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

A study is presented on the effects of discourse–semantic and lexical–syntactic information during spoken sentence processing. Event-related brain potentials (ERPs) were registered while subjects listened to discourses that ended in a sentence with a temporary syntactic ambiguity. The prior discourse–semantic information biased toward one analysis of the temporary ambiguity, whereas the lexical-syntactic information allowed only for the alternative analysis. The ERP results show that discourse–semantic information can momentarily take precedence over syntactic information, even if this violates grammatical gender agreement rules.

Abstract

Two experiments are reported that provide evidence on task-induced effects during
visual lexical processing in a primetarget semantic priming paradigm. The research focuses on target expectancy effects by manipulating the proportion of semantically related and unrelated word pairs. In Experiment 1, a lexical decision task was used and reaction times (RTs) and event-related brain potentials (ERPs) were obtained. In Experiment 2, subjects silently read the stimuli, without any additional task demands, and ERPs were recorded. The RT and ERP results of Experiment 1 demonstrate that an expectancy mechanism contributed to the priming effect when a high proportion of related word pairs was presented. The ERP results of Experiment 2 show that in the absence of extraneous task requirements, an expectancy mechanism is not active. However, a standard ERP semantic priming effect was obtained in Experiment 2. The combined results show that priming effects due to relatedness proportion are induced by task demands and are not a standard aspect of online lexical processing.

Abstract

Rede uitgesproken op 12 mei 2000 bij de aanvaarding van het ambt van hoogleraar in de neuropsychologie aan de Faculteit Sociale Wetenschappen KUN.

Abstract

In this study, event-related brain potential ffects of speech processing are obtained and compared to similar effects in sentence reading. In two experiments sentences were presented that contained three different types of grammatical violations. In one experiment sentences were presented word by word at a rate of four words per second. The grammatical violations elicited a Syntactic Positive Shift (P600/SPS), 500 ms after the onset of the word that rendered the sentence ungrammatical. The P600/SPS consisted of two phases, an early phase with a relatively equal anterior-posterior distribution and a later phase with a strong posterior distribution. We interpret the first phase as an indication of structural integration complexity, and the second phase as an indication of failing parsing operations and/or an attempt at reanalysis. In the second experiment the same syntactic violations were presented in sentences spoken at a normal rate and with normal intonation. These violations elicited a P600/SPS with the same onset as was observed for the reading of these sentences. In addition two of the three violations showed a preceding frontal negativity, most clearly over the left hemisphere.

Abstract

In this study, event-related brain potential effects of speech processing are obtained and compared to similar effects insentence reading. In two experiments spoken sentences were presented with semantic violations in sentence-signal or mid-sentence positions. For these violations N400 effects were obtained that were very similar to N400 effects obtained in reading. However, the N400 effects in speech were preceded by an earlier negativity (N250). This negativity is not commonly observed with written input. The early effect is explained as a manifestation of a mismatch between the word forms expected on the basis of the context, and the actual cohort of activated word candidates that is generated on the basis of the speech signal.

Abstract

Based on the results of an event-related brain potentials (ERP) experiment (van Berkum, Brown, & Hagoort. 1999a, b), we have recently argued that discourse-level referential context can be taken into account extremely rapidly by the parser. Moreover, our ERP results indicated that local grammatical gender information, although available within a few hundred milliseconds from word onset, is not always used quickly enough to prevent the parser from considering a discourse-supported, but agreement-violating, syntactic analysis. In a comment on our work, Brysbaert and Mitchell (2000) have raised concerns about the methodology of our ERP experiment and have challenged our interpretation of the results. In this reply, we argue that these concerns are unwarranted and, that, in contrast to our own interpretation, the alternative explanations provided by Brysbaert and Mitchell do not account for the full pattern of ERP results.