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.

Implicit sequence learning in a search task

2008 ◽  
Vol 61 (11) ◽  
pp. 1650-1657 ◽  
Author(s):  
Luis Jiménez ◽  
Gustavo A. Vázquez
Keyword(s):  
Sequence Learning ◽  
Search Task ◽  
Explicit Learning ◽  
Implicit Sequence Learning ◽  
Generation Task ◽  
Central Location ◽  
Indirect Measures ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning ◽  
Central Condition

This study investigated the effects of selection demands on implicit sequence learning. Participants in a search condition looked for a target among seven distractors and responded on the target identity. The responses followed a deterministic sequence, and sequence learning was compared to that found in two control conditions in which the targets were presented alone, either at a central location or over a series of unpredictable locations. Sequence learning was obtained in all conditions, and it was equivalent for the two variable location conditions, regardless of the perceptual demands. Larger effects of learning were observed in the central location, both on the indirect measures and on the measures taken from a cued-generation task. The expression of learning decreased selectively in this condition when the sequence validity was reduced over a test block. These results are consistent with the claims that implicit and explicit learning are mixed in this central condition and that implicit learning is not affected by selection difficulty.

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.

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.

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.

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.

EPILOGUE

2021 ◽  
Vol 43 (3) ◽  
pp. 692-697
Author(s):  
Robert DeKeyser ◽  
Shaofeng Li
Keyword(s):  
Convergent Validity ◽  
Explicit Knowledge ◽  
Outcome Variable ◽  
Future Research ◽  
Explicit Learning ◽  
Language Aptitude ◽  
Language Research ◽  
Instruction Type ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning

AbstractIn this commentary, we summarize the findings of the seven included studies that examined implicit language aptitude from various perspectives and highlight issues to be resolved in the validation of this new construct in second language research. We start by providing an overview of the contributions of the studies. We then identify the lack of convergent validity of the measures of implicit aptitude reported in the included studies and problematize the equally varied nature of the measurement of implicit knowledge—the outcome variable of aptitude research—and related concepts. In particular, by drawing on empirical evidence and theoretical claims, we attempt to clarify the relationships between implicit and explicit knowledge, implicit and explicit learning, and implicit and explicit instruction. Next, we draw attention to the interactions reported by the included studies between aptitude and outcome measures and between aptitude and instruction type, emphasizing the value and importance of interactional research. We conclude by making recommendations for future research.

Implicit versus explicit: An act-r learning perspective

1999 ◽  
Vol 22 (5) ◽  
pp. 785-786 ◽  
Author(s):  
Niels A. Taatgen
Keyword(s):  
Implicit Learning ◽  
Explicit Memory ◽  
Explicit Knowledge ◽  
Cognitive Architecture ◽  
Explicit Learning ◽  
Implicit And Explicit Memory ◽  
The Difference ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning

Dienes & Perner propose a theory of implicit and explicit knowledge that is not entirely complete. It does not address many of the empirical issues, nor does it explain the difference between implicit and explicit learning. It does, however, provide a possible unified explanation, as opposed to the more binary theories like the systems and the processing theories of implicit and explicit memory. Furthermore, it is consistent with a theory in which implicit learning is viewed as based on the mechanisms of the cognitive architecture, and explicit learning as strategies that exploit these mechanisms.

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.

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