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In reasoning about situations in which several causes lead to a common effect, a much studied and yet still not well-understood inference is that of explaining away. Assuming that the causes contribute independently to the effect, if we learn that the effect is present, then this increases the probability that one or more of the causes are present. But if we then learn that a particular cause is present, this cause “explains” the presence of the effect, and the probabilities of the other causes decrease again. People tend to show this explaining away effect in their probability judgments, but to a lesser extent than predicted by the causal structure of the situation. We investigated further the conditions under which explaining away is observed. Participants estimated the probability of a cause, given the presence or the absence of another cause, for situations in which the effect was either present or absent, and the evidence about the effect was either certain or uncertain. Responses were compared to predictions obtained using Bayesian network modeling as well as a sensitivity analysis of the size of normative changes in probability under different information conditions. One of the conditions investigated: when there is certainty that the effect is absent, is special because under the assumption of causal independence, the probabilities of the causes remain invariant, that is, there is no normative explaining away or augmentation. This condition is therefore especially diagnostic of people’s reasoning about common-effect structures. The findings suggest that, alongside earlier explanations brought forward in the literature, explaining away may occur less often when the causes are assumed to interact in their contribution to the effect, and when the normative size of the probability change is not large enough to be subjectively meaningful. Further, people struggled when given evidence against negative evidence, resembling a double negation effect.

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.

It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast, Hahn, Prat-Sala, Pothos, and Brumby (2010) have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by Anderson & Betz, 2001) can account for the Hahn et al. dataset. This model implements Nosofsky and Palmeri’s (1997) Exemplar-Based Random Walk (EBRW) model as its exemplar route, and combines it with an implementation of Nosofsky, Palmeri and McKinley’s (1994) rule-based model RULEX. A thorough search of the model’s parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modelling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition.

Politicians, philosophers, and rhetors engage in co-value argumentation: appealing to one value in order to support another value (e.g., "equality leads to freedom"). Across four experiments in the United Kingdom and India, we found that the psychological relatedness of values affects the persuasiveness of the arguments that bind them. Experiment 1 found that participants were more persuaded by arguments citing values that fulfilled similar motives than by arguments citing opposing values. Experiments 2 and 3 replicated this result using a wider variety of values, while finding that the effect is stronger among people higher in need for cognition and that the effect is mediated by the greater plausibility of co-value arguments that link motivationally compatible values. Experiment 4 extended the effect to real-world arguments taken from political propaganda and replicated the mediating effect of argument plausibility. The findings highlight the importance of value relatedness in argument persuasiveness.