Institutionen för klinisk neurovetenskap / Department of Clinical Neuroscience
Abstract
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
