Are humans still necessary?

Our long accepted and historically-persistent human narrative almost exclusively places us at the motivational centre of events. The wellspring of this anthropocentric fable arises from the unitary and bounded nature of personal consciousness. Such immediate conscious experience frames the heroic vision we have told to, and subsequently sold to ourselves. But need this centrality necessarily be a given? The following work challenges this, oft unquestioned, foundational assumption, especially in light of developments in automated, autonomous, and artificially-intelligent systems. For, in these latter technologies, human contributions are becoming ever more peripheral and arguably unnecessary. The removal of the human operator from the inner loops of momentary control has progressed to now an ever more remote function as some form of supervisory monitor. The natural progression of that line of evolution is the eventual excision of humans from access to any form of control loop at all. This may even include system maintenance and then, prospectively, even initial design. The present argument features a 'unit of analysis' provocation which explores the proposition that socially, and even ergonomically, the human individual no longer occupies priority or any degree of pre-eminent centrality. Rather, we are witnessing a transitional phase of development in which socio-technical collectives are evolving as the principle sources of what, may well be profoundly unhuman motivation. These developing proclivities occupy our landscape of technological innovations that daily act to magnify, rather than diminish, such progressive inhumanities. Where this leaves a science focused on work as a human-centred enterprise serves to occupy the culminating consideration of the present discourse.

Understanding the changes in discretionary, as compared to obligatory, roles of human users and operators in systems is central to Ergonomic practice. Envisioning this path of potential progress, and then witnessing and impacting its actual realisation, permits practitioners to optimise their professional and personal strategies as they deal with this next critical step in the relationship between humans and technology.

Isometric Arm Forces Exerted by Females at Different Levels of Physical Comfort and Their EEG Signatures.

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users' force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

Simplicity and predictability: a phenomenological study of psychological flow in transactional workers.

Psychological flow is a positive experience achieved through a near-balance of task challenge and skill capability, creating a merging of awareness and action and leading to an intrinsically rewarding feeling. Flow has typically been documented in persons who participate in work and leisure activities where they can exercise a large degree of creativity and agency over their actions in pursuit of their goals. The objective of the present study is to explore the lived experiences of flow in workers in roles where creativity and agency are typically not expected. An interpretative phenomenological analysis approach was employed to attain this objective. Semi-structured interviews were conducted with 17 adults whose role is to perform transactional work, which by its nature affords less opportunity for creative execution. Common themes about participants' flow experiences are documented. Two broad types of flow are described and a connection is made that the present study's participants achieve one of those flow types while working. Participants' feelings, preferences, and actions are mapped to the nine conventional dimensions of flow. Specific non-task work system factors are discussed relative to their influence on participants' attainment of flow. Limitations of the present study and recommended future research are discussed.

How and why humans trust: A meta-analysis and elaborated model.

Trust exerts an impact on essentially all forms of social relationships. It affects individuals in deciding whether and how they will or will not interact with other people. Equally, trust also influences the stance of entire nations in their mutual dealings. In consequence, understanding the factors that influence the decision to trust, or not to trust, is crucial to the full spectrum of social dealings. Here, we report the most comprehensive extant meta-analysis of experimental findings relating to such human-to-human trust. Our analysis provides a quantitative evaluation of the factors that influence interpersonal trust, the initial propensity to trust, as well as an assessment of the general trusting of others. Over 2,000 relevant studies were initially identified for potential inclusion in the meta-analysis. Of these, (n = 338) passed all screening criteria and provided therefrom a total of (n = 2,185) effect sizes for analysis. The identified dependent variables were trustworthiness, propensity to trust, general trust, and the trust that supervisors and subordinates express in each other. Correlational results demonstrated that a large range of trustor, trustee, and shared, contextual factors impact each of trustworthiness, the propensity to trust, and trust within working relationships. The emphasis in the present work on contextual factors being one of several trust dimensions herein originated. Experimental results established that the reputation of the trustee and the shared closeness of trustor and trustee were the most predictive factors of trustworthiness outcome. From these collective findings, we propose an elaborated, overarching descriptive theory of trust in which special note is taken of the theory's application to the growing human need to trust in non-human entities. The latter include diverse forms of automation, robots, artificially intelligent entities, as well as specific implementations such as driverless vehicles to name but a few. Future directions as to the momentary dynamics of trust development, its sustenance and its dissipation are also evaluated.

Quintessential Solutions to Existential Problems: How Human Factors and Ergonomics Can and Should Address the Imminent Challenges of Our Times.

OBJECTIVE: To examine and evaluate ways in which an understanding of the quintessential element of Human Factors/Ergonomics can address the spectrum of existential threats that confront contemporary civilization. BACKGROUND: HF/E is dedicated to improving quality of life. Paradoxically, many processes which sustain contemporary civilization act to reduce that overall quality. Some technological developments themselves now even present existential threats to the fragile skein of civilization itself. Many disciplines address these diverse threats, and each may be advised and facilitated by HF/E knowledge and methods. It is a moral imperative of our science to contribute what we can to proposed resolutions. METHOD: A primary conduit, by the established strengths of HF/E can contribute to potential solutions is identified. The present work advocates for specific, practical interventions using a direct-perception mediated, panopticon principle, that derives from the corpus of our science. RESULT: Limitations upon a general, social understanding of imminent global concerns, which are largely ignorable when not actually present, are brought to immediate consciousness via an HF/E principle emphasizing the direct-perception of threat. It is argued that this, and allied HF/E insights can generate practical steps toward problem resolution at both macroscopic and localized levels of implementation. APPLICATIONS: The primary, practical application of the proposed panopticon principle is to use our science to save global civilization. It is postulated that this represents useful employment of the knowledge we have adduced and accumulated across our discipline's existence.

Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.

(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.

A Distracted Scientist: The Life and Contributions of John Senders.

OBJECTIVE: To provide an evaluative and personal overview of the life and contributions of Professor John Senders and to introduce this Special Issue dedicated to his memory. BACKGROUND: John Senders made many profound contributions to HF/E. These various topics are exemplified by the range of papers which compose the Special Issue. Collectively, these works document and demonstrate the impact of his many valuable research works. METHOD: The Special Issue serves to summarize Senders' collective body of work as can be extracted from archival sources. This introductory paper recounts a series of remembrances derived from personal relationships, as well as the products of cooperative investigative research. RESULTS: This collective evaluative process documents Senders' evident and deserved status in the highest pantheon of HF/E pioneers. It records his extraordinary life, replete with accounts of his insights and joie de vivre in exploring and explaining the world which surrounded him. APPLICATIONS: Senders' record of critical contributions provides the example, par excellence, of the successful and fulfilling life in science. It encourages all, both researchers and practitioners alike, in their own individual search for excellence.

On Senders's Models of Visual Sampling Behavior.

OBJECTIVE: We review the sampling models described in John Senders's doctoral thesis on "visual sampling processes" via a ready and accessible exposition. BACKGROUND: John Senders left a significant imprint on human factors/ergonomics (HF/E). Here, we focus on one preeminent aspect of his career, namely visual attention. METHODS: We present, clarify, and expand the models in his thesis through computer simulation and associated visual illustrations. RESULTS: One of the key findings of Senders's work on visual sampling concerns the linear relationship between signal bandwidth and visual sampling rate. The models that are used to describe this relationship are the periodic sampling model (PSM), the random constrained sampling model (RCM), and the conditional sampling model (CSM). A recent replication study that used results from modern eye-tracking equipment showed that Senders's original findings are manifestly replicable. CONCLUSIONS: Senders's insights and findings withstand the test of time and his models continue to be both relevant and useful to the present and promise continued impact in the future. APPLICATION: The present paper is directed to stimulate a broad spectrum of researchers and practitioners in HF/E and beyond to use these important and insightful models.

Reacting and responding to rare, uncertain and unprecedented events.

This work examines how we may be able to anticipate, respond to, and train for the occurrence of rare, uncertain, and unexpected events in human-machine systems operations. In particular, it uses a foundational matrix which describes the combinations of the state-of-the-world and the state-of-the-respondent, to formulate preferred response strategies, contingent upon what is knowable and actionable in each circumstance. It employs the dichotomy of System I and System II forms of cognitive response and augments these perspectives with a further form of decision-making, namely Systems III. The latter is predicated upon reactions to novel, unprecedented, and even 'unthinkable' events. The degree to which any human operator, the associated automation and/or the autonomy of a system, or each of these acting in concert, can best deal with these 'blue swan' events is explored. Potential forms of remediation, especially featuring training, are discussed, and evaluated in light of the skills needed to respond to even prohibitive degrees of situational uncertainty.Practitioners summary: Practitioners are liable to witness a growing spectrum of unusual and, on occasion, even unprecedented events in the operation of systems for which they are responsible. They will be required to account for their response to these circumstances to a spectrum of involved constituencies to whom they answer. This work aids them in succeeding to bring clarity to such difficult and challenging processes.Abbreviations: K: Known; Unk: Unknown; AI: Artificial Intelligence; ML: Machine Learning; CHARM: Cockpit Human-Automation Resource Management; SDT: signal detection theory; ASRS: Aviation Safety Reporting System.

Trust in Artificial Intelligence: Meta-Analytic Findings.

OBJECTIVE: The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. BACKGROUND: There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. METHOD: Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. RESULTS: Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. CONCLUSION: Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. APPLICATION: Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Attribution Errors by People and Intelligent Machines.

OBJECTIVE: To explore the ramifications of attribution errors (AEs), initially in the context of vehicle collisions and then to extend this understanding into the broader and diverse realms of all forms of human-machine interaction. BACKGROUND: This work focuses upon a particular topic that John Senders was examining at the time of his death. He was using the lens of attribution, and its associated errors, to seek to further understand and explore dyadic forms of driver collision. METHOD: We evaluated the utility of the set of Senders' final observations on conjoint AE in two-vehicle collisions. We extended this evaluation to errors of attribution generally, as applicable to all human-human, human-technology, and prospectively technology-technology interactions. RESULTS: As with Senders and his many other contributions, we find evident value in this perspective on how humans react to each other and how they react to emerging forms of technology, such as autonomous systems. We illustrate this value through contemporary examples and prospective analyses. APPLICATIONS: The comprehension and mitigation of AEs can help improve all interactions between people, between intelligent machines and between humans and the machines they work with.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies. Here, we propose an end-to-end graph convolutional network (GCN) framework with three convolutional layers to classify task fMRI data from the Human Connectome Project dataset. We compared the predictive performance of our GCN model across four of the most widely used node embedding algorithms-NetMF, RandNE, Node2Vec, and Walklets-to automatically extract the structural properties of the nodes in the functional graph. The empirical results indicated that our GCN framework accurately predicted individual differences (0.978 and 0.976) with the NetMF and RandNE embedding methods, respectively. Furthermore, to assess the effects of individual differences, we tested the classification performance of the model on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data.

Corrigendum: Human mental workload: A survey and a novel inclusive definition.

[This corrects the article DOI: 10.3389/fpsyg.2022.883321.].

In defense of the maximal adaptability model.

INTRODUCTION: In a recent work, Doohan and colleagues (this Journal) have reported an experiment designed to evaluate the predictions of the Maximal Adaptability Model. METHOD: The experimental data and results of the above study were analyzed in order to reassess the validity and applicability of the conclusions offered therein. RESULTS: The stress insensitivity of the congruent Stroop task employed is entirely consistent with the predictions of the Maximal Adaptability Model, but this same outcome strongly questions the validity of the standard, inverted-U description of the relationship of stress and performance. In this respect, theirs are valuable and useful data. Similarly, the utility of the incongruent performance measure here is also of limited determinability. Doohan and colleagues' inappropriate concatenation of stress with stable body temperatures, albeit at elevated levels, fails to consider the criticality of the need for the occurrence of dynamic instability in which such responses are actually predicted to exhibit consistent and significant performance decrement. CONCLUSIONS: Doohan and colleagues findings argue that the standard inverted-U description relating stress to performance is doubtful as a comprehensive description. In contrast, the extended-U, Maximal Adaptability Model withstands the present assault since the performance task employed imposes little in terms of absolute levels of attentional demand. Indeed, it is strongly supported by this outcome. Recommendations for future evaluations of the Maximal Adaptability Model are promulgated.

Development of the Smart Tools Proneness Questionnaire (STP-Q): an instrument to assess the individual propensity to use smart tools.

Humans have developed a prolonged and special relationship with their tools, which themselves exhibit the propensity to become ever more intelligent across the years. A 'smart tool' is defined as to representing any entity, machine, or device that can complete an informational, mechanical, or electronic work. This work explains the development of the Smart Tool Proneness Questionnaire (STP-Q), which is designed to measure an individual's propensity to use smart tools. Data collection was designed to (1) identify the psychological dimensions underlying smart tool use (2) establish the questionnaire's reliability (3) validity, (4) propose a normalisation, and (5) provide an English translation of the French original. The work therefore implements a reliable and valid questionnaire, sensitive to inter-individual differences regarding the propensity to use smart tools. Statistical analysis reveals that the individual self-reported propensity for smart tool use rests on three factors (1) utilitarian use, (2) hedonic and social use, and (3) proneness to delegate. From a theoretical perspective, this individual propensity to use smart tools might be considered key to our species development. In practical terms, measuring an individual's propensity to use smart tools can be of considerable benefit to the design of future smart tools in both professional and non-professional settings. Practitioner summary: The STP-Q, a self-reported measure of an individual's propensity to use smart tools, was developed. STP-Q offers practitioners a measure of individual propensity to use smart tools along three dimensions: utilitarian use, hedonic and social use, and proneness to task delegate. Individual results can easily be interpreted from normalizations that STP-Q provides. Abbreviations: CFI: comparative fit index; GFI: goodness of fit index; IFI: incremental fit index; ISO: International Standardization Organization; IRB: institutional review board of the university of central Florida; IT: information technology; MATB: multi-attribute task battery; NMP-Q: no more phone phobia; RMSEA: root mean square error of approximation; STP-Q: smart tools proneness questionnaire; TAM: technology acceptance model; TRI: technology readiness index; UTAUT: unified theory of acceptance and use of technology; WAIS IV: Wechsler adult intelligence scale.

How human factors and ergonomics save lives.

This work is directed to an understanding as to how the knowledge of, and the application of human factors and ergonomics (HF/E) can save lives. To achieve this, the paper features an assessment of the achievements of one particular scientist, Neville Anthony Stanton, and how his body of contributions has impacted the realm of ground transportation and, in particular, driver behavior assessment. On the widest scale, it is objectively and obviously the case that Stanton is one of the most fecund scientists of our discipline ever. His impact is evident globally and results not simply from the sum total of his written and published works but through an extensive record of international scientific presentations, mutual investigative collaborations across the globe, and mentoring at all levels of the Academy and beyond. As well as mastering and elucidating the HF/E dimensions of a number of content domains, he has generated vital, and even unique tools and methods through which we can explore and understand the problem space of HF/E. Placing those attainments in context permits us a wider window upon how the discipline itself exerts practical and positive influences across the wide swath of real-world systems.

Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study.

Introduction: The ability to perform optimally under pressure is critical across many occupations, including the military, first responders, and competitive sport. Despite recognition that such performance depends on a range of cognitive factors, how common these factors are across performance domains remains unclear. The current study sought to integrate existing knowledge in the performance field in the form of a transdisciplinary expert consensus on the cognitive mechanisms that underlie performance under pressure. Methods: International experts were recruited from four performance domains [(i) Defense; (ii) Competitive Sport; (iii) Civilian High-stakes; and (iv) Performance Neuroscience]. Experts rated constructs from the Research Domain Criteria (RDoC) framework (and several expert-suggested constructs) across successive rounds, until all constructs reached consensus for inclusion or were eliminated. Finally, included constructs were ranked for their relative importance. Results: Sixty-eight experts completed the first Delphi round, with 94% of experts retained by the end of the Delphi process. The following 10 constructs reached consensus across all four panels (in order of overall ranking): (1) Attention; (2) Cognitive Control-Performance Monitoring; (3) Arousal and Regulatory Systems-Arousal; (4) Cognitive Control-Goal Selection, Updating, Representation, and Maintenance; (5) Cognitive Control-Response Selection and Inhibition/Suppression; (6) Working memory-Flexible Updating; (7) Working memory-Active Maintenance; (8) Perception and Understanding of Self-Self-knowledge; (9) Working memory-Interference Control, and (10) Expert-suggested-Shifting. Discussion: Our results identify a set of transdisciplinary neuroscience-informed constructs, validated through expert consensus. This expert consensus is critical to standardizing cognitive assessment and informing mechanism-targeted interventions in the broader field of human performance optimization.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

The seat of happiness? The effect of seat comfort on the achievement of psychological flow during transactional work.

Psychological flow is highly pleasurable, time-limited form of engagement in a task which has been shown to produce benefits in the workplace. Flow has historically been studied in the context of the interaction between the task and the performer. However, in work settings such as an office, many other factors may contribute to or hinder the achievement of flow. This present study broadens the research on flow to test the extent to which seating comfort while executing a challenging task influences an individual's ability to achieve flow. Fifty-four participants in this study were randomly assigned to one of two seat types and given a set of tasks to perform via a computer simulation. Seat comfort, coupled with participants' perceptions of their ability to concentrate on the simulation's set of tasks, was found to predict participants' flow experiences. Implications and future directions are discussed.