Information gain and decision-theoretic approaches to data selection: Response to Klauer (1999).

K. C. Klauer (1999) argued that a Bayesian decision-theoretic rational analysis of Wason's selection task is preferable to an information gain account (M. Oaksford & N. Chater, 1994) because it has a better normative justification and may provide a better fit with the empirical data. The authors argue that Klauer's proposal and their proposal are equally well justified from a normative perspective and that, where the predictions of the 2 approaches diverge, the existing empirical evidence is consistent with the information gain approach. However, more empirical research is required to decide between these 2 accounts. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Connectionism,classical cognitive science and experimental psychology

Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this behaviour. In addition to capturing the gross human performance profile, Connectionist systems seem well suited to accounting for the systematic patterns of errors observed in the human data. We take these arguments to counter Fodor and Pylyshyn's (1988) recent claim that Connectionism is, in principle, irrelevant to psychology.     

Reasoning with conditionals containing negated constituents.

Investigated matching bias in conditional reasoning tasks in 3 experiments. Matching bias occurs when Ss ignore negations and match named items. Exp 1 used an abstract and a thematic version of J. S. Evans's (1972) construction task. Results showed that matching may be due to an interaction between task demands and constructing contrast classes when interpreting negations. Exp 2, which used P. C. Wason's (1968) selection task, introduced a manipulation to ease contrast-class construction. Confirmation plus falsification dominated over matching. Exp 3 introduced 2 other manipulations to aid contrast-class construction with abstract material. Confirmation was facilitated, matching was suppressed, and falsification remained unchanged. These results suggest that matching occurs only when insufficient or ambiguous information prevents the intended interpretation of negations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mood, reasoning, and central executive processes.

How positive induced mood states affect reasoning was investigated in three experiments. In Experiment 1, consistent with resource allocation theory (H. C. Ellis & P. W. Ashbrook, 1987), both positive and negative mood suppressed performance on a deontic version of Wason's selection task (P. W. Cheng & K. J. Holyoak, 1985)—participants confirmed where they normally falsify. Experiment 2 revealed the same confirmatory responses for participants performing a concurrent distracter task, indicating that induced mood states suppress reasoning by depleting central executive resources. This hypothesis was directly tested in Experiment 3. Participants in a positive, but not in a negative, mood state showed suppressed performance on the Tower of London task (T. Shallice, 1982)—the classical central executive task. The robust positive mood effects and the confirmation effects are discussed in terms of the D. A. Norman and T. Shallice (1986) model of central executive function and recent accounts of selection task performance (L. Cosmides, 1989; K. I. Manktelow & D. E. Over, 1991; M. Oaksford & N. Chater, 1994). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Rational explanation of the selection task.

M. Oaksford and N. Chater (O&C, see record 1995-08271-001) presented the first quantitative model of R C. Wason's (1966) selection task in which performance is rational. J. St. B. T. Evans and D. E. Over (see record 83:25190) reply that O&C's account is normatively incorrect and cannot model K. N. Kirby's (see record 1995-04302-001) or R Pollard and J. St. B. T. Evans's (see record 1984-30572-001) data. It is argued that an equivalent measure satisfies their normative concerns and that a modification of O&C's model accounts for their empirical concerns. D. Laming (see record 83:25220) argues that O&C made unjustifiable psychological assumptions and that a "correct" Bayesian analysis agrees with logic. It is argued that O&C's model makes normative and psychological sense and that Laming's analysis is not Bayesian. A. Almor and S. A. Sloman (see record 83:25168) argue that O&C cannot explain their data. It is argued that Almor and Sloman's data do not bear on O&C's model because they alter the nature of the task. It is concluded that O&C's model remains the most compelling and comprehensive account of the selection task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computational levels and conditional inference: Reply to Schroyens and Schaeken (2003).

This article is a reply to W. Schroyens and W. Schaeken's (2003; see record 2002-08431-014) critique of M. Oaksford, N. Chater, and J. Larkin's (2000; see record 2000-08540-005) conditional probability model (CP) of conditional inference. It is argued that their meta-analysis does not falsify CP because the evidence may bear on more than one computational level of explanation. Moreover, it is argued that CP provides a rational account of more of the data than W. Schroyens and W. Schaeken's mental models theory. Other points are also addressed. It is suggested that W. Schroyens and W. Schaeken's model and CP converge on the importance of probabilistic prior knowledge in conditional inference. This is consistent with the normative literature, which (like CP) treats conditionals in terms of subjective conditional probabilities. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Bayesian Approach to the Argument From Ignorance.

In this paper, we re-examine a classic informal reasoning fallacy, the so-called argumentam ad ignorantiam. We argue that the structure of some versions of this argument parallels examples of inductive reasoning that are widely viewed as unproblematic. Viewed probabilistically, these versions of the argument from ignorance constitute a legitimate form of reasoning; the textbook examples are inductive arguments that are not unsound but simply weak, due to the nature of the premises and conclusions involved. In an experiment, we demonstrated some of the variables affecting the strength of the argument, and conclude with some general considerations towards an empirical theory of argument strength. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A rational analysis of the selection task as optimal data selection.

Human reasoning in hypothesis-testing tasks like P. C. Wason's (1968) selection task has been depicted as prone to systematic biases. However, performance on this task has been assessed against a now outmoded falsificationist philosophy of science. Therefore, the experimental data is reassessed in the light of a Bayesian model of optimal data selection in inductive hypothesis testing. The model provides a rational analysis (J. R. Anderson, 1990) of the selection task that fits well with people's performance on both abstract and thematic versions of the task. The model suggests that reasoning in these tasks may be rational rather than subject to systematic bias. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Probabilities and polarity biases in conditional inference.

A probabilistic computational level model of conditional inference is proposed that can explain polarity biases in conditional inference (e.g., J. St.B. T. Evans, 1993). These biases are observed when J. St.B. T. Evans's (1972) negations paradigm is used in the conditional inference task. The model assumes that negations define higher probability categories than their affirmative counterparts (M. Oaksford & K. Stenning, 1992); for example, P(not-dog) > P(dog). This identification suggests that polarity biases are really a rational effect of high-probability categories. Three experiments revealed that, consistent with this probabilistic account, when high-probability categories are used instead of negations, a high-probability conclusion effect is observed. The relationships between the probabilistic model and other phenomena and other theories in conditional reasoning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)