Faculty Bibliography

In our dynamic environments, predictive processing is vital for auditory perception and its associated behaviors. Predictive coding formalizes inferential processes by implementing them as information exchange across cortical layers and areas. With laminar-specific blood oxygenation level-dependent imaging we measured responses to a cascading oddball paradigm, to ground predictive auditory processes on the mesoscopic human cortical architecture. Using a modeling approach, we derive putative changes in neural dynamics while accounting for draining effects. We show that the violations of predictions are potentially hierarchically organized and associated with responses in superficial layers of the planum polare and middle layers of the lateral temporal cortex. Moreover, we relate the updating of the brain's internal model to changes in deep layers. Our results support the role of temporal cortical architecture in the implementation of predictive coding and highlight the ability of laminar fMRI to investigate mesoscopic processes in a large extent of temporal areas.

Mobile eye-tracking has revolutionized the study of human behavior and cognition by enabling researchers to record eye movements in the real world. However, the dynamic and multimodal nature of mobile eye-tracking data also introduces significant analytical challenges, including the alignment, integration, and interpretation of complex data. To fill these gaps, we present PyNeon, a versatile, community-oriented Python package designed to streamline the analysis of mobile eye-tracking, motion, and video data from the Neon eye-tracking system (Pupil Labs GmbH). We describe how PyNeon provides accessible APIs for reading, preprocessing, epoching, and exporting Neon data. Furthermore, it supports advanced video processing such as mapping between eye movement data and real-world coordinates and dynamic scanpath estimation. PyNeon presents an open-source and extendable framework for analyzing mobile eye-tracking data and forms the foundation for higher-level applications.

We present a functional magnetic resonance (fMRI) dataset collected as part of an adversarial collaboration aimed at arbitrating between the Global Neuronal Workspace theory (GNWT) and the Integrated Information Theory (IIT) of consciousness. Participants (N = 118) were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) with three orientations (front, left, right view), and three durations (0.5, 1.0, 1.5 seconds). Participants were asked to identify infrequent targets that changed in each block, thereby rendering two categories task-relevant and two task-irrelevant. The simplicity of the experimental design and of the task given to the participants ensures that these data are broadly reusable. Besides testing predictions from other theories of consciousness, these data can be used to examine various aspects of visual processing. The anonymized data were converted to Brain Imaging Data Structure (BIDS), and can be easily accessed through a web platform or an API. The dataset contains quality reports, demographics, behavioral performance, and eye-tracking data. We also provide code for preprocessing and analyzing the data.

Metaphors have long played multiple roles in conceptualizing the mind and brain, guiding the development and refinement of theoretical models and empirical questions. Early analogies (comparing the brain to hydraulic systems, telephone exchanges, factories, or libraries) offered shortcuts to understanding aspects of cognition, memory, and brain dynamics. From theoretical frameworks, metaphors like the mind as a computer evolved into central scientific metaphors, shaping core theoretical frameworks, inspiring predictions, and informing research methodologies. As such, metaphors play a key role in guiding scientific inquiries. Building on that premise, we propose music as a scientific metaphor for understanding multiple brain dynamics and cognitive functions. Unlike metaphors focusing on static components or linear flows, music emphasizes continuous adaptation, context-dependence, and cultural embedding, and presents a model for simultaneous engagement with multiple layers of meaning. Integrating analytical techniques from music theory and experiential insights from performance and listening, we can deepen our understanding of mind and brain dynamics and provide fresh epistemological pathways for interdisciplinary research. Music has a hierarchical structure, temporal complexity, and capacity to integrate multiple processes that parallel key features of the brain's architecture and cognitive functions. Drawing from research on neural oscillations, plasticity, predictive coding, and emotional processing, we illustrate how the musical paradigm can capture the rich entanglement of mind and brain, from large-scale brain dynamics and developmental trajectories to the emergence of consciousness and the interplay of affective states.

Understanding consciousness remains a significant challenge in science. What distinguishes conscious beings from unconscious systems, such as organoids, artificial intelligence or other non-sentient entities? Research on consciousness often focuses on identifying brain activity associated with conscious and non-conscious states, primarily in neurotypical human adults. However, this approach is limited in scope when applied to entities with developmental or evolutionary trajectories different from our own. How do we investigate consciousness in infants, whose brains are still maturing or in non-human animals, shaped by diverse ecological and evolutionary pressures? This opinion piece encourages consciousness studies to adopt a neuroethological perspective, drawing on Tinbergen's framework for studying behaviour. By examining the (1) mechanisms, (2) development, (3) adaptive functions and (4) evolutionary origins of consciousness, we can move beyond a human-centric focus to explore its diversity across life forms. Most investigators now accept that consciousness is not confined to humans alone but that some other animals have it, and it is a continuum shaped by evolutionary pressures. By adopting this broader approach, consciousness studies can better investigate and understand consciousness in its various forms and contexts, with significant scientific, ethical and societal implications.This article is part of the theme issue 'Evolutionary functions of consciousness'.

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain's linguistic capabilities. ECoG offers high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research questions, from auditory perception to narrative integration. In addition to the neural data, we extracted linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.

We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.

Different theories explain how subjective experience arises from brain activity^1,2. These theories have independently accrued evidence, but have not been directly compared³. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)^4,5 and global neuronal workspace theory (GNWT)^6-10 via a theory-neutral consortium^11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof¹². Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here^14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.