Practical versus Pragmatic: Enlarging the Selection Task, extending reasoning

A Computational Model of Wason’s Selection Task

We apply an algorithmic learning model of inquiry to model reasoning carried by experimental subjects in Wason’s Selection Task, that represents reasoning in the task as computation of a decision tree that supervenes on (partial) semantic representations. We argue that the resulting model improves on previous probabilistic (Bayesian) and pragmatic (Relevance theory) models of the task. In parti...

Extending Storage Duration of Mother Scales for Enlarging Scale Bulblets and Soluble Carbohydrates Content in Lily “Arabian Red”

Oriental hybrid lily “Arabian Red” mother scales were stored in moist cocopeat and perlite (1:1) for 3 or 5 months at 25 ̊C. The numbers of scale bulblets per mother scale were nearly identical in two durations. Increasing storage duration promoted the growth of roots (number and length) and No. scales per scale bulblets. And, diameters of scale bulblets were more in longer storage duration (5 m...

The Effect of Task Repetition on Pragmatic Awareness of Iranian EFL Learners

Pragmatic reasoning schemas.

We propose that people typically reason about realistic situations using neither content-free syntactic inference rules nor representations of specific experiences. Rather, people reason using knowledge structures that we term pragmatic reasoning schemas, which are generalized sets of rules defined in relation to classes of goals. Three experiments examined the impact of a “permission schema” o...

DiPRA: Distributed Practical Reasoning Architecture

DiPRA (Distributed Practical Reasoning Architecture) implements the main principles of practical reasoning via the distributed action selection paradigm. We introduce and motivate the underlying theoretical and computational peculiarities of DiPRA and we describe its components, also providing as a case study a guards-and-thieves task.