Modeling sequential context effects in judgment analysis: A time series approach

Abstract

AbstractIn this article a broad perspective incorporating elements of time series theory is presented for conceptualizing the data obtained in multi-trial judgment experiments. Recent evidence suggests that sequential context effects, assimilation and contrast, commonly found in psychophysical judgment tasks, may be present in judgments of abstract magnitudes. A time series approach for analyzing single-subject data is developed and applied to expert prognostic judgments of risk for heart disease with an emphasis on detecting possible sequential context effects. The results demonstrate that sequential context effects do exist in such expert prognostic judgments. Contrast and assimilation were produced by cue series; the latter occurring more frequently. Experts also showed assimilation of prior responses that was independent of the cue series input. Time series analysis also revealed that abrupt or large trial-by-trial changes in the value of cues that receive the most attention in prognostic judgment tasks can disrupt the accuracy of these judgments. These findings suggest that a time series approach is a useful alternative to ordinary least squares regression, providing additional insights into the cognitive processes operating during multi-cue judgment experiments.