Approaching future rewards or waiting for them to arrive: Spatial representations of time and intertemporal choice.

Our mental representation of the passage of time is structured by concepts of spatial motion, including an ego-moving perspective in which the self is perceived as approaching future events and a time-moving perspective in which future events are perceived as approaching the self. While previous research has found that processing spatial information in one's environment can preferentially activate either an ego-moving or time-moving temporal perspective, potential downstream impacts on everyday decision-making have received less empirical attention. Based on the idea people may feel closer to positive events they see themselves as actively approaching rather than passively waiting for, in this pre-registered study we tested the hypothesis that spatial primes corresponding to an ego-moving (vs. time-moving) perspective would attenuate temporal discounting by making future rewards feel more proximal. 599 participants were randomly assigned to one of three spatial prime conditions (ego-moving, time-moving, control) resembling map-based tasks people may engage with on digital devices, before completing measures of temporal perspective, perceived wait time, perceived control over time, and temporal discounting. Partly consistent with previous research, the results indicated that the time-moving prime successfully activated the intended temporal perspective-though the ego-moving prime did not. Contrary to our primary hypotheses, the spatial primes had no effect on either perceived wait time or temporal discounting. Processing spatial information in a map-based task therefore appears to influence how people conceptualise the passage of time, but there was no evidence for downstream effects on intertemporal preferences. Additionally, exploratory analysis indicated that greater perceived control over time was associated with lower temporal discounting, mediated by a reduction in perceived wait time, suggesting a possible area for future research into individual differences and interventions in intertemporal decision-making.

Defining human-centricity in Industry 5.0 and assessing the readiness of ergonomics/human factors communities in UK.

There is a lack of a clear and consistent definition of human-centricity in Industry 5.0. This study identified the definition of human-centricity in Industry 5.0 through a systematic literature review and used it to assess the readiness of Ergonomics/Human Factors communities in the UK. The assessment of the communities readiness was conducted by reviewing UK accredited courses and events of three professional bodies; and interviewing practitioners (n = 8). Eleven themes were identified as elements of human-centricity from the thematic analysis of 30 publications. Gaps that had to be addressed to better equip UK practitioners to support the realisation of human-centricity in Industry 5.0 were also identified.

The meaning of human-centricity in Industry 5.0 and its bearing on Ergonomics/Human Factors communities are not fully understood. Eleven themes that define human-centricity in Industry 5.0 are extracted. Gaps that have to be addressed by Ergonomics/Human Factors communities in UK are also identified.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers.

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

Association between cyclic variation in the heart rate index and biomarkers of neurodegenerative diseases in obstructive sleep apnea syndrome: A pilot study.

PURPOSE: Obstructive sleep apnea syndrome (OSAS) has mostly been examined using in-laboratory polysomnography (Lab-PSG), which may overestimate severity. This study compared sleep parameters in different environments and investigated the association between the plasma levels of neurochemical biomarkers and sleep parameters. METHODS: Thirty Taiwanese participants underwent Lab-PSG while wearing a single-lead electrocardiogram patch. Participants' blood samples were obtained in the morning immediately after the recording. Participants wore the patch for the subsequent three nights at home. Sleep disorder indices were calculated, including the apnea-hypopnea index (AHI), chest effort index, and cyclic variation of heart rate index (CVHRI). The 23 eligible participants' derived data were divided into the normal-to-moderate (N-M) group and the severe group according to American Association of Sleep Medicine (AASM) guidelines (Lab-PSG) and the recommendations of a previous study (Rooti Rx). Spearman's correlation was used to examine the correlations between sleep parameters and neurochemical biomarker levels. RESULTS: The mean T-Tau protein level was positively correlated with the home-based CVHRI (r = 0.53, p < 0.05), whereas no significant correlation was noted between hospital-based CVHRI and the mean T-tau protein level (r = 0.25, p = 0.25). The home-based data revealed that the mean T-Tau protein level in the severe group was significantly higher than that in the N-M group (severe group: 24.75 ± 6.16 pg/mL, N-M group: 19.65 ± 3.90 pg/mL; p < 0.05). Furthermore, the mean in-hospital CVHRI was higher than the mean at-home values (12.16 ± 13.66 events/h). CONCLUSION: Severe OSAS patients classified by home-based CVHRI demonstrated the higher T-Tau protein level, and CVHRI varied in different sleep environments.

Understanding factors that influence unintentional insider threat: a framework to counteract unintentional risks.

The exploitation of so-called insiders is increasingly recognised as a common vector for cyberattacks. Emerging work in this area has considered the phenomenon from various perspectives including the technological, the psychological and the sociotechnical. We extend this work by specifically examining unintentional forms of insider threat and report the outcomes of a series of detailed Critical Decision Method (CDM) led interviews with those who have experienced various forms of unwitting cybersecurity breaches. We also articulate factors likely to contribute firmly in the context of everyday work-as-done. CDM's probing questions were used to elicit expert knowledge around how decision making occurred prior, during and post an unintentional cyber breach whilst participants were engaged in the delivery of cognitive tasks. Through the application of grounded theory to data, emerging results included themes of decision making, task factors, accidents and organisational factors. These results are utilised to inform an Epidemiological Triangle to represent the dynamic relationship between three vectors of exploit, user and the work environment that can in turn affect the resilience of cyber defences. We conclude by presenting a simple framework, which for the purposes of this work is a set of recommendations applicable in specific scenarios to reduce negative impact for understanding unintentional insider threats. We also suggest practical means to counteract such threats rooted in the lived experience of those who have fallen prey to them.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

The field becomes the laboratory? The impact of the contextual digital footprint on the discipline of E/HF.

The increasing prevalence of affordable digital sensors, ubiquitous networking and computation puts us at what is only the start of a new era in terms of the volume, coverage and granularity of data that we can access about individuals and workplaces. This paper examines the consequences of harnessing this data deluge for the practice of E/HF. Focusing on what we term the 'contextual digital footprint', the trail of data we produce through interactions with many different digital systems over the course of even a single day, we describe three example scenarios (drawn from health care, distributed work and transportation) and examine how access to data directly drawn in considerable volume from the field will potentially change our application of design and evaluation methods. We conclude with a discussion of issues relevant to ethical and professional practice within this new environment including the increased challenges of respecting anonymity, working with n = all data-sets and the central role of ergonomists in promulgating positive uses of data while retaining a systems-based humanistic approach to work design. Practitioner summary: The paper envisions the impact of new and emerging sources of data about people and workplaces upon future practice in E/HF. We identify practical consequences for ergonomics practice, highlight new areas of professional competence likely to be required and flag both the risks and benefits of adopting a more data-driven approach.

Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis.

Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions. PRACTITIONER SUMMARY: In this paper, a technique for building social network diagrams from CWA is demonstrated.The approach allows analysts to appreciate the potential impact of organisational structure on a command system.

The attribution of success when using navigation aids.

Attitudes towards geographic information technology is a seldom explored research area that can be explained with reference to established theories of attribution. This article reports on a study of how the attribution of success and failure in pedestrian navigation varies with level of automation, degree of success and locus of control. A total of 113 participants took part in a survey exploring reflections on personal experiences and vignettes describing fictional navigation experiences. A complex relationship was discovered in which success tends to be attributed to skill and failure to the navigation aid when participants describe their own experiences. A reversed pattern of results was found when discussing the navigation of others. It was also found that navigation success and failure are associated with personal skill to a greater extent when using paper maps, as compared with web-based routing engines or satellite navigation systems. PRACTITIONER SUMMARY: This article explores the influences on the attribution of success and failure when using navigation aids. A survey was performed exploring interpretations of navigation experiences. Level of success, self or other as navigator and type of navigation aid used are all found to influence the attribution of outcomes to internal or external factors.

Increasing self-regulatory energy using an Internet-based training application delivered by smartphone technology.

Self-control resources can be defined in terms of "energy." Repeated attempts to override desires and impulses can result in a state of reduced self-control energy termed "ego depletion" leading to a reduced capacity to regulate future self-control behaviors effectively. Regular practice or "training" on self-control tasks may improve an individual's capacity to overcome ego depletion effectively. The current research tested the effectiveness of training using a novel Internet-based smartphone application to improve self-control and reduce ego depletion. In two experiments, participants were randomly assigned to either an experimental group, which received a daily program of self-control training using a modified Stroop-task Internet-based application delivered via smartphone to participants over a 4-week period, or a no-training control group. Participants assigned to the experimental group performed significantly better on post-training laboratory self-control tasks relative to participants in the control group. Findings support the hypothesized training effect on self-control and highlight the effectiveness of a novel Internet-based application delivered by smartphone as a practical means to administer and monitor a self-control training program. The smartphone training application has considerable advantages over other means to train self-control adopted in previous studies in that it has increased ecological validity and enables effective monitoring of compliance with the training program.

Command and control in emergency services operations: a social network analysis.

There is increasing interest in the use of social network analysis as a tool to study the performance of teams and organizations. In this paper, processes of command and control in the emergency services are explored from the perspective of social network theory. We report a set of network analyses (comprising visualization, a selection of mathematical metrics, and a discussion of procedures) based on the observation of six emergency service incidents: three fire service operations involving the treatment of hazardous chemicals, and three police operations involving immediate response to emergency calls. The findings are discussed in terms of our attempts to categorize the network structures against a set of extant command and control network archetypes and the relationship between those structures; comments on the qualities the networks display are put into the contexts of the incidents reported. We suggest that social network analysis may have a valuable part to play in the general study of command and control.

Attentional modulation of the visual motion aftereffect has a central cognitive locus: evidence of interference by the postcategorical on the precategorical.

An attentionally demanding task undertaken during adaptation to motion reduces the duration of the subsequent motion aftereffect (A. Chaudhuri, 1990). Previous studies have suggested that this effect is intramodal in character, reflecting the selective deployment of visual attention. The present study demonstrates that nonvisual tasks, performed concurrently with motion adaptation, can significantly reduce the duration of the ensuing aftereffect. Three experiments converge on the conclusionthat postcategorical processes can influence otherwise unrelated concurrent precategorical processes. The experiments also show that neither perceptual input nor motor output components of the attentional task are responsible for the subsequent reduction in motion of aftereffect. The results suggest a reappraisal of findings in this area and of the general distinction between perception and cognition.

Does auditory streaming require attention? Evidence from attentional selectivity in short-term memory.

R. P. Carlyon, R. Cusack, J. M. Foxton, and I. H. Robertson (2001) have argued that attention is crucial for auditory streaming. The authors review R. P. Carlyon et al.'s (2001) arguments and suggest that a pertinent literature, the irrelevant sound paradigm--demonstrating preattentive auditory streaming--has been overlooked. In illustration of this alternative approach, the authors include a novel single experiment demonstrating the impact of preattentive auditory streaming on short-term serial memory. It is concluded that R. P. Carlyon et al.'s (2001) results do not definitively demonstrate that auditory streaming processes are dependent on attention; indeed, they are compatible with alternative accounts of the relationship between perceptual organization and attention.