Mercier & Sperber (M&S) argue for their argumentative theory in terms of communicative abilities. Insights can be gained by extending the discussion beyond human reasoning to rodent and robot navigation. The selection of arguments and conclusions that are mutually reinforcing can be cast as a form of abductive reasoning that I argue underlies the construction of cognitive maps in navigation tasks.

Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...) evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. (shrink)

This paper puts forward a general framework for thought about human information processing. It is intended to avoid some of the problems of pipeline or stage models of function. At the same time it avoids the snare of supposing a welter of indefinitely many separate processes. The approach is not particularly original, but rather represents the common elements or presuppositions in a number of modern theories. These presuppositions are not usually explicit, however, and making them so reduces the danger of (...) slipping back into earlier modes of thought.The key point is to distinguish between persisting representations and the processes that translate one representation into another. Various classes or groups of persisting representations can be distinguished by the experimental treatments that interfere with them. In particular, there now seem to be several kinds of short-term or temporary storage, different from each other as well as from longterm memory; the translating processes also have several different modes or kinds. A particularly important aspect of the current position is that a model of this general type no longer requires some external agent to direct and control long sequences of behaviour. (shrink)

Mercier & Sperber (M&S) claim that the phenomenon of belief bias provides fundamental support for their argumentative theory and its basis in intuitive judgement. We propose that chronometric evidence necessitates a more nuanced account of belief bias that is not readily captured by argumentative theory.

Good explanations are not only true or probably true, but are also relevant to a causal question. Current models of causal explanation either only address the question of the truth of an explanation, or do not distinguish the probability of an explanation from its relevance. The tasks of scenario construction and conversational explanation are distinguished, which in turn shows how scenarios can interact with conversational principles to determine the truth and relevance of explanations. The proposed model distinguishes causal discounting from (...) causal backgrounding , and makes predictions concerning the differential effects of contextual information about alternative explanations on: (a) the kind of mental models constructed; (b) belief revision about probable cause; and (c) the perceived quality of a focal explanation. Four experiments are reported that test these predictions. The significance of the notion of explanatory relevance for research on causal explanation is then discussed. (shrink)

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI (...) programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations. (shrink)

Mercier and Sperber (M&S) suggest that human reasoning is reflective and has evolved to support social interaction. Cognitive agents benefit from being able to reflect on their beliefs whether they are acting alone or socially. A formal framework for argumentation that has emerged from research on artificial cognitive systems that parallels M&S's proposals may shed light on mental processes that underpin social interactions.