Publications

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  • Zhu, Z., Bastiaansen, M. C. M., Hakun, J. G., Petersson, K. M., Wang, S., & Hagoort, P. (2019). Semantic unification modulates N400 and BOLD signal change in the brain: A simultaneous EEG-fMRI study. Journal of Neurolinguistics, 52: 100855. doi:10.1016/j.jneuroling.2019.100855.

    Abstract

    Semantic unification during sentence comprehension has been associated with amplitude change of the N400 in event-related potential (ERP) studies, and activation in the left inferior frontal gyrus (IFG) in functional magnetic resonance imaging (fMRI) studies. However, the specificity of this activation to semantic unification remains unknown. To more closely examine the brain processes involved in semantic unification, we employed simultaneous EEG-fMRI to time-lock the semantic unification related N400 change, and integrated trial-by-trial variation in both N400 and BOLD change beyond the condition-level BOLD change difference measured in traditional fMRI analyses. Participants read sentences in which semantic unification load was parametrically manipulated by varying cloze probability. Separately, ERP and fMRI results replicated previous findings, in that semantic unification load parametrically modulated the amplitude of N400 and cortical activation. Integrated EEG-fMRI analyses revealed a different pattern in which functional activity in the left IFG and bilateral supramarginal gyrus (SMG) was associated with N400 amplitude, with the left IFG activation and bilateral SMG activation being selective to the condition-level and trial-level of semantic unification load, respectively. By employing the EEG-fMRI integrated analyses, this study among the first sheds light on how to integrate trial-level variation in language comprehension.
  • Araújo, S., Bramão, I., Faísca, L., Petersson, K. M., & Reis, A. (2012). Electrophysiological correlates of impaired reading in dyslexic pre-adolescent children. Brain and Cognition, 79, 79-88. doi:10.1016/j.bandc.2012.02.010.

    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.
  • Bramão, I., Francisco, A., Inácio, F., Faísca, L., Reis, A., & Petersson, K. M. (2012). Electrophysiological evidence for colour effects on the naming of colour diagnostic and noncolour diagnostic objects. Visual Cognition, 20, 1164-1185. doi:10.1080/13506285.2012.739215.

    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.
  • Bramão, I., Faísca, L., Petersson, K. M., & Reis, A. (2012). The contribution of color to object recognition. In I. Kypraios (Ed.), Advances in object recognition systems (pp. 73-88). Rijeka, Croatia: InTech. Retrieved from http://www.intechopen.com/books/advances-in-object-recognition-systems/the-contribution-of-color-in-object-recognition.

    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.
  • Bramão, I., Faísca, L., Forkstam, C., Inácio, F., Araújo, S., Petersson, K. M., & Reis, A. (2012). The interaction between surface color and color knowledge: Behavioral and electrophysiological evidence. Brain and Cognition, 78, 28-37. doi:10.1016/j.bandc.2011.10.004.

    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.
  • Menenti, L., Petersson, K. M., & Hagoort, P. (2012). From reference to sense: How the brain encodes meaning for speaking. Frontiers in Psychology, 2, 384. doi:10.3389/fpsyg.2011.00384.

    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.
  • Petersson, K. M., & Hagoort, P. (2012). The neurobiology of syntax: Beyond string-sets [Review article]. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 1971-1883. doi:10.1098/rstb.2012.0101.

    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.
  • Petersson, K. M., Folia, V., & Hagoort, P. (2012). What artificial grammar learning reveals about the neurobiology of syntax. Brain and Language, 120, 83-95. doi:10.1016/j.bandl.2010.08.003.

    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.
  • Scheeringa, R., Petersson, K. M., Kleinschmidt, A., Jensen, O., & Bastiaansen, M. C. M. (2012). EEG alpha power modulation of fMRI resting state connectivity. Brain Connectivity, 2, 254-264. doi:10.1089/brain.2012.0088.

    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.
  • Segaert, K., Menenti, L., Weber, K., Petersson, K. M., & Hagoort, P. (2012). Shared syntax in language production and language comprehension — An fMRI study. Cerebral Cortex, 22, 1662-1670. doi:10.1093/cercor/bhr249.

    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.
  • Silva, C., Faísca, L., Ingvar, M., Petersson, K. M., & Reis, A. (2012). Literacy: Exploring working memory systems. Journal of Clinical and Experimental Neuropsychology, 34(4), 369-377. doi:10.1080/13803395.2011.645017.

    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.

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  • Udden, J., Ingvar, M., Hagoort, P., & Petersson, K. M. (2012). Implicit acquisition of grammars with crossed and nested non-adjacent dependencies: Investigating the push-down stack model. Cognitive Science, 36, 1078-1101. doi:10.1111/j.1551-6709.2012.01235.x.

    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.
  • De Vries, M. H., Petersson, K. M., Geukes, S., Zwitserlood, P., & Christiansen, M. H. (2012). Processing multiple non-adjacent dependencies: Evidence from sequence learning. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 2065-2076. doi:10.1098/rstb.2011.0414.

    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.
  • Carlsson, K., Petrovic, P., Skare, S., Petersson, K. M., & Ingvar, M. (2000). Tickling expectations: Neural processing in anticipation of a sensory stimulus. Journal of Cognitive Neuroscience, 12(4), 691-703. doi:10.1162/089892900562318.
  • Ingvar, M., & Petersson, K. M. (2000). Functional maps and brain networks. In A. W. Toga (Ed.), Brain mapping: The systems (pp. 111-140). San Diego: Academic Press.
  • Lansner, A., Sandberg, A., Petersson, K. M., & Ingvar, M. (2000). On forgetful attractor network memories. In H. Malmgren, M. Borga, & L. Niklasson (Eds.), Artificial neural networks in medicine and biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13-16 May 2000 (pp. 54-62). Heidelberg: Springer Verlag.

    Abstract

    A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.
  • Petersson, K. M., Reis, A., Askelöf, S., Castro-Caldas, A., & Ingvar, M. (2000). Language processing modulated by literacy: A network analysis of verbal repetition in literate and illiterate subjects. Journal of Cognitive Neuroscience, 12(3), 364-382. doi:10.1162/089892900562147.
  • Petrovic, P., Petersson, K. M., Ghatan, P., Stone-Elander, S., & Ingvar, M. (2000). Pain related cerebral activation is altered by a distracting cognitive task. Pain, 85, 19-30.

    Abstract

    It has previously been suggested that the activity in sensory regions of the brain can be modulated by attentional mechanisms during parallel cognitive processing. To investigate whether such attention-related modulations are present in the processing of pain, the regional cerebral blood ¯ow was measured using [15O]butanol and positron emission tomography in conditions involving both pain and parallel cognitive demands. The painful stimulus consisted of the standard cold pressor test and the cognitive task was a computerised perceptual maze test. The activations during the maze test reproduced findings in previous studies of the same cognitive task. The cold pressor test evoked signi®cant activity in the contralateral S1, and bilaterally in the somatosensory association areas (including S2), the ACC and the mid-insula. The activity in the somatosensory association areas and periaqueductal gray/midbrain were significantly modified, i.e. relatively decreased, when the subjects also were performing the maze task. The altered activity was accompanied with significantly lower ratings of pain during the cognitive task. In contrast, lateral orbitofrontal regions showed a relative increase of activity during pain combined with the maze task as compared to only pain, which suggests the possibility of the involvement of frontal cortex in modulation of regions processing pain
  • Sandberg, A., Lansner, A., Petersson, K. M., & Ekeberg, Ö. (2000). A palimpsest memory based on an incremental Bayesian learning rule. Neurocomputing, 32(33), 987-994. doi:10.1016/S0925-2312(00)00270-8.

    Abstract

    Capacity limited memory systems need to gradually forget old information in order to avoid catastrophic forgetting where all stored information is lost. This can be achieved by allowing new information to overwrite old, as in the so-called palimpsest memory. This paper describes a new such learning rule employed in an attractor neural network. The network does not exhibit catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits recency e!ects in retrieval
  • Sandberg, A., Lansner, A., Petersson, K. M., & Ekeberg, Ö. (2000). A palimpsest memory based on an incremental Bayesian learning rule. In J. M. Bower (Ed.), Computational Neuroscience: Trends in Research 2000 (pp. 987-994). Amsterdam: Elsevier.

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