A general framework for the analysis of concept identification tasks

Raaijmakers, Jeroen G. W.

doi:10.1016/0001-6918(81)90018-4

Cited by 3 publications

(2 citation statements)

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“…Markov models were used frequently in psychology in the 1970's and 1980's [45]. Applications of Markov models included paired-associate learning [4] conceptidentification [32], forgetting [20], and conservation learning [3]. Judging by the large number of specialized programs for different applications -Markovforget for models of forgetting [20], Markov-count for 2-stage learning [19], an SAS module for testing homogeneity in Markov response sequences [11] -a comprehensive framework for parameter estimation seemed to be lacking.…”

Section: Concept Identificationmentioning

confidence: 99%

Fitting Hidden Markov Models to Psychological Data

Visser

Raijmakers

Molenaar

2002

Scientific Programming

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Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

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Section: Concept Identificationmentioning

confidence: 99%

Fitting Hidden Markov Models to Psychological Data

Visser

Raijmakers

Molenaar

2002

Scientific Programming

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“…; and 3) Can individual differences in the latent cognitive abilities supporting these processes be precisely measured? To answer these questions we conducted cognitive and psychometric modeling analyses of the Army STARRS' PCET data by combining elements of item response theory (e.g., Embretson & Reise, 2000; Lord, 1980; Thomas, 2011) with classic associative- and hypothesis-based Markov models of concept identification learning (see Atkinson, Bower, & Crothers, 1965; Millward & Wickens, 1974; Raijmakers, 1981; Schmittmann, Visser, & Raijmakers, 2006; Wickens & Millward, 1971). Prior research and theory (e.g., Kongs, Thompson, Iverson, & Heaton, 2000; Schmittmann, Visser, & Raijmakers, 2006) suggests that a multidimensional, hypothesis-based model ought to fit PCET response data best.…”

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confidence: 99%

Measurement of latent cognitive abilities involved in concept identification learning

Michael

Brown

Gur

et al. 2015

Journal of Clinical and Experimental Neuropsychology

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Introduction We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET). Method Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and fitted to 35,553 Army soldiers' PCET data from the Army Study To Assess Risk and Resilience in Servicemembers (Army STARRS). Results Data were consistent with a hypothesis-testing model with multiple latent abilities— abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. Abstraction was most precisely assessed for participants with abilities ranging from one-and-a-half standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors. Conclusions The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set shifting ability, but reliable assessment is limited to the impaired range of ability reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.

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