Publications

Abstract

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modelled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding, and we show that it solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod neuron can be used efficiently in simulations of larger cortical circuits.

Abstract

Formal language hierarchy describes levels of increasing syntactic complexity (adjacent dependencies, nonadjacent nested, nonadjacent crossed) of which the transcription into a hierarchy of cognitive complexity remains under debate. The cognitive foundations of formal language hierarchy have been contradicted by two types of evidence: First, adjacent dependencies are not easier to learn compared to nonadjacent; second, crossed nonadjacent dependencies may be easier than nested. However, studies providing these findings may have engaged confounds: Repetition monitoring strategies may have accounted for participants’ high performance in nonadjacent dependencies, and linguistic experience may have accounted for the advantage of crossed dependencies. We conducted two artificial grammar learning experiments where we addressed these confounds by manipulating reliance on repetition monitoring and by testing participants inexperienced with crossed dependencies. Results showed relevant differences in learning adjacent versus nonadjacent dependencies and advantages of nested over crossed, suggesting that formal language hierarchy may indeed translate into a hierarchy of cognitive complexity

Abstract

This ERP study investigated the cognitive nature of the P1–N1 components during orthographic processing. We used an implicit reading task with various types of stimuli involving different amounts of sublexical or lexical orthographic processing (words, pseudohomophones, pseudowords, nonwords, and symbols), and tested average and dyslexic readers. An orthographic regularity effect (pseudowords– nonwords contrast) was observed in the average but not in the dyslexic group. This suggests an early sensitivity to the dependencies among letters in word-forms that reflect orthographic structure, while the dyslexic brain apparently fails to be appropriately sensitive to these complex features. Moreover, in the adults the N1-response may already reflect lexical access: (i) the N1 was sensitive to the familiar vs. less familiar orthographic sequence contrast; (ii) and early effects of the phonological form (words-pseudohomophones contrast) were also found. Finally, the later N320 component was attenuated in the dyslexics, suggesting suboptimal processing in later stages of phonological analysis.

Abstract

Evidence that rapid naming skill is associated with reading ability has become increasingly prevalent in recent years. However, there is considerable variation in the literature concerning the magnitude of this relationship. The objective of the present study was to provide a comprehensive analysis of the evidence on the relationship between rapid automatized naming (RAN) and reading performance. To this end, we conducted a meta-analysis of the correlational relationship between these 2 constructs to (a) determine the overall strength of the RAN–reading association and (b) identify variables that systematically moderate this relationship. A random-effects model analysis of data from 137 studies (857 effect sizes; 28,826 participants) indicated a moderate-to-strong relationship between RAN and reading performance (r = .43, I2 = 68.40). Further analyses revealed that RAN contributes to the 4 measures of reading (word reading, text reading, non-word reading, and reading comprehension), but higher coefficients emerged in favor of real word reading and text reading. RAN stimulus type and type of reading score were the factors with the greatest moderator effect on the magnitude of the RAN–reading relationship. The consistency of orthography and the subjects’ grade level were also found to impact this relationship, although the effect was contingent on reading outcome. It was less evident whether the subjects’ reading proficiency played a role in the relationship. Implications for future studies are discussed.

Abstract

In this study, event related potentials (ERPs) were used to investigate the extent to which dyslexics (aged 9–13 years) differ from normally reading controls in early ERPs, which reflect prelexical orthographic processing, and in late ERPs, which reflect implicit phonological processing. The participants performed an implicit reading task, which was manipulated in terms of letter-specific processing, orthographic familiarity, and phonological structure. Comparing consonant- and symbol sequences, the results showed significant differences in the P1 and N1 waveforms in the control but not in the dyslexic group. The reduced P1 and N1 effects in pre-adolescent children with dyslexia suggest a lack of visual specialization for letter-processing. The P1 and N1 components were not sensitive to the familiar vs. less familiar orthographic sequence contrast. The amplitude of the later N320 component was larger for phonologically legal (pseudowords) compared to illegal (consonant sequences) items in both controls and dyslexics. However, the topographic differences showed that the controls were more left-lateralized than the dyslexics. We suggest that the development of the mechanisms that support literacy skills in dyslexics is both delayed and follows a non-normal developmental path. This contributes to the hemispheric differences observed and might reflect a compensatory mechanism in dyslexics.

Abstract

In this study, we investigated the level of visual processing at which surface colour information improves the naming of colour diagnostic and noncolour diagnostic objects. Continuous electroencephalograms were recorded while participants performed a visual object naming task in which coloured and black-and-white versions of both types of objects were presented. The black-and-white and the colour presentations were compared in two groups of event-related potentials (ERPs): (1) The P1 and N1 components, indexing early visual processing; and (2) the N300 and N400 components, which index late visual processing. A colour effect was observed in the P1 and N1 components, for both colour and noncolour diagnostic objects. In addition, for colour diagnostic objects, a colour effect was observed in the N400 component. These results suggest that colour information is important for the naming of colour and noncolour diagnostic objects at different levels of visual processing. It thus appears that the visual system uses colour information, during naming of both object types, at early visual stages; however, for the colour diagnostic objects naming, colour information is also recruited during the late visual processing stages.

Abstract

The cognitive processes involved in object recognition remain a mystery to the cognitive
sciences. We know that the visual system recognizes objects via multiple features, including
shape, color, texture, and motion characteristics. However, the way these features are
combined to recognize objects is still an open question. The purpose of this contribution is to
review the research about the specific role of color information in object recognition. Given
that the human brain incorporates specialized mechanisms to handle color perception in the
visual environment, it is a fair question to ask what functional role color might play in
everyday vision.

Abstract

In this study, we used event-related potentials (ERPs) to evaluate the contribution of surface color and color knowledge information in object identification. We constructed two color-object verification tasks – a surface and a knowledge verification task – using high color diagnostic objects; both typical and atypical color versions of the same object were presented. Continuous electroencephalogram was recorded from 26 subjects. A cluster randomization procedure was used to explore the differences between typical and atypical color objects in each task. In the color knowledge task, we found two significant clusters that were consistent with the N350 and late positive complex (LPC) effects. Atypical color objects elicited more negative ERPs compared to typical color objects. The color effect found in the N350 time window suggests that surface color is an important cue that facilitates the selection of a stored object representation from long-term memory. Moreover, the observed LPC effect suggests that surface color activates associated semantic knowledge about the object, including color knowledge representations. We did not find any significant differences between typical and atypical color objects in the surface color verification task, which indicates that there is little contribution of color knowledge to resolve the surface color verification. Our main results suggest that surface color is an important visual cue that triggers color knowledge, thereby facilitating object identification.

Abstract

In speaking, semantic encoding is the conversion of a non-verbal mental representation (the reference) into a semantic structure suitable for expression (the sense). In this fMRI study on sentence production we investigate how the speaking brain accomplishes this transition from non-verbal to verbal representations. In an overt picture description task, we manipulated repetition of sense (the semantic structure of the sentence) and reference (the described situation) separately. By investigating brain areas showing response adaptation to repetition of each of these sentence properties, we disentangle the neuronal infrastructure for these two components of semantic encoding. We also performed a control experiment with the same stimuli and design but without any linguistic task to identify areas involved in perception of the stimuli per se. The bilateral inferior parietal lobes were selectively sensitive to repetition of reference, while left inferior frontal gyrus showed selective suppression to repetition of sense. Strikingly, a widespread network of areas associated with language processing (left middle frontal gyrus, bilateral superior parietal lobes and bilateral posterior temporal gyri) all showed repetition suppression to both sense and reference processing. These areas are probably involved in mapping reference onto sense, the crucial step in semantic encoding. These results enable us to track the transition from non-verbal to verbal representations in our brains.

Abstract

The human capacity to acquire language is an outstanding scientific challenge to understand. Somehow our language capacities arise from the way the human brain processes, develops and learns in interaction with its environment. To set the stage, we begin with a summary of what is known about the neural organization of language and what our artificial grammar learning (AGL) studies have revealed. We then review the Chomsky hierarchy in the context of the theory of computation and formal learning theory. Finally, we outline a neurobiological model of language acquisition and processing based on an adaptive, recurrent, spiking network architecture. This architecture implements an asynchronous, event-driven, parallel system for recursive processing. We conclude that the brain represents grammars (or more precisely, the parser/generator) in its connectivity, and its ability for syntax is based on neurobiological infrastructure for structured sequence processing. The acquisition of this ability is accounted for in an adaptive dynamical systems framework. Artificial language learning (ALL) paradigms might be used to study the acquisition process within such a framework, as well as the processing properties of the underlying neurobiological infrastructure. However, it is necessary to combine and constrain the interpretation of ALL results by theoretical models and empirical studies on natural language processing. Given that the faculty of language is captured by classical computational models to a significant extent, and that these can be embedded in dynamic network architectures, there is hope that significant progress can be made in understanding the neurobiology of the language faculty.

Abstract

In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial grammar learning paradigm in 32 healthy Dutch university students (natural language FMRI data were already acquired for these participants). We predicted that artificial syntax processing would engage the left inferior frontal region (BA 44/45) and that this activation would overlap with syntax-related variability observed in the natural language experiment. The main findings of this study show that the left inferior frontal region centered on BA 44/45 is active during artificial syntax processing of well-formed (grammatical) sequence independent of local subsequence familiarity. The same region is engaged to a greater extent when a syntactic violation is present and structural unification becomes difficult or impossible. The effects related to artificial syntax in the left inferior frontal region (BA 44/45) were essentially identical when we masked these with activity related to natural syntax in the same subjects. Finally, the medial temporal lobe was deactivated during this operation, consistent with the view that implicit processing does not rely on declarative memory mechanisms that engage the medial temporal lobe. In the context of recent FMRI findings, we raise the question whether Broca’s region (or subregions) is specifically related to syntactic movement operations or the processing of hierarchically nested non-adjacent dependencies in the discussion section. We conclude that this is not the case. Instead, we argue that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner, independent of whether there are any processing requirements related to syntactic movement or hierarchically nested structures. In addition, we argue that the Chomsky hierarchy is not directly relevant for neurobiological systems.

Abstract

In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include fMRI-based functional and/or effective connectivity, and EEG/MEG-based measures of neuronal synchronization. Here we use simultaneously recorded EEG and fMRI to assess whether fMRI-based BOLD connectivity and frequency-specific EEG power are related. Using data collected during resting state, we studied whether posterior EEG alpha power fluctuations are correlated with connectivity within the visual network and between visual cortex and the rest of the brain. The results show that when alpha power increases BOLD connectivity between primary visual cortex and occipital brain regions decreases and that the negative relation of the visual cortex with anterior/medial thalamus decreases and ventral-medial prefrontal cortex is reduced in strength. These effects were specific for the alpha band, and not observed in other frequency bands. Decreased connectivity within the visual system may indicate enhanced functional inhibition during higher alpha activity. This higher inhibition level also attenuates long-range intrinsic functional antagonism between visual cortex and other thalamic and cortical regions. Together, these results illustrate that power fluctuations in posterior alpha oscillations result in local and long range neural connectivity changes.

Abstract

During speaking and listening syntactic processing is a crucial step. It involves specifying syntactic relations between words in a sentence. If the production and comprehension modality share the neuronal substrate for syntactic processing then processing syntax in one modality should lead to adaptation effects in the other modality. In the present functional magnetic resonance imaging experiment, participants either overtly produced or heard descriptions of pictures. We looked for brain regions showing adaptation effects to the repetition of syntactic structures. In order to ensure that not just the same brain regions but also the same neuronal populations within these regions are involved in syntactic processing in speaking and listening, we compared syntactic adaptation effects within processing modalities (syntactic production-to-production and comprehension-to-comprehension priming) with syntactic adaptation effects between processing modalities (syntactic comprehension-to-production and production-to-comprehension priming). We found syntactic adaptation effects in left inferior frontal gyrus (Brodmann's area [BA] 45), left middle temporal gyrus (BA 21), and bilateral supplementary motor area (BA 6) which were equally strong within and between processing modalities. Thus, syntactic repetition facilitates syntactic processing in the brain within and across processing modalities to the same extent. We conclude that that the same neurobiological system seems to subserve syntactic processing in speaking and listening.

Abstract

Previous research showed an important association between reading and writing skills (literacy) and the phonological loop. However, the effects of literacy on other working memory components remain unclear. In this study, we investigated performance of illiterate subjects and their matched literate controls on verbal and nonverbal working memory tasks. Results revealed that the phonological loop is significantly influenced by literacy, while the visuospatial sketchpad appears to be less affected or not at all. Results also suggest that the central executive might be influenced by literacy, possibly as an expression of cognitive reserve.

Abstract

A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.

Abstract

Processing non-adjacent dependencies is considered to be one of the hallmarks of human language. Assuming that sequence-learning tasks provide a useful way to tap natural-language-processing mechanisms, we cross-modally combined serial reaction time and artificial-grammar learning paradigms to investigate the processing of multiple nested (A1A2A3B3B2B1) and crossed dependencies (A1A2A3B1B2B3), containing either three or two dependencies. Both reaction times and prediction errors highlighted problems with processing the middle dependency in nested structures (A1A2A3B3_B1), reminiscent of the ‘missing-verb effect’ observed in English and French, but not with crossed structures (A1A2A3B1_B3). Prior linguistic experience did not play a major role: native speakers of German and Dutch—which permit nested and crossed dependencies, respectively—showed a similar pattern of results for sequences with three dependencies. As for sequences with two dependencies, reaction times and prediction errors were similar for both nested and crossed dependencies. The results suggest that constraints on the processing of multiple non-adjacent dependencies are determined by the specific ordering of the non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved (i.e. two or three). Furthermore, these constraints may not be specific to language but instead derive from limitations on structured sequence learning.