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

Affective focus increases the concordance between implicit and explicit attitudes

Two attitude dichotomies - implicit versus explicit and affect versus cognition - are presumed to be related. Following a manipulation of attitudinal focus (affective or cognitive), participants completed two implicit measures (Implicit Association Test and the Sorting Paired Features task) and three explicit attitude measures toward cats/dogs (Study 1) and gay/straight people (Study 2). Based on confirmatory factor analysis, both studies showed that explicit attitudes were more related to implicit attitudes in an affective focus than in a cognitive focus. We suggest that, although explicit evaluations can be meaningfully parsed into affective and cognitive components, implicit evaluations are more related to affective than cognitive components of attitudes

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

A method for enhancing the stability and robustness of explicit schemes in astrophysical fluid dynamics

A method for enhancing the stability and robustness of explicit schemes in computational fluid dynamics is presented. The method is based in reformulating explicit schemes in matrix form, which cane modified gradually into semi or strongly-implicit schemes. From the point of view of matrix-algebra, explicit numerical methods are special cases in which the global matrix of coefficients is reduced to the identity matrix $I$. This extreme simplification leads to severer stability range, hence of their robustness. In this paper it is shown that a condition, which is similar to the Courant-Friedrich-Levy (CFL) condition can be obtained from the stability requirement of inversion of the coefficient matrix. This condition is shown to be relax-able, and that a class of methods that range from explicit to strongly implicit methods can be constructed, whose degree of implicitness depends on the number of coefficients used in constructing the corresponding coefficient-matrices. Special attention is given to a simple and tractable semi-explicit method, which is obtained by modifying the coefficient matrix from the identity matrix $I$ into a diagonal-matrix $D$. This method is shown to be stable, robust and it can be applied to search for stationary solutions using large CFL-numbers, though it converges slower than its implicit counterpart. Moreover, the method can be applied to follow the evolution of strongly time-dependent flows, though it is not as efficient as normal explicit methods. In addition, we find that the residual smoothing method accelerates convergene toward steady state solutions considerably and improves the efficiency of the solution procedure.Comment: 33 pages, 15 figure

Using ICT tools to manage knowledge: a student perspective in determining the quality of education

Within the e-learning context of a university, technology has the potential to facilitate the knowledge interaction between the source (instructor) and the recipient (students). From a literature review, it can be concluded that prior studies have not explored the types of channels that encourage knowledge transfer in this environment. For example, how explicit knowledge travels through the e-learning environment and goes through interaction processes and is received and acquired is largely unknown. According to Alavi & Leidner (2001), Information and Communication Technology (ICT) can help speed up the processes of transferring knowledge from those who have knowledge to those seeking knowledge. Within the university context, technologies such as email, Internet, IRC chat, bulletin boards and tools such as WebCT and BlackBoard have the potential to facilitate the transfer of knowledge and act as a link between source and recipient. Effective knowledge transfer has to consider effective knowledge acquisition, which are therefore inexplicably linked. Nonaka's spiral model addresses knowledge acquisition through spiraling processes in which an individual would be able to convert tacit knowledge to explicit knowledge and vice versa. According to Nonaka & Takeuchi (1995) there are four types of interaction, which give way to the conversion of one form of knowledge into another, namely tacit-to-tacit, tacit-to-explicit, explicit-to-tacit and explicit-to-explicit. In an academic environment, this can be studied as the source, either transferring tacit or explicit knowledge, and similarly as the recipient, receiving knowledge either in tacit or explicit form. Nonaka & Takeuchi (1995) also refer to this as the SECI model, where SECI stands for Socialisation, Externalisation, Combination and Internalisation. This 'Research in Progress' reports the outcomes of a study undertaken to understand how and to what extent knowledge spiraling processes and accompanying characteristics of SECI can be ICT-enabled to contribute towards the studying and learning processes for university education. A survey instrument was developed for this purpose and it is currently undergoing peer-review and other customary validity and reliability tests. Once the instrument is validated, it will be administered on about 50 tertiary students. It is hoped that the results obtained from this survey will be reported in the QIK 2005 conference

On the Sum of the Square of a Prime and a Square-Free Number

We prove that every integer $n \geq 10$ such that $n \not\equiv 1 \text{mod} 4$ can be written as the sum of the square of a prime and a square-free number. This makes explicit a theorem of Erd\H{o}s that every sufficiently large integer of this type may be written in such a way. Our proof requires us to construct new explicit results for primes in arithmetic progressions. As such, we use the second author's numerical computation regarding GRH to extend the explicit bounds of Ramar\'e-Rumely.Comment: 12 page