in a simulation study. Additionally, a test of model fit is proposed. Finally, the model will be used for the analysis of an empirical data set.

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“…In psychology, the iterative MLE technique is definitely the most commonly used technique (Cousineau & Larochelle, 1997; Dolan, 2000; Heathcote, 1996; Heathcote, Brown, & Cousineau, 2004; Rouder, Sun, Speckman, Lu, & Zhou, 2003; see Cousineau, Brown, & Heathcote, 2004, for a review and Van Zandt, 2000, for alternatives).…”

Section: The Iterative and Weighted Mle Equationsmentioning

confidence: 99%

“…Burbeck & Luce, 1982; Palmer, 1998) or test a model (e.g. Cousineau & Shiffrin, 2004; Cousineau, Goodman, & Shiffrin, 2002). In some applications, the Weibull distribution is just a convenient tool used to highlight the properties of interests in the data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Nearly unbiased estimators for the three‐parameter Weibull distribution with greater efficiency than the iterative likelihood method

Cousineau

2009

Brit J Math & Statis

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The maximum likelihood estimation (MLE) method is the most commonly used method to estimate the parameters of the three-parameter Weibull distribution. However, it returns biased estimates. In this paper, we show how to calculate weights which cancel the biases contained in the MLE equations. The exact weights can be computed when the population parameters are known and the expected weights when they are not. Two of the three weights' expected values are dependent only on the sample size, whereas the third also depends on the population shape parameters. Monte Carlo simulations demonstrate the practicability of the weighted MLE method. When compared with the iterative MLE technique, the bias is reduced by a factor of 7 (irrespective of the sample size) and the variability of the parameter estimates is also reduced by a factor of 7 for very small sample sizes, but this gain disappears for large sample sizes.

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Merging race models and adaptive networks: A parallel race network

Cited by 14 publications

References 42 publications

The rise of quantitative methods in psychology

The rise of quantitative methods in psychology

A Race Model for Responses and Response Times in Tests

Nearly unbiased estimators for the three‐parameter Weibull distribution with greater efficiency than the iterative likelihood method

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