Biology subjects

van der Meer, J. N.

Publications and source records attributed to van der Meer, J. N..

5 recordsLinked to original sources

bioRxiv2025

Distinct brain mechanisms support trust violations, belief integration, and bias in human-AI teams

This study provides an integrated electrophysiological and behavioral account of the neuro-cognitive markers underlying trust evolution during human interaction with artificial intelligence (AI). Trust is essential for effective collaboration and plays a key role in realizing the benefits of human-AI teaming in information-rich and decision-critical contexts. Using electroencephalography (EEG), we identified neural signatures of dynamic shifts in human trust during a face classification task involving an AI agent. Viewing the AIs classification elicited an N2-P3a-P3b event-related potential (ERP) complex that was sensitive to agreement with the participants own judgment and modulated by individual response biases. In addition, we observed a centro-parietal positivity (CPP) prior to participants responses, and found that ongoing EEG activity in this time window co-varied with subsequent changes in AI trust ratings. These neural effects showed substantial individual variability, indicating the use of diverse metacognitive strategies. Together, these findings suggest that trust in AI is shaped by internal confidence signals and evaluative processing of feedback.

neuroscience↗

bioRxiv2025

Brain-heart coupling shapes large scale brain dynamics

Functional brain networks reconfigure to support adaptive behaviour, balancing integration and segregation. These dynamics are influenced by neuromodulatory arousal, reflected in heart rate (HR) fluctuations. We leverage intracranial EEG (iEEG), functional MRI (fMRI), and HR recordings during emotional movie viewing to probe neurocardiac contributions to large-scale network dynamics. Network dynamics using high-frequency iEEG activity (60-140 Hz), an established marker of neuronal firing, reveal that greater network integration tracks elevated HR, while segregation aligns with lower HR. However, we observe an inverted relationship between HR and network dynamics in a separate fMRI dataset utilising the same movie, and replicate this in another independent fMRI dataset. Biophysical modelling of BOLD from iEEG reproduces the iEEG-HR findings, suggesting neurovascular transformation may obscure neural-cardiac links in fMRI. These results link large-scale network dynamics to physiological arousal through HR driven adrenergic-cholinergic neuromodulation and highlight the complex interplay between neural activity, autonomic regulation, and haemodynamics.

neuroscience↗

bioRxiv2020

Evidence for modulation of EEG microstate sequence by vigilance level

The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

neuroscience↗

bioRxiv2020

Intracranial-EEG evidence for medial temporal pole driving amygdala activity in multi-modal emotional stimuli

The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, functional mapping of the TP with functional magnetic resonance imaging is technically challenging and thus understanding of its interaction with other key emotional circuitry, such as the amygdala, remain elusive. We exploited the unique advantages of stereo-electroencephalography (SEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested the TP driving this effect in the theta-alpha frequency range and which was modulated by the emotional salience of the stimuli. Of note, cross-frequency analysis indicated the phase of theta-alpha oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible with three types of stimuli including naturalistic stimuli suggesting a hierarchical influence of the TP over the amygdala in non-threatening stimuli.

neuroscience↗

bioRxiv2019

The anterior insula channels prefrontal expectancy signals during affective processing

Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N1 = 37 & N2 = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.

neuroscience↗