Errors rarely occur in isolation, but rather within behavioral sequences and are known to shape subsequent behavior. However, understanding the full time course of the impact of errors has been challenging given data limitations. Here, to gain key insights, two groups of precisely matched participants (n > 35,000) were compared; those who made versus did not make an error in a short object discrimination task. Linear mixed-effects models revealed error-induced effects both preceding and following errors. First, the error group showed pre-error speeding up to eight trials before the error occurred. Second, post-error slowing occurred and rapidly diminished over trials. Finally, leave-one-out cross-validation models using individual trial behavior predicted an upcoming error eight trials before the error occurred. This project highlights the systematic nature of behavioral changes around errors and demonstrates the potential for early error prediction and intervention using only simple metrics and models.

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.

Autism Spectrum Disorder (ASD) is defined as a unidimensional condition, and autism traits are measured on a continuum where the high end of the spectrum represents individuals likely to have an ASD diagnosis. However, the large heterogeneity of ASD has thrown this unidimensional conceptualization into question. With the exact underlying cause(s) of autism yet to be identified, there is a pressing need to establish core, underlying dimensions of ASD that can capture heterogeneity within the autism spectrum, thereby better specifying both autistic traits and ASD symptoms. Here we describe one important transdiagnostic dimension, the cognitive rigidity-flexibility dimension, that may impact autistic traits and symptoms across symptom-relevant cognitive domains. We first discuss how diminished cognitive flexibility manifests in core autistic traits and autism symptoms in perception, attention, learning, social cognition, and communication. We then propose to supplement assessments of autistic traits in the general population and autism symptoms in individuals with an ASD diagnosis with a comprehensive batter of cognitive flexibility measures in these symptom-relevant domains. We conjecture that systematic differences in domain-general versus domain-specific cognitive flexibility can distill subgroups within the autism phenotype. While we focus on the cognitive flexibility dimension here, we believe that it is important to extend this framework to other higher order dimensions that can capture core autism symptoms and transdiagnostic symptom severity. This approach can characterize the latent, multi-faceted structure of autism, thereby yielding greater precision in diagnostic classification and the creation of more targeted interventions.

The behavioral sciences have had great success in their study of the mechanisms that drive behavior. However, they have had less impact on applied settings or policy. This gap results from the very adaptability that makes human behavior useful. Adaptability implies that behavior will be highly specific to the context in which it occurs. Thus, building a bridge between the lab and application requires testing in the specific applied setting, which runs afoul of the high cost of data collection. This cost has also led to a focus on simple paradigms that poorly match applied settings. However, crowdsourcing enables data collection at vastly reduced budgets and schedules. This new cost regime also enables paradigms better suited to applied settings. Behavioral science should now be used throughout applied- and policy-focused projects.

Satisfaction of Search (SOS), a phenomenon studied by medical imaging and cognitive science researchers, refers to the diminished visual search performance for a target in a search image when a prior target has already been detected. Much has been learned about the SOS effect by studying its pervasiveness across many different types of medical images, including chest radiography, abdominal contrasts, and breast imaging. Much has also been learned about the SOS effect by using simplified search images with targets that take little training to detect (see Adamo et al., 2021 for a review). In this study, we used simplified 2D and segmented-3D search images and investigated whether observers' search performance differs between these imaging types. Consistent with research in breast imaging, Adamo et al. (2018) found that when novice and experienced observers searched for a single target, they: 1) made fewer false positives, 2) improved their hit rates, and 3) spent longer searching in segmented-3D images compared to 2D images. Here, we replicated this pattern when observers searched for multiple targets. Importantly, we also found that the SOS effect was reduced in segmented-3D images compared to 2D images, suggesting that segmented-3D imaging can improve search performance for multiple targets (abnormalities) within medical imaging.

Large-scale replication failures have shaken confidence in the social sciences, psychology in particular. Most researchers acknowledge the problem, yet there is widespread debate about the causes and solutions. Using “big data,” the current project demonstrates that unintended consequences of three common questionable research practices (retaining pilot data, adding data after checking for significance, and not publishing null findings) can explain the lion’s share of the replication failures. A massive dataset was randomized to create a true null effect between two conditions, and then these three practices were applied. They produced false discovery rates far greater than 5% (the generally accepted rate), and were strong enough to obscure, or even reverse, the direction of real effects. These demonstrations suggest that much of the replication crisis might be explained by simple, misguided experimental choices. This approach also produces empirically-based corrections to account for these practices when they are unavoidable, providing a viable path forward.

Visual search—looking for targets among distractors—underlies many critical professions (e.g., aviation security, radiology, military operations), making it important to understand the mechanisms that govern performance. Feature repetition across trials benefits subsequent search performance, however this has not been thoroughly studied through the lens of associative learning, wherein relationships between temporally or spatially co-occurring stimuli are repeated and learned across consecutive search trials. Complex visual search tasks provide a window into associative learning that can potentially inform a debate about whether the learning operates over task-irrelevant information (e.g., backgrounds, distractors). The “associative blocking” account suggests only task-relevant, highly salient features bind with targets. Yet recent findings of trial sequence effects in search suggest that even task-irrelevant information impacts subsequent performance. Accordingly, the current study hypothesized that search performance is influenced by a mechanism of indiscriminate implicit learning wherein all information, regardless of task-relevance, is processed and available for learning. Performance was assessed for task-relevant and task-irrelevant features repeating both together and independently across consecutive trials pairs. Data were drawn from a massive (>3.8B trials, >15.5M participants) visual search dataset (Airport Scanner; Kedlin Co.). Contrary to the blocking account, the co-occurrence of both task-irrelevant and task-relevant information influenced performance. Specifically, the performance advantage for consecutive trials containing the same target and same irrelevant feature (e.g., bag-type) exceeded the summed benefit of each element repeating individually. Preliminary findings on relative Euclidean distance in the search arrays between the repeated targets provides possible evidence for an allocentric representation relative to the bag. The results suggest that learning may be a natural consequence of visual processing that is strengthened by, but not reliant on, relevance; suggesting that attentional selection may be unnecessary for associative learning. In sum, the current study supports that implicit learning, even of associations, could shape behavior without directed attention.

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.

Visual search—looking for targets among distractors—underlies many critical professions (e.g., aviation security, radiology), making it important to understand the mechanisms that govern performance. Previous research has demonstrated that repeating features benefit search performance, however this has not been thoroughly studied through the lens of associative binding, wherein co-occurring information links into singular memory representations that strengthen encoding. Complex visual search tasks, arguably, provide a highly sensitive window into associative binding mechanisms that can potentially inform an open debate about whether associative binding operates over task-irrelevant information (e.g., backgrounds, distractors). The “associative blocking” account suggests only task-relevant and highly salient features bind with targets. Yet, recent findings of trial sequence effects in search suggest that even task-irrelevant information impacts subsequent performance. Accordingly, the current study hypothesized that search performance is influenced by a unitary mechanism wherein all information, regardless of task relevance, is processed and available for binding. Performance was assessed across consecutive trials, wherein both task-relevant and task-irrelevant features co-occurred. Data were drawn from a massive (>3.8B trials, >15.5M participants) visual search dataset (Airport Scanner; Kedlin Co.). In line with the prediction that associative binding can operate over task-irrelevant features in search, the co-occurrence of both task-irrelevant and task-relevant information influenced performance. Specifically, the performance advantage for consecutive trials containing the same target and same irrelevant feature (e.g., bag type) exceeded the summed benefit of a repeated target or repeated bag individually. The results suggest that binding may be a natural consequence of visual processing that is strengthened by, but not reliant on, relevance. This research may also provide insights into existing debates surrounding associative blocking; suggesting that attentional selection is nonessential for associative binding. In sum, these results suggest that implicit learning, even of associations, can profoundly shape behavior without conscious awareness or attention.

Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians’ accuracy and performance, improving patient outcomes, and reducing diagnostician burnout. Medical image perception remains substantially understudied. In September 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the “Cognition and Medical Image Perception Think Tank.” The Think Tank’s key objectives were to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians, discuss how these clinically relevant questions could be addressed through cognitive and perception research, identify barriers and solutions for transdisciplinary collaborations, define ways to elevate the profile of cognition and perception research within the medical image community, determine the greatest needs to advance medical image perception, and outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians’ perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This article reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception.

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.