Publications

Abstract

According to the language marker hypothesis language has provided homo sapiens with a rich symbolic system that plays a central role in interpreting signals delivered by our sensory apparatus, in shaping action goals, and in creating a powerful tool for reasoning and inferencing. This view provides an important correction on embodied accounts of language that reduce language to action, perception, emotion and mental simulation. The presence of a language system has, however, also important consequences for perception, action, emotion, and memory. Language stamps signals from perception, action, and emotional systems with rich cognitive markers that transform the role of these signals in the overall cognitive architecture of the human mind. This view does not deny that language is implemented by means of universal principles of neural organization. However, language creates the possibility to generate rich internal models of the world that are shaped and made accessible by the characteristics of a language system. This makes us less dependent on direct action-perception couplings and might even sometimes go at the expense of the veridicality of perception. In cognitive (neuro)science the pendulum has swung from language as the key to understand the organization of the human mind to the perspective that it is a byproduct of perception and action. It is time that it partly swings back again.

Abstract

EEG and eye-tracking provide complementary information when investigating language comprehension. Evidence that speech processing may be facilitated by speech prediction comes from the observation that a listener's eye gaze moves towards a referent before it is mentioned if the remainder of the spoken sentence is predictable. However, changes to the trajectory of anticipatory fixations could result from a change in prediction or an attention shift. Conversely, N400 amplitudes and concurrent spectral power provide information about the ease of word processing the moment the word is perceived. In a proof-of-principle investigation, we combined EEG and eye-tracking to study linguistic prediction in naturalistic, virtual environments. We observed increased processing, reflected in theta band power, either during verb processing - when the verb was predictive of the noun - or during noun processing - when the verb was not predictive of the noun. Alpha power was higher in response to the predictive verb and unpredictable nouns. We replicated typical effects of noun congruence but not predictability on the N400 in response to the noun. Thus, the rich visual context that accompanied speech in virtual reality influenced language processing compared to previous reports, where the visual context may have facilitated processing of unpredictable nouns. Finally, anticipatory fixations were predictive of spectral power during noun processing and the length of time fixating the target could be predicted by spectral power at verb onset, conditional on the object having been fixated. Overall, we show that combining EEG and eye-tracking provides a promising new method to answer novel research questions about the prediction of upcoming linguistic input, for example, regarding the role of extralinguistic cues in prediction during language comprehension.

Abstract

During listening, brain activity tracks the rhythmic structures of speech signals. Here, we directly dissociated the contribution of neural envelope tracking in the processing of speech acoustic cues from that related to linguistic processing. We examined the neural changes associated with the comprehension of Noise-Vocoded (NV) speech using magnetoencephalography (MEG). Participants listened to NV sentences in a 3-phase training paradigm: (1) pre-training, where NV stimuli were barely comprehended, (2) training with exposure of the original clear version of speech stimulus, and (3) post-training, where the same stimuli gained intelligibility from the training phase. Using this paradigm, we tested if the neural responses of a speech signal was modulated by its intelligibility without any change in its acoustic structure. To test the influence of spectral degradation on neural envelope tracking independently of training, participants listened to two types of NV sentences (4-band and 2-band NV speech), but were only trained to understand 4-band NV speech. Significant changes in neural tracking were observed in the delta range in relation to the acoustic degradation of speech. However, we failed to find a direct effect of intelligibility on the neural tracking of speech envelope in both theta and delta ranges, in both auditory regions-of-interest and whole-brain sensor-space analyses. This suggests that acoustics greatly influence the neural tracking response to speech envelope, and that caution needs to be taken when choosing the control signals for speech-brain tracking analyses, considering that a slight change in acoustic parameters can have strong effects on the neural tracking response.

Abstract

Affective behaviors enable social robots to not only establish better connections with humans but also serve as a tool for the robots to express their internal states. It has been well established that emotions are important to signal understanding in Human-Robot Interaction (HRI). This work aims to harness the power of Large Language Models (LLM) and proposes an approach to control the affective behavior of robots. By interpreting emotion appraisal as an Emotion Recognition in Conversation (ERC) tasks, we used GPT-3.5 to predict the emotion of a robot’s turn in real-time, using the dialogue history of the ongoing conversation. The robot signaled the predicted emotion using facial expressions. The model was evaluated in a within-subjects user study (N = 47) where the model-driven emotion generation was compared against conditions where the robot did not display any emotions and where it displayed incongruent emotions. The participants interacted with the robot by playing a card sorting game that was specifically designed to evoke emotions. The results indicated that the emotions were reliably generated by the LLM and the participants were able to perceive the robot’s emotions. It was found that the robot expressing congruent model-driven facial emotion expressions were perceived to be significantly more human-like, emotionally appropriate, and elicit a more positive impression. Participants also scored significantly better in the card sorting game when the robot displayed congruent facial expressions. From a technical perspective, the study shows that LLMs can be used to control the affective behavior of robots reliably in real-time. Additionally, our results could be used in devising novel human-robot interactions, making robots more effective in roles where emotional interaction is important, such as therapy, companionship, or customer service.

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

We hypothesized that areas in the temporal lobe that have been implicated in the phonological processing of spoken words would also be activated during the generation and phonological processing of imagined speech. We tested this hypothesis using functional magnetic resonance imaging during a behaviorally controlled task of metrical stress evaluation. Subjects were presented with bisyllabic words and had to determine the alternation of strong and weak syllables. Thus, they were required to discriminate between weak-initial words and strong-initial words. In one condition, the stimuli were presented auditorily to the subjects (by headphones). In the other condition the stimuli were presented visually on a screen and subjects were asked to imagine hearing the word. Results showed activation of the supplementary motor area, inferior frontal gyrus (Broca's area) and insula in both conditions. In the superior temporal gyrus (STG) and in the superior temporal sulcus (STS) strong activation was observed during the auditory (perceptual) condition. However, a region located in the posterior part of the STS/STG also responded during the imagery condition. No activation of this same region of the STS was observed during a control condition which also involved processing of visually presented words, but which required a semantic decision from the subject. We suggest that processing of metrical stress, with or without auditory input, relies in part on cortical interface systems located in the posterior part of STS/STG. These results corroborate behavioral evidence regarding phonological loop involvement in auditory–verbal imagery.

Abstract

Oscillatory neuronal dynamics, observed in the human electroencephalogram (EEG) during language processing, have been related to the dynamic formation of functionally coherent networks that serve the role of integrating the different sources of information needed for understanding the linguistic input. To further explore the functional role of oscillatory synchrony during language processing, we quantified event-related EEG power changes induced by the presentation of open-class (OC) words and closed-class (CC) words in a wide range of frequencies (from 1 to 30 Hz), while subjects read a short story. Word presentation induced three oscillatory components: a theta power increase (4–7 Hz), an alpha power decrease (10–12 Hz), and a beta power decrease (16–21 Hz). Whereas the alpha and beta responses showed mainly quantitative differences between the two word classes, the theta responses showed qualitative differences between OC words and CC words: A theta power increase was found over left temporal areas for OC words, but not for CC words. The left temporal theta increase may index the activation of a network involved in retrieving the lexical–semantic properties of the OC items.

Abstract

In speaking and comprehending language, word information is retrieved from memory and combined into larger units (unification). Unification operations take place in parallel at the semantic, syntactic and phonological levels of processing. This article proposes a new framework that connects psycholinguistic models to a neurobiological account of language. According to this proposal the left inferior frontal gyrus (LIFG) plays an important role in unification. Research in other domains of cognition indicates that left prefrontal cortex has the necessary neurobiological characteristics for its involvement in the unification for language. I offer here a psycholinguistic perspective on the nature of language unification and the role of LIFG.

Abstract

Children begin to talk at about age one. The vocabulary they need to do so must be built on perceptual evidence and, indeed, infants begin to recognize spoken words long before they talk. Most of the utterances infants hear, however, are continuous, without pauses between words, so constructing a vocabulary requires them to decompose continuous speech in order to extract the individual words. Here, we present electrophysiological evidence that 10-month-old infants recognize two-syllable words they have previously heard only in isolation when these words are presented anew in continuous speech. Moreover, they only need roughly the first syllable of the word to begin doing this. Thus, prelinguistic infants command a highly efficient procedure for segmentation and recognition of spoken words in the absence of an existing vocabulary, allowing them to tackle effectively the problem of bootstrapping a lexicon out of the highly variable, continuous speech signals in their environment.

Abstract

The chronic fatigue syndrome (CFS) is a disabling disorder of unknown etiology. The symptomatology of CFS (central fatigue, impaired concentration, attention and memory) suggests that this disorder could be related to alterations at the level of the central nervous system. In this study, we have used an automated and unbiased morphometric technique to test whether CFS patients display structural cerebral abnormalities. We mapped structural cerebral morphology and volume in two cohorts of CFS patients (in total 28 patients) and healthy controls (in total 28 controls) from high-resolution structural magnetic resonance images, using voxel-based morphometry. Additionally, we recorded physical activity levels to explore the relation between severity of CFS symptoms and cerebral abnormalities. We observed significant reductions in global gray matter volume in both cohorts of CFS patients, as compared to matched control participants. Moreover, the decline in gray matter volume was linked to the reduction in physical activity, a core aspect of CFS. These findings suggest that the central nervous system plays a key role in the pathophysiology of CFS and point to a new objective and quantitative tool for clinical diagnosis of this disabling disorder.

Abstract

We have used implicit motor imagery to investigate the neural correlates of motor planning independently from actual movements. Subjects were presented with drawings of left or right hands and asked to judge the hand laterality, regardless of the stimulus rotation from its upright orientation. We paired this task with a visual imagery control task, in which subjects were presented with typographical characters and asked to report whether they saw a canonical letter or its mirror image, regardless of its rotation. We measured neurovascular activity with fast event-related fMRI, distinguishing responses parametrically related to motor imagery from responses evoked by visual imagery and other task-related phenomena. By quantifying behavioral and neurovascular correlates of imagery on a trial-by-trial basis, we could discriminate between stimulusrelated, mental rotation-related, and response-related neural activity. We found that specific portions of the posterior parietal and precentral cortex increased their activity as a function of mental rotation only during the motor imagery task. Within these regions, the parietal cortex was visually responsive, whereas the dorsal precentral cortex was not. Response- but not rotation-related activity was found around the left central sulcus (putative primary motor cortex) during both imagery tasks. Our study provides novel evidence on the topography and content of movement representations in the human brain. During intended action, the posterior parietal cortex combines somatosensory and visuomotor information, whereas the dorsal premotor cortex generates the actual motor plan, and the primary motor cortex deals with movement execution. We discuss the relevance of these results in the context of current models of action planning.

Abstract

The authors examined whether people can use their knowledge of the wider discourse rapidly enough to anticipate specific upcoming words as a sentence is unfolding. In an event-related brain potential (ERP) experiment, subjects heard Dutch stories that supported the prediction of a specific noun. To probe whether this noun was anticipated at a preceding indefinite article, stories were continued with a gender-marked adjective whose suffix mismatched the upcoming noun's syntactic gender. Prediction-inconsistent adjectives elicited a differential ERP effect, which disappeared in a no-discourse control experiment. Furthermore, in self-paced reading, prediction-inconsistent adjectives slowed readers down before the noun. These findings suggest that people can indeed predict upcoming words in fluent discourse and, moreover, that these predicted words can immediately begin to participate in incremental parsing operations.

Abstract

An event-related brain potential experiment was carried out to investigate on-line syntactic processing in patients with Broca’s aphasia. Subjects were visually presented with sentences that were either syntactically correct or contained violations of word-category. Three groups of subjects were tested: Broca patients (N=11), non-aphasic patients with a right hemisphere (RH) lesion (N=9), and healthy aged-matched controls (N=15). Both control groups appeared sensitive to the violations of word-category as shown by clear P600/SPS effects. The Broca patients displayed only a very reduced and delayed P600/SPS effect. The results are discussed in the context of a lexicalist parsing model. It is concluded that Broca patients are hindered to detect on-line violations of word-category, if word class information is incomplete or delayed available.