Background beliefs and evidence interpretation

Missing the dog that failed to bark in the nighttime: on the overestimation of occurrences over non-occurrences in hypothesis testing

In three studies, we investigated whether and to what extent the evaluation of two mutually exclusive hypotheses is affected by a feature-positive effect, wherein present clues are weighted more than absent clues. Participants (N = 126) were presented with abstract problems concerning the most likely provenance of a card that was drawn from one of two decks. We factored the correct response (the hypothesis favored by the consideration of all clues) and the ratio of present-to-absent features in each set of observations. Furthermore, across the studies, we manipulated the presentation format of the features' probabilities by providing the probability distributions of occurrences (Study 1), non-occurrences (Study 3) or both (Study 2). In all studies, both participant preference and accuracy were mostly determined by an over-reliance on present features. Moreover, across participants, both confidence in the responses and the informativeness of the present clues correlated positively with the number of responses given in line with an exclusive consideration of present features. These results were mostly independent of both the rarity of the absent clues and the presentation format. We concluded that the feature-positive effect influences hypothesis evaluation, and we discussed the implications for confirmation bias.

A Bayesian view of covariation assessment

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants' prior beliefs about the variables' relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.

Deciding between accounts of the selection task: a reply to Oaksford (2002)

In this paper we report on our attempts to fit the optimal data selection (ODS) model (Oaksford & Chater, 1994; Oaksford, Chater, & Larkin, 2000) to the selection task data reported in Feeney and Handley (2000) and Handley, Feeney, and Harper (2002). Although Oaksford (2002b) reports good fits to the data described in Feeney and Handley (2000), the model does not adequately capture the data described in Handley et al. (2002). Furthermore, across all six of the experiments modelled here, the ODS model does not predict participants' behaviour at the level of selection rates for individual cards. Finally, when people's probability estimates are used in the modelling exercise, the model adequately captures only 1 out of 18 conditions described in Handley et al. We discuss the implications of these results for models of the selection task and claim that they support deductive, rather than probabilistic, accounts of the task.