Categorization influences detection: A perceptual advantage for representative exemplars of natural scene categories.

Traditional models of recognition and categorization proceed from registering low-level features, perceptually organizing that input, and linking it with stored representations. Recent evidence, however, suggests that this serial model may not be accurate, with object and category knowledge affecting rather than following early visual processing. Here, we show that the degree to which an image exemplifies its category influences how easily it is detected. Participants performed a two-alternative forced-choice task in which they indicated whether a briefly presented image was an intact or phase-scrambled scene photograph. Critically, the category of the scene is irrelevant to the detection task. We nonetheless found that participants "see" good images better, more accurately discriminating them from phase-scrambled images than bad scenes, and this advantage is apparent regardless of whether participants are asked to consider category during the experiment or not. We then demonstrate that good exemplars are more similar to same-category images than bad exemplars, influencing behavior in two ways: First, prototypical images are easier to detect, and second, intact good scenes are more likely than bad to have been primed by a previous trial.

Portable Weather Applications for General Aviation Pilots.

OBJECTIVE: The objective of this study was to examine the potential benefits and impact on pilot behavior from the use of portable weather applications. METHOD: Seventy general aviation (GA) pilots participated in the study. Each pilot was randomly assigned to an experimental or a control group and flew a simulated single-engine GA aircraft, initially under visual meteorological conditions (VMC). The experimental group was equipped with a portable weather application during flight. We recorded measures for weather situation awareness (WSA), decision making, cognitive engagement, and distance from the aircraft to hazardous weather. RESULTS: We found positive effects from the use of the portable weather application, with an increased WSA for the experimental group, which resulted in credibly larger route deviations and credibly greater distances to hazardous weather (≥30 dBZ cells) compared with the control group. Nevertheless, both groups flew less than 20 statute miles from hazardous weather cells, thus failing to follow current weather-avoidance guidelines. We also found a credibly higher cognitive engagement (prefrontal oxygenation levels) for the experimental group, possibly reflecting increased flight planning and decision making on the part of the pilots. CONCLUSION: Overall, the study outcome supports our hypothesis that portable weather displays can be used without degrading pilot performance on safety-related flight tasks, actions, and decisions as measured within the constraints of the present study. However, it also shows that an increased WSA does not automatically translate to enhanced flight behavior. APPLICATION: The study outcome contributes to our knowledge of the effect of portable weather applications on pilot behavior and decision making.

Simple line drawings suffice for functional MRI decoding of natural scene categories.

Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary visual cortex, the parahippocampal place area (PPA), and the retrosplenial cortex (RSC). Here we ask whether, and where, we can still decode scene category if we reduce the scenes to mere lines. We collected fMRI data while participants viewed photographs and line drawings of beaches, city streets, forests, highways, mountains, and offices. Despite the marked difference in scene statistics, we were able to decode scene category from fMRI data for line drawings just as well as from activity for color photographs, in primary visual cortex through PPA and RSC. Even more remarkably, in PPA and RSC, error patterns for decoding from line drawings were very similar to those from color photographs. These data suggest that, in these regions, the information used to distinguish scene category is similar for line drawings and photographs. To determine the relative contributions of local and global structure to the human ability to categorize scenes, we selectively removed long or short contours from the line drawings. In a category-matching task, participants performed significantly worse when long contours were removed than when short contours were removed. We conclude that global scene structure, which is preserved in line drawings, plays an integral part in representing scene categories.

Precision recruitment for high-risk participants in a COVID-19 cohort study.

Background: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. Methods: We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. Results: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. Conclusion: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

Leveraging Clinical Informatics to Address the Quintuple Aim for End-of-Life Care.

As the population of older adults grows at an unprecedented rate, there is a large gap to provide culturally tailored end-of-life care. This study describes a payor-led, informatics-based approach to identify Medicare members who may benefit from a Compassionate CareSM Program (CCP), which was designed to provide specialized care management services and support to members who have end-stage and/or life-limiting illnesses by addressing the quintuple aim. Potential participants are identified through machine learning models whereby nurse care managers then provide tailored outreach via telephone. A retrospective, observational cohort analysis of propensity-weighted Medicare members was performed to compare decedents who did or did not participate in the CCP. This program enhanced the end-of-life care experience while providing equitable outcomes regardless of age, gender, and geography and decreased inpatient (-37%) admissions with concomitant reduced (-59%) medical spend when compared to decedents that did not utilize the end-of-life care management program.

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors.

Importance: The severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented, and their public health burden is not accurately estimated. Objective: To estimate the true burden of influenza-like illness (ILI) in the US population using a surrogate measure of daily steps lost as measured by commercial wearable sensors. Design, Setting, and Participants: This cohort study modeled data from 15 122 US adults who reported ILI symptoms during the 2018-2019 influenza season (before the COVID-19 pandemic) and who had a sufficient density of wearable sensor data at symptom onset. Participants' minute-level step data as measured by commercial wearable sensors were collected from October 1, 2018, through June 30, 2019. Minute-level activity time series were transformed into day-level time series per user, indicating the total number of steps daily. Main Outcomes and Measures: The primary end point was the number of steps lost during the period of 4 days before symptom onset (the latent phase) through 11 days after symptom onset (the symptomatic phase). The association between covariates and steps lost during this interval was also examined. Results: Of the 15 122 participants in this study, 13 108 (86.7%) were women, and the median age was 32 years (IQR, 27-38 years). For their ILI event, 2836 of 15 080 participants (18.8%) sought medical attention, and only 61 (0.4%) were hospitalized. Over the course of an ILI lasting 10 days, the mean cumulative loss was 4437 steps (95% CI, 4143-4731 steps). After weighting, there was an estimated overall nationwide reduction in mobility equivalent to 255.2 billion steps (95% CI, 232.9-277.6 billion steps) lost because of ILI symptoms during the study period. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, 60.6% of this reduction in steps (154.6 billion steps [95% CI, 138.1-171.2 billion steps]) occurred among persons who sought no medical care. Age and educational level were positively associated with steps lost. Conclusions and Relevance: These findings suggest that most of the burden of ILI in this study would have been invisible to health care and public health reporting systems. This approach has applications for public health, health care, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes such as during long COVID-19.

Natural scene categories revealed in distributed patterns of activity in the human brain.

Human subjects are extremely efficient at categorizing natural scenes, despite the fact that different classes of natural scenes often share similar image statistics. Thus far, however, it is unknown where and how complex natural scene categories are encoded and discriminated in the brain. We used functional magnetic resonance imaging (fMRI) and distributed pattern analysis to ask what regions of the brain can differentiate natural scene categories (such as forests vs mountains vs beaches). Using completely different exemplars of six natural scene categories for training and testing ensured that the classification algorithm was learning patterns associated with the category in general and not specific exemplars. We found that area V1, the parahippocampal place area (PPA), retrosplenial cortex (RSC), and lateral occipital complex (LOC) all contain information that distinguishes among natural scene categories. More importantly, correlations with human behavioral experiments suggest that the information present in the PPA, RSC, and LOC is likely to contribute to natural scene categorization by humans. Specifically, error patterns of predictions based on fMRI signals in these areas were significantly correlated with the behavioral errors of the subjects. Furthermore, both behavioral categorization performance and predictions from PPA exhibited a significant decrease in accuracy when scenes were presented up-down inverted. Together these results suggest that a network of regions, including the PPA, RSC, and LOC, contribute to the human ability to categorize natural scenes.

Saccadic repulsion in pop-out search: how a target's dodgy history can push the eyes away from it.

Previous studies have shown that even in the context of fairly easy selection tasks, as is the case in a pop-out task, selection of the pop-out stimulus can be sped up (in terms of eye movements) when the target-defining feature repeats across trials. Here, we show that selection of a pop-out target can actually be delayed (in terms of saccadic latencies) and made less accurate (in terms of saccade accuracy) when the target-defining feature has recently been associated with distractor status. This effect was observed even though participants' task was to fixate color oddballs (when present) and simply press a button when their eyes reached the target to advance to the next trial. Importantly, the inter-trial effect was also observed in response time (time to advance to the next trial). In contrast, this response time effect was completely eliminated in a second experiment when eye movements were eliminated from the task. That is, when participants still had to press a button to advance to the next trial when an oddball target was present in the display (an oddball detection task experiment). This pattern of results closely links the "need for selection" in a task to the presence of an inter-trial bias of attention (and eye movements) in pop-out search.

Induced visual fading of complex images.

Visual stimuli fade from awareness under retinal stabilization or careful fixation, a phenomenon documented by Troxler more than 200 years ago. Research on visual fading during normal visual fixation typically has been restricted to discrete, simple, low-contrast shapes presented peripherally against a uniform or textured background. In four experiments, we document a striking new visual fading effect in which entire photographs of scenes fade to a uniform luminance and hue during normal visual fixation. Critically, this "scene fading" can be induced almost instantaneously by some types of visual transients but not by others. These induced fading effects are sufficiently robust that they can be experienced by most observers in a single trial. Taken as a whole, the effects are inconsistent with simple contrast adaptation, gradual Troxler fading, or transient-induced fading. They are, however, consistent with the idea that small contrast decrements can induce fading of entire scenes. The methods provide a robust tool for the exploration of visual fading, and the results could have important implications for the role of filling-in and neural adaptation in our visual awareness of natural scenes and other complex stimuli.

Modeling the effect of selection history on pop-out visual search.

While attentional effects in visual selection tasks have traditionally been assigned "top-down" or "bottom-up" origins, more recently it has been proposed that there are three major factors affecting visual selection: (1) physical salience, (2) current goals and (3) selection history. Here, we look further into selection history by investigating Priming of Pop-out (POP) and the Distractor Preview Effect (DPE), two inter-trial effects that demonstrate the influence of recent history on visual search performance. Using the Ratcliff diffusion model, we model observed saccadic selections from an oddball search experiment that included a mix of both POP and DPE conditions. We find that the Ratcliff diffusion model can effectively model the manner in which selection history affects current attentional control in visual inter-trial effects. The model evidence shows that bias regarding the current trial's most likely target color is the most critical parameter underlying the effect of selection history. Our results are consistent with the view that the 3-item color-oddball task used for POP and DPE experiments is best understood as an attentional decision making task.

Good exemplars of natural scene categories elicit clearer patterns than bad exemplars but not greater BOLD activity.

Within the range of images that we might categorize as a "beach", for example, some will be more representative of that category than others. Here we first confirmed that humans could categorize "good" exemplars better than "bad" exemplars of six scene categories and then explored whether brain regions previously implicated in natural scene categorization showed a similar sensitivity to how well an image exemplifies a category. In a behavioral experiment participants were more accurate and faster at categorizing good than bad exemplars of natural scenes. In an fMRI experiment participants passively viewed blocks of good or bad exemplars from the same six categories. A multi-voxel pattern classifier trained to discriminate among category blocks showed higher decoding accuracy for good than bad exemplars in the PPA, RSC and V1. This difference in decoding accuracy cannot be explained by differences in overall BOLD signal, as average BOLD activity was either equivalent or higher for bad than good scenes in these areas. These results provide further evidence that V1, RSC and the PPA not only contain information relevant for natural scene categorization, but their activity patterns mirror the fundamentally graded nature of human categories. Analysis of the image statistics of our good and bad exemplars shows that variability in low-level features and image structure is higher among bad than good exemplars. A simulation of our neuroimaging experiment suggests that such a difference in variance could account for the observed differences in decoding accuracy. These results are consistent with both low-level models of scene categorization and models that build categories around a prototype.