Unlike laboratory experiments, real-world visual search can contain multiple targets. Searching for an unknown number of targets creates a unique set of challenges for the observer, and often produces serious errors. We propose a Bayesian optimal foraging model to predict and describe behavior in such search scenarios, and investigate whether people adapt their search strategies based on complex statistics of target distributions. Separate groups searched arrays drawn from three target distributions with the same average number of targets per display, but different target-clustering properties. As predicted, participants searched longer when they expected more targets to remain and adjusted their expectations as searches unfolded, indicating that searchers are sensitive to the target distribution, consistent with both an optimal foraging framework and an ideal Bayesian observer. However, compared to the ideal observers, searchers systematically under-adjusted to the target distribution, suggesting that training could improve multipletarget search in radiology and other crucial applications.
Practice can improve performance on visual search tasks; the neural mechanisms underlying such improvements, however, are not clear. Response time typically shortens with practice, but which components of the stimulus-response processing chain facilitate this behavioral change? Improved search performance could result from enhancements in various cognitive processing stages, including (1) sensory processing, (2) attentional allocation, (3) target discrimination, (4) motor-response preparation, and/or (5) response execution. We measured event-related potentials (ERPs) as human participants completed a five-day visual-search protocol in which they reported the orientation of a color popout target within an array of ellipses. We assessed changes in behavioral performance and in ERP components associated with various stages of processing. After practice, response time decreased in all participants (while accuracy remained consistent), and electrophysiological measures revealed modulation of several ERP components. First, amplitudes of the early sensoryevoked N1 component at 150 ms increased bilaterally, indicating enhanced visual sensory processing of the array. Second, the negativepolarity posterior-contralateral component (N2pc, 170 -250 ms) was earlier and larger, demonstrating enhanced attentional orienting. Third, the amplitude of the sustained posterior contralateral negativity component (SPCN, 300 -400 ms) decreased, indicating facilitated target discrimination. Finally, faster motor-response preparation and execution were observed after practice, as indicated by latency changes in both the stimulus-locked and response-locked lateralized readiness potentials (LRPs). These electrophysiological results delineate the functional plasticity in key mechanisms underlying visual search with high temporal resolution and illustrate how practice influences various cognitive and neural processing stages leading to enhanced behavioral performance.
Mobile technology (e.g., smartphones and tablets) has provided psychologists with a wonderful opportunity: through careful design and implementation, mobile applications can be used to crowd source data collection. By garnering massive amounts of data from a wide variety of individuals, it is possible to explore psychological questions that have, to date, been out of reach. Here we discuss 2 examples of how data from the mobile game Airport Scanner (Kedlin Co., http://www.airportscannergame.com) can be used to address questions about the nature of visual search that pose intractable problems for laboratory-based research. Airport Scanner is a successful mobile game with millions of unique users and billions of individual trials, which allows for examining nuanced visual search questions. The goals of the current Observation Report were to highlight the growing opportunity that mobile technology affords psychological research and to provide an example roadmap of how to successfully collect usable data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.