Cortical brain regions associated with color processing: an FMRI study

To clarify whether the neural pathways concerning color processing are the same for natural objects, for artifacts objects and for non-objects we examined brain responses measured with functional magnetic resonance imaging (FMRI) during a covert naming task including the factors color (color vs. black&white (B&W)) and stimulus type (natural vs. artifacts vs. non-objects). Our results indicate that the superior parietal lobule and precuneus (BA 7) bilaterally, the right hippocampus and the right fusifom gyrus (V4) make part of a network responsible for color processing both for natural objects and artifacts, but not for non-objects. When color objects (both natural and artifacts) were contrasted with color non-objects we observed activations in the right parahippocampal gyrus (BA 35/36), the superior parietal lobule (BA 7) bilaterally, the left inferior middle temporal region (BA 20/21) and the inferior and superior frontal regions (BA 10/11/47). These additional activations s uggest that colored objects recruit brain regions that are related to visual semantic information/retrieval and brain regions related to visuo-spatial processing. Overall, the results suggest that color information is an attribute that can improve object recognition (behavioral results) and activate a specific neural network related to visual semantic information that is more extensive than for B&W objects during object recognitio

Cortical Brain Regions Associated with Color Processing: An FMRi Study

To clarify whether the neural pathways concerning color processing are the same for natural objects, for artifacts objects and for non-objects we examined brain responses measured with functional magnetic resonance imaging (FMRI) during a covert naming task including the factors color (color vs. black&white (B&W)) and stimulus type (natural vs. artifacts vs. non-objects). Our results indicate that the superior parietal lobule and precuneus (BA 7) bilaterally, the right hippocampus and the right fusifom gyrus (V4) make part of a network responsible for color processing both for natural objects and artifacts, but not for non-objects. When color objects (both natural and artifacts) were contrasted with color non-objects we observed activations in the right parahippocampal gyrus (BA 35/36), the superior parietal lobule (BA 7) bilaterally, the left inferior middle temporal region (BA 20/21) and the inferior and superior frontal regions (BA 10/11/47). These additional activations suggest that colored objects recruit brain regions that are related to visual semantic information/retrieval and brain regions related to visuo-spatial processing. Overall, the results suggest that color information is an attribute that can improve object recognition (behavioral results) and activate a specific neural network related to visual semantic information that is more extensive than for B&W objects during object recognition

A aprendizagem implícita em crianças disléxicas

This study investigates the implicit sequence learning abilities of dyslexic children using an artificial grammar learning task and an extended exposure period. Twenty children with developmental dyslexia participated in the study and were matched with two control groups—one matched for age and the second for reading skills. During 3 days, all participants performed an acquisition task in which they were exposed to sequences of colored geometrical forms with an underlying grammatical structure. On the last day, after the acquisition task, participants were tested in a grammaticality classification task. Sequence learning was present in dyslexic children, as well as in both control groups, and no differences between groups were observed. These results suggest that implicit learning deficits cannot explain the characteristic reading difficulties of the dyslexics.info:eu-repo/semantics/publishedVersio

Implicit sequence learning is preserved in dyslexic children

This study investigates the implicit sequence learning abilities of dyslexic children using an artificial grammar learning task with an extended exposure period. Twenty children with developmental dyslexia participated in the study and were matched with two control groups-one matched for age and other for reading skills. During 3 days, all participants performed an acquisition task, where they were exposed to colored geometrical forms sequences with an underlying grammatical structure. On the last day, after the acquisition task, participants were tested in a grammaticality classification task. Implicit sequence learning was present in dyslexic children, as well as in both control groups, and no differences between groups were observed. These results suggest that implicit learning deficits per se cannot explain the characteristic reading difficulties of the dyslexics.info:eu-repo/semantics/publishedVersio

Acquisition and manipulation of mental structures : Investigations on artificial grammar learning and implicit sequence processing

This thesis introduces repetitive artificial grammar learning as a paradigm in the investigation of sequential implicit learning, in particular as a model for language acquisition and processing. Implicit learning of sequential structure captures an essential cognitive processing capacity of interest from a larger cognitive neuroscience perspective. We investigate in this thesis the underlying neural processing architecture for implicit learning/acquisition to acquire and process non-motor sequences, an implicit non-motor procedural learning ability present in the human cognitive system. In doing this, we validate and explore the repeated artificial grammar learning paradigm as a laboratory model to investigate the acquisition and processing of structural aspects of language, e.g. (morpho-) syntax processing, to further our understanding of the specific neural processing architecture subserving the syntax processing ability of the language faculty. A theoretical background on sequential procedural learning and formal grammars in cognitive processing is presented together with a general outline of the neuronal implementation of the cognitive functions involved. We suggest a lexical view on the processing and acquisition of artificial grammars to be beneficial to understand the nature and representation of the acquired knowledge. From this perspective we suggest that formal grammar acquisition and processing of the (regular) grammar type commonly studies in artificial grammar learning can be used as a model to investigate the neuronal infrastructure supporting language acquisition and processing, including to characterize the neuronal infrastructure supporting syntax processing and unification (cf. e.g., Hagoort, 2003; Jackendoff, 1997; Jackendoff, 2007; Kaan & Swaab, 2002; Shieber, 1986; Vosse & Kempen, 2000). In study 1 we describe the neuronal implementation using a setup based on the seminal study on implicit learning by Reber (1967), and report an overlap in the neural activation on artificial syntax violation and similar natural syntax violation. In study 2 we replicate this finding using a more elaborated model with repeated acquisition sessions to simulate a prolonged acquisition period, and using a sequential presentation forcing the cognitive processing into a sequential processing mode. A neuronal activation pattern is reported which suggests that frontostriatal circuits are at play during artificial grammar classification, specifically the left inferior frontal region Broddmann s area 44/45 and the head of the caudate nucleus. In study 3 we repeate the behaviour performance, introducing a preference classification instruction to further the cognitive system into an implicit learning mode, and report a clear and increasing preference for grammatical structure over repeated sessions. In study 4 we investigated the basal ganglia component in Huntington patients with specific caudate head lesions. While the patients did not show any deficit in their behaviour performance, structures in the basal ganglia including the caudate head showed abnormal activation patterns compared to their matched normal controls. Also, a cooperative activation between basal ganglia and hippocampus typically involved in declarative memory was found. We interpret this to reflect attempts of the cognitive system to compensate the damaged procedural processing with declarative knowledge processing. In summary, in the studies of this thesis we have gained an initial characterization of the neural infrastructure subserving artificial grammar processing. We have done so by characterising the end-state of the learning process as well as characterizing the learning curves reflecting the outcome of acquisition at different time points. This thesis reports findings supporting the view that the extended artificial grammar learning model is useful to capture structural aspects in language acquisition processing in the laboratory

Syntactic Classification of Acquired Structural Regularities

In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation

Artificial syntactic violations activate Broca’s region

In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar.We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this studywas to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the present study. Recent meta-analyses of functional neuroimaging studies indicate that syntactic processing is related to the left inferior frontal gyrus (Brodmann's areas 44 and 45) or Broca's region. In the present study, we observed that artificial grammaticality violations activated Broca's region in all participants. This observation lends some support to the suggestions that artificial grammar learning represents a model for investigating aspects of language learning in infants

Maintaining and Restoring Privacy through Communication in Different Types of Relationships

Recent FMRI studies indicate that language related brain regions are engaged in artificial grammar (AG) processing. In the present study we investigate the Reber grammar by means of formal analysis and network simulations. We outline a new method for describing the network dynamics and propose an approach to grammar extraction based on the state-space dynamics of the network. We conclude that statistical frequency-based and rule-based acquisition procedures can be viewed as complementary perspectives on grammar learning, and more generally, that classical cognitive models can be viewed as a special case of a dynamical systems perspective on information processing

Instruction effects in implicit artificial grammar learning: a preference for grammaticality

Human implicit learning can be investigated with implicit artificial grammar learning, a paradigm that has been proposed as a simple model for aspects of natural language acquisition. In the present study we compared the typical yes-no grammaticality classification, with yes-no preference classification. in the case of preference instruction no reference to the underlying generative mechanism (i.e., grammar) is needed and the subjects are therefore completely uninformed about an underlying structure in the acquisition material. in experiment 1, subjects engaged in a short-term memory task using only grammatical strings without performance feedback for 5 days. As a result of the 5 acquisition days, classification performance was independent of instruction type and both the preference and the grammaticality group acquired relevant knowledge of the underlying generative mechanism to a similar degree. Changing the grammatical stings to random strings in the acquisition material (experiment 2) resulted in classification being driven by local substring familiarity. Contrasting repeated vs. non-repeated preference classification (experiment 3) showed that the effect of local substring familiarity decreases with repeated classification. This was not the case for repeated grammaticality classifications. We conclude that classification performance is largely independent of instruction type and that forced-choice preference classification is equivalent to the typical grammaticality classification. (C) 2008 Elsevier B.V. All rights reserved.info:eu-repo/semantics/publishedVersio