How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment.

According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is amongst the most characteristic indicators of meaningful deliberative thought in an organism or agent. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achieved by a particular kind of learning agent. More specifically, we provide and motivate a concrete operational definition of what it means for these agents to be in possession of abstract concepts, before presenting an explicit example of a minimal architecture that supports this capability. We then proceed to demonstrate how the existence of abstract conceptual structures can be operationally useful in the process of employing previously acquired knowledge in the face of new experiences, thereby vindicating the natural conjecture that the cognitive functions of abstraction and generalisation are closely related.

Bayesian argumentation and the value of logical validity.

According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, in which the fundamental norms are traditionally assumed to be logical. Here, we present a major generalization of extant Bayesian approaches to argumentation that (a) utilizes a new class of Bayesian learning methods that are better suited to modeling dynamic and conditional inferences than standard Bayesian conditionalization, (b) is able to characterize the special value of logically valid argument schemes in uncertain reasoning contexts, (c) greatly extends the range of inferences and argumentative phenomena that can be adequately described in a Bayesian framework, and (d) undermines some influential theoretical motivations for dual function models of human cognition. We conclude that the probabilistic norms given by the Bayesian approach to rationality are not necessarily at odds with the norms given by classical logic. Rather, the Bayesian theory of argumentation can be seen as justifying and enriching the argumentative norms of classical logic. (PsycINFO Database Record (c) 2018 APA, all rights reserved).