Deconstructing RTK: How to explicate a theory of implicit knowledge

1999 ◽  
Vol 22 (5) ◽  
pp. 790-801 ◽  
Author(s):  
Josef Perner ◽  
Zoltan Dienes
Keyword(s):  
Executive Control ◽  
First Principles ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Theory Of Knowledge ◽  
Memory Development ◽  
Target Article ◽  
Linguistic Representations ◽  
Implicit And Explicit ◽  
The Relationship

In this response, we start from first principles, building up our theory to show more precisely what assumptions we do and do not make about the representational nature of implicit and explicit knowledge (in contrast to the target article, where we started our exposition with a description of a fully fledged representational theory of knowledge (RTK). Along the way, we indicate how our analysis does not rely on linguistic representations but it implies that implicit knowledge is causally efficacious; we discuss the relationship between property structure implicitness and conceptual and nonconceptual content; then we consider the factual, fictional, and functional uses of representations and how we go from there to consciousness. Having shown how the basic theory deals with foundational criticisms, we indicate how the theory can elucidate issues that commentators raised in the particular application areas of explicitation, voluntary control, visual perception, memory, development (with discussion on infancy, theory of mind [TOM] and executive control, gestures), and finally models of learning.

Fishing with the wrong nets: How the implicit slips through the Representational Theory of Mind

1999 ◽  
Vol 22 (5) ◽  
pp. 771-771
Author(s):  
Luis Jiménez ◽  
Axel Cleeremans
Keyword(s):  
Theory Of Mind ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Theory Of Knowledge ◽  
Causal Efficacy ◽  
Observable Effect ◽  
Fully Implicit ◽  
Target Article ◽  
Representational Theory Of Mind ◽  
Satisfactory Theory

Dienes & Perner's target article is not a satisfactory theory of implicit knowledge because in endorsing the representational theory of knowledge, the authors also inadvertently accept that only explicit knowledge can be causally efficacious, and hence that implicit knowledge is an inert category. This conflation between causal efficacy, knowledge, and explicitness is made clear through the authors' strategy, which consists of attributing any observable effect to the existence of representations that are as minimally explicit as needed to account for behavior. In contrast, we believe that causally efficacious and fully implicit knowledge exists, and is best embodied in frameworks that depart radically from classical assumptions.

scholarly journals Beyond paradigm: The ‘what’ and the ‘how’ of classroom research

2014 ◽  
Vol 30 (4) ◽  
pp. 551-568 ◽  
Author(s):  
Melinda Whong ◽  
Kook-Hee Gil ◽  
Heather Marsden
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Language Development ◽  
Explicit Knowledge ◽  
Teaching Practice ◽  
Implicit Knowledge ◽  
Developmental Differences ◽  
Classroom Research ◽  
Language Classroom ◽  
Implicit And Explicit

This article reviews studies in second language classroom research from a cross-theoretic perspective, arguing that the classroom holds the potential for bringing together researchers from opposing theoretical orientations. It shows how generative and general cognitive approaches share a view of language that implicates both implicit and explicit knowledge, and that holds a bias towards implicit knowledge. Arguing that it is implicit knowledge that should be the object of research, it proposes that classroom research would benefit from incorporating insights from a generative understanding of language. Specifically, there is a need for a more nuanced view of the complexity of language in terms of linguistic domain, and the interaction between those domains. Generative second language acquisition research that shows developmental differences in terms of both linguistic domain and interface is reviewed. The core argument is a call for more attention to the ‘what’ of language development in classroom research and, by implication, teaching practice. As such, the language classroom is seen to offer potential for research that goes beyond paradigm to address both the ‘what’ and the ‘how’ of language development.

scholarly journals Assumptions of the Process-Dissociation Procedure are Violated in Implicit Sequence Learning

2016 ◽  
Author(s):  
Marius Barth ◽  
Christoph Stahl ◽  
Hilde Haider
Keyword(s):  
Sequence Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Process Dissociation ◽  
Implicit Sequence Learning ◽  
Generation Task ◽  
Accurate Performance ◽  
Exclusion Condition ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning

In implicit sequence learning, a process-dissociation (PD) approach has been proposed to dissociate implicit and explicit learning processes. Applied to the popular generation task, participants perform two different task versions: inclusion instructions require generating the transitions that form the learned sequence; exclusion instructions require generating transitions other than those of the learned sequence. Whereas accurate performance under inclusion may be based on either implicit or explicit knowledge, avoiding to generate learned transitions requires controllable explicit sequence knowledge. The PD approach yields separate estimates of explicit and implicit knowledge that are derived from the same task; it therefore avoids many problems of previous measurement approaches. However, the PD approach rests on the critical assumption that the implicit and explicit processes are invariant across inclusion and exclusion conditions. We tested whether the invariance assumptions hold for the PD generation task. Across three studies using first-order as well as second-order regularities, invariance of the controlled process was found to be violated. In particular, despite extensive amounts of practice, explicit knowledge was not exhaustively expressed in the exclusion condition. We discuss the implications of these findings for the use of process-dissociation in assessing implicit knowledge.

Indian study shows that encouraging knowledge sharing at work can help spark innovative behavior

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Explicit Knowledge ◽  
Manufacturing Sector ◽  
Critical Role ◽  
Underlying Mechanism ◽  
Innovative Behavior ◽  
Content Type ◽  
Indian Manufacturing Sector ◽  
Implicit And Explicit ◽  
The Relationship

Purpose The authors wanted to find out the most important mechanisms for encouraging innovative behavior in the Indian manufacturing sector. Design/methodology/approach The researchers collected data from Indian manufacturing organizations. They distributed questionnaires and received 288 complete ones. Items measured critical concepts. For OJ one example was “I have been fairly rewarded for the effort I put forth”. For KS, one sample was, “When I have learned something new, I tell my colleagues about it” and, “When they have learned something new, my colleagues tell me about it”. Meanwhile, IB was measured using items such as “I generate original solutions for problems”. Findings It highlighted the pivotal role of OJ in bolstering employees’ IB. When companies treat employees fairly, it encourages positive social interactions that lead to perceptions of supportiveness and trustworthiness. Employees reciprocate these sentiments with positive behavior. The study also showed the positive predictive influence of KS on IB. Finally, the results showed that the relationship between OJ and IB is complex, but KS is a pivotal mediator. Promotion of OJ, KS and IM is “vital” to spark innovation. Originality/value The authors felt their most important finding was to highlight the critical role of the underlying mechanism of KS, which is where individuals exchange implicit and explicit knowledge to create new knowledge. In addition, previous researchers have looked at the role of organizational justice in encouraging innovative behavior, but evidence from non-Western countries is scarce.

The Implicit/Explicit Strategy

2019 ◽  
pp. 89-129
Author(s):  
Eli Alshanetsky
Keyword(s):  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Perceptual Classification ◽  
Color Recognition ◽  
Implicit And Explicit

On the proposed solution to the puzzle, we recognize the correct formulations of our thoughts by relying on our implicit knowledge of what we are thinking. After discussing an analogous puzzle in the case of basic perceptual classification and constructing a model of implicit knowledge for the simpler case of color recognition, the chapter extends the model to the trickier case of thought. On this model, our implicit knowledge of an item consists in its stored signature—the invariant aspect of experience that its instances share. On the proposed solution, the process that mediates between implicit and explicit knowledge is not itself wholly sub-personal. Instead, it is best understood as straddling the personal/sub-personal divide. A deeper source of the puzzle that emerges from this chapter’s discussion of our involvement in articulation lies in the conflation between two types of freedom (or control) that we may have over a response.

Temporal Stability of Implicit Sequence Knowledge

2015 ◽  
Vol 62 (4) ◽  
pp. 240-253 ◽  
Author(s):  
Ricardo Tamayo ◽  
Peter A. Frensch
Keyword(s):  
Explicit Knowledge ◽  
Common Knowledge ◽  
Random Noise ◽  
Temporal Stability ◽  
Implicit Knowledge ◽  
Single System ◽  
Artificial Grammars ◽  
Implicit And Explicit ◽  
Explicit And Implicit Knowledge ◽  
Best Fit

Abstract. Previous research has shown that explicit and implicit knowledge of artificial grammars may decay at different rates (e.g., Tamayo & Frensch, 2007 ; Tunney, 2003 ). We extend these findings to sequential regularities embedded in serial reaction time (SRT) tasks. We compared the forgetting patterns of implicit and explicit knowledge after a retention interval of 7 days without rehearsal. Explicit knowledge decayed after 7 days, whereas implicit knowledge was retained. These data were modeled according to the assumptions involved in the single-system model suggested by Shanks, Wilkinson, and Channon (2003) . The best fit for the model was obtained by modifying the parameters related to (a) the common knowledge-strength variable for implicit and explicit knowledge, and (b) reliability of the explicit test. We interpret these dissociations as a boundary condition for single-system models that assume constant random noise to explain dissociations in the forgetting patterns of implicit and explicit sequential knowledge.

scholarly journals Implicit and explicit learning of Bayesian priors differently impacts bias during perceptual decision-making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
V. N. Thakur ◽  
M. A. Basso ◽  
J. Ditterich ◽  
B. J. Knowlton
Keyword(s):  
Decision Making ◽  
Explicit Knowledge ◽  
Drift Rate ◽  
Implicit Knowledge ◽  
Perceptual Decision Making ◽  
Model Framework ◽  
Perceptual Decision ◽  
General Orientation ◽  
Starting Point ◽  
Implicit And Explicit

AbstractKnowledge without awareness, or implicit knowledge, influences a variety of behaviors. It is unknown however, whether implicit knowledge of statistical structure informs visual perceptual decisions or whether explicit knowledge of statistical probabilities is required. Here, we measured visual decision-making performance using a novel task in which humans reported the orientation of two differently colored translational Glass patterns; each color associated with different orientation probabilities. The task design allowed us to assess participants’ ability to learn and use a general orientation prior as well as a color specific feature prior. Classifying decision-makers based on a questionnaire revealed that both implicit and explicit learners implemented a general orientation bias by adjusting the starting point of evidence accumulation in the drift diffusion model framework. Explicit learners additionally adjusted the drift rate offset. When subjects implemented a stimulus specific bias, they did so by adjusting primarily the drift rate offset. We conclude that humans can learn priors implicitly for perceptual decision-making and depending on awareness implement the priors using different mechanisms.

Explicit to whom? Accessibility, representational homogeneity, and dissociable learning mechanisms

1999 ◽  
Vol 22 (5) ◽  
pp. 777-778
Author(s):  
David C. Noelle
Keyword(s):  
Explicit Knowledge ◽  
Propositional Attitudes ◽  
Implicit Knowledge ◽  
Learning Performance ◽  
Learning Mechanisms ◽  
Active Representation ◽  
Implicit And Explicit ◽  
Rough Outline

Distinguishing explicit from implicit knowledge on the basis of the active representation of certain propositional attitudes fails to provide an explanation for dissociations in learning performance under implicit and explicit conditions. This suggests an account of implicit and explicit knowledge grounded in the presence of multiple learning mechanisms, and multiple brain systems more generally. A rough outline of a connectionist account of this kind is provided.

Implicit knowledge as automatic, latent knowledge

1999 ◽  
Vol 22 (5) ◽  
pp. 787-788 ◽  
Author(s):  
John R. Vokey ◽  
Philip A. Higham
Keyword(s):  
Knowledge Representation ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Practical Matter ◽  
Definition Of ◽  
Implicit And Explicit

Implicit knowledge is perhaps better understood as latent knowledge so that it is readily apparent that it contrasts with explicit knowledge in terms of the form of the knowledge representation, rather than by definition in terms of consciousness or awareness. We argue that as a practical matter any definition of the distinction between implicit and explicit knowledge further involves the notion of control.

scholarly journals Implicit and explicit knowledge: One representational medium or many?

1999 ◽  
Vol 22 (5) ◽  
pp. 769-770
Author(s):  
James A. Hampton
Keyword(s):  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Self Awareness ◽  
Implicit And Explicit

In Dienes & Perner's analysis, implicitly represented knowledge differs from explicitly represented knowledge only in the attribution of properties to specific events and to self-awareness of the knower. This commentary questions whether implicit knowledge should be thought of as being represented in the same conceptual vocabulary; rather, it may involve a quite different form of representation.

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