Biomedical subjects

Derek J Koehler

Publications and source records attributed to Derek J Koehler.

3 recordsLinked to original sources

PubMed2004

Missing information in multiple-cue probability learning.

In a multiple-cue probability learning task, participants learned to use six discrete symptoms (i.e., cues) to diagnose which of three possible flu strains a hypothetical patient suffered from. For some patients, information regarding the status of certain symptoms was not available. Various possible ways in which the missing cue information might be processed were distinguished and tested in a series of three experiments (Ns = 80, 109, and 61). The results suggest that the judged probability of the outcome variable (i.e., flu strain) was assessed by "filling in" the missing cue information with a mean value based on previous observations. The predictions of other methods of processing missing cue information are inconsistent with the data.

Cues↗

PubMed2004

Dilution and confirmation of probability judgments based on nondiagnostic evidence.

Previous research has shown that probability judgments based on a mix of diagnostic and nondiagnostic information are less extreme than judgments based on the diagnostic information alone. Results of the present experiments suggest that this dilution effect holds only under a limited set of conditions. When judgments based on a mix of diagnostic and nondiagnostic information are compared with separately elicited judgments based on the diagnostic information alone, the dilution effect is consistently observed. When judgments based on the diagnostic evidence are revised in light of additional, nondiagnostic evidence, by contrast, the dilution effect is eliminated or even reversed (yielding a confirmation effect) depending on the type of nondiagnostic evidence under evaluation.

Adolescent↗

PubMed2003

An evidential support accumulation model of subjective probability.

A model of cue-based probability judgment is developed within the framework of support theory. Cue diagnosticity is evaluated from experience as represented by error-free frequency counts. When presented with a pattern of cues, the diagnostic implications of each cue are assessed independently and then summed to arrive at an assessment of the support for a hypothesis, with greater weight placed on present than on absent cues. The model can also accommodate adjustment of support in light of the baserate or prior probability of a hypothesis. Support for alternatives packed together in a "residual" hypothesis is discounted; fewer cues are consulted in assessing support for alternatives as support for the focal hypothesis increases. Results of fitting this and several alternative models to data from four new multiple-cue probability learning experiments are reported.

Cues↗