Humans are extremely adept at categorizing complex visual environments, an ability supported by a network of scene-selective cortical areas in occipitotemporal cortex (OTC), primarily parahippocampal- and occipital-place area (PPA, OPA, respectively). Despite increasing knowledge on the development of the scene-selective network, it is still not well-understood how experience impacts scene-related activity in the adult brain. A key question is how activity in scene-selective cortex changes as people gain experience in categorizing scenes. To address this question, we conducted an fMRI training study focused on the categorization of aerial and terrestrial scenes. Unlike terrestrial scenes, aerial scenes lack the same environmental regularities the brain has adapted to, and thus ideal for testing the impact of experience on scene-selective cortex. Over six training sessions, 39 participants (19 males and 20 females) were shown scenes of different categories from aerial and terrestrial viewpoints, with half the participants categorizing the scenes at a specific level (e.g., truss bridge/suspension bridge), whereas the other performed an unrelated task on the same images. Both groups were scanned before, during, and after training. We found that categorization training had a group-specific effect on responses in OPA and PPA, with greater neural sensitivity to viewpoint in the trained versus the untrained group. In contrast, nonscene-selective regions, such as object-selective LOC and early visual cortex showed no training effects. Improvements in behavioral performance, including learning transfer, were linked to changes in PPA activity level pre- versus-posttraining. We conclude that scene-selective cortex can support the learning of novel spatial geometries.

INTRODUCTION:Online experiential learning can benefit learners with scalable techniques to self-debrief and to develop cognitive skills in recognizing clinical cues. METHODS:We developed techniques for cue-detection exercises and self-debriefing, based on cognitive engineering-inspired expertise development approaches that focus on tacit knowledge, sensemaking, and mental models. Self-debriefing was structured by asking the learners to compare and then reflect on their choices and rationales against those provided by a panel of experts. Using these techniques, we generated scenario-based experiential learning materials in a virtual environment for a 2-hour module on deteriorating patient conditions that can lead to imminent cardiac arrests. The module was tested in a senior nursing course as an optional assignment. The volume of voluntarily submitted reflections by learners was used to assess engagement and subsequent thematic analysis to assess feasibility of the techniques. RESULTS:The module was completed by 189 of the 197 students invited (95.9%). Engagement level was high with all completed students submitting reflections after self-debriefing, most of which (between 53.4% and 87.8%) were specific enough for thematic analysis. The main theme of reflections was "missing something" in the scenario, followed by the themes of importance of reading the patient monitor and refining actions and priorities. CONCLUSIONS:We demonstrated the feasibility of the techniques based on cognitive engineering-inspired approaches for virtual simulation learning in health care that structures self-debriefing by comparing a learner's situation assessment and responses with those of experts. The techniques have the potential to help learners in health care efficiently and consistently develop key critical thinking skills, especially those based on tacit knowledge to detect cues.

This paper presents a framework for eliciting and identifying key decision-maker attributes in uncertain and complex environments. Developed through semi-structured cognitive interviews with civilian and combat medics, the framework consists of four components: identifying sources of decision difficulty, categorizing decision types, mapping the key attributes across the types of difficulty and types of decisions, and evaluating trust-related factors. This approach provides a method for examining expert decision-making and trust in other highstakes domains and supports the development of algorithmic decision-makers and AI-driven support systems.

Mental models are characterized as a person's mental representation of how something works, guiding how they process information, anticipate future events, and interact with devices and tools in complex sociotechnical systems. This paper introduces the Mental Model Matrix (MMM), a novel framework for conceptualizing mental models in the context of human-system interactions. The MMM framework partitions aspects of an individual's knowledge about a target system into two primary dimensions (system and user), each containing beliefs regarding capabilities and limitations. The system-based dimension includes knowledge for the system's parts, connections, and causal relationships (capabilities), as well as knowledge for how the system can fail (limitations). The user-based dimension incorporates tacit knowledge for working with the system (capabilities) along with an appreciation for one's own confusions and misunderstandings (limitations). The MMM provides a conceptual framework and guidance for end-users, trainers, and system designers seeking to elicit and codify different dimensions of an individual's mental model of the systems they work with. The MMM can be used to reveal knowledge gaps and misalignments between different stakeholders in an organization, which can facilitate the development of human-centered technologies and systems, and the creation of effective training programs.

This panel brings together a diverse set of experts to discuss training for high-stakes domains such as emergency medicine, law enforcement, combat medicine, and others. Work in these domains typically involves managing uncertainty, time pressure, and often life and death risk. These high-stakes domains are often leaders in simulation-based training, creating virtual environments and seeking the right types of fidelity to prepare personnel to develop the perceptual, sensemaking, and stress management skills needed to perform in some of the toughest situations. Panelists will discuss some of the challenges for training and evaluating the effectiveness of training in these complex domains. They will share examples of successful strategies and lessons learned from less-than-successful strategies.

Throughout evolution, the human visual system has adapted to efficiently encode several environmental constants to deal with the huge complexity involved in representing large-scale spatial environments. Being terrestrial animals, these constants reflect a specific ground-based viewpoint. For example, people show a strong affinity for detecting a perceptual upright layout relative to the horizon. But, what happens when humans leave this terrestrial perspective, for example when taking a flight, or looking down from the Empire State building? Are the mechanisms originally evolved for terrestrial scene recognition also recruited for the recognition of novel large-scale environments people rarely encounter on a daily basis? We propose that studying how people learn to recognize aerial scene images can reveal how the scene recognition system develops through experience and shed light on the putative mechanisms underlying its malleability. We conducted an intensive six-session behavioral training study in which naive participants learned to categorize manmade and natural scenes at a specific-subordinate level (‘suspension bridge’). Scene images depicted real-world places from a terrestrial and an aerial viewpoint, allowing us to establish how people learn to categorize two visually-different images as the same environment. We found that performance constantly improved over the first five training sessions, with greater learning for the aerial compared to the terrestrial scenes. This viewpoint performance gap manifested more for manmade than natural scenes. In addition to memory improvements, we also found learning transfer, the hallmark of perceptual learning. Performance in the sixth session (in which participants categorized scenes they had not been trained with) was significantly better compared to the first session, and equivalent to average performance across training-sessions. Our findings provide novel evidence for the potential mechanisms underlying plasticity in the scene recognition system, showing both memory and perception contribute to experience-based enhancements in scene recognition.

Visual analysis of complex real-world scenes (e.g. overhead imagery) is a skill essential tomany professional domains. However, little is currently known about how this skill is formed and develops with experience. The present work adopts a neuroergonomic approach to uncover the underlying mechanisms associated with the acquisition of scene expertise, and establish neurobehavioral markers for the effectiveness of training in scene imagery analysis. We conducted an intensive six-session behavioral training study combined with multiple functionalMRI scans using a large set of high-resolution color images of real-world scenes varying in their viewpoint (aerial/terrestrial) and naturalness (manmade/natural). Participants were trained to categorize the scenes at a specific-subordinate level (e.g. suspension bridge). Participants categorized the same stimuli for five sessions; the sixth session consisted of a novel set of scenes. Following training, participants categorized the scenes faster and more accurately, reflectingmemory-based improvement. Learning also generalized to novel scene images, demonstrating learning transfer, a hallmark of perceptual expertise. Critically, brain activity in scene-selective cortex across all sessions significantly correlated with learning transfer effects. Moreover, baseline activity (pre-training) was highly predictive of subsequent perceptual performance. Whole-brain activity following training indicated changes to scene- and object-selective cortex, as well as posterior-parietal cortex, suggesting potential involvement of top-down visuospatial-attentional networks. We conclude that scene-selective activity can be used to predict enhancement in perceptual performance following training in scene categorization and ultimately be used to reveal the point when trainees transition to an expert-user level, reducing costs and enhancing existing training paradigms.

Human visual perception entails a complex interplay between top-down and bottom-up signals yielding fast and accurate object recognition. Recent neuroimaging findings have demonstrated that observer goals (manipulated by task context) modulate visual object processing across the cortex. While these findings reveal where task context influences object representations, they do not uncover when these effects emerge (i.e. early vs. late). To identify how early the impact of task context can be observed, we recorded Event-Related Potentials (ERPs) from participants as they viewed objects from four categories spanning animacy and real-world size dimensions under four tasks, two of which required judments of the objects’ animacy and size. We examined how the relevance of the task context impacted object processing across both time and space by measuring the effects of task relevance in two time windows (0-300ms and 300-600ms post stimulus onset), and across two sites: an occipital electrode site (Oz) and a central-parietal location (Cz). We found that activity distinguishing animate and inanimate objects was greatest under the task relevance context (i.e. under the animacy task) compared to the task irrelevant context (i.e. under the size task). However, the effect of task relevance was relegated to cognitive, post-perceptual stages, occurring primarily in the later time-window and specific to the central-parietal site. Interestingly, the effect of task relevance on real-world size discrimination while also observed in the late time-window, was not site-specific. Together, these results suggest task-related processing occurs post-perceptually (>300ms post-stimulus onset) following initial object processing and suggests task-related information is first processed outside of early visual areas in frontoparietal regions.

Recognizing aerial scene imagery is an essential skill in a variety of work domains, yet little is currently known about how this skill forms and develops with experience. The current study aimed to elucidate the neural mechanisms underlying expertise in aerial scene recognition in order to understand the acquisition of experience with aerial imagery. We conducted an intensive six-session behavioral training study combined with multiple fMRI scans using a large set of high-resolution color images of real-world scenes varying in their viewpoint (aerial/terrestrial) and naturalness (manmade/natural). Half of the participants were trained to categorize these visual scenes at a specific-subordinate level (e.g., truss bridge, suspension bridge) and half of the participants passively viewed the same images while performing an orthogonal task. Both groups saw the same scene stimuli for five of the six training sessions; the sixth session consisted of a novel set of scenes to assess learning transfer. We found group-specific improvements in behavioral performance across training sessions and scene dimensions, including learning transfer, with greatest behavioral improvements observed for aerial scenes. In contrast, the passive-viewing group showed no major improvements despite equal exposure to the stimuli. Complementing the behavioral effects, we found experience-related neural changes in the experimental group: response magnitudes in scene-selective cortex (PPA and OPA) were correlated with improvements in behavioral performance for aerial imagery, but not in control regions (e.g., EVC, FFA). Whole-brain analyses revealed that over the course of training with aerial scenes, additional regions were recruited beyond scene-selective cortex, primarily lateral ventral-occipitotemporal cortex and posterior parietal cortex. Together these findings suggest that acquiring experience in categorizing aerial scenes entails the engagement of multiple visual areas involved in object and scene recognition, as well as the potential involvement of top-down attention mechanisms, and visuospatial processing. This research is supported by ONR BAA N00014-16-R-BA01.

This paper describes lessons we have learned about presenting cognitive skills training. We have used ShadowBox as our training approach (Klein and Borders in J Cogn Eng Decis Mak 10:268–280, 2016), but the lessons apply regardless of specific techniques employed. We analyze key takeaways and lessons learned throughout the course of multiple ShadowBox projects. We explain how the original ShadowBox mission statement has evolved based on these lessons learned. Recommendations are offered for others who are engaged in cognitive skills training.

Visual analysis of complex real-world scenes is essential to a variety of professional contexts, ranging from defense and intelligence to architecture and urban planning. Expertise in recognizing information-rich yet highly variable scenes is putatively achieved through experience, yet little is currently known about how skills in scene recognition are formed and evolve during learning, and what underlying neural mechanisms support their acquisition. The present study is a first attempt at addressing these questions, quantifying the behavioral changes associated with the acquisition of scene expertise. We assembled a rich stimulus-set consisting of high-resolution color scene images varying across five dimensions: Viewpoint (aerial/terrestrial), Naturalness (manmade/natural), and three hierarchical categorization levels: Basic-level, Subordinate, and Exemplar. For instance, the category "deserts" contained three deserts types (Sandy, Shrub and Rocky), and each desert type contained ten individual images of specific deserts. Critically, each individual scene was presented both in an aerial and terrestrial viewpoint, to assess generalization across viewpoints. We trained 15 participants to categorize these scenes for a total of 12 hours. Each individual training regimen was comprised of six sessions; participants trained on half of the stimuli for five sessions, and in the sixth session they viewed he other half of the scenes. To assess the efficiency of training, we employed two behavioral metrics: (1) within-set learning (i.e. learning across the five sessions), and (2) generalization (i.e. transfer of learning). Learning occurred within the five sessions (evident in a monotonic decrease in reaction times and increase in accuracy), and notably, we also found transfer of learning, as performance in the sixth session was pronouncedly better than performance in the first four training sessions. Together, these results suggest that expertise in scene recognition can be trained in the lab and will form the basis for future studies on the neural substrates of scene expertise. Meeting abstract presented at VSS 2018

One recurrent finding in the neuroimaging of vision is category-selective regions in occipitotemporal cortex (OTC). It is still unclear, however, how object information is organized in OTC. Konkle and Caramazza (2013) suggested that object representations in OTC are organized based on their animacy and real-world size. These two dimensions, they argue, do not operate independently, but rather interact; small and large inanimate objects are represented in separate OTC regions, while in contrast, animate objects are grouped together irrespective of their size. We investigated the early neurophysiological signatures of this organizational principle by asking whether the N1, a category-selective event-related potential (ERP) component, is differentially affected by animacy and size. We recorded ERPs from participants while they viewed object images from four categories spanning animacy (Inanimate: roller-skate, motorbike; Animate: cow, butterfly) and size (Large: motorbike, cow; Small: roller-skate, butterfly) dimensions. To ensure active categorization of the objects along all dimensions, participants were asked to categorize the objects based on their size and animacy (as well as based on physical properties, as a control condition). We found that the combined effect of animacy and size can be observed as early as 170ms post-stimulus onset. Specifically, there was a significant interaction effect on N1 amplitude reflecting the organizational principle suggested by Konkle and Caramazza: N1 amplitude in right posterior lateral regions was more negative for animate than inanimate objects, and critically, size had an effect on N1 amplitude which was evident only for inanimate objects. A more negative response was recorded in response to large objects compared to the small objects. Together, these data support previous neuroimaging findings suggesting object representations in OTC are represented based on their animacy and size, and, importantly, indicate that this organizational principle can be observed in a relatively early stage along the visual processing hierarchy. Meeting abstract presented at VSS 2018

Event Abstract Back to Event Using behavioral and neural measures to assess training in scene categorization Joseph Borders1*, Birken Noesen1, Bethany Dennis1 and Assaf Harel1 1 Wright State University, Department of Psychology, United States Visual analysis of complex real-world scenes is essential to a variety of professional contexts, ranging from defense intelligence (e.g., overhead imagery analysis and target detection) to urban planning. Expertise in recognizing information-rich yet highly variable scenes is putatively achieved through experience, yet little is currently known about how skills in scene recognition are formed and evolve during learning, and what are the underlying neural mechanisms that support the acquisition and deployment of these skills. The goal of the present study is to quantify the behavioral changes associated with the acquisition of scene expertise, elucidate the neural mechanisms underlying the acquisition of scene expertise, and ultimately, to establish the importance of changes in neural scene representations as novel markers of scene expertise. To track down the development of scene expertise and its underlying neural correlates, we conducted a long-term multi-session behavioral training study in which participants were trained in the categorization of visual scenes, and measured changes in neural responses to these scenes over multiple scanning sessions. We constructed a rich stimulus set consisting of high-resolution color images of scenes varying across five dimensions: Viewpoint (aerial/terrestrial), Naturalness (manmade/natural), and three hierarchical categorization levels: Basic, Subordinate, and Exemplar. For example, the category “airports” contained three airport types (large hub, small hub, military), and each airport type contained ten individual images of specific airports. Critically, each individual scene was presented both in an aerial and terrestrial viewpoint to assess generalization across viewpoints. A group of naïve participants were trained to categorize these scenes for a total of 12 hours across a period of three weeks. Each individual training regimen was comprised of six sessions, interspersed with four functional magnetic resonance imaging (fMRI) scanning sessions. Participants trained on half of the stimuli for five sessions, and in the sixth session they viewed the other half of the scenes. We employed two behavioral metrics to assess scene categorization learning: (1) within-set learning (i.e., learning across the five sessions), and (2) generalization (i.e., transfer of learning) to assess the behavioral effects with the acquisition of scene expertise. Learning occurred within the five sessions (evident in a monotonic decrease in reaction times and increase in accuracy), and notably, we also found transfer of learning, as performance in the sixth session was pronouncedly better than performance in the first four training sessions. Thus, performance substantially improved on all measures, validating our training paradigm. This is a clear demonstration that naïve participants can be trained to develop expertise that goes beyond their initial capabilities in scene categorization. Preliminary analysis of the neuroimaging findings revealed that the effects of training in scene categorization can be measured not only by using behavior, but also by examining how scene representations in scene-selective cortex change as a function of learning. We conducted a region of interest (ROI) analysis, examining the magnitude of neural response in scene-selective regions (i.e., Parahippocampal Place Area (PPA) and the Occipital Place Area (OPA) as well as non-scene-selective visual regions, such as the Fusiform Face Area (FFA) and retinotopic cortex (Early Visual Cortex: EVC). Importantly, all functional ROIs were localized using an independent task. Overall, we observed a robust effect of naturalness, in which manmade scenes evoked a greater response than natural scenes within scene-selective region. Notably, increased levels of training yielded viewpoint effects, evident primarily for the natural scenes. Such an interaction suggests that as participants develop experience with the natural scenes, scene selective regions become more selective for viewpoint, specifically for terrestrial scenes. In comparison, the non-scene-selective visual regions did not demonstrate these effects, suggesting the observed effects are specific and were not due to attentional effects. Together, these results suggest that expertise in scene recognition can be trained in the lab and will form the basis for future studies on the neural substrates of scene expertise. These findings highlight the need for deeper understanding of neural circuits and mechanisms underlying expertise in scene recognition. Overcoming this gap will enable the design of neuroscientific-grounded personalized training regimens. Acknowledgements The present study was sponsored by an Office of Naval Research grant (BAA N00014-16-R-BA01). Keywords: Scene Recognition, Neuroimaging, Expertise Development, human neuroscience, neuroergonomics, scene categorization Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Poster Presentation Topic: Neuroergonomics Citation: Borders J, Noesen B, Dennis B and Harel A (2019). Using behavioral and neural measures to assess training in scene categorization. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00111 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Apr 2018; Published Online: 27 Sep 2019. * Correspondence: Mr. Joseph Borders, Wright State University, Department of Psychology, Dayton, United States, borders.9@wright.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Joseph Borders Birken Noesen Bethany Dennis Assaf Harel Google Joseph Borders Birken Noesen Bethany Dennis Assaf Harel Google Scholar Joseph Borders Birken Noesen Bethany Dennis Assaf Harel PubMed Joseph Borders Birken Noesen Bethany Dennis Assaf Harel Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Unlike behavioral skills training, cognitive skills training attempts to impart concepts that typically depend on tacit knowledge. Subject-matter experts (SMEs) often deliver cognitive training, but SMEs are expensive and in short supply, causing a training bottleneck. Recently, Hintze developed the ShadowBox method to overcome this limitation. As part of the Defense Advanced Research Projects Agency’s Social Strategic Interaction Modules, Klein, Hintze, and Saab adapted the ShadowBox approach to train large numbers of trainees without relying on expert facilitators. As part of this program, we used the ShadowBox approach to train warfighters on the social cognitive skills needed to successfully manage civilian encounters without creating hostility or resentment. ShadowBox training was evaluated in two studies. Evaluation 1 provided 3 hr of nonfacilitated, paper-based training to Marines at Camp Pendleton and Camp Lejeune (N = 59), and improved performance (i.e., match to the SME rankings) by 28% compared to a control group. Evaluation 2 provided 1 hr of nonfacilitated training, administered via Android tablet, to soldiers at Fort Benning (N = 30) and improved performance by 21%. These results, both statistically significant, suggest ways to use scenario-based training to develop cognitive skills in the military.

The objective of this project was to understand why and how some police officers and military personnel are more effective than others at managing civilian encounters without creating hostility – ‘Good Strangers’ (GSs). We conducted cognitive task analysis (CTA) interviews with 17 US police officers and 24 US warfighters (Marines and Army soldiers). The interviews yielded a total of 38 incidents (17 police and 21 military), which we used to identify critical skills for functioning as GSs. These skills centred on having a sensemaking frame that established a professional identity as a GS – Someone who seeks opportunities to increase civilian trust in police/military. This frame requires skills in gaining voluntary compliance, building rapport, trading off security and other frames versus trust building, and taking the perspective of civilians.Practitioner points To work effectively with civilians, police and military personnel need to use a Good Stranger frame, which casts each encounter as an opportunity to build trust. This GS frame requires skills such as trading off security to be seen as trustworthy, perspective taking, gaining rapport, gaining voluntary compliance rather than coercive compliance, and de‐escalating tense situations. The GS frame may be surprisingly easy to acquire for some police and military; observation of role models and their effectiveness seems to be a powerful training opportunity. Other training leverage points involve peer pressure, becoming more effective at gaining civilian cooperation, and recognizing the problems created by failing to build trust.

When experts leave organizations due to retirement or turnover, their valuable experience, skills and knowledge often depart with them. A major challenge facing organizations across various domains is their ability to capture and transfer this expertise to the younger workforce. This becomes increasingly problematic as organizations grow and hire large quantities of new employees. Ideally, novices would be trained to think like those experts that they must replace. However, it is not easy to design and deliver the right kind of training. There are several conceptual and practical considerations to be addressed. The ShadowBox method [1,2] offers organizations a flexible and innovative training solution. ShadowBox training uses scenario-based instruction to train novices in the perceptual and cognitive skills of experts within their domain, and it can also serve as a diagnostic tool prior to training. The underlying approach and training format allows ShadowBox to accommodate many of the organizational constraints that face training developers today.

US military personnel can function as “Good Strangers” (GS), cultivating co-operation and safety with civilians, or they can act in ways that increase hostility. Current work investigates the dynamics of social interactions and training technologies attempting to increase positive outcomes of military social encounters. Soldiers at Ft. Benning completed the GS Diagnostic Tool measuring the GS cognitive frame and its strategic, behavioral and affect components. First, we examined differences between a GS frame and other cognitive frames: Mission, Rules/Procedures, and Authority. Next, we investigated priorities for tactics and skills: Perspective Taking, Deescalation, Building Rapport, Voluntary Compliance, and Security. Finally, we assessed confidence and competence levels for the tactics listed above. We found differences among warfighters as well as significant relationships among frame preference, response tendencies, and competencies. The research suggests how social interaction training might be most effective when addressing gaps between initial capacities of the learner and GS essentials.

We sought to understand how some police officers and military personnel are more effective than others at increasing civilian good will following encounters. Such officers can be termed “Good Strangers” (GSs). We conducted Cognitive Task Analysis (CTA) interviews with 17 U.S. police officers and 24 warfighters (Marines and Army soldiers). The CTA interviews yielded a total of 92 incidents, which were used to identify critical skills for training warfighters to become GSs. These skills supported a professional identity as a GS – seeking opportunities to increase civilian trust in police/military. Increasing trust from civilians requires skills in gaining voluntary compliance, building rapport, de-escalating conflicts, trading-off risk versus trust building, and taking the perspective of civilians.