Application of process models in assessment psychology: Potential assets and challenges.

Abstract

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Stochastic dynamics of stimulus encoding in schizophrenia: Theory, testing, and application

“…In the case of mixture models in particular, the criterion of coherence of grouplevel, and (Bayesian mediated) individual testing of empirical fit can be brought to bear on the issue of model selection (below). Data aggregation across participants nevertheless risks folding systematic individual differences into an amalgam that then emerges as unrepresentative of any of its constituents (Carter, Neufeld, & Benn, 1998;Neufeld & Gardner, 1990). An associated risk is the application of model predictions to an ''unreal computational creation''.…”

“…Overall, the above developments show how symptom significance can be brokered to MRI-monitored neuro-(co)activation by overlaying onto MRI-signal analysis the estimated stochastic dynamical template of a symptom-significant cognitive function (Carter et al, 1998); potential symptom significance of the present function is taken up further in the Section 8.1.…”

“…In this way, mathematical cognitive psychology is uniquely poised to advance the ''functional side of cognitive neuroscience'' in general, and clinical cognitive neuroscience in particular. Along the way, to the degree that model-parameterized pathocognition is symptom significant, clinical mathematical cognitive psychology stands to broker relations between deviations in patterns of regional neuro-(co)activation, on the one hand, and symptomatology on the other (Carter et al, 1998).…”

Application of stochastic modeling to the assessment of group and individual differences in cognitive functioning.

“…Such patterns may become aligned with a stringently specified model architecture and/or parameter values. Clinical significance, in turn, is endowed to the degree that titrated features (see above) can be identified with measured deviations in activation (Carter, Neufeld, & Benn, 1998).…”

“…Finally, psychometric properties of measured response parameters, such as latencies, in principle can be introduced into formal process models. Psychometric measurement errors, or modelexogenous noise (described in Carter et al, 1998), stand to be integrated with cognitive-process completion latencies (technically, by convolving the posited distribution of measurement errors with that of encoding-process latencies, the latter portrayed in Figures 2 and 8 -10). Measurement errors thus are expressed in an expanded version of the theoretical base distribution of trialwise latencies (see Figure 5).…”