Spatial Configuration Search Research Articles

Visual search proceeds concurrently during the attentional blink and response selection bottleneck.

It is well known that human information processing comprises several distinct subprocesses-namely, the perceptual, central, and motor stage. In each stage, attention plays an important role. Specifically, a type of attention-perceptual attention-operates to detect and identify a sensory input. Following this, another class of attention-central attention-is involved in working memory encoding and response selection at the central stage. While perceptual attention and central attention are known to be separate, distinct processes, some researchers reported findings that loading central attention postponed the deployment of perceptual attention needed to perform a spatial configuration search. We tested whether a similar pattern of results would emerge when a different kind of search task is used. To do so, we had participants perform a visual-search task of searching for a feature conjunction target, taxing perceptual attention while they are engaged in central processes, such as working memory encoding and response selection. The results showed that perceptual processing of conjunction search stimuli could be carried out concurrently with central processes. These results suggest that the nature of the concurrent visual search process is a determinant responsible for the dynamic relationship between perceptual attention deployed for visual search and central attention needed for working memory encoding and response selection.

Studying the dynamics of visual search behavior using RT hazard and micro-level speed-accuracy tradeoff functions: A role for recurrent object recognition and cognitive control processes.

Thanks to the work of Anne Treisman and many others, the visual search paradigm has become one of the most popular paradigms in the study of visual attention. However, statistics like mean correct response time (RT) and percent error do not usually suffice to decide between the different search models that have been developed. Recently, to move beyond mean performance measures in visual search, RT histograms have been plotted, theoretical waiting time distributions have been fitted, and whole RT and error distributions have been simulated. Here we promote and illustrate the general application of discrete-time hazard analysis to response times, and of micro-level speed-accuracy tradeoff analysis to timed response accuracies. An exploratory analysis of published benchmark search data from feature, conjunction, and spatial configuration search tasks reveals new features of visual search behavior, such as a relatively flat hazard function in the right tail of the RT distributions for all tasks, a clear effect of set size on the shape of the RT distribution for the feature search task, and individual differences in the presence of a systematic pattern of early errors. Our findings suggest that the temporal dynamics of visual search behavior results from a decision process that is temporally modulated by concurrently active recurrent object recognition, learning, and cognitive control processes, next to attentional selection processes.

Visual search proceeds concurrently during the attentional blink and response selection bottleneck

It is well known that human information processing comprises several distinct subprocesses-namely, the perceptual, central, and motor stage. In each stage, attention plays an important role. Specifically, a type of attention-perceptual attention-operates to detect and identify a sensory input. Following this, another class of attention-central attention-is involved in working memory encoding and response selection at the central stage. While perceptual attention and central attention are known to be separate, distinct processes, some researchers reported findings that loading central attention postponed the deployment of perceptual attention needed to perform a spatial configuration search. We tested whether a similar pattern of results would emerge when a different kind of search task is used. To do so, we had participants perform a visual-search task of searching for a feature conjunction target, taxing perceptual attention while they are engaged in central processes, such as working memory encoding and response selection. The results showed that perceptual processing of conjunction search stimuli could be carried out concurrently with central processes. These results suggest that the nature of the concurrent visual search process is a determinant responsible for the dynamic relationship between perceptual attention deployed for visual search and central attention needed for working memory encoding and response selection.

Serial versus parallel search: A model comparison approach based on reaction time distributions

ABSTRACTFor 50 years or so, visual search experiments have been used to examine how humans find behaviourally relevant objects in complex visual scenes. For the same length of time, there has been a dispute over whether this search is performed in a serial or parallel fashion. In this paper, we approach this dispute by numerically fitting a serial search model and a parallel search model to reaction time (RT) distributions from three visual search experiments (feature search, conjunction search, spatial configuration search). In order to do so, we used a free-likelihood method based on a novel kernel density estimator (KDE).The serial search model was the Competitive Guided Search (CGS) model by Moran et al. [(2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), 24–24.]. We were able to replicate the ability of CGS to model RT distributions from visual search experiments, and demonstrated that CGS generalizes well to new data. The parallel model was based on the biased-competition theory and utilized a very simple biologically-plausible winner-take-all (WTA) mechanism from Heinke and Humphreys’s [(2003). Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1), 29–87.]. With this mechanism, SAIM has been able to explain a broad range of attentional phenomena but it was not specifically designed to model RT distributions in visual search. Nevertheless, the WTA was able to reproduce these distributions.However, a direct comparison of the two models suggested that the serial CGS is slightly better equipped to explain the RT distributions than the WTA mechanism. The CGS’s success was mainly down the usage of the Wald distribution which was specifically designed to model visual search. Future WTA versions will have to find a biologically plausible mechanism to reproduce such a RT distribution. Finally, both models suffered from a failure to generalize across all display sizes. From these comparisons, we developed suggestions for improving the models and motivated empirical studies to devise a stronger test for the two types of searches.

Serial vs. parallel models of attention in visual search: accounting for benchmark RT-distributions.

Visual search is central to the investigation of selective visual attention. Classical theories propose that items are identified by serially deploying focal attention to their locations. While this accounts for set-size effects over a continuum of task difficulties, it has been suggested that parallel models can account for such effects equally well. We compared the serial Competitive Guided Search model with a parallel model in their ability to account for RT distributions and error rates from a large visual search data-set featuring three classical search tasks: 1) a spatial configuration search (2 vs. 5); 2) a feature-conjunction search; and 3) a unique feature search (Wolfe, Palmer & Horowitz Vision Research, 50(14), 1304-1311, 2010). In the parallel model, each item is represented by a diffusion to two boundaries (target-present/absent); the search corresponds to a parallel race between these diffusors. The parallel model was highly flexible in that it allowed both for a parametric range of capacity-limitation and for set-size adjustments of identification boundaries. Furthermore, a quit unit allowed for a continuum of search-quitting policies when the target is not found, with "single-item inspection" and exhaustive searches comprising its extremes. The serial model was found to be superior to the parallel model, even before penalizing the parallel model for its increased complexity. We discuss the implications of the results and the need for future studies to resolve the debate.

Sleep inertia, sleep homeostatic and circadian influences on higher-order cognitive functions.

Sleep inertia, sleep homeostatic and circadian processes modulate cognition, including reaction time, memory, mood and alertness. How these processes influence higher-order cognitive functions is not well known. Six participants completed a 73-day-long study that included two 14-day-long 28-h forced desynchrony protocols to examine separate and interacting influences of sleep inertia, sleep homeostasis and circadian phase on higher-order cognitive functions of inhibitory control and selective visual attention. Cognitive performance for most measures was impaired immediately after scheduled awakening and improved during the first ~2-4h of wakefulness (decreasing sleep inertia); worsened thereafter until scheduled bedtime (increasing sleep homeostasis); and was worst at ~60° and best at ~240° (circadian modulation, with worst and best phases corresponding to ~09:00 and ~21:00hours, respectively, in individuals with a habitual wake time of 07:00hours). The relative influences of sleep inertia, sleep homeostasis and circadian phase depended on the specific higher-order cognitive function task examined. Inhibitory control appeared to be modulated most strongly by circadian phase, whereas selective visual attention for a spatial-configuration search task was modulated most strongly by sleep inertia. These findings demonstrate that some higher-order cognitive processes are differentially sensitive to different sleep-wake regulatory processes. Differential modulation of cognitive functions by different sleep-wake regulatory processes has important implications for understanding mechanisms contributing to performance impairments during adverse circadian phases, sleep deprivation and/or upon awakening from sleep.

Signal detection evidence for limited capacity in visual search

The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a "2" among "5"s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1-8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models.

What are the shapes of response time distributions in visual search?

Many visual search experiments measure response time (RT) as their primary dependent variable. Analyses typically focus on mean (or median) RT. However, given enough data, the RT distribution can be a rich source of information. For this paper, we collected about 500 trials per cell per observer for both target-present and target-absent displays in each of three classic search tasks: feature search, with the target defined by color; conjunction search, with the target defined by both color and orientation; and spatial configuration search for a 2 among distractor 5s. This large data set allows us to characterize the RT distributions in detail. We present the raw RT distributions and fit several psychologically motivated functions (ex-Gaussian, ex-Wald, Gamma, and Weibull) to the data. We analyze and interpret parameter trends from these four functions within the context of theories of visual search.

The impact of item clustering on visual search: It all depends on the nature of the visual search

Decades of vision research on how people search for a target item among distractor items have always avoided item clustering. Instead, researchers made sure that items were evenly distributed in search displays. This, however, is rarely the case in our everyday visual environment. Consequently, it is largely unknown how item clustering may impact visual search performance. In this study, I manipulated item clustering in search displays. In an easy feature search, observers looked for a target letter "T" among distractor letters "Os" and reported whether the target was pointing to the left or to the right. In a difficult spatial configuration search, observers searched and reported the orientation of the target letter "T" among distractor letters "Ls". The two types of searches thus had the same target but different distractors. In two experiments, I found that while item clustering slowed down the easy feature search, it speeded up the difficult spatial configuration search. Together, these results show that item clustering significantly affects visual search performance and its exact impact (negative or positive) depends on the nature of the visual search.

Effects of Individual Differences on Visual Search Task Performance

Working memory capacity (WMC) is generally referred to as a quantitative measure of the ability to maintain relevant information while performing unrelated tasks (Delaney & Sahakyan, 2007). Although studies have shown that WMC can vary by individual (Engle et al., 1992; Just, 1992), performance can be taxed by varying degrees of subjective workload depending on the task parameters. Most studies have shown task performance benefits for individuals with high working memory capacity, with some exceptions for individuals with low working memory capacity (Barrett, Tugade, & Engle, 2004; Kane et al, 2001; Beilock et al, 2005). Kane and colleagues (2006) tested the relationship of WMC and executive attention control and found no relationship with individual differences in WMC and performance on visual search tasks, namely on feature-absence, conjunction, and spatial configuration search tasks. However, other studies have found that loading working memory interferes with search (Peterson, Beck & Wong, 2008; Han & Kim, 2004; He & McCarley, 2010). To explore how WMC affects visual search performance, a study is proposed to test whether the use of a complex visual search task can demonstrate a relationship between WMC and visual search performance. It is hypothesized that a visual search task aimed at engaging the control of attention and limiting automatic forms of information processing will be performed better by high WMC individuals compared to low WMC individuals. The implications of this proposal would help in designing for cockpit operation, driving, or other operations that require effortful control where the contributions and limitations of an individual's working memory capacity should be considered.

What, when and how of target detection in visual search

When and how do we decide whether the target is present in the scene or not? There are surprisingly few quantitative models that address this question. We propose a generative model of visual search. First, the observer obtains stimulus information (corrupted by additive white noise of variance σ) at each location and time instant. Second, information across scene locations is combined using optimal Bayesian inference to compute the instantaneous evidence for target presence in the scene (log likelihood ratio of target presence vs. absence at time t). Third, the observer accumulates evidence over time and decides ‘yes’ if the accumulated evidence exceeds criterion ζ+Δ, ‘no’ if the evidence falls below criterion −ζ+Δ, and otherwise continues to observe the scene. Δ is a criterion-shift which depends on the target frequency, and the reward for different responses. This model has only 2 free parameters (noise σ, criterion ζ), compared to many parameters in drift diffusion models. The model can explain several search phenomena including how accuracy and RT are affected by set-size, target-distractor discriminability, distractor heterogeneity, target frequency and reward. It can capture the shape of the RT distribution in target present and absent trials for different tasks like feature, conjunction and spatial configuration search. It explains that rare targets are missed (Wolfe et. al, 2005) because decreasing target frequency (e.g., from 50% to 2%) shifts the starting point of the decision process closer to ‘no’ than ‘yes’ criterion, leading to high miss error rates, and fast abandoning of search. Our model predicts that increasing penalties on miss errors will decrease these errors and increase RTs. We validated these predictions through psychophysics experiments with 4 human subjects. To summarize, we have proposed a generative model of visual search that with only 2 free parameters, can explain a wide range of search phenomena.

Why do we miss rare targets? Exploring the boundaries of the low prevalence effect.

Observers tend to miss a disproportionate number of targets in visual search tasks with rare targets. This 'prevalence effect' may have practical significance since many screening tasks (e.g., airport security, medical screening) are low prevalence searches. It may also shed light on the rules used to terminate search when a target is not found. Here, we use perceptually simple stimuli to explore the sources of this effect. Experiment 1 shows a prevalence effect in inefficient spatial configuration search. Experiment 2 demonstrates this effect occurs even in a highly efficient feature search. However, the two prevalence effects differ. In spatial configuration search, misses seem to result from ending the search prematurely, while in feature search, they seem due to response errors. In Experiment 3, a minimum delay before response eliminated the prevalence effect for feature but not spatial configuration search. In Experiment 4, a target was present on each trial in either two (2AFC) or four (4AFC) orientations. With only two response alternatives, low prevalence produced elevated errors. Providing four response alternatives eliminated this effect. Low target prevalence puts searchers under pressure that tends to increase miss errors. We conclude that the specific source of those errors depends on the nature of the search.

Working memory capacity and the top-down control of visual search: Exploring the boundaries of "executive attention".

The executive attention theory of working memory capacity (WMC) proposes that measures of WMC broadly predict higher order cognitive abilities because they tap important and general attention capabilities (R. W. Engle & M. J. Kane, 2004). Previous research demonstrated WMC-related differences in attention tasks that required restraint of habitual responses or constraint of conscious focus. To further specify the executive attention construct, the present experiments sought boundary conditions of the WMC-attention relation. Three experiments correlated individual differences in WMC, as measured by complex span tasks, and executive control of visual search. In feature-absence search, conjunction search, and spatial configuration search, WMC was unrelated to search slopes, although they were large and reliably measured. Even in a search task designed to require the volitional movement of attention (J. M. Wolfe, G. A. Alvarez, & T. S. Horowitz, 2000), WMC was irrelevant to performance. Thus, WMC is not associated with all demanding or controlled attention processes, which poses problems for some general theories of WMC.

Attention capacity and task difficulty in visual search

When a visual search task is very difficult (as when a small feature difference defines the target), even detection of a unique element may be substantially slowed by increases in display set size. This has been attributed to the influence of attentional capacity limits. We examined the influence of attentional capacity limits on three kinds of search task: difficult feature search (with a subtle featural difference), difficult conjunction search, and spatial-configuration search. In all 3 tasks, each trial contained sixteen items, divided into two eight-item sets. The two sets were presented either successively or simultaneously. Comparison of accuracy in successive versus simultaneous presentations revealed that attentional capacity limitations are present only in the case of spatial-configuration search. While the other two types of task were inefficient (as reflected in steep search slopes), no capacity limitations were evident. We conclude that the difficulty of a visual search task affects search efficiency but does not necessarily introduce attentional capacity limits.

Searching night and day: a dissociation of effects of circadian phase and time awake on visual selective attention and vigilance.

How does sleepiness affect selective attention? We studied the effect of circadian phase and time awake on visual search. The generalized-cognitive-slowing hypothesis predicts that search rate will be slower, feature guidance less effective, and response time (RT) lengthened when observers are sleepy. Observers performed spatial-configuration (finding a 5 among 2s) and conjunction (finding red vertical among red horizontal and green vertical) search tasks during 38 hr of wakefulness under constant conditions. Adverse circadian phases and elapsed time awake did lead to increased RT (corrected for errors). However, contrary to the hypothesis, search rates (indexed by RT x Set Size slopes) were constant across the protocol. This was true for conjunction as well as for spatial-configuration search, indicating that feature guidance was also insensitive to sleepiness. The locus of sleepiness effects on search is probably downstream from the bottleneck of attentional selection. Observers did trade accuracy for speed when sleepy. This implicates decision-stage impairments.