On people’s understanding of the diagnostic implications of probabilistic data

Abstract

Two lines of prior research into the conditions under which people seek information are examined in light of two statistical definitions of diagnosticity. Five experiments are reported. In two, subjects selected information in order to test a hypothesis. In the remaining three, they selected information in order to convince someone else of the truth of a known hypothesis. A total of 567 university students served as subjects. The two primary conclusions were as follows: (l) When the task is highly structured by the environment, subjects select information diagnostically, and (2) when the task is less structured, so that subjects must seek relevant information not manifest, they select information pseudodiagnostically. Possible relations to other laboratory inference tasks and to clinical judgment are discussed.

Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain.

“…If two questions are equally useful, give them the same rank. (Doherty, Mynatt, Tweney, & Schiavo, 1979;Doherty, Schiavo, Tweney, & Mynatt, 1981;Doherty, Chadwick, Garavan, Barr, & Mynatt, 1996;Maggi, Butera, Legrenzi, & Mugny, 1998) also use diagnosticity as a sampling norm. In a typical experiment subjects are given d 1 (datum 1 ) and d 2…”

Reaching a verdict

“…This information allowed us to examine the proposal (Kuhn et al, 1994) that individuals generally construct a single account of the events at issue. Doherty, Chadwick, Garavan, Barr, and Mynatt (1996) have gone further than most in supposing that individuals can only entertain one hypothesis or possibility at a time (see also Hogarth & Einhorn, 1992). But how individuals typically behave does not preclude their being able to entertain more than one possibility (e.g., Evans, Venn, & Feeney, 2002;McKenzie, 1999).…”

Obstetrician-gynecologists' decision making about the diagnosis of major depressive disorder and premenstrual dysphoric disorder

“…Another potential source of bias in decision making under uncertainty is base-rate neglect, or the failure to appropriately use frequency data when predicting probability [28,30,31]. Theoreticallybased research has shown that physicians tend to overestimate low probability events and underestimate high probability events [32].…”