Predicting human behavior in smart environments: theory and application to gaze prediction

Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure.While analytic methods have enabled more detailed morphometric characterization,findings are often equivocal. In this meta-analysis, we employed the harmonizedENIGMA shape analysis protocols to collaboratively investigate subcortical brainstructure shape differences between individuals with schizophrenia and healthy con-trol participants. The study analyzed data from 2,833 individuals with schizophreniaand 3,929 healthy control participants contributed by 21 worldwide research groupsparticipating in the ENIGMA Schizophrenia Working Group. Harmonized shape analy-sis protocols were applied to each site's data independently for bilateral hippocam-pus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained fromT1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens,and thalamus in individuals with schizophrenia compared with control participants,more-convex-than-concave shape differences in the putamen and pallidum, and bothconcave and convex shape differences in the caudate. Patterns of exaggerated asym-metry were observed across the hippocampus, amygdala, and thalamus in individualswith schizophrenia compared to control participants, while diminished asymmetryencompassed ventral striatum and ventral and dorsal thalamus. Our analyses also rev-ealed that higher chlorpromazine dose equivalents and increased positive symptomlevels were associated with patterns of contiguous convex shape differences acrossmultiple subcortical structures. Findings from our shape meta-analysis suggest thatcommon neurobiological mechanisms may contribute to gray matter reduction acrossmultiple subcortical regions, thus enhancing our understanding of the nature of net-work disorganization in schizophrenia

IE-Map: a novel in-vivo atlas and template of the human inner ear

Brain atlases and templates are core tools in scientific research with increasing importance also in clinical applications. Advances in neuroimaging now allowed us to expand the atlas domain to the vestibular and auditory organ, the inner ear. In this study, we present IE-Map, an in-vivo template and atlas of the human labyrinth derived from multi-modal high-resolution magnetic resonance imaging (MRI) data, in a fully non-invasive manner without any contrast agent or radiation. We reconstructed a common template from 126 inner ears (63 normal subjects) and annotated it with 94 established landmarks and semi-automatic segmentations of all relevant macroscopic vestibular and auditory substructures. We validated the atlas by comparing MRI templates to a novel CT/micro-CT atlas, which we reconstructed from 21 publicly available post-mortem images of the bony labyrinth. Templates in MRI and micro-CT have a high overlap, and several key anatomical measures of the bony labyrinth in IE-Map are in line with micro-CT literature of the inner ear. A quantitative substructural analysis based on the new template, revealed a correlation of labyrinth parameters with total intracranial volume. No effects of gender or laterality were found. We provide the validated templates, atlas segmentations, surface meshes and landmark annotations as open-access material, to provide neuroscience researchers and clinicians in neurology, neurosurgery, and otorhinolaryngology with a widely applicable tool for computational neuro-otology

Engagement with Digital Behaviour Change Interventions: Conceptualisation, Measurement and Promotion

Digital behaviour change interventions (DBCIs) can help people change various health behaviours; however, engagement is low on average and there is a positive association of engagement with intervention effectiveness. The extent to which this relationship is confounded or subject to reverse causality is unclear, and evidence-based models of how to promote engagement are lacking. Progress is hindered by the existence of multiple definitions and measures of engagement; this hampers attempts to aggregate data in meta-analyses. Using smartphone applications (apps) for smoking cessation and alcohol reduction as case studies, this thesis investigated how to conceptualise and measure engagement and identified factors that influence engagement with DBCIs in general, and with apps for smoking cessation and alcohol reduction in particular. Six studies using qualitative and quantitative methods were conducted. Study 1 was a systematic, interdisciplinary literature review, which synthesised existing conceptualisations and generated an integrative definition of engagement with behavioural and experiential dimensions, and a conceptual framework of factors that influence engagement with DBCIs. Studies 3 and 4 involved the development and evaluation of a self-report measure of the behavioural and experiential dimensions of engagement. Studies 2, 5 and 6 used mixed-methods to identify factors that influence engagement with apps for smoking cessation and alcohol reduction. Engagement with DBCIs can usefully be defined in both behavioural and experiential terms: the self-report measure demonstrated promising psychometric properties and was underpinned by two distinct factors, labelled ‘Experiential Engagement’ and ‘Behavioural Engagement’. Design features that support users’ motivation to change, foster their beliefs about the perceived usefulness and relevance of the technology, and spark their interest were found to be most important in the promotion of engagement with apps for smoking cessation and alcohol reduction. These findings can be used to inform the design of new, or modification of existing, apps for these behaviours

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Micro-, Meso- and Macro-Dynamics of the Brain

Neurosciences, Neurology, Psychiatr

Learning what to expect (in visual perception)

Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is 'Bayes-optimal' under some constraints. In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. A number of questions remain unsolved, however, for example: How fast do priors change over time? Are there limits in the complexity of the priors that can be learned? How do an individual’s priors compare to the true scene statistics? Can we unlearn priors that are thought to correspond to natural scene statistics? Where and what are the neural substrate of priors? Focusing on the perception of visual motion, we here review recent studies from our laboratories and others addressing these issues. We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors