Hits and misses: Kirby on the selection task

Deduction from Uncertain Premises

We investigate how the perceived uncertainty of a conditional affects a person's choice of conclusion. We use a novel procedure to introduce uncertainty by manipulating the conditional probability of the consequent given the antecedent. In Experiment 1, we show first that subjects reduce their choice of valid conclusions when a conditional is followed by an additional premise that makes the major premise uncertain. In this we replicate Byrne (1989). These subjects choose, instead, a qualified conclusion expressing uncertainty. If subjects are given a third statement that qualifies the likelihood of the additional premise, then the uncertainty of the conclusions they choose is systematically related to the suggested uncertainty. Experiment 2 confirms these observations in problems that omit the additional premise and qualify the first premise directly. Experiment 3 shows that the qualifying statement also affects the perceived probability of the consequent given the antecedent of the conditional. Experiment 4 investigates the effect of suggested uncertainty on the fallacies and shows that increases in uncertainty reduce the number of certain conclusions that are chosen while affirming the consequent but have no effect on denying the antecedent. We discuss our results in terms of rule theories and mental models and conclude that the latter give the most natural account of our results.

Externalization, Counter-examples, and the Abstract Selection Task

The five experiments reported in this paper examined the effect of two theoretically motivated, explicit mental procedures on performance in different versions of the four-card, abstract selection task. There was no overall benefit as a result of carrying out these procedures; instead, performance depended upon the extent to which subjects carried out the procedures successfully. Most crucially, the experiments demonstrated that correct selection depends not simply upon envisaging the right counter-example, but upon identifying those cards that could contain potential counter-examples. Such findings are broadly consistent with the theory of mental models. However, contrary to the theory, identification is not sufficient for correct selection. The externalization technique used in these studies provides a way to examine the loci of performance differences in the selection task and therefore provides a way to test rival theoretical claims.

Précis of bayesian rationality: The probabilistic approach to human reasoning

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic--the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards. Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1-4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5-7 the psychology of "deductive" reasoning is tackled head-on: It is argued that purportedly "logical" reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider "probabilistic turn" in cognitive science and artificial intelligence, for understanding human rationality.

Optimal data selection: revision, review, and reevaluation

Since it first appeared, there has been much research and critical discussion on the theory of optimal data selection as an explanation of Wason's (1966,1968) selection task (Oaksford & Chater, 1994). In this paper, this literature is reviewed, and the theory of optimal data selection is reevaluated in its light. The information gain model is first located in the current theoretical debate in the psychology of reasoning concerning dual processes in human reasoning. A model comparison exercise is then presented that compares a revised version of the model with its theoretical competitors. Tests of the novel predictions of the model are then reviewed. This section also reviews experiments claimed not to be consistent with optimal data selection. Finally, theoretical criticisms of optimal data selection are discussed. It is argued either that the revised model accounts for them or that they do not stand up under analysis. It is concluded that some version of the optimal data selection model still provides the best account of the selection task. Consequently, the conclusion of Oaksford and Chater's (1994) original rational analysis (Anderson, 1990), that people's hypothesis-testing behavior on this task is rational and well adapted to the environment, still stands.

A quantitative model of optimal data selection in Wason's selection task

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

Confirmation Bias: A Ubiquitous Phenomenon in Many Guises

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed.