1,193 research outputs found

    Neuroimaging investigations of cortical specialisation for different types of semantic knowledge

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    Embodied theories proposed that semantic knowledge is grounded in motor and perceptual experiences. This leads to two questions: (1) whether the neural underpinnings of perception are also necessary for semantic cognition; (2) how do biases towards different sensorimotor experiences cause brain regions to specialise for particular types of semantic information. This thesis tackles these questions in a series of neuroimaging and behavioural investigations. Regarding question 1, strong embodiment theory holds that semantic representation is reenactment of corresponding experiences, and brain regions for perception are necessary for comprehending modality-specific concepts. However, the weak embodiment view argues that reenactment may not be necessary, and areas near to perceiving regions may be sufficient to support semantic representation. In the particular case of motion concepts, lateral occipital temporal cortex (LOTC) has been long identified as an important area, but the roles of its different subregions are still uncertain. Chapter 3 examined how different parts of LOTC reacted to written descriptions of motion and static events, using multiple analysis methods. A series of anterior to posterior sub-regions were analyzed through univariate, multivariate pattern analysis (MVPA), and psychophysical interaction (PPI) analyses. MVPA revealed strongest decoding effects for motion vs. static events in the posterior parts of LOTC, including both visual motion area (V5) and posterior middle temporal gyrus (pMTG). In contrast, only the middle portion of LOTC showed increased activation for motion sentences in univariate analyses. PPI analyses showed increased functional connectivity between posterior LOTC and the multiple demand network for motion events. These findings suggest that posterior LOTC, which overlapped with the motion perception V5 region, is selectively involved in comprehending motion events, while the anterior part of LOTC contributes to general semantic processing. Regarding question 2, the hub-and-spoke theory suggests that anterior temporal lobe (ATL) acts as a hub, using inputs from modality-specific regions to construct multimodal concepts. However, some researchers propose temporal parietal cortex (TPC) as an additional hub, specialised in processing and integrating interaction and contextual information (e.g., for actions and locations). These hypotheses are summarized as the "dual-hub theory" and different aspects of this theory were investigated in in Chapters 4 and 5. Chapter 4 focuses on taxonomic and thematic relations. Taxonomic relations (or categorical relations) occur when two concepts belong to the same category (e.g., ‘dog’ and ‘wolf’ are both canines). In contrast, thematic relations (or associative relations) refer to situations that two concepts co-occur in events or scenes (e.g., ‘dog’ and ‘bone’), focusing on the interaction or association between concepts. Some studies have indicated ATL specialization for taxonomic relations and TPC specialization for thematic relations, but others have reported inconsistent or even converse results. Thus Chapter 4 first conducted an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies contrasting taxonomic and thematic relations. This found that thematic relations reliably engage action and location processing regions (left pMTG and SMG), while taxonomic relations only showed consistent effects in the right occipital lobe. A primed semantic judgement task was then used to test the dual-hub theory’s prediction that taxonomic relations are heavily reliant on colour and shape knowledge, while thematic relations rely on action and location knowledge. This behavioural experiment revealed that action or location priming facilitated thematic relation processing, but colour and shape did not lead to priming effects for taxonomic relations. This indicates that thematic relations rely more on action and location knowledge, which may explain why the preferentially engage TPC, whereas taxonomic relations are not specifically linked to shape and colour features. This may explain why they did not preferentially engage left ATL. Chapter 5 concentrates on event and object concepts. Previous studies suggest ATL specialization for coding similarity of objects’ semantics, and angular gyrus (AG) specialization for sentence and event structure representation. In addition, in neuroimaging studies, event semantics are usually investigated using complex temporally extended stimuli, unlike than the single-concept stimuli used to investigate object semantics. Thus chapter 5 used representational similarity analysis (RSA), univariate analysis, and PPI analysis to explore neural activation patterns for event and object concepts presented as static images. Bilateral AGs encoded semantic similarity for event concepts, with the left AG also coding object similarity. Bilateral ATLs encoded semantic similarity for object concepts but also for events. Left ATL exhibited stronger coding for events than objects. PPI analysis revealed stronger connections between left ATL and right pMTG, and between right AG and bilateral inferior temporal gyrus (ITG) and middle occipital gyrus, for event concepts compared to object concepts. Consistent with the meta-analysis in chapter 4, the results in chapter 5 support the idea of partial specialization in AG for event semantics but do not support ATL specialization for object semantics. In fact, both the meta-analysis and chapter 5 findings suggest greater ATL involvement in coding objects' associations compared to their similarity. To conclude, the thesis provides support for the idea that perceptual brain regions are engaged in conceptual processing, in the case of motion concepts. It also provides evidence for a specialised role for TPC regions in processing thematic relations (pMTG) and event concepts (AG). There was mixed evidence for specialisation within the ATLs and this remains an important target for future research

    Explanation Strategies for Image Classification in Humans vs. Current Explainable AI

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    Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users

    On the path integration system of insects: there and back again

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    Navigation is an essential capability of animate organisms and robots. Among animate organisms of particular interest are insects because they are capable of a variety of navigation competencies solving challenging problems with limited resources, thereby providing inspiration for robot navigation. Ants, bees and other insects are able to return to their nest using a navigation strategy known as path integration. During path integration, the animal maintains a running estimate of the distance and direction to its nest as it travels. This estimate, known as the `home vector', enables the animal to return to its nest. Path integration was the technique used by sea navigators to cross the open seas in the past. To perform path integration, both sailors and insects need access to two pieces of information, their direction and their speed of motion over time. Neurons encoding the heading and speed have been found to converge on a highly conserved region of the insect brain, the central complex. It is, therefore, believed that the central complex is key to the computations pertaining to path integration. However, several questions remain about the exact structure of the neuronal circuit that tracks the animal's heading, how it differs between insect species, and how the speed and direction are integrated into a home vector and maintained in memory. In this thesis, I have combined behavioural, anatomical, and physiological data with computational modelling and agent simulations to tackle these questions. Analysis of the internal compass circuit of two insect species with highly divergent ecologies, the fruit fly Drosophila melanogaster and the desert locust Schistocerca gregaria, revealed that despite 400 million years of evolutionary divergence, both species share a fundamentally common internal compass circuit that keeps track of the animal's heading. However, subtle differences in the neuronal morphologies result in distinct circuit dynamics adapted to the ecology of each species, thereby providing insights into how neural circuits evolved to accommodate species-specific behaviours. The fast-moving insects need to update their home vector memory continuously as they move, yet they can remember it for several hours. This conjunction of fast updating and long persistence of the home vector does not directly map to current short, mid, and long-term memory accounts. An extensive literature review revealed a lack of available memory models that could support the home vector memory requirements. A comparison of existing behavioural data with the homing behaviour of simulated robot agents illustrated that the prevalent hypothesis, which posits that the neural substrate of the path integration memory is a bump attractor network, is contradicted by behavioural evidence. An investigation of the type of memory utilised during path integration revealed that cold-induced anaesthesia disrupts the ability of ants to return to their nest, but it does not eliminate their ability to move in the correct homing direction. Using computational modelling and simulated agents, I argue that the best explanation for this phenomenon is not two separate memories differently affected by temperature but a shared memory that encodes both the direction and distance. The results presented in this thesis shed some more light on the labyrinth that researchers of animal navigation have been exploring in their attempts to unravel a few more rounds of Ariadne's thread back to its origin. The findings provide valuable insights into the path integration system of insects and inspiration for future memory research, advancing path integration techniques in robotics, and developing novel neuromorphic solutions to computational problems

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Developmental Bootstrapping of AIs

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    Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Building better than we know: The residential built environment, trust, social behaviour, biology, and health

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    Over the last decade there has been a renewed interest in identifying exactly how aspects of the residential built environment “get under the skin” and affect the physical health of not only of those who dwell within, but reside and commute among, disorderly and deteriorating neighbourhoods. This thesis is focused on better understanding how aspects of the social environment are crystallised in the residential built environment, and in particular the proximate environmental, behavioural, and perceptual mechanisms that account for how our interaction with the residential built environment modulates both our social behaviour and physical health. Building on Wilson and O’Brien’s evolutionary construct of Community Perception, Chapter 1 reviews the relevant literature from across the evolutionary human sciences, social psychology, applied social epidemiology, and social neuroscience to propose a biologically plausible pathway from the residential built environment to physical health. The empirical chapters (Chapters 2 to 4), then test this framework through both experimental and observational studies. Employing an eye tracking paradigm, in Chapter 2 we learn about the perceptual mechanisms that account for how residential maintenance has a significant impact on our assessment of the social environment. In Chapter 3 we find no significant difference in social behaviour, assayed through a behavioural economics paradigm, following affective priming via different levels of residential maintenance. A result which could be a consequence of methodological factors, or a finding due to the absence of task-specific relevance of the maintenance cue in a socially neutral experimental framing. In Chapter 4, through an analysis of the UK Household Longitudinal Study biomarker data asset, we find that residential maintenance is significantly associated with poor physical health. Chapter 5 then assesses the validity of the thesis’s proposed framework, the thesis’s contribution to the burgeoning field of inquiry, and considers future work towards generating impactful evidence-based public policy proposals

    Structural and functional largescale brain network dynamics: Examples from mental disorders

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    Hjernen er organisert i ulike funksjonelle og strukturelle nettverk. Til tross for omfattende forskning, er fremdeles ikke funksjonen og dynamikken i slike nettverk godt forstått. En økt innsikt kan være avgjørende for å forstå symptomer, og mekanismene som kontrollerer disse, hos pasienter med psykiske lidelser som schizofreni. Avhandlingen omfatter tre studier som hver adresserer ulike delmål i forskningen. Den første studien undersøker endringer i strukturelle nettverk hos en gruppe pasienter med schizofreni. Studien viser på gruppenivå at det er dels utbredte strukturelle forskjeller i hvit substans hos pasienter med schizofreni som opplever hørselshallusinasjoner sammenlignet med pasienter som ikke opplever disse hallusinasjonen. For å undersøke mulig samsvarende funksjonelle endringer har det vært behov for først å utvikle en ny tilnærming for å måle forskjeller i dynamikken mellom hjernens nettverk i hvile (DMN) og i aktiv oppgaveløsing av krevende kognitive oppgaver (EMN) hos en gruppe friske frivillige deltakere. I korte trekk, ble tre ulike visuelle, kognitive oppgaver presentert for deltakerne gjennom et fMRI blokk design. Resultatene i studien viste en antikorrelasjon i tid i områder som er involvert i henholdsvis hvile (DMN) og aktiv tilstand (EMN). For å gjøre undersøkelser hos pasienter med psykiske lidelser mindre tidkrevende, beskrives i avhandlingen også en studie som undersøker om hvileområder i hjernen (DMN) som er aktivert nettopp som del av en fMRI blokk design studier overlapper med en tilleggsundersøkelse med femminutters kontinuerlig hvile («resting state»). Sammenligningen er også interessant fra et mer basalforskningsperspektiv fordi en rask endring mellom aktiv tilstand og hvile kanskje bedre reflekterer en realistisk hviletilstand enn den kontinuerlige undersøkelsen som i dag representerer «gullstandarden» i denne type forskning. Resultatene fra studien viste stor grad av overlapp mellom aktiverte områder og at den foreslåtte tilnærmingen dermed kan ha et stort potensial i videre undersøkelser. I sum beskriver forskningen i avhandlingen muligheter for å undersøke strukturelle og funksjonelle nettverk hos pasienter med psykiske lidelser. Avhandlingen viser første resultater hos pasienter med schizofreni som strukturelle forskjeller i hvit substans mellom pasientgrupper avhengig om de opplever hørselshallusinasjoner eller ikke. Slike undersøkelser kan og bør komplementeres med undersøkelser av funksjonelle nettverk slik som foreslått i de andre studiene i avhandlingen, og i sum bidra til et godt rammeverk for videre undersøkelser hos pasienter.The human brain is organized in various networks both functionally and structurally. However, despite the extensive research on brain connectivity, which was made possible due to the development of in vivo brain imaging techniques, the neuroscientific field is still far from fully comprehending networks function and dynamics. Detailed knowledge about the relationship between various brain networks is essential for understanding the function of the healthy brain. However, many studies on mental disorders such as schizophrenia suggest that it might be caused by abnormal brain network functioning and structural aberrations. Therefore, the knowledge of the brain network's dynamics and structure might be critical for revealing the underpinnings of mental disorders such as schizophrenia. The presented thesis had three main goals, resulting in three structural and functional imaging studies. Firstly, the brain's structural connectivity affected by schizophrenia has been investigated to determine the nature and extent of its changes. Hence, Diffusion Tensor Imaging (DTI) and tract-based spatial statistics (TBSS) were employed to explore white matter differences between subtypes of schizophrenia patients compared to healthy controls. This study revealed widespread FA-value reduction in the hallucinating schizophrenia subjects' white matter compared to non-hallucinating ones. Since widespread aberrations of the white matter should affect the function of the large-scale brain networks, the second goal was to explore the two main functional brain networks, Default Mode Network (DMN) and Extrinsic Mode Network (EMN). This is because dysfunction of DMN and EMN networks has been previously suggested to be significant for the generation of symptoms of schizophrenia disorder, such as Auditory Verbal Hallucinations (AVH). Since the concept of EMN is relatively new and not yet deeply explored, and additionally protocol used in that study has not been previously utilized to study EMN and DMN, it was first necessary to test the design in a group of healthy participants. This study used the novel protocol based on the classic block design fMRI experiment with three different visual tasks: mental rotation, working memory, and mental arithmetic. The results of study II proved the existence of the EMN that is anti-correlated with the DMN and is domain-general. Lastly, the neuroimaging studies of the participants suffering from mental disorders such as schizophrenia require relatively short and effective examination protocols. Therefore, the last project investigated both similarities and differences in DMN activity between two experimental designs: block design and resign state. A classic block design experiment would be a good candidate for the investigation reflecting the fluctuating activity of the brain during typical daily activity. The results of Study III showed that the activity of the DMN was generally similar in the two experiments, though with some discrepancies. These differences were in the DMN architecture itself and concerning the relations of the DMN with other brain networks. These findings, in combination with the results of study number two suggest that the block design experiment could be the most effective for studying the function of the brain in schizophrenia. The studies incorporated in that thesis add to the current findings on the white matter alterations in schizophrenia disorder and contribute to a better understanding of the function and dynamics of the large-scale brain networks: EMN and DMN. Last but not least, the performed studies give a good background for future clinical studies on schizophrenia disorder.Doktorgradsavhandlin
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