A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

The effectiveness of virtual reality interventions for improvement of neurocognitive performance post-traumatic brain injury: a systematic review

Objective: To evaluate current evidence for the effectiveness of virtual reality (VR) interventions in improving neurocognitive performance in individuals who have sustained a traumatic brain injury (TBI). Methods: A systematic literature search across multiple databases (PubMed, EMBASE, Web of Science) for articles of relevance. Studies were evaluated according to study design, patient cohort, VR intervention, neurocognitive parameters assessed, and outcome. VR interventions were evaluated qualitatively with respect to methodology and extent of immersion and quantitatively with respect to intervention duration. Outcomes: Our search yielded 324 articles, of which only 13 studies including 132 patients with TBI met inclusion criteria. A wide range of VR interventions and cognitive outcome measures is reported. Cognitive measures included learning and memory, attention, executive function, community skills, problem solving, route learning, and attitudes about driving. Several studies (n = 10) reported statistically significant improvements in outcome, and 2 studies demonstrated successful translation to real-life performance. Conclusions: VR interventions hold significant potential for improving neurocognitive performance in patients with TBI. While there is some evidence for translation of gains to activities of daily living, further studies are required to confirm the validity of cognitive measures and reliable translation to real-life performance

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Introspective physicalism as an approach to the science of consciousness

Most ‘theories of consciousness’ are based on vague speculations about the properties of conscious experience. We aim to provide a more solid basis for a science of consciousness. We argue that a theory of consciousness should provide an account of the very processes that allow us to acquire and use information about our own mental states – the processes underlying introspection. This can be achieved through the construction of information processing models that can account for ‘Type-C’ processes. Type-C processes can be specified experimentally by identifying paradigms in which awareness of the stimulus is necessary for an intentional action. The Shallice (1988b) framework is put forward as providing an initial account of Type-C processes, which can relate perceptual consciousness to consciously performed actions. Further, we suggest that this framework may be refined through the investigation of the functions of prefrontal cortex. The formulation of our approach requires us to consider fundamental conceptual and methodological issues associated with consciousness. The most significant of these issues concerns the scientific use of introspective evidence. We outline and justify a conservative methodological approach to the use of introspective evidence, with attention to the difficulties historically associated with its use in psychology

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness

DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES

Abstract The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Article reviews opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments