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

Cognitive processing among schizophrenia participants, entailing encoding of presenting stimulation into a format facilitating collateral processes (e.g., memory search), is examined in light of stochastic mathematical models of performance. Results implicate additional encoding operations (encoding subprocesses) as the source of schizophrenia encoding-process elongation. Convergent evidence for this inference, including that from auxiliary neuro-connectionist simulations, are brought forth. Developments from initial, fixed-parameter accounts include random-parameter mixtures, and their Bayesian extensions, formally mediating group-level results to assessment of individual performance. Outgrowths bear on model-selection methodology, according to coherence of group-level and individual-level model functioning (in part addressing the issue of “small-trial-sample model testing”); longitudinal monitoring of encoding-specific treatment response; evaluation of treatment-regimen efficacy with respect to encoding efficiency; and specification of times of measurement interest, in fMRI. The symptom significance of encoding elongation, strongly hinted at by model developments, along with a model-endowed window on exacerbating effects of stress, are drawn out.