Shifting expectations: Lapses in spatial attention are driven by anticipatory attentional shifts.

Attention is dynamic, constantly shifting between different locations - sometimes imperfectly. How do goal-driven expectations impact dynamic spatial attention? A previous study (Dowd & Golomb, Psychological Science, 30(3), 343-361, 2019) explored object-feature binding when covert attention needed to be either maintained at a single location or shifted from one location to another. In addition to revealing feature-binding errors during dynamic shifts of attention, this study unexpectedly found that participants sometimes made correlated errors on trials when they did not have to shift attention, mistakenly reporting the features and location of an object at a different location. The authors posited that these errors represent "spatial lapses" attention, which are perhaps driven by the implicit sampling of other locations in anticipation of having to shift attention. To investigate whether these spatial lapses are indeed anticipatory, we conducted a series of four experiments. We first replicated in Psychological Science, 30(3), the original finding of spatial lapses, and then showed that these spatial lapses were not observed in contexts where participants are not expecting to have to shift attention. We then tested contexts where the direction of attentional shifts was spatially predictable, and found that participants lapse preferentially to more likely shift locations. Finally, we found that spatial lapses do not seem to be driven by explicit knowledge of likely shift locations. Combined, these results suggest that spatial lapses of attention are induced by the implicit anticipation of making an attentional shift, providing further insight into the interplay between implicit expectations, dynamic spatial attention, and visual perception.

The Binding Problem after an eye movement.

Spatial attention is thought to be the "glue" that binds features together (e.g., Treisman & Gelade, 1980, Psychology, 12[1], 97-136)-but attention is dynamic, constantly moving across multiple goals and locations. For example, when a person moves her eyes, visual inputs that are coded relative to the eyes (retinotopic) must be rapidly updated to maintain stable world-centered (spatiotopic) representations. Here, we examined how dynamic updating of spatial attention after a saccadic eye movement affects object-feature binding. Immediately after a saccade, participants were simultaneously presented with four colored and oriented bars (one at a precued spatiotopic target location) and instructed to reproduce both the color and orientation of the target item. Object-feature binding was assessed by applying probabilistic mixture models to the joint distribution of feature errors: feature reports for the target item could be correlated (and thus bound together) or independent. We found that compared with holding attention without an eye movement, attentional updating after an eye movement produced more independent errors, including illusory conjunctions, in which one feature of the item at the spatiotopic target location was misbound with the other feature of the item at the initial retinotopic location. These findings suggest that even when only one spatiotopic location is task relevant, spatial attention-and thus object-feature binding-is malleable across and after eye movements, heightening the challenge that eye movements pose for the binding problem and for visual stability.

Object-Feature Binding Survives Dynamic Shifts of Spatial Attention.

Visual object perception requires integration of multiple features; spatial attention is thought to be critical to this binding. But attention is rarely static-how does dynamic attention impact object integrity? Here, we manipulated covert spatial attention and had participants (total N = 48) reproduce multiple properties (color, orientation, location) of a target item. Object-feature binding was assessed by applying probabilistic models to the joint distribution of feature errors: Feature reports for the same object could be correlated (and thus bound together) or independent. We found that splitting attention across multiple locations degrades object integrity, whereas rapid shifts of spatial attention maintain bound objects. Moreover, we document a novel attentional phenomenon, wherein participants exhibit unintentional fluctuations- lapses of spatial attention-yet nevertheless preserve object integrity at the wrong location. These findings emphasize the importance of a single focus of spatial attention for object-feature binding, even when that focus is dynamically moving across the visual field.

Working memory-driven attention towards a distractor does not interfere with target feature perception.

The contents of working memory (WM) can influence where we attend-but can it also interfere with what we see? Active maintenance of visual items in WM biases attention towards WM-matching objects, and also enhances early perceptual processing of WM-matching items (e.g., more accurate perceptual discrimination). Here, we asked whether a WM-matching distractor interferes with perceptual processing of a target's features. In a dual-task paradigm, participants maintained a shape in WM across an intervening visual search task, during which they had to reproduce the colour of a designated target item using a continuous-report technique. Importantly, the WM shape could match the target item, a distractor item, or no item in the search array. When the WM shape matched a distractor, we found no evidence of systematic perceptual interference (i.e., swapping or mixing with the distractor colour), but observed only general disruptions in target processing (i.e., decreased target accuracy). These results suggest that when visual attention is inadvertently drawn to a WM-matching distractor, any resultant automatic perceptual processing may be too transient or weak to significantly interfere with perceptual processing of the target's features.

Neural Representation of Working Memory Content Is Modulated by Visual Attentional Demand.

Recent theories assert that visual working memory (WM) relies on the same attentional resources and sensory substrates as visual attention to external stimuli. Behavioral studies have observed competitive tradeoffs between internal (i.e., WM) and external (i.e., visual) attentional demands, and neuroimaging studies have revealed representations of WM content as distributed patterns of activity within the same cortical regions engaged by perception of that content. Although a key function of WM is to protect memoranda from competing input, it remains unknown how neural representations of WM content are impacted by incoming sensory stimuli and concurrent attentional demands. Here, we investigated how neural evidence for WM information is affected when attention is occupied by visual search-at varying levels of difficulty-during the delay interval of a WM match-to-sample task. Behavioral and fMRI analyses suggested that WM maintenance was impacted by the difficulty of a concurrent visual task. Critically, multivariate classification analyses of category-specific ventral visual areas revealed a reduction in decodable WM-related information when attention was diverted to a visual search task, especially when the search was more difficult. This study suggests that the amount of available attention during WM maintenance influences the detection of sensory WM representations.

Decoding working memory content from attentional biases.

What we are currently thinking influences where we attend. The finding that active maintenance of visual items in working memory (WM) biases attention toward memory-matching objects-even when WM content is irrelevant for attentional goals-suggests a tight link between WM and attention. To test whether this link is reliable enough to infer specific WM content from measures of attentional bias, we applied multivariate pattern classification techniques to response times from an unrelated visual search task during a WM delay. Single-trial WM content was successfully decoded from incidental attentional bias within an individual, highlighting the specificity and reliability of the WM-attention link. Furthermore, classifiers trained on a group of individuals predicted WM content in another, completely independent individual-implying a shared cognitive mechanism of memory-driven attentional bias. The existence of such classifiers demonstrates that memory-based attentional bias is both a robust and generalizable probe of WM.

Fear generalization gradients in visuospatial attention.

Fear learning can be adaptively advantageous, but only if the learning is integrated with higher-order cognitive processes that impact goal-directed behaviors. Recent work has demonstrated generalization (i.e., transfer) of conditioned fear across perceptual dimensions and conceptual categories, but it is not clear how fear generalization influences other cognitive processes. The current study investigated how associative fear learning impacts higher-order visuospatial attention, specifically in terms of attentional bias toward generalized threats (i.e., the heightened assessment of potentially dangerous stimuli). We combined discriminative fear conditioning of color stimuli with a subsequent visual search task, in which targets and distractors were presented inside colored circles that varied in perceptual similarity to the fear-conditioned color. Skin conductance responses validated the fear-conditioning manipulation. Search response times indicated that attention was preferentially deployed not just to the specific fear-conditioned color, but also to similar colors that were never paired with the aversive shock. Furthermore, this attentional bias decreased continuously and symmetrically from the fear-conditioned value along the color spectrum, indicating a generalization gradient based on perceptual similarity. These results support functional accounts of fear learning that promote broad, defensive generalization of attentional bias toward threat. (PsycINFO Database Record

Attentional guidance by working memory differs by paradigm: an individual-differences approach.

The contents of working memory (WM) have been repeatedly found to guide the allocation of visual attention; in a dual-task paradigm that combines WM and visual search, actively holding an item in WM biases visual attention towards memory-matching items during search (e.g., Soto et al., Journal of Experimental Psychology: Human Perception and Performance, 31(2), 248-261, 2005). A key debate is whether such memory-based attentional guidance is automatic or under strategic control. Generally, two distinct task paradigms have been employed to assess memory-based guidance, one demonstrating that attention is involuntarily captured by memory-matching stimuli even at a cost to search performance (Soto et al., 2005), and one demonstrating that participants can strategically avoid memory-matching distractors to facilitate search performance (Woodman & Luck, Journal of Experimental Psychology: Human Perception and Performance, 33(2), 363-377, 2007). The current study utilized an individual-differences approach to examine why the different paradigms--which presumably tap into the same attentional construct--might support contrasting interpretations. Participants completed a battery of cognitive tasks, including two types of attentional guidance paradigms (see Soto et al., 2005; Woodman & Luck, 2007), a visual WM task, and an operation span task, as well as attention-related self-report assessments. Performance on the two attentional guidance paradigms did not correlate. Subsequent exploratory regression analyses revealed that memory-based guidance in each task was differentially predicted by visual WM capacity for one paradigm, and by attention-related assessment scores for the other paradigm. The current results suggest that these two paradigms--which have previously produced contrasting patterns of performance--may probe distinct aspects of attentional guidance.

Examining perceptual and conceptual set biases in multiple-target visual search.

Visual search is a common practice conducted countless times every day, and one important aspect of visual search is that multiple targets can appear in a single search array. For example, an X-ray image of airport luggage could contain both a water bottle and a gun. Searchers are more likely to miss additional targets after locating a first target in multiple-target searches, which presents a potential problem: If airport security officers were to find a water bottle, would they then be more likely to miss a gun? One hypothetical cause of multiple-target search errors is that searchers become biased to detect additional targets that are similar to a found target, and therefore become less likely to find additional targets that are dissimilar to the first target. This particular hypothesis has received theoretical, but little empirical, support. In the present study, we tested the bounds of this idea by utilizing "big data" obtained from the mobile application Airport Scanner. Multiple-target search errors were substantially reduced when the two targets were identical, suggesting that the first-found target did indeed create biases during subsequent search. Further analyses delineated the nature of the biases, revealing both a perceptual set bias (i.e., a bias to find additional targets with features similar to those of the first-found target) and a conceptual set bias (i.e., a bias to find additional targets with a conceptual relationship to the first-found target). These biases are discussed in terms of the implications for visual-search theories and applications for professional visual searchers.

What can 1 billion trials tell us about visual search?

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.

Attentional guidance by working memory overrides salience cues in visual search.

Many factors influence visual search, including how much targets stand out (i.e., their visual salience) and whether they are currently relevant (i.e., Are they in working memory?). Although these are two known influences on search performance, it is unclear how they interact to guide attention. The present study explored this interplay by having participants hold an item in memory for a subsequent test while simultaneously conducting a multiple-target visual search. Importantly, the memory item could match one or neither of two targets from the search. In Experiment 1, when the memory item did not match either target, participants found a high-salience target first, demonstrating a baseline salience effect. This effect was exaggerated when a high-salience target was in working memory and completely reversed when a low-salience target was in memory, demonstrating a powerful influence of working memory guidance. Experiment 2 amplified the salience effect by including very high-salience, "pop-out"-like targets. Yet this salience effect was still attenuated when the memory item matched a less salient target. Experiment 3 confirmed these were memory-based effects and not priming. Collectively, these findings illustrate the influential role of working memory in guiding visual attention, even in the face of competing bottom-up salience cues.