Re-evaluating Dissociations between Implicit and Explicit Category Learning: An Event-related fMRI Study

2011 ◽  
Vol 23 (7) ◽  
pp. 1697-1709 ◽  
Author(s):  
Todd M. Gureckis ◽  
Thomas W. James ◽  
Robert M. Nosofsky
Keyword(s):  
Implicit Learning ◽  
Category Learning ◽  
Neural Activity ◽  
Incidental Learning ◽  
Brain Activity ◽  
Neural Systems ◽  
Explicit Learning ◽  
Learning Condition ◽  
Perceptual Category Learning ◽  
Implicit And Explicit

Recent fMRI studies have found that distinct neural systems may mediate perceptual category learning under implicit and explicit learning conditions. In these previous studies, however, different stimulus-encoding processes may have been associated with implicit versus explicit learning. The present design was aimed at decoupling the influence of these factors on the recruitment of alternate neural systems. Consistent with previous reports, following incidental learning in a dot-pattern classification task, participants showed decreased neural activity in occipital visual cortex (extrastriate region V3, BA 19) in response to novel exemplars of a studied category compared to members of a foil category, but did not show this decreased neural activity following explicit learning. Crucially, however, our results show that this pattern was primarily modulated by aspects of the stimulus-encoding instructions provided at the time of study. In particular, when participants in an implicit learning condition were encouraged to evaluate the overall shape and configuration of the stimuli during study, we failed to find the pattern of brain activity that has been taken to be a signature of implicit learning, suggesting that activity in this area does not uniquely reflect implicit memory for perceptual categories but instead may reflect aspects of processing or perceptual encoding strategies.

Complementary Category Learning Systems Identified Using Event-Related Functional MRI

2000 ◽  
Vol 12 (6) ◽  
pp. 977-987 ◽  
Author(s):  
Howard J. Aizenstein ◽  
Angus W. MacDonald ◽  
V. Andrew Stenger ◽  
Robert D. Nebes ◽  
Jeris K. Larson ◽  
...  
Keyword(s):  
Implicit Learning ◽  
Category Learning ◽  
Medial Temporal Lobe ◽  
Learning Task ◽  
Neural Systems ◽  
Explicit Learning ◽  
Imaging Data ◽  
Activation Patterns ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning

Event-related fMRI was used to dissociate the neural systems involved in category learning with and without awareness. Ten subjects performed a speeded response category learning task. Functional MR images were acquired during both explicit and implicit learning conditions. Behavioral data showed evidence of learning in both conditions. Functional imaging data showed different activation patterns in implicit and explicit trials. Decreased activation in extrastriate region V3 was found with implicit learning, and increased activation in V3, the medial temporal lobe, and frontal regions were found with explicit learning. These results support the theory that implicit and explicit learning utilize dissociable neural systems. Moreover, in both the implicit and explicit conditions a similar pattern of decreased activation was found in parietal regions. This commonality suggests that these dissociable systems also operate in parallel.

scholarly journals Effect of coordinate frame compatibility on the transfer of implicit and explicit learning across limbs

2016 ◽  
Vol 116 (3) ◽  
pp. 1239-1249 ◽  
Author(s):  
Eugene Poh ◽  
Timothy J. Carroll ◽  
Jordan A. Taylor
Keyword(s):  
Implicit Learning ◽  
Neural Representation ◽  
Coordinate Frame ◽  
Explicit Learning ◽  
Verbal Reports ◽  
Visuomotor Rotation ◽  
Left And Right ◽  
Task Conditions ◽  
Opposite Limb ◽  
Implicit And Explicit

Insights into the neural representation of motor learning can be obtained by investigating how learning transfers to novel task conditions. We recently demonstrated that visuomotor rotation learning transferred strongly between left and right limbs when the task was performed in a sagittal workspace, which afforded a consistent remapping for the two limbs in both extrinsic and joint-based coordinates. In contrast, transfer was absent when performed in horizontal workspace, where the extrinsically defined perturbation required conflicting joint-based remapping for the left and right limbs. Because visuomotor learning is thought to be supported by both implicit and explicit forms of learning, however, it is unclear to what extent these distinct forms of learning contribute to interlimb transfer. In this study, we assessed the degree to which interlimb transfer, following visuomotor rotation training, reflects explicit vs. implicit learning by obtaining verbal reports of participants' aiming direction before each movement. We also determined the extent to which these distinct components of learning are constrained by the compatibility of coordinate systems by comparing transfer between groups of participants who reached to targets arranged in the horizontal and sagittal planes. Both sagittal and horizontal conditions displayed complete transfer of explicit learning to the untrained limb. In contrast, transfer of implicit learning was incomplete, but the sagittal condition showed greater transfer than the horizontal condition. These findings suggest that explicit strategies developed with one limb can be fully implemented in the opposite limb, whereas implicit transfer depends on the degree to which new sensorimotor maps are spatially compatible for the two limbs.

The complex relationship between conscious/unconscious learning and conscious/unconscious knowledge: The mediating effects of salience in form–meaning connections

2021 ◽  
pp. 026765832110449
Author(s):  
Junya Fukuta ◽  
Junko Yamashita
Keyword(s):  
Incidental Learning ◽  
Choice Task ◽  
Explicit Learning ◽  
Conscious Awareness ◽  
Mediating Effects ◽  
Thinking Aloud ◽  
Target Form ◽  
Forced Choice Task ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning

This study investigates how implicit and explicit learning and knowledge are associated, by focusing on the salience of target form–meaning connections. The participants were engaged in incidental learning of artificial determiner systems that included grammatical rules of [± plural] (a taught rule), [± actor] (a more salient hidden rule), and [± animate] (a less salient hidden rule). They completed immediate and delayed post-tests by means of a two-alternative forced-choice task with subjective judgments of source attributions. Awareness during the learning phase was identified through analysis of thinking aloud protocols. The results did not support a one-to-one relation between either explicit learning and conscious knowledge, or implicit learning and unconscious knowledge; rather, they indicated that implicit and explicit learning are intricately linked to conscious and unconscious knowledge mediated by the salience of form–meaning connections in target items. This result also suggests the possibility of the later emergence of knowledge without any conscious awareness of it.

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.

Implicit and explicit learning in a hybrid architecture of cognition

1999 ◽  
Vol 22 (5) ◽  
pp. 772-773
Author(s):  
Christian Lebiere ◽  
Dieter Wallach
Keyword(s):  
Implicit Learning ◽  
Conceptual Analysis ◽  
Human Cognition ◽  
Explicit Learning ◽  
Hybrid Architecture ◽  
Integrated Architecture ◽  
The Common ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning ◽  
Analysis Of Knowledge

We present a theoretical account of implicit and explicit learning in terms of act-r, an integrated architecture of human cognition as a computational supplement to Dienes & Perner's conceptual analysis of knowledge. Explicit learning is explained in act-r by the acquisition of new symbolic knowledge, whereas implicit learning amounts to statistically adjusting subsymbolic quantities associated with that knowledge. We discuss the common foundation of a set of models that are able to explain data gathered in several signature paradigms of implicit learning.

An algebraic perspective on implicit and explicit knowledge

2016 ◽  
Vol 3 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Omid Khatin Zadeh ◽  
Sedigheh Vahdat ◽  
Babak Yazdani Fazlabadi
Keyword(s):  
Implicit Learning ◽  
Infinite Number ◽  
Explicit Knowledge ◽  
Algebraic Groups ◽  
The Other ◽  
Explicit Learning ◽  
Abstract Group ◽  
Abstract Structure ◽  
The Mind ◽  
Implicit And Explicit

The isomorphic relationship between an infinite number of concrete algebraic groups and the existence of a single abstract group that underlies all these concrete groups is one of the most fundamental subjects in Abstract Algebra. Looking at the process of explicit learning from a mathematical perspective, this article suggests that explicit knowledge of a certain concrete structure can be viewed as consciousness of an abstract algebraic structure that underlies that structure. On the other hand, implicit knowledge can be regarded as knowing something without being conscious of the abstract structure that underlies that knowledge. Explicit knowledge enables the learner to know what features are shared by these concrete groups or structures. These shared features are the defining elements of underlying abstract structure. The abstract structure is constructed in the mind by the suppression of irrelevant data. Therefore, it is suggested that while implicit learning is a receiving-oriented mode of learning, explicit learning is a suppression-oriented one. The sub-process of suppression enables the cognitive system to focus on abstract structure and its defining features, making the process of explicit learning deeper.

A Multinomial Model to Assess Fluency and Recollection in a Sequence Learning Task

1997 ◽  
Vol 50 (3) ◽  
pp. 631-663 ◽  
Author(s):  
Axel Buchner ◽  
Melanie C. Steffens ◽  
Edgar Erdfelder ◽  
Rainer Rothkegel
Keyword(s):  
Sequence Learning ◽  
Incidental Learning ◽  
Measurement Model ◽  
Learning Task ◽  
Process Dissociation ◽  
Learning Condition ◽  
Auditory Event ◽  
Process Dissociation Procedure ◽  
Sequence Identification ◽  
Implicit And Explicit

We suggest that well-formedness judgements in conjunction with L.L. Jacoby's (1991) process dissociation procedure and an appropriate measurement model can be used to obtain measures of implicit and explicit sequence knowledge. We introduce a new measurement model designed specifically for the sequence learning task. The model assumes that sequence identification is based on recollection, perceptual or motor fluency, systematicity detection, and guessing. The model and the application of the process dissociation procedure were empirically evaluated using auditory event sequences. In Experiment 1, the parameter reflecting recollection was higher in an intentional than in an incidental learning condition. Experiment 2 showed that random sequences interspersed among the systematic sequences during the acquisition phase may change this pattern of results. A manipulation of processing fluency in Experiment 3 was reflected in the appropriate model parameter. In sum, the new measurement model and the application of the process dissociation procedure appear to be useful tools in sequence learning research.

scholarly journals Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

2021 ◽  
Author(s):  
Sabrina A Jones ◽  
Jacob H Barfield ◽  
Woodrow L Shew
Keyword(s):  
Neural Activity ◽  
Cortical Neurons ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Neural Systems ◽  
Scale Free ◽  
Behavioral Dynamics ◽  
Free Behavior ◽  
Neural Underpinnings ◽  
Winner Take All

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure, thought to confer functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here we show that scale-free dynamics of behavior and certain subsets of cortical neurons are one-to-one related. Surprisingly, the scale-free neural subsets exhibit stochastic winner-take-all competition with other neural subsets, inconsistent with prevailing theory of scale-free neural systems. We develop a computational model which accounts for known cell-type-specific circuit structure and explains our findings. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity, which was previously thought to represent background noise in cerebral cortex.

scholarly journals Acquisition of skilled finger movements is accompanied by reorganization of the corticospinal system

2018 ◽  
Vol 119 (2) ◽  
pp. 573-584 ◽  
Author(s):  
Masato Hirano ◽  
Shinji Kubota ◽  
Shinichi Furuya ◽  
Yoshiki Koizume ◽  
Shinya Tanaka ◽  
...  
Keyword(s):  
Implicit Learning ◽  
Sequence Learning ◽  
Explicit Learning ◽  
Voluntary Movements ◽  
Finger Movements ◽  
Corticospinal System ◽  
Learning Conditions ◽  
Implicit And Explicit ◽  
Implicit And Explicit Learning ◽  
Coordination Patterns

Dexterous finger movements are often characterized by highly coordinated movements. Such coordination might be derived from reorganization of the corticospinal system. In this study, we investigated 1) the manner in which finger movement covariation patterns are acquired, by examining the effects of the implicit and explicit learning of a serial reaction time task (SRTT), and 2) how such changes in finger coordination are represented in the corticospinal system. The subjects learned a button press sequence in both implicit and explicit learning conditions. In the implicit conditions, they were naive about what they were learning, whereas in the explicit conditions the subjects consciously learned the order of the sequence elements. Principal component analysis decomposed both the voluntary movements produced during the SRTT and the passive movements evoked by transcranial magnetic stimulation (TMS) over the primary motor cortex into a set of five finger joint covariation patterns. The structures of the voluntary and passive TMS-evoked movement patterns were reorganized by implicit learning but not explicit learning. Furthermore, in the implicit learning conditions the finger covariation patterns derived from the TMS-evoked and voluntary movements spanned similar movement subspaces. These results provide the first evidence that skilled sequential finger movements are acquired differently through implicit and explicit learning, i.e., the changes in finger coordination patterns induced by implicit learning are accompanied by functional reorganization of the corticospinal system, whereas explicit learning results in faster recruitment of individual finger movements without causing any changes in finger coordination. NEW & NOTEWORTHY Skilled sequential multifinger movements are characterized as highly coordinated movement patterns. These finger coordination patterns are represented in the corticospinal system, yet it still remains unclear how these patterns are acquired through implicit and explicit motor sequence learning. A direct comparison of learning-related changes between actively generated finger movements and passively evoked finger movements by TMS provided evidence that finger coordination patterns represented in the corticospinal system are reorganized through implicit, but not explicit, sequence learning.

scholarly journals Implicit and Explicit Learning of Temporal Sequences Studied With the Process Dissociation Procedure

2008 ◽  
Vol 100 (2) ◽  
pp. 733-739 ◽  
Author(s):  
Anke Karabanov ◽  
Fredrik Ullén
Keyword(s):  
Implicit Learning ◽  
Serial Recall ◽  
Explicit Knowledge ◽  
Stimulus Presentation ◽  
Explicit Learning ◽  
Process Dissociation ◽  
Process Dissociation Procedure ◽  
Significant Difference ◽  
Implicit And Explicit ◽  
Temporal Sequences

We studied whether temporal sequences can be learned implicitly using a process dissociation procedure (PDP). Participants performed repeated serial recalls of sequential stimuli with a random ordinal structure and fixed temporal structure. Explicit knowledge was evaluated through verbal questions and PDP analysis of two generation tasks (inclusion and exclusion). Participants were divided into two groups: in the Ordinal group, stimulus presentation was visual and the participants were instructed to repeat the ordinal structure; in the Temporal+Ordinal group, stimulus presentation was audio-visual and the participants were instructed to repeat temporal and ordinal structure. We expected predominantly implicit learning in the Ordinal group and explicit learning in the Temporal+Ordinal group. This was supported by two findings. First, a significant difference between inclusion and exclusion performance was seen only in the Temporal+Ordinal group. Second, in both groups, a negative relation was found between the degree of improvement during serial recall and a measure of explicit knowledge in the generation tasks. This relation was independent of the final level of performance during serial recall. These findings suggest that distinct implicit and explicit systems may exist for learning of temporal sequences: implicit learning is gradual and gives rise to knowledge that is inaccessible to conscious control while the explicit system is fast and results in representations that can be used to control performance in inclusion and exclusion tasks.

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