This article describes a representation-based framework of distributed cognition. This framework considers distributed cognition as a cognitive system whose structures and processes are distributed between internal and external representations, across a group of individuals, and across space and time. The major issue for distributed research, under this framework, are the distribution, transformation, and propagation of information across the components of the distributed cognitive system and how they affect the performance of the system as a whole. To demonstrate the value of (...) this representation-based approach, the framework was used to describe and explain an important, challenging, and controversial issue — the concept of affordance. (shrink)

This article describes a representation-based framework of distributed cognition. This framework considers distributed cognition as a cognitive system whose structures and processes are distributed between internal and external representations, across a group of individuals, and across space and time. The major issue for distributed research, under this framework, are the distribution, transformation, and propagation of information across the components of the distributed cognitive system and how they affect the performance of the system as a whole. To demonstrate the value of (...) this representation-based approach, the framework was used to describe and explain an important, challenging, and controversial issue — the concept of affordance. (shrink)

Although we agree with Newell and Anderson & Lebiere (A&L) that a unified theory of cognition is needed to advance cognitive science, we disagree on how to achieve it. A hybrid system can score high in the Newell Test but may not offer a veridical and coherent theory of cognition. A multilevel approach, involving theories at both psychological and brain levels, is suggested.

This article presents three experiments that examine the relation between order effects and frequency learning, with the following results. First, when frequencies of occurrence are presented as sequences of real events, base rates can be learned and used with a high degree of accuracy. However, conditional probabilities for multiple sequentially presented evidence items cannot be completely learned, due to the distortion of a recency order effect for actual decisions. Second, there is also a recency order effect for belief evaluations, which (...) cannot be eliminated even if base rates are used correctly. Third, base rates learned in one environment can be transferred to another environment, but the transfer soon diminishes due to learning in the new environment. However, belief evaluations are not transferred from one environment to another The existing models of frequency learning cannot explain the order effect for actual decisions because they do not consider sequential information. The existing models of belief updating can explain both types of order effects, but they do not have any mechanisms for frequency learning. To account for the complete spectrum of frequency learning and order effects, we outline our initial effort in developing a unified model that integrates frequency learning and order effects. (shrink)