Virtual Reality for Enhanced Ecological Validity and Experimental Control in the Clinical, Affective and Social Neurosciences

This article highlights the potential of virtual reality environments for enhanced ecological validity in the clinical, affective, and social neurosciences

High-dimensional Metaverse Platforms and the Virtually Extended Self

The study of cognition has traditionally used low-dimensional measures and stimulus presentations that emphasize laboratory control over high-dimensional (i.e., ecologically valid) tools that reflect the activities and interactions in everyday living. Although controlled experimental presentations in laboratories have enhanced our understanding of cognition for both healthy and clinical cohorts, high dimensionality may extend reality and cognition. High-dimensional Metaverse approaches use extended reality (XR) platforms with dynamic stimulus presentations that couple humans and simulation technologies to extend cognition. The plan for this paper is as follows: The “Extending from low to high-dimensional studies of cognition” section discusses current needs for high-dimensional stimulus presentations that reflect everyday cognitive activities. In the “Algorithmic devices and digital extension of cognition” section, technologies of the extended mind are introduced with the Metaverse as a candidate cognitive process for extension. Next, in the “A neurocognitive framework for understanding technologies of the extended mind” section, a framework and model are proposed for understanding the neural correlates of human technology couplings in terms of automatic algorithmic processes (limbic-ventral striatal loop); reflective cognition (prefrontal-dorsal striatal loop); and algorithmic processing (insular cortex). The algorithmic processes of human-technology interactions can, over time, become an automated and algorithmic coupling of brain and technology. The manuscript ends with a brief summary and discussion of the ways in which the Metaverse can be used for studying how persons respond to high-dimensional stimuli in simulations that approximate real-world activities and interactions

Cognitive tasks as measures of pig welfare: a systematic review

Cognitive approaches are increasingly used to assess animal welfare, but no systematic review has been conducted on pigs despite their cognitive capacities. Our aims were two-fold: first, to assess the popularity and heterogeneity of this approach by quantifying the different cognitive tasks used and welfare interventions studied. The second was to assess how often results from cognitive tasks supported treatment effects. The search yielded 36 studies that met our criteria. Eleven different cognitive tasks were applied (three most common: judgment bias, learned approach/aversion, and holeboard). Welfare interventions investigated were also diverse: the impact of 19 other different events/conditions/states were reported (most common: housing enrichment). We defined “supportive” as the observation of a significant difference between treatment groups consistent with an author’s expectation or hypothesis. Supportive findings were reported in 44% of papers. Interventions yielded no significant difference in 33% of studies. In another 21% of reports, outcomes were mixed and a single study refuted the author’s predictions. When considering specific cognitive tasks, authors’ predictions of welfare differences were supported most often when using learned approach/aversion (55% of these studies). Similar supportive results were observed less commonly (40% each) when using judgment bias and holeboard tests. Analysis of additional concomitant measures of welfare (health, physiology or behavior) revealed that behavioral measures were most frequently supportive of author’s expectations (41%) as well as often matching the actual outcomes of these cognitive tasks (47%). This systematic review highlights the growing popularity of cognitive tasks as measures of pig welfare. However, overall rates of supportive results, i.e., changes in performance on cognitive tasks due to welfare interventions, have been limited so far, even for the most employed task, judgment bias. The numerous different combinations of experimental paradigms and welfare interventions reported in the literature creates challenges for a critical meta-analysis of the field especially in evaluating the efficiency of specific cognitive tasks in assessing animal welfare. This work also highlights important knowledge gaps in the use of cognitive tasks that will require both further validation as well as novel innovation to ensure that their potential is fully realized in the measurement of pig welfare

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

The First 10 Years of NeuroIS: A Systematic Literature Review of NeuroIS Publications (2007 - 2017)

NeuroIS is an emerging and promising academic field that has attracted increasing attention. The year 2017 signifies the 10th year of existence of NeuroIS as a research field in information systems area. In this study, we conduct a systematic literature review of the NeuroIS academic research publications of last 10 years (2007-2017). As a result, we categorize the existent NeuroIS literature into 8 groups, explore the correlations among various NeuroIS concepts/ constructs, and demonstrate how the study enhances our understanding of the granulated inter-relationships between pairs of NeuroIS elements. The implications of the result to the NeuroIS research community are discussed

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Evaluating player task engagement and arousal using electroencephalography

This paper from the 6th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conference, AHFE 2015 conference proceedings coordinate task engagement data with arousal-valence data for application to expressive transformations to video game play in real time by tuning different performance parameters in an Engagement-Arousal rule system

Extended Reality for the Clinical, Affective, and Social Neurosciences

Article highlighting the promise of extended reality platforms for greater ecological validity in the clinical, affective, and social neurosciences. This article belongs to the Special Issue: A Decade of Brain Sciences

Complexity Synchronization in Emergent Intelligence

In this work, we use a simple multi-agent-based model (MABM), implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using modified diffusion entropy analysis (MDEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been experimentally shown to exist among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence (i.e., without macroscopic control and based on self-interest) between two groups of agents playing an anti-coordination game, thereby suggesting the potential for the same CS in real-world social phenomena and in human-machine interactions.Comment: 28 pages, 12 Figure