The human visual system adapts to statistical regularities in the environment to facilitate visual processing. While laboratory-based tasks make clear distinctions between how task-relevant and task-irrelevant visual information can guide this adaptation, such discretization is rarely available in the real world. As such, it remains unclear exactly what information the visual system tracks to flexibly adapt to a given task. The current study used a massive visual search dataset from the mobile game Airport Scanner. Effects of exposure over a range of more task-relevant (e.g., target presence) to less task-relevant (e.g., background context) features were analyzed in an omnibus model to predict response times in both target-present and target-absent trials. As in previous work (Kramer et al., Journal of Experimental Psychology: General, 151 (8), 1854, 2022), increased exposure to target-present trials significantly sped up the detection of targets and slowed the rejection of target-absent trials. Exposure to salient distractors reduced response times for target-present trials, potentially as a result of learned distractor suppression (Gaspelin Luck, Trends in cognitive sciences, 22 (1), 79-92, 2018) or increased familiarity (Mruczek Sheinberg, Perception psychophysics, 67 (6), 1016-1031, 2005), but had no effect on target-absent trials. Exposure to background information decreased response times in both target-present and target-absent trials, with notable interactions between target and background exposure. Specifically, the effect of background information was more pronounced when target exposure was low, suggesting that less task-relevant context information is more likely to be tracked in the absence of more task-relevant information, namely, the presentation of targets. The findings highlight the importance of considering multiple sources of exposure in visual search tasks and demonstrate the value of large datasets in quantifying their complex interactions.

Object category learning is a foundational cognitive process. Most human category learning studies involve brief paradigms lasting a few hours and show increased shape tuning in visual areas and task-dependent responses in PFC. Other studies also identify a "frontal bottleneck" that limits multitasking. However, real-world categorization often involves months or years of practice, potentially producing qualitative shifts toward automaticity. We tested the hypothesis that extensive training causes a spatio-temporal shift in the neural circuitry supporting categorization. Participants were trained over >30,000 trials across 5-10 weeks to categorize novel morphed car stimuli via a mobile app. We used fMRI and EEG rapid adaptation techniques to examine neural responses after initial learning (∼4 hr in 1-2 weeks) and after extensive training (∼16 additional hours over another 4-8 weeks). Converging fMRI and EEG results showed that extensive training fundamentally remodeled task-related circuitry: Visual areas in ventral occipito-temporal cortex (vOTC) were initially shape-selective, but category-selective responses emerged in the vOTC after extensive training. The vOTC also showed decreased functional connectivity with the PFC and increased connectivity with motor output areas. This supports the hypothesis that extensive experience enables category decisions to occur outside of the "frontal bottleneck." Critically, the decrease in connectivity between vOTC and PFC was associated with improved categorization performance while dual tasking, indicating increased automaticity. These findings demonstrate that prolonged training reshapes the neural basis of categorization, shifting it from a flexible but attentionally controlled process to a more streamlined, automatic process.

The existence of a neural representation for whole words (i.e., a lexicon) is a common feature of many models of speech processing. Prior studies have provided evidence for a visual lexicon containing representations of whole written words in an area of the ventral visual stream known as the visual word form area. Similar experimental support for an auditory lexicon containing representations of spoken words has yet to be shown. Using functional magnetic resonance imaging rapid adaptation techniques, we provide evidence for an auditory lexicon in the auditory word form area in the human left anterior superior temporal gyrus that contains representations highly selective for individual spoken words. Furthermore, we show that familiarization with novel auditory words sharpens the selectivity of their representations in the auditory word form area. These findings reveal strong parallels in how the brain represents written and spoken words, showing convergent processing strategies across modalities in the visual and auditory ventral streams.

Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as “shadow” biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample’s representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged “shadow” biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Objective The long-term consequences of the COVID-19 pandemic on college students' mental health remains unknown. The current study explored self-reported Obsessive-Compulsive symptomatology among college student cohorts from pre-, peak-, and later-pandemic time points. Participants Undergraduate college students (N = 524) who volunteered for course credit. Methods Self-report responses on the Dimensional Obsessive-Compulsive Scale (DOCS), which includes subscales for contamination, unacceptable thoughts, harm responsibility, and symmetry, were collected from November 29, 2016 through April 27, 2021 and assessed for differences between the pre-, peak-, and later-pandemic cohorts. Results Peak-pandemic responders reported higher symptomatology for contamination and unacceptable thoughts compared to pre-pandemic responders (and for pre- vs. later-pandemic for contamination), with no significant effects for symmetry or harm responsibility. Conclusions Although the longer-term consequences of the COVID-19 pandemic on students remains unknown, a greater shift in college mental health services from prevention to assessing and addressing more immediate challenges may be necessary.

Human behavior does not exist in a bubble—it is influenced by countless forces, including each individual’s current goals, pre-existing cognitive biases, and prior experience. The current project leveraged a massive behavioral dataset to provide a data-driven quantification of the relationship between prior experience and current behavior. Data from two different behavioral tasks (a categorization task and a visual search task) demonstrated that prior history had a precise, systematic, and meaningful influence on subsequent performance. Specifically, the greater the evidence for (or against) all aspects of the current trial, the more (or less) efficient behavior was on that trial. The robust influence of prior experience was present for even distracting and likely unattended information. The ubiquity and consistency of the effect for features both related and unrelated to stimulus presence suggests a domain-general mechanism that increases the efficiency of behavior in contexts that match prior experience. These findings are theoretically important for understanding behavioral adaptation, experimentally powerful for directly addressing effects of previous trials when designing and analyzing research projects, and potentially useful for optimizing behavior in various applied contexts.

Response inhibition—the suppression of prepotent motor responses—typically triggers a cascade of behavioral changes in decision making and motor planning which effectively reduces the efficiency of subsequent performance. These effects have traditionally been hypothesized to arise from high-level cognitive processes localized to the frontoparietal cortices. However, recent evidence noting bidirectional interference between top-down processing and perception (Teng & Kravitz, 2019) suggests that post-inhibition interference might also arise from perceptual processing and extend to even task-irrelevant stimulus features; arguing against traditional views that such interference originates in the frontoparietal network. To test this prediction, a simple go/no-go paradigm with colored and oriented Gabor patches was presented to participants recruited via Amazon Mechanical Turk. On each trial, participants responded to either the color (Experiment 1) or orientation (Experiment 2) of a Gabor patch while the other, task-irrelevant feature was orthogonal to task goals. Critically, the task-irrelevant feature (of orientation or color) on the go-trial (probe) immediately following the singular no-go trial was manipulated between participants such that it was either 0º or 72º away from that of the preceding inhibition event. Accuracy and response time on the probe trial was worse/slower when the task-irrelevant features of the probe matched (0º) the task-irrelevant feature of preceding inhibition trial than when it differed (72º). Additional experiments replicated this finding and extended it to intermediate task-irrelevant differences (18º, 36º, 54º), allowing for direct comparisons with the known tuning properties of the task-irrelevant feature (of orientation or color) in early perceptual areas. Taken together, these findings suggest that task-irrelevant stimulus features shape post-inhibition performance deficits. Importantly, these results lend support to an alternative theoretical model in which there is extensive interplay between response inhibition and perceptual processing.

The visual world often presents dense and complex scenes, creating a critical need for efficient visual search—looking for targets while limiting interference from non-target distractors. Visual search is key for many professions (e.g., radiology, aviation security, military combat) and understanding the impact of distractors is key to optimizing operations. For example, assessing whether the ability to filter out distracting information is a stable difference between individuals could have important consequences for recruitment and training efforts. The goal of the current project was to simultaneously examine multiple ways in which distractors might impact search performance, necessitating a large and diverse dataset so that possible influences can be examined both in isolation and in concert with others. As such, the current project took advantage of a massive dataset (>3.8 billion trials, >15.5 million individuals) from the Airport Scanner mobile game (Kedlin Co.). The size of the dataset allows for testing multiple potential effects within the same paradigm with the same data. In the game, players serve as aviation security screeners, using their finger to tap on prohibited (targets) amongst allowed items (distractors) in simulated bags. A number of effects were explored, including the impact of featural overlap between targets and distractors on performance. For example, higher amounts of overlap between the colors of distractors and the set of possible targets caused greater interference. This and other examples of the interplay between distractor influences on search performance, such as changes in interference as exposure to individual distractors builds over experience, will be discussed.

Visual search—looking for targets among distractors—underlies many critical professions (e.g., radiology, aviation security) that demand optimal performance. As such, it is important to identify, understand, and ameliorate negative factors such as fatigue—mental and/or physical tiredness that leads to diminished function. One way to reduce the detrimental effects is to minimize fatigue itself (e.g., scheduled breaks, adjusting pre-shift behaviors), but this is not always possible or sufficient. The current study explored whether some individuals are less susceptible to the impact of fatigue than others; specifically, if conscientiousness, the ability to control impulses and plan, moderates fatigue’s impact. Participants ( N = 374) self-reported their energy (i.e., the inverse of fatigue) and conscientiousness levels and completed a search task. Self-report measures were gathered prior to completing the search task as part of a large set of surveys so that participants could not anticipate any particular research question. Preregistered linear mixed-effect analyses revealed main effects of energy level (lower state energy related to lower accuracy) and conscientiousness (more trait conscientiousness related to higher accuracy), and, critically, a significant interaction between energy level and conscientiousness. A follow-up analysis, that was designed to illustrate the nature of the primary result, divided participants into above- vs. below-median conscientiousness groups and revealed a significant negative relationship between energy level and accuracy for the below median, but not above-median, group. The results raise intriguing operational possibilities for visual search professions, with the most direct implication being the incorporation of conscientiousness measures to personnel selection processes.

Realizing the benefits of research for human factors applications requires that academic theory and applied research in operational environments work in tandem, each informing the other. Mechanistic theories about cognitive processing gain insight from incorporating information from practical applications. Likewise, human factors implementations require an understanding of the underlying nature of the human operators that will be using those very implementations. This interplay holds great promise, but is too often thwarted by information from one side not flowing to the other. On one hand, basic researchers are often reluctant to accept research findings from complex environments and a relatively small number of highly-specialized participants. On the other hand, industry decision makers are often reluctant to believe results from simplified testing environments using non-expert research participants. The argument put forward here is that both types of data are fundamentally important, and explicit efforts should bring them together into unified and integrated research programs. Moreover, effectively understanding expert performance requires assessing non-expert populations.For many fields, it is critically important to understand how operators (e.g., radiologists, aviation security officers, military personnel) perform in their professional setting. Extensive research has explored a breadth of factors that can improve, or hinder, operators’ success, however, the vast majority of these research endeavors hit the same roadblock—it is practically difficult to test specialized operators. They can be hard to gain access to, have limited availability, and sometimes there just are not enough of them to conduct the needed research. Therefore, non-expert populations can provide a much-needed resource. Specifically, it can be highly useful to create a closed-loop ecosystem wherein an idea rooted in an applied realm (e.g., radiologists are more likely to miss an abnormality if they just found another abnormality) is explored with non-experts (e.g., undergraduate students) to affordably and extensively explore a number of theoretical and mechanistic possibilities. Then, the most promising candidate outcomes can be brought back to the expert population for further testing. With such a process, researchers can explore possible ideas with the more accessible population and then only use the specialized population with vetted research paradigms and questions.While such closed-looped research practices offer a way to best use available resources, the argument here is also that it is necessary to assess non-experts to fully understand expert performance. That is, even if researchers have full access to a large number of experts, they still need to test non-experts. Specifically, assessing non-experts allows for quantifying fundamentally important factors, such as strategic vs. perceptual drivers of performance and the time course of learning. Many of the potential gains in the applied sphere come from selecting the best people to train into becoming experts; without non-expert performance it is impossible to know how to enact that selection or to divorce the effects of extensive practice and expertise from the operational environment. While there has been an, at times, adversarial relationship between research practices that use non-expert vs. expert participants, the proposal here is that embracing both is vital for fully understanding the nature of expert performance.

Professions such as radiology and aviation security screening that rely on visual search—the act of looking for targets among distractors—often cannot provide operators immediate feedback, which can create situations where performance may be largely driven by the searchers’ own expectations. For example, if searchers do not expect relatively hard-to-spot targets to be present in a given search, they may find easy-to-spot targets but systematically quit searching before finding more difficult ones. Without feedback, searchers can create self-fulfilling prophecies where they incorrectly reinforce initial biases (e.g., first assuming and then, perhaps wrongly, concluding hard-to-spot targets are rare). In the current study, two groups of searchers completed an identical visual search task but with just a single difference in their initial task instructions before the experiment started; those in the “high-expectation” condition were told that each trial could have one or two targets present (i.e., correctly implying no target-absent trials) and those in the “low-expectation” condition were told that each trial would have up to two targets (i.e., incorrectly implying there could be target-absent trials). Compared to the high-expectation group, the low-expectation group had a lower hit rate, lower false alarm rate and quit trials more quickly, consistent with a lower quitting threshold (i.e., performing less exhaustive searches) and a potentially higher target-present decision criterion. The expectation effect was present from the start and remained across the experiment—despite exposure to the same true distribution of targets, the groups’ performances remained divergent, primarily driven by the different subjective experiences caused by each groups’ self-fulfilling prophecies. The effects were limited to the single-targets trials, which provides insights into the mechanisms affected by the initial expectations set by the instructions. In sum, initial expectations can have dramatic influences—searchers who do not expect to find a target, are less likely to find a target as they are more likely to quit searching earlier.

Professions such as radiology and aviation security rely on visual search—the act of looking for targets among distractors. Often the searchers must perform in the absence of immediate feedback, which can create situations where performance may be disproportionately driven by the searchers’ expectations. For example, if searchers do not expect difficult targets, they may find easy-to-spot targets but systematically quit searching before finding more difficult ones. Without feedback, as is often the case in real-world search, searchers may reinforce their initial expectations (e.g., falsely believing difficult targets are rare) and create self-fulfilling biases (e.g., I need only search for easy targets). Here, two groups of participants completed an identical multiple-target visual search task which differed only in the initial instructions. Those in the “high-expectation” condition were told that each trial would have 1 or 2 targets present (i.e., suggesting no target-absent trials) and those in the “low-expectation” condition were told that each trial would have up to 2 targets (i.e., suggesting there could be target-absent trials). The low-expectation group had a lower hit rate and quit trials more quickly, consistent with a shift in quitting threshold. This effect was present from the start and remained stable across blocks. In sum, the current results suggest initial expectations can have long-term consequences, such that searchers who do not expect to find a target become less likely to find a target.

Decades of research in cognitive psychology have largely relied on simple key or button presses to quantify human behavior. While many valuable discoveries have been made, a richer response modality may reveal more information regarding the different processes that underlie complex human behavior. This study provides a proof of concept for using a touch-and-swipe response method to separate response time into two components to extract more meaningful behavioral insights. Across several analyses, the two components were consistently shown to be separable, independent measurements of behavior. Furthermore, evaluating these isolated response time components improved inferential power and clarity of behavioral patterns. The touch-and-swipe response method is simple and easy-to-use, and it shows promise for more accurately targeting mechanisms of interest.

Cognitive psychologists often recruit through university-organized subject pools. Such pools are effective for data collection, providing access to a convenient sample. However, there are potential concerns, including the commonly held belief that the students who volunteer for participation at the end of the academic term might provide less reliable data. Such intuitions are not unfounded, as previous research has found demographic, personality, and motivational differences between students who participate in subject pool studies at the start versus the end of an academic term. However, empirical data on cognitive performance are equivocal, with some studies finding evidence for cognitive differences based on time-of-term participation and some studies failing to. The current study administered a visual search task in a subject pool at the start and end of multiple academic terms. The end-of-term participants were less accurate than the start-of-term participants, with a larger performance difference on difficult targets. Furthermore, the end-of-term participants were also less reliable - more likely to arrive late or fail to show up altogether. These results suggest that extra care needs to be taken when recruiting from subject pools, and suggestions are provided for how the visual cognition community can best make use of this important research tool.

Visual search, finding targets among distractors, is theoretically interesting and practically important as it involves many cognitive abilities and is vital for several critical industries (e.g., radiology, baggage screening). Unfortunately, search is especially error prone when more than one target is present in a display (a phenomenon termed the satisfaction of search effect or the subsequent search miss effect). The general effect is that observers are more likely to miss a second target if a first was already detected. Unpacking the underlying mechanisms requires two key aspects in analysis and design. First, to speak to the "subsequent" nature of the effect, the analyses must compare performance on single-target trials to performance for a second target in dual-target displays after a first has been found. Second, the design must include single-target displays that are matched in difficulty to each dual-target display to enable fair comparisons. However, it is not clear that prior research has met these two standards simultaneously. Work from academic radiology has primarily used designs with well-matched single- and dual-target trials, but most employed analyses that do not focus solely on performance after a first target has been detected. Work from cognitive psychology has generally performed the correct analyses, but relied on unmatched single- and dual-target trials, introducing a confound that could distort the results. In the current paper, we demonstrate the impact of this confound in empirical data and provide a roadmap for proper study design and analyses.

Studies of visual search—looking for targets among distractors—typically focus on quantifying the impact of general factors (e.g., number of distractors) on search performance. However, search efficiency, particularly in complex environments, is undoubtedly a function of the particular similarity relationships between the specific target(s) and distractors. Further, the visual system represents many different dimensions (color, location, category) that can be flexibly weighted according to current goals, implying that different similarity relationships may be important in distinct contexts. In the current study we examined the impact of similarity in complex visual search by using “big data” from the mobile app Airport Scanner, where the player serves as an airport security officer searching bags for a diverse set of prohibited items among a large heterogeneous set of potential distractors. This large variability in possible targets and distractors, combined with the volume of data (~3.6 billion trials, ~14.8 million users), provide a means to explore the impact of target-distractor similarity on search. The game also includes levels that players advance through in sequence, enabling an investigation of the effect of experience. The data were used to calculate the impact of every distractor on every target at each level, and the resulting behavioral matrices were then compared to a number of different similarity metrics derived from image statistics (e.g., color, pixelwise) and biologically-inspired models of vision (e.g., HMAX). The analyses revealed that experience shaped the impact of distractors, with lower-level metrics dominant early and higher-level features becoming increasingly important as target and distractor familiarity increased. The detailed understanding of search revealed by these analyses provides key insights for generating a detailed model of real-world search difficulty.

The human visual system can detect objects in streams of rapidly presented images at presentation rates of 70 Hz and beyond. Yet, target detection is often impaired when multiple targets are presented in quick temporal succession. Here, we provide evidence for the hypothesis that such impairments can arise from interference between "top-down" feedback signals and the initial "bottom-up" feedforward processing of the second target. Although it is has been recently shown that feedback signals are important for visual detection, this "crash" in neural processing affected both the detection and categorization of both targets. Moreover, experimentally reducing such interference between the feedforward and feedback portions of the two targets substantially improved participants' performance. The results indicate a key role of top-down re-entrant feedback signals and show how their interference with a successive target's feedforward process determine human behavior. These results are not just relevant for our understanding of how, when, and where capacity limits in the brain's processing abilities can arise, but also have ramifications spanning topics from consciousness to learning and attention.