Knowledge In Chunks Research Articles

Neural Basis of the Implicit Learning of Complex Artificial Grammar with Nonadjacent Dependencies.

The capacity for the implicit learning/processing of complex grammar with nonadjacent dependencies is an important feature of human language learning. In this fMRI study, using an implicit AGL paradigm, we explored the neural basis of the implicit learning of the nonadjacent dependency rule, disentangling from sequence-based chunk knowledge (i.e., local sequential regularities or substring) by focusing on the low chunk strength items (which were naturally less similar to training strings), based on tracking neural responses during training and test phases. After listening to and memorizing a series of strings of 10 syllables generated from nonadjacent artificial grammar in the training phase, participants implicitly acquired the knowledge of grammar and chunks. Regarding grammaticality, Broca's area was specifically related to low chunk strength grammatical strings relative to nongrammatical strings in the test phase. This region showed decreased activity with time in the training phase, and a lesser decrease in activity was associated with higher performance in grammar learning. Furthermore, Broca's area showed significantly higher strength of functional connectivity with the left superior temporal gyrus in the low chunk strength grammatical string compared with nongrammatical strings, and this functional connectivity increased with the training time. For the chunks, the performance of accurate discrimination of high chunk strength from low chunk strength nongrammatical strings was predicted by hippocampal activity in the training phase. Converging evidence from the training and test phases showed that Broca's area and its functional connectivity with the left superior temporal gyrus were engaged in the implicit learning/processing of the nonadjacent dependency rule, separating the effects of chunks.

Educational Content Development Process in "CleverUniversity": Our Dynamic Adaptive Hypermedia Environment

The implementation of a simple and flexible dynamic adaptive hypermedia environment meets several needs of teachers. Using a fitting system, teachers will be able to share their knowledge in chunks of content in order to create quality teaching resources.
 In this paper, we propose a development approach through our simple system dedicated to teachers and learners alike taking into account the different work done on the subject. Moreover, our approach is based on the various elements of our system such as the learner’s model, the domain model, the pedagogical model, the courses generator and the multimedia database.
 Our focus in this paper will be on the domain model given its importance in the development and adaptation of educational content as needed, and which consists of finding educational content related to a given field of knowledge to be adapted for a particular learner.

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro-rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

Language adaptation and learning: Getting explicit about implicit learning

AbstractLinguistic adaptation is a phenomenon where language representations change in response to linguistic input. Adaptation can occur on multiple linguistic levels such as phonology (tuning of phonotactic constraints), words (repetition priming), and syntax (structural priming). The persistent nature of these adaptations suggests that they may be a form of implicit learning and connectionist models have been developed which instantiate this hypothesis. Research on implicit learning, however, has also produced evidence that explicit chunk knowledge is involved in the performance of these tasks. In this review, we examine how these interacting implicit and explicit processes may change our understanding of language learning and processing.

Prodigious calculation performance and neural plasticity

Event Abstract Back to Event Prodigious calculation performance and neural plasticity Thorsten Fehr1, 2, 3*, P. Erhard1, 3 and M. Herrmann1, 3 1 Dept. of Neuropsychology/Behavioral Neurobiology, University of Bremen, Germany 2 Dept. of Neurologie II, Otto-von-Guericke-University , Germany 3 Center for Advanced Imaging – Bremen, University of Bremen, Germany Prodigies are individuals with exceptional mental abilities. We examined CP, a mental calculation prodigy, and a control group of 11 normal calculators for moderate mental arithmetic tasks. CP has also repeatedly been tested for exceptionally difficult exponentiations with a time gap of 20 months. We hypothesized that, if CP would activate similar brain regions as controls for both moderate and very difficult tasks, his special exceptional abilities could rather be explained by neuroplastic changes as a result of obsessive practice than by unusual neuro-cognitive mechanisms. For very difficult exponentiation tasks (first measurement), CP showed activation patterns in brain regions adjacent to those which were activated during moderate task calculation both by CP and control participants. We hypothesize that most importantly neo-cortical networks should be involved in highly over-learned and excessively trained appliance of constantly used algorithms and the recall of number-related chunk knowledge. For mental arithmetic task processing, these networks were discussed to include particularly neo-cortical parietal and frontal brain regions (e.g., Fehr et al., 2007, 2008). For complex exponentiations, CP showed remarkable changes in activation patterns between measurement sessions. These changes inter alia suggest a regional shift of brain activation foci from intra-parietal to adjacent regions, and from the posterior to the anterior part of the middle frontal gyrus (Fig. 1). Left perisylvian activation patterns were observed for the second measurement session only. Inconsistencies of results between sessions were discussed in relation to both potential neural plastic changes due to pronounced involvement of language-related arithmetic processing and strategy-related changes in recall schemes. We concluded that CP´s exceptional calculation performance is based on neuroplastic changes sub-stantially caused by years of daily hours of training com-bined with excellent working memory capabilities. Furthermore, results of the present study imply that the neural substrate, which is necessary to enhance specific skills dramatically by excessive mental training, is highly adaptive and potentially present in every individual. pic