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

Language processing involves the ability to store and integrate pieces of
information in working memory over short periods of time. According to
the dominant view, information is maintained through sustained, elevated
neural activity. Other work has argued that short-term synaptic facilitation
can serve as a substrate of memory. Here, we propose an account where
memory is supported by intrinsic plasticity that downregulates neuronal
firing rates. Single neuron responses are dependent on experience and we
show through simulations that these adaptive changes in excitability pro-
vide memory on timescales ranging from milliseconds to seconds. On this
account, spiking activity writes information into coupled dynamic variables
that control adaptation and move at slower timescales than the membrane
potential. From these variables, information is continuously read back into
the active membrane state for processing. This neuronal memory mech-
anism does not rely on persistent activity, excitatory feedback, or synap-
tic plasticity for storage. Instead, information is maintained in adaptive
conductances that reduce firing rates and can be accessed directly with-
out cued retrieval. Memory span is systematically related to both the time
constant of adaptation and baseline levels of neuronal excitability. Inter-
ference effects within memory arise when adaptation is long-lasting. We
demonstrate that this mechanism is sensitive to context and serial order
which makes it suitable for temporal integration in sequence processing
within the language domain. We also show that it enables the binding of
linguistic features over time within dynamic memory registers. This work
provides a step towards a computational neurobiology of language.

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.