Publications

Abstract

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the “machine language” of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

Abstract

Purpose of review: The human brain supports acquisition mechanisms that can extract structural regularities implicitly from experience without the induction of an explicit model. Reber defined the process by which an individual comes to respond appropriately to the statistical structure of the input ensemble as implicit learning. He argued that the capacity to generalize to new input is based on the acquisition of abstract representations that reflect underlying structural regularities in the acquisition input. We focus this review of the implicit learning literature on studies published during 2004 and 2005. We will not review studies of repetition priming ('implicit memory'). Instead we focus on two commonly used experimental paradigms: the serial reaction time task and artificial grammar learning. Previous comprehensive reviews can be found in Seger's 1994 article and the Handbook of Implicit Learning. Recent findings: Emerging themes include the interaction between implicit and explicit processes, the role of the medial temporal lobe, developmental aspects of implicit learning, age-dependence, the role of sleep and consolidation. Summary: The attempts to characterize the interaction between implicit and explicit learning are promising although not well understood. The same can be said about the role of sleep and consolidation. Despite the fact that lesion studies have relatively consistently suggested that the medial temporal lobe memory system is not necessary for implicit learning, a number of functional magnetic resonance studies have reported medial temporal lobe activation in implicit learning. This issue merits further research. Finally, the clinical relevance of implicit learning remains to be determined.

Abstract

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.

Abstract

The posterior medial parietal cortex and left prefrontal cortex (PFC) have both been implicated in the recollection of past episodes. In a previous study, we found the posterior precuneus and left lateral inferior frontal cortex to be activated during episodic source memory retrieval. This study further examines the role of posterior precuneal and left prefrontal activation during episodic source memory retrieval using a similar source memory paradigm but with longer latency between encoding and retrieval. Our results suggest that both the precuneus and the left inferior PFC are important for regeneration of rich episodic contextual associations and that the precuneus activates in tandem with the left inferior PFC during correct source retrieval. Further, results suggest that the left ventro-lateral frontal region/ frontal operculum is involved in searching for task-relevant information (BA 47) and subsequent monitoring or scrutiny (BA 44/45) while regions in the dorsal inferior frontal cortex are important for information selection (BA 45/46).

Abstract

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

Abstract

We present two simple arguments for the potential relevance of a neurobiological analogue of the finite-state architecture. The first assumes the classical cognitive framework, is wellknown, and is based on the assumption that the brain is finite with respect to its memory organization. The second is formulated within a general dynamical systems framework and is based on the assumption that the brain sustains some level of noise and/or does not utilize infinite precision processing. We briefly review the classical cognitive framework based on Church–Turing computability and non-classical approaches based on analog processing in dynamical systems. We conclude that the dynamical neurobiological analogue of the finitestate architecture appears to be relevant, at least at an implementational level, for cognitive brain systems

Abstract

Learning a specific skill during childhood may partly determine the functional organization of the adult brain. This hypothesis led us to study oral language processing in illiterate subjects who, for social reasons, had never entered school and had no knowledge of reading or writing. In a brain activation study using PET and statistical parametric mapping, we compared word and pseudoword repetition in literate and illiterate subjects. Our study confirms behavioural evidence of different phonological processing in illiterate subjects. During repetition of real words, the two groups performed similarly and activated similar areas of the brain. In contrast, illiterate subjects had more difficulty repeating pseudowords correctly and did not activate the same neural structures as literates. These results are consistent with the hypothesis that learning the written form of language (orthography) interacts with the function of oral language. Our results indicate that learning to read and write during childhood influences the functional organization of the adult human brain.

Abstract

A key function of attention is to select an appropriate subset of available information by facilitation of attended processes and/or inhibition of irrelevant processing. Functional imaging studies, using positron emission tomography, have during different experimental tasks revealed decreased neuronal activity in areas that process input from unattended sensory modalities. It has been hypothesized that these decreases reflect a selective inhibitory modulation of nonrelevant cortical processing. In this study we addressed this question using a continuous arithmetical task with and without concomitant disturbing auditory input (task-irrelevant speech). During the arithmetical task, irrelevant speech did not affect task-performance but yielded decreased activity in the auditory and midcingulate cortices and increased activity in the left posterior parietal cortex. This pattern of modulation is consistent with a top down inhibitory modulation of a nonattended input to the auditory cortex and a coexisting, attention-based facilitation of taskrelevant processing in higher order cortices. These findings suggest that task-related decreases in cortical activity may be of functional importance in the understanding of both attentional mechanisms and taskrelated information processing.