Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Representational geometry: integrating cognition, computation, and the brain

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure

The left inferior frontal gyrus under focus: an fMRI study of the production of deixis via syntactic extraction and prosodic focus: An fMRI study of the production of deixis

International audienceThe left inferior frontal gyrus (LIFG, BA 44, 45, 47) has been associated with linguistic processing (from sentence- to syllable- parsing) as well as action analysis. We hypothesize that the function of the LIFG may be the monitoring of action, a function well adapted to agent deixis (verbal pointing at the agent of an action). The aim of this fMRI study was therefore to test the hypothesis that the LIFG is involved during the production of agent deixis. We performed an experiment whereby three kinds of deictic sentences were pronounced, involving prosodic focus, syntactic extraction and prosodic focus with syntactic extraction. A common pattern of activation was found for the three deixis conditions in the LIFG (BA 45 and/or 47), the left insula and the bilateral premotor (BA 6) cortex. Prosodic deixis additionally activated the left anterior cingulate gyrus (BA 24, 32), the left supramarginal gyrus (LSMG, BA 40) and Wernicke's area (BA 22). Our results suggest that the LIFG is involved during agent deixis, through either prosody or syntax, and that the LSMG and Wernicke's area are additionally required in prosody-driven deixis. Once grammaticalized, deixis would be handled solely by the LIFG, without the LSMG and Wernicke's area

Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition

The age of acquisition (AoA) of objects and their names is a powerful determinant of processing speed in adulthood, with early-acquired objects being recognized and named faster than late-acquired objects. Previous research using fMRI (Ellis et al., 2006. Traces of vocabulary acquisition in the brain: evidence from covert object naming. NeuroImage 33, 958–968) found that AoA modulated the strength of BOLD responses in both occipital and left anterior temporal cortex during object naming. We used magnetoencephalography (MEG) to explore in more detail the nature of the influence of AoA on activity in those two regions. Covert object naming recruited a network within the left hemisphere that is familiar from previous research, including visual, left occipito-temporal, anterior temporal and inferior frontal regions. Region of interest (ROI) analyses found that occipital cortex generated a rapid evoked response (~ 75–200 ms at 0–40 Hz) that peaked at 95 ms but was not modulated by AoA. That response was followed by a complex of later occipital responses that extended from ~ 300 to 850 ms and were stronger to early- than late-acquired items from ~ 325 to 675 ms at 10–20 Hz in the induced rather than the evoked component. Left anterior temporal cortex showed an evoked response that occurred significantly later than the first occipital response (~ 100–400 ms at 0–10 Hz with a peak at 191 ms) and was stronger to early- than late-acquired items from ~ 100 to 300 ms at 2–12 Hz. A later anterior temporal response from ~ 550 to 1050 ms at 5–20 Hz was not modulated by AoA. The results indicate that the initial analysis of object forms in visual cortex is not influenced by AoA. A fastforward sweep of activation from occipital and left anterior temporal cortex then results in stronger activation of semantic representations for early- than late-acquired objects. Top-down re-activation of occipital cortex by semantic representations is then greater for early than late acquired objects resulting in delayed modulation of the visual response

Towards a Neuronally Consistent Ontology for Robotic Agents

The Collaborative Research Center for Everyday Activity Science & Engineering (CRC EASE) aims to enable robots to perform environmental interaction tasks with close to human capacity. It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences. To properly describe these human experiences, the ontology will strongly benefit from incorporating characteristics of neuronal information processing which are not accessible from a behavioral perspective alone. We, therefore, propose the analysis of human neuroimaging data for evaluation and validation of concepts and events defined in the ontology model underlying most of the CRC projects. In an exploratory analysis, we employed an Independent Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data from participants who were presented with the same complex video stimuli of activities as robotic and human agents in different environments and contexts. We then correlated the activity patterns of brain networks represented by derived components with timings of annotated event categories as defined by the ontology model. The present results demonstrate a subset of common networks with stable correlations and specificity towards particular event classes and groups, associated with environmental and contextual factors. These neuronal characteristics will open up avenues for adapting the ontology model to be more consistent with human information processing.Comment: Preprint of paper accepted for the European Conference on Artificial Intelligence (ECAI) 2023 (minor typo corrections

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin