Explicit learning in ACT-R

A popular distinction in the learning literature is the distinction between implicit and explicit learning. Although many studies elaborate on the nature of implicit learning, little attention is left for explicit learning. The unintentional aspect of implicit learning corresponds well to the mechanistic view of learning employed in architectures of cognition. But how to account for deliberate, intentional, explicit learning? This chapter argues that explicit learning can be explained by strategies that exploit implicit learning mechanisms. This idea is explored and modelled using the ACT-R theory (Anderson, 1993). An explicit strategy for learning facts in ACT-R’s declarative memory is rehearsal, a strategy that uses ACT-R’s activation learning mechanisms to gain deliberate control over what is learned. In the same sense, strategies for explicit procedural learning are proposed. Procedural learning in ACT-R involves generalisation of examples. Explicit learning rules can create and manipulate these examples. An example of these explicit rules will be discussed. These rules are general enough to be able to model the learning of three different tasks. Furthermore, the last of these models can explain the difference between adults and children in the discrimination-shift task

A Rational Analysis of Alternating Search and Reflection Strategies in Problem Solving

In this paper two approaches to problem solving, search and reflection, are discussed, and combined in two models, both based on rational analysis (Anderson, 1990). The first model is a dynamic growth model, which shows that alternating search and reflection is a rational strategy. The second model is a model in ACT-R, which can discover and revise strategies to solve simple problems. Both models exhibit the explore-insight pattern normally attributed to insight problem solving

A model of learning task-specific knowledge for a new task

In this paper I will present a detailed ACT-R model of how the task-specific knowledge for a new, complex task is learned. The model is capable of acquiring its knowledge through experience, using a declarative representation that is gradually compiled into a procedural representation. The model exhibits several characteristics that concur with Fitt’s theory of skill learning, and can be used to show that individual differences in working memory capacity initially have a large impact on performance, but that this impact diminished after sufficient experience. Some preliminary experimental data support these findings

Cognitive Architectures: Innate or Learned?

Cognitive architectures are generally considered to be theo- ries of the innate capabilities of the (human) cognitive system. Any knowledge that is not innate is encoded in the architec- tures memory systems, either by the modeler or learned by the architecture itself. However, in human intelligent behav- ior few things are innate. An alternative is to acknowledge that learning occurs at different levels of abstraction. A stan- dard model of the mind should therefore span multiple levels of abstraction, encouraging research efforts to establish learn- ing mechanism that connect them

Cognitive Architectures: Innate or Learned?

Cognitive architectures are generally considered to be theo- ries of the innate capabilities of the (human) cognitive system. Any knowledge that is not innate is encoded in the architec- tures memory systems, either by the modeler or learned by the architecture itself. However, in human intelligent behav- ior few things are innate. An alternative is to acknowledge that learning occurs at different levels of abstraction. A stan- dard model of the mind should therefore span multiple levels of abstraction, encouraging research efforts to establish learn- ing mechanism that connect them

Implicit and explicit learning in ACT-R

A useful way to explain the notions of implicit and explicit learning in ACT-R is to define implicit learning as learning by ACT-R's learning mechanisms, and explicit learning as the results of learning goals. This idea complies with the usual notion of implicit learning as unconscious and always active and explicit learning as intentional and conscious. Two models will be discussed to illustrate this point. First a model of a classical implicit memory task, the SUGARFACTORY scenario by Berry & Broadbent (1984) will be discussed, to show how ACT-R can model implicit learning. The second model is of the so-called Fincham task (Anderson & Fincham, 1994), and exhibits both implicit and explicit learning

Transfer of Cognitive Skills in Developmental Tasks

The main question we try to answer in this paper is whetherstage-like progression in cognitive development can beexplained by transfer of cognitive skill among tasks. Wefocus on the following question: To what extent does trainingon one task improve the performance on another task? Thetasks are Piaget’s (1959) Balance Scale Task and NumberConservation Task, and a task that we will call the Une-Sentence Task, which is taken from Karmiloff-Smith's (1979)experiment on the acquisition of determiners in French. Were-implemented already existing models within theframework of the PRIMs cognitive architecture (Taatgen,2013). Each task was subdivided in certain stages related tothe complexity of the problem-solving strategies. We showthat mastery of a certain stage of a problem becomes easier ifa higher stage of another task is mastered first