The empirical study of norms is just what we are missing.

This paper argues that the goals people have when reasoning determine their own norms of reasoning. A radical descriptivism which avoids norms never worked for any science; nor can it work for the psychology of reasoning. Norms as we understand them are illustrated with examples from categorical syllogistic reasoning and the "new paradigm" of subjective probabilities. We argue that many formal systems are required for psychology: classical logic, non-monotonic logics, probability logics, relevance logic, and others. One of the hardest challenges is working out what goals reasoners have and choosing and tailoring the appropriate logics to model the norms those goals imply.

To naturalize or not to naturalize? An issue for cognitive science as well as anthropology.

Several of Beller, Bender, and Medin's (2012) issues are as relevant within cognitive science as between it and anthropology. Knowledge-rich human mental processes impose hermeneutic tasks, both on subjects and researchers. Psychology's current philosophy of science is ill suited to analyzing these: Its demand for ''stimulus control'' needs to give way to ''negotiation of mutual interpretation.'' Cognitive science has ways to address these issues, as does anthropology. An example from my own work is about how defeasible logics are mathematical models of some aspects of simple hermeneutic processes. They explain processing relative to databases of knowledge and belief-that is, content. A specific example is syllogistic reasoning, which raises issues of experimenters' interpretations of subjects' reasoning. Science, especially since the advent of understandings of computation, does not have to be reductive. How does this approach transfer onto anthropological topics? Recent cognitive science approaches to anthropological topics have taken a reductive stance in terms of modules. We end with some speculations about a different cognitive approach to, for example, religion.

Visual/verbal-analytic reasoning bias as a function of self-reported autistic-like traits: a study of typically developing individuals solving Raven's Advanced Progressive Matrices.

People with autism spectrum condition (ASC) perform well on Raven's matrices, a test which loads highly on the general factor in intelligence. However, the mechanisms supporting enhanced performance on the test are poorly understood. Evidence is accumulating that milder variants of the ASC phenotype are present in typically developing individuals, and that those who are further along the autistic-like trait spectrum show similar patterns of abilities and impairments as people with clinically diagnosed ASC. We investigated whether self-reported autistic-like traits in a university student sample, assessed using the Autism-Spectrum Quotient (AQ; Baron-Cohen, Wheelwright, Skinner, et al., 2001), predict performance on Raven's Advanced Progressive Matrices. We found that reporting poorer social skills but better attention switching predicted a higher Advanced matrices score overall. DeShon, Chan, and Weissbein (1995) classified Advanced matrices items as requiring a visuospatial, or a verbal-analytic strategy. We hypothesised that higher AQ scores would predict better performance on visuospatial items than on verbal-analytic items. This prediction was confirmed. These results are consistent with the continuum view and can be explained by the enhanced perceptual functioning theory of performance peaks in ASC. The results also confirm a new prediction about Raven's Advanced Progressive Matrices performance in people with ASC.

Reasoning, logic, and psychology.

We argue that reasoning has been conceptualized so narrowly in what is known as 'psychology of reasoning' that reasoning's relevance to cognitive science has become well-nigh invisible. Reasoning is identified with determining whether a conclusion follows validly from given premises, where 'valid' is taken to mean 'valid according to classical logic'. We show that there are other ways to conceptualize reasoning, more in line with current logical theorizing, which give it a role in psychological processes ranging from (verbal) discourse comprehension to (nonverbal) planning. En route we show that formal logic, at present marginalized in cognitive science, can be an extremely valuable modeling tool. In particular, there are cases in which probabilistic modeling must fail, whereas logical models do well. WIREs Cogni Sci 2011 2 555-567 DOI: 10.1002/wcs.134 For further resources related to this article, please visit the WIREs website.

Reconnecting interpretation to reasoning through individual differences.

Computational theories of mind assume that participants interpret information and then reason from those interpretations. Research on interpretation in deductive reasoning has claimed to show that subjects' interpretation of single syllogistic premises in an "immediate inference" task is radically different from their interpretation of pairs of the same premises in syllogistic reasoning tasks (Newstead, 1989, 1995; Roberts, Newstead, & Griggs, 2001). Narrow appeal to particular Gricean implicatures in this work fails to bridge the gap. Grice's theory taken as a broad framework for credulous discourse processing in which participants construct speakers' "intended models" of discourses can reconcile these results, purchasing continuity of interpretation through variety of logical treatments. We present exploratory experimental data on immediate inference and subsequent syllogistic reasoning. Systematic patterns of interpretation driven by two factors (whether the subject's model of the discourse is credulous, and their degree of reliance on information packaging) are shown to transcend particular quantifier inferences and to drive systematic differences in subjects' subsequent syllogistic reasoning. We conclude that most participants do not understand deductive tasks as experimenters intend, and just as there is no single logical model of reasoning, so there is no reason to expect a single "fundamental human reasoning mechanism".

Semantic interpretation as computation in nonmonotonic logic: the real meaning of the suppression task.

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed-world reasoning of particular interest for human cognition. Byrne's "suppression" data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine-grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3-valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning.