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Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations (Endsley, 2019). How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained at-least-one significant effect, yet most papers reported multiple effects, and the number of non-significant effects did not impact the score. Thus, the predictiveness score was rarely less than the maximum. In addition, only significant effects were included in Endsley’s quantitative synthesis. Filtering excluded half of all reported effects, with guaranteed minimum effect sizes based on sample size. Results for filtered compared to as-reported effects clearly diverged. Compared to the mean of as-reported effects, the filtered mean was overestimated by 56%. Furthermore, 92% (or 222 out of 241) of the as-reported effects were below the mean of filtered effects. We conclude that outcome-dependent selection of effects is circular, predetermining results and running contrary to the purpose of meta-analysis. Instead of using significance to score and filter effects, meta-analyses should follow established research practices.

The increasing use of Artificial Intelligence (AI) systems in face recognition and video processing in recent times creates higher stakes for their application in daily life. Increasingly, critical decisions are being made using these AI systems in application domains such as employment, finance, and crime prevention. These applications are done through the use of more abstract concepts such as emotions, trait evaluations (e.g., trustworthiness), and behavior (e.g., deception). These abstract concepts are learned by the AI system using the verbal and non-verbal cues from the human subject stimuli (e,g., facial expressions, movements, audio, text) for inference. Because the use of AI systems often happens in high stakes scenarios, it is of utmost importance that the AI system participating in the decision-making process is highly reliable and credible. In this paper, we specifically consider the feasibility of using such an AI system for deception detection. We examine if deception can be caught using multimodal aspects such as facial expressions and movements, audio cues, video cues, etc. We experiment using three different datasets with varying degrees of deception to explore the problem of deception detection. We also study state-of-the-art deception detection systems and investigate whether we can extend their algorithm into new datasets. We conclude that there is a lack of reasonable evidence that AI-based deception detection is generalizable over different scenarios of lying (lying deliberately, lying under duress, and lying through half-truths) and that in the future additional factors will need to be considered to make such a claim.

Augmented-reality (AR) displays render virtual content overlaid on the real world. AR offers great potential for Soldiers to immediately perceive relevant tactical information without the need to consult a physical map or other reference material, or divert their eyes away from the immediate environment. However, realizing the potential benefits of using AR requires that the conformal subset of virtual content is perceived in the correct physical location. Conformal virtual content must be correctly associated with the physical location, object, or person intended to be augmented. In this report, we provide emerging recommendations aimed at improving spatial perception of virtual content.

Social sensing techniques were designed for analyzing unreliable data [1], but not explicitly built for adversarial generated and manipulated data. The adversarial use of social media to spread deceptive or misleading information poses a social, economic, and political threat [2]. Deceptive information spreads quickly and inexpensively online relative to traditional methods of dissemination (e.g., print, radio, and television). For example, bots (i.e., dedicated software for sharing text information [3]) can distribute information faster than humans. Such deceptive information is commonly referred to as fake (fabricated) news, which can be a form of propaganda (i.e., manipulation to advance a particular view or agenda). Information spread is particularly effective if the content resonates with the preconceptions and biases of social groups or communities because the spread will be reinforced by implied trust in information coming from other members (echo chambers and filter bubbles) [4]. We conjecture that the future of online deception, including fake news, will extend beyond text to high-quality, massproduced machine-generated and manipulated images, video, and audio [5].

Summary Stereotypes can harm human performance, especially when activated in individuals with diminished working memory capacity (WMC). Performance implications for the stereotype of poor driving in older adults were investigated. Using a sample of older adults, WMC (the ability to maintain task goals and ignore distractions) and driving performance [brake reaction time (RT), following distance, and crashes] were assessed, the latter using a high-fidelity simulator. Elderly participants under stereotype threat with reduced WMC exhibited slower brake RTs and longer following distances compared with a control condition that was not threatened. This driving profile was characteristic of cognitive distraction. Stereotype threat has clear consequences for human performance in a common real-world task—driving—that is critical to public safety. Furthermore, these findings suggest caution in how the media and public policy communicate information about older adult driving.Copyright © 2015 John Wiley & Sons, Ltd.

Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks., 12 pages 6 figures

In the wake of rapid maturing of Internet of Things (IoT) approaches and technologies in the commercial sector,\ud the IoT is increasingly seen as a key ‘disruptive’ technology in military environments. Future operational environments\ud are expected to be characterized by a lower proportion of human participants and a higher proportion of\ud autonomous and semi-autonomous devices. This view is reflected in both US ‘third offset’ and UK ‘information\ud age’ thinking and is likely to have a profound effect on how multinational coalition operations are conducted\ud in the future. Much of the initial consideration of IoT adoption in the military domain has rightly focused\ud on security concerns, reflecting similar cautions in the early era of electronic commerce. As IoT approaches\ud mature, this initial technical focus is likely to shift to considerations of interactivity and policy. In this paper,\ud rather than considering the broader range of IoT applications in the military context, we focus on roles for IoT\ud concepts and devices in future intelligence, surveillance and reconnaissance (ISR) tasks, drawing on experience\ud in sensor-mission resourcing and human-computer collaboration (HCC) for ISR. We highlight the importance of\ud low training overheads in the adoption of IoT approaches, and the need to balance proactivity and interactivity\ud (push vs pull modes). As with sensing systems over the last decade, we emphasize that, to be valuable in ISR\ud tasks, IoT devices will need a degree of mission-awareness in addition to an ability to self-manage their limited\ud resources (power, memory, bandwidth, computation, etc). In coalition operations, the management and potential\ud sharing of IoT devices and systems among partners (e.g., in cross-coalition tactical-edge ISR teams) becomes a\ud key issue due heterogeneous factors such as language, policy, procedure and doctrine. Finally, we briefly outline a\ud platform that we have developed in order to experiment with human-IoT teaming on ISR tasks, in both physical\ud and virtual settings.

Cybernetics provides a framework for understanding the behavior of closed-loop systems, including the feedback control intrinsic to cognitive systems (Smith and Smith in continuing the conversation: a newsletter of ideas in cybernetics. Greg and Pat Williams, Gravel Switch, KY, [1]). We propose adopting our interpretation of the cybernetics concept of feedback control of cognition by integrating across metacognition, performance, computational cognitive modeling, and physiological levels of analysis. To accomplish this objective, we tie cognitive variables to each level of analysis, including: (1) metacognition—self-evaluation of cognition; (2) performance—objective measures of progress toward a goal state; (3) physiology—indications of cognitive function (e.g., heart rate variability as an index of the level of task engagement); and (4) cognitive models—prediction and understanding of empirical results using sequences of cognitive steps. We call this integrative approach, Multi-Level Cognitive Cybernetics (MLCC). In this paper, we define the MLCC framework, discuss how MLCC can inform the design of adaptive automation technologies, and discuss the benefits of the MLCC approach in human factors.

Previous research has suggested that perceived distances are scaled by the action capabilities of the body. The present studies showed that when "reachability" is constrained due to a difficult grasp required to pick up an object, perceived distance to the object increases. Participants estimated the distances to tools with handle orientations that made them either easy or difficult to grasp with their dominant and nondominant hands. Right-handed participants perceived tools that were more difficult to grasp to be farther away than tools that were easier to grasp. However, perceived distance did not differ in left-handed participants. These studies suggest that, when reaching toward a target, the distance to that target is scaled in terms of how far one can effectively reach, given the type of reaching posture that is executed. Furthermore, this effect is modulated by handedness.

Spatial perception is sensitive to the energetic costs required to perform intended actions. For example, hills look steeper to people who are fatigued or burdened by a heavy load. Similarly, perceived distance is also influenced by the energy required to walk or throw to a target. Such experiments demonstrate that perception is a function, not just of optical information, but also of the perceiver’s potential to act and the energetic costs associated with the intended action. In the current paper, we expand on the notion of “cost” by examining perceived distance in patients diagnosed with chronic pain, a multifactorial disease, which is experienced while walking. We found that chronic pain patients perceive target distances to be farther away compared with a control group. These results indicate the physical, and perhaps emotional, costs of chronic pain affect spatial perceptions.

This work investigates whether large-scale indoor layouts can be learned and navigated non-visually, using verbal descriptions of layout geometry that are updated, e.g. contingent on a participant's location in a building. In previous research, verbal information has been used to facilitate route following, not to support free exploration and wayfinding. Our results with blindfolded-sighted participants demonstrate that accurate learning and wayfinding performance is possible using verbal descriptions and that it is sufficient to describe only local geometric detail. In addition, no differences in learning or navigation performance were observed between the verbal study and a control study using visual input. Verbal learning was also compared to the performance of a random walk model, demonstrating that human search behavior is not based on chance decision-making. However, the model performed more like human participants after adding a constraint that biased it against reversing direction.

People have a lifetime of experience in which to calibrate their self-produced locomotion with the resultant optical flow. Contrary to walking across the ground, however, walking on a treadmill produces minimal optical flow, and consequentially, a perceptual-motor aftereffect results. We demonstrate that the magnitude of this perceptual-motor aftereffect—measured by forward drift while attempting to march in-place following treadmill walking—decreases as experience walking on a treadmill is acquired over time. Experience with treadmill walking enables walking in this context to become sufficiently distinguished from walking in other contexts. Consequently, two distinct perceptual-motor calibration states are maintained, each linked to the context in which walking occurs. Experience with treadmill walking maintains perceptual-motor calibration accuracy in both walking contexts, despite changes to the relationship between perception and action.

Perception of one's body is related not only to the physical appearance of the body, but also to the neural representation of the body. The brain contains many body maps that systematically differ between right- and left-handed people. In general, the cortical representations of the right arm and right hand tend to be of greater area in the left hemisphere than in the right hemisphere for right-handed people, whereas these cortical representations tend to be symmetrical across hemispheres for left-handers. We took advantage of these naturally occurring differences and examined perceived arm length in right- and left-handed people. When looking at each arm and hand individually, right-handed participants perceived their right arms and right hands to be longer than their left arms and left hands, whereas left-handed participants perceived both arms accurately. These experiments reveal a possible relationship between implicit body maps in the brain and conscious perception of the body.

When people are engaged in a skilled behavior, such as occurs in sports, their perceptions relate optical information to their performance. In the present research, we demonstrate the effects of performance on size perception in golfers. We found that golfers who played better judged the hole to be bigger than did golfers who did not play as well. In follow-up laboratory experiments, participants putted on a golf mat from a location near or far from the hole and then judged the size of the hole. Participants who putted from the near location perceived the hole to be bigger than did participants who putted from the far location. Our results demonstrate that perception is influenced by the perceiver's current ability to act effectively in the environment.