Human attention filters for single colors

What is the basis of ensemble subset selection?

The visual system can rapidly calculate the ensemble statistics of a set of objects; for example, people can easily estimate an average size of apples on a tree. To accomplish this, it is not always useful to summarize all the visual information. If there are various types of objects, the visual system should select a relevant subset: only apples, not leaves and branches. Here, we ask what kind of visual information makes a "good" ensemble that can be selectively attended to provide an accurate summary estimate. We tested three candidate representations: basic features, preattentive object files, and full-fledged bound objects. In four experiments, we presented a target and several distractors' sets of differently colored objects. We found that conditions where a target ensemble had at least one unique color (basic feature) provided ensemble averaging performance comparable to the baseline displays without distractors. When the target subset was defined as a conjunction of two colors or color-shape partly shared with distractors (so that they could be differentiated only as preattentive object files), subset averaging was also possible but less accurate than in the baseline and feature conditions. Finally, performance was very poor when the target subset was defined by an exact feature relationship, such as in the spatial conjunction of two colors (spatially bound object). Overall, these results suggest that distinguishable features and, to a lesser degree, preattentive object files can serve as the representational basis of ensemble selection, while bound objects cannot.

Deriving the number of salience maps an observer has from the number and quality of concurrent centroid judgments

[C. Koch, S. Ullman, Hum. Neurobiol.4, 219-227 (1985)] proposed a 2D topographical salience map that took feature-map outputs as its input and represented the importance "saliency" of the feature inputs at each location as a real number. The computation on the map, "winner-take-all," was used to predict action priority. We propose that the same or a similar map is used to compute centroid judgments, the center of a cloud of diverse items. [P. Sun, V. Chu, G. Sperling, Atten. Percept. Psychophys.83, 934-955 (2021)] demonstrated that following a 250-msec exposure of a 24-dot array of 3 intermixed colors, subjects could accurately report the centroid of each dot color, thereby indicating that these subjects had at least three salience maps. Here, we use a postcue, partial-report paradigm to determine how many more salience maps subjects might have. In 11 experiments, subjects viewed 0.3-s flashes of 28 to 32 item arrays composed of M, M = 3,...,8, different features followed by a cue to mouse-click the centroid of items of just the post-cued feature. Ideal detector response analyses show that subjects utilized at least 12 to 17 stimulus items. By determining whether a subject's performance in (M-1)-feature experiments could/could-not predict performance in M-feature experiments, we conclude that one subject has at least 7 and the other two have at least five salience maps. A computational model shows that the primary performance-limiting factors are channel capacity for representing so many concurrently presented groups of items and working-memory capacity for so many computed centroids.

Efficient quantization of painting images by relevant colors

Realistic images often contain complex variations in color, which can make economical descriptions difficult. Yet human observers can readily reduce the number of colors in paintings to a small proportion they judge as relevant. These relevant colors provide a way to simplify images by effectively quantizing them. The aim here was to estimate the information captured by this process and to compare it with algorithmic estimates of the maximum information possible by colorimetric and general optimization methods. The images tested were of 20 conventionally representational paintings. Information was quantified by Shannon's mutual information. It was found that the estimated mutual information in observers' choices reached about 90% of the algorithmic maxima. For comparison, JPEG compression delivered somewhat less. Observers seem to be efficient at effectively quantizing colored images, an ability that may have applications in the real world.

Variability of dot spread is overestimated

Previous research has demonstrated that individuals exhibit a tendency to overestimate the variability of both low-level features (e.g., color, orientation) and mid-level features (e.g., size) when items are presented dynamically in a sequential order, a finding we will refer to as the variability overestimation effect. Because previous research on this bias used sequential displays, an open question is whether the effect is due to a memory-related bias or a vision-related bias. To assess whether the bias would also be apparent with static, simultaneous displays, and to examine whether the bias generalizes to spatial properties, we tested participants' perception of the variability of a cluster of dots. Results showed a consistent overestimation bias: Participants judged the dots as being more spread than they actually were. The variability overestimation effect was observed when there were 10 or 20 dots but not when there were 50 dots. Taken together, the results of the current study contribute to the ensemble perception literature by providing evidence that simultaneously presented stimuli are also susceptible to the variability overestimation effect. The use of static displays further demonstrates that this bias is present in both dynamic and static contexts, suggesting an inherent bias existent in the human visual system. A potential theoretical account-boundary effect-is discussed as a potential underlying mechanism. Moreover, the present study has implications for common visual tasks carried out in real-world scenarios, such as a radiologist making judgments about distribution of calcification in breast cancer diagnoses.

On the flexibility of strategies for center estimation

When subjects are asked to indicate the center of a spatially distributed stimulus, the features that control their responses tend to vary (1) across subjects and (2) as stimulus properties are altered. Here we ask: can subjects bring these different response tendencies under top-down control? In each of three tasks, all using briefly displayed, Gaussian dot-clouds, subjects were trained to perform different center-estimation responses. In the "mass task," the target was the centroid of the dots. In the "hull task," the target was the centroid of the region circumscribed by the convex hull of the dot-cloud. In the "hull-vertex task," the target was the centroid of the vertices of the convex hull. Subjects were able to perform each of the mass- and hull-tasks accurately and reliably. However, they found the hull-vertex task more difficult; errors were substantially larger in this task, and responses tended to be closer to both of the hull- and mass-task centers. The finding that subjects can intentionally target either the centroid of the dots in the stimulus or the centroid of the stimulus convex hull suggests that individual differences in feedback-free experiments may reflect idiosyncratic decisions by different subjects about what combination of these statistics to use in responding.

Information gains from commercial spectral filters in anomalous trichromacy

Red-green color discrimination is compromised in anomalous trichromacy, the most common inherited color vision deficiency. This computational analysis tested whether three commercial optical filters with medium-to-long-wavelength stop bands increased information about colored surfaces. The surfaces were sampled from 50 hyperspectral images of outdoor scenes. At best, potential gains in the effective number of surfaces discriminable solely by color reached 9% in protanomaly and 15% in deuteranomaly, much less than with normal trichromacy. Gains were still less with lower scene illumination and more severe color vision deficiency. Stop-band filters may offer little improvement in objective real-world color discrimination.

Visual bias could impede diagnostic accuracy of breast cancer calcifications

Purpose: Diagnosing breast cancer based on the distribution of calcifications is a visual task and thus prone to visual biases. We tested whether a recently discovered visual bias that has implications for breast cancer diagnosis would be present in expert radiologists, thereby validating the concern of this bias for accurate diagnoses. Approach: We ran a vision experiment with expert radiologists and untrained observers to test the presence of visual bias when judging the spread of dots that resembled calcifications and when judging the spread of line orientations. We calculated visual bias scores for both groups for both tasks. Results: Participants overestimated the spread of the dots and the spread of the line orientations. This bias, referred to as the variability overestimation effect, was of similar magnitudes in both expert radiologists and untrained observers. Even though the radiologists were better at both tasks, they were similarly biased compared with the untrained observers. Conclusions: The results justify the concern of the variability overestimation effect for accurate diagnoses based on breast calcifications. Specifically, the bias is likely to lead to an increased number of false-negative results, thereby leading to delayed treatments.

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Scatterplots can encode a third dimension by using additional channels like size or color (e.g. bubble charts). We explore a potential misinterpretation of trivariate scatterplots, which we call the weighted average illusion, where locations of larger and darker points are given more weight toward x- and y-mean estimates. This systematic bias is sensitive to a designer's choice of size or lightness ranges mapped onto the data. In this paper, we quantify this bias against varying size/lightness ranges and data correlations. We discuss possible explanations for its cause by measuring attention given to individual data points using a vision science technique called the centroid method. Our work illustrates how ensemble processing mechanisms and mental shortcuts can significantly distort visual summaries of data, and can lead to misjudgments like the demonstrated weighted average illusion.

Is there a serial bottleneck in visual object recognition?

Divided attention has little effect for simple tasks, such as luminance detection, but it has large effects for complex tasks, such as semantic categorization of masked words. Here, we asked whether the semantic categorization of visual objects shows divided attention effects as large as those observed for words, or as small as those observed for simple feature judgments. Using a dual-task paradigm with nameable object stimuli, performance was compared with the predictions of serial and parallel models. At the extreme, parallel processes with unlimited capacity predict no effect of divided attention; alternatively, an all-or-none serial process makes two predictions: a large divided attention effect (lower accuracy for dual-task trials, compared to single-task trials) and a negative response correlation in dual-task trials (a given response is more likely to be incorrect when the response about the other stimulus is correct). These predictions were tested in two experiments examining object judgments. In both experiments, there was a large divided attention effect and a small negative correlation in responses. The magnitude of these effects was larger than for simple features, but smaller than for words. These effects were consistent with serial models, and rule out some but not all parallel models. More broadly, the results help establish one of the first examples of likely serial processing in perception.

An explicit investigation of the roles that feature distributions play in rapid visual categorization

Ensemble representations are often described as efficient tools when summarizing features of multiple similar objects as a group. However, it can sometimes be more useful not to compute a single summary description for all of the objects if they are substantially different, for example when they belong to entirely different categories. It was proposed that the visual system can efficiently use the distributional information of ensembles to decide whether simultaneously displayed items belong to single or several different categories. Here we directly tested how the feature distribution of items in a visual array affects an ability to discriminate individual items (Experiment 1) and sets (Experiments 2-3) when participants were instructed explicitly to categorize individual objects based on the median of size distribution. We varied the width (narrow or fat) as well as the shape (smooth or two-peaked) of distributions in order to manipulate the ease of ensemble extraction from the items. We found that observers unintentionally relied on the grand mean as a natural categorical boundary and that their categorization accuracy increased as a function of the size differences among individual items and a function of their separation from the grand mean. For ensembles drawn from two-peaked size distributions, participants showed better categorization performance. They were more accurate at judging within-category ensemble properties in other dimensions (centroid and orientation) and less biased by superset statistics. This finding corroborates the idea that the two-peaked feature distributions support the "segmentability" of spatially intermixed sets of objects. Our results emphasize important roles of ensemble statistics (mean, range, distribution shape) in explicit visual categorization.

Fluctuating environmental light limits number of surfaces visually recognizable by colour

Small changes in daylight in the environment can produce large changes in reflected light, even over short intervals of time. Do these changes limit the visual recognition of surfaces by their colour? To address this question, information-theoretic methods were used to estimate computationally the maximum number of surfaces in a sample that can be identified as the same after an interval. Scene data were taken from successive hyperspectral radiance images. With no illumination change, the average number of surfaces distinguishable by colour was of the order of 10,000. But with an illumination change, the average number still identifiable declined rapidly with change duration. In one condition, the number after two minutes was around 600, after 10 min around 200, and after an hour around 70. These limits on identification are much lower than with spectral changes in daylight. No recoding of the colour signal is likely to recover surface identity lost in this uncertain environment.

Spatially intermixed objects of different categories are parsed automatically

Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited "bottlenecks" of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age-22.7) participated in our study. Loading participants' attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience.

Multiple concurrent centroid judgments imply multiple within-group salience maps

Subjects viewed a brief flash of 8-24 dots of either two or three colors randomly arrayed. Their task was to move a mouse cursor to the centroid (center-of-gravity) of each color in a pre-designated order. Conventional and idea-detector analyses show that subjects accurately judged all three centroids utilizing an astounding 13/24 stimulus dots, with only a modest loss of accuracy compared to judging a single-predesignated color centroid. The ability to concurrently compute three centroids is important because it is believed that centroid judgments are made on salience maps that record only salience and are ignorant of the features that produced the salience. Our explanation, instantiated in a computational model of salience processing, is that subjects have three salience maps. Dots are initially segregated into three groups according to color, then each color-group is recorded on a different salience map to compute a centroid. In Part 2, the data are analyzed in terms of Attention Operating Characteristics to characterize impairments in subjects' color-attention filters (mostly insignificant) and encoding efficiency (20% drop for the hardest task) in making multiple versus single centroid judgments. A new, more sensitive analysis measured five sources of subject error variance, four independent, additive sources of error variance: imperfect color-attention filters; a Bayesian-like bias towards a central tendency; storage, retrieval, and cursor misplacement error; a large residual error due mostly to inefficient encoding; and fifth, an interactive source - error in all four components that increases when multiple centroid judgments versus a single centroid judgment are required on each trial. SIGNIFICANCE STATEMENT: An important brain process is a salience map, a representation of the relative importance (salience) of the locations of visual space. It is needed to guide where to look next, for computing the center (technically "centroid") of a cluster of items, and for other important computations. Here we show that in a brief flash of dots of three different colors, randomly interleaved, subjects can compute all three centroids. As a single salience map cannot discriminate dots of different colors, accurately reporting three centroids demonstrates that subjects have not just one, as is commonly believed, but at least three salience maps.

A direct comparison of central tendency recall and temporal integration in the successive field iconic memory task

The ensemble coding literature suggests the existence of a fast, automatic formation of some ensemble codes. Can statistical representations, such as memory for the central tendency along a particular visual feature dimension, be extracted from information held in the sensory register? Furthermore, can knowledge of early, iconic memory processes be used to determine how central tendency is extracted? We focused on the potential role of visible persistence mechanisms that support temporal integration. We tested whether mean orientation could be accurately recalled from brief visual displays using the successive field task. On separate blocks of trials, participants were asked to report the location of a split element (requiring differentiation of frames), a missing element (requiring integration across frames), and the average orientation of elements pooled across both frames (central tendency recall). Results replicate the expected tradeoff between differentiation and integration performance across inter-frame interval (IFI). In contrast, precision of mean estimates was high and invariant across IFIs. A manipulation of within-frame distributional similarity coupled with simulations using 12 models supported 2-item subsampling. The results argue against the “strategic” interpretation of subsampling since 2-item readout was predicted by information theoretic estimates of STM encoding rate: the 2 items were not from a superset in STM. Most crucially, the results argue against the various early “preattentive/parallel/global pooling” accounts and instead suggest that non-selective readout of information from iconic memory supplies a relatively small amount of item information to STM, and it is only at this point that the computation of ensemble averages begins.

Which search are you on? Adapting to color while searching for shape

Human observers adjust their attentional control settings when searching for a target in the presence of predictable changes in the target-defining feature dimension. We investigated whether observers also adapt to changes in a nondefining target dimension. According to feature integration theory, stimuli that are unique in their environment in a single feature dimension can be detected with little effort. In two experiments, we studied how observers searching for such singletons adapt their attentional control settings to a dynamical change in a nondefining target dimension. Participants searched for a shape singleton and freely chose between two targets in each trial. The two targets differed in color, and the ratio of distractors colored like each target varied dynamically across trials. A model-based analysis with a Bayesian estimation approach showed that participants adapted their target choices to the color ratio: They tended to select the target from the smaller color subset, and switched their preference both when the color ratio changed between gray and heterogeneous colors (Exp. 1) and when it changed between red and blue (Exp. 2). Participants thus tuned their attentional control settings toward color, although the target was defined by shape. We concluded that observers spontaneously adapted their behavior to changing regularities in the environment. Because adaptation was more pronounced when color homogeneity allowed for element grouping, we suggest that observers adapt to regularities that can be registered without attentional resources. They do so even if the changes are not relevant for accomplishing the task-a process presumably based on statistical learning.

Categorical grouping is not required for guided conjunction search

Knowledge of target features can guide attention in many conjunction searches in a top-down manner. For example, in search of a red vertical line among blue vertical and red horizontal lines, observers can guide attention toward all red items and all vertical items. In typical conjunction searches, distractors often form perceptually vivid, categorical groups of identical objects. This could favor the efficient search via guidance of attention to these "segmentable" groups. Can attention be guided if the distractors are not neatly segmentable (e.g., if colors vary continuously from red through purple to blue)? We tested search for conjunctions of color × orientation (Experiments 1, 3, 4, 5) or length × orientation (Experiment 2). In segmentable conditions, distractors could form two clear groups (e.g., blue steep and red flat). In non-segmentable conditions, distractors varied smoothly from red to blue and/or steep to flat; thus, discouraging grouping and increasing overall heterogeneity. We found that the efficiency of conjunction search was reasonably high and unaffected by segmentability. The same lack of segmentability had a detrimental effect on feature search (Experiment 4) and on conjunction search, if target information was limited to one feature (e.g., find the odd item in the red set, "subset search," Experiment 3). Guidance in conjunction search may not require grouping and segmentation cues that are very important in other tasks like texture discrimination. Our results support an idea of simultaneous, parallel top-down guidance by multiple features and argue against models suggesting sequential guidance by each feature in turn.

What is a preattentive feature?

The concept of a preattentive feature has been central to vision and attention research for about half a century. A preattentive feature is a feature that guides attention in visual search and that cannot be decomposed into simpler features. While that definition seems straightforward, there is no simple diagnostic test that infallibly identifies a preattentive feature. This paper briefly reviews the criteria that have been proposed and illustrates some of the difficulties of definition.

Variation in target and distractor heterogeneity impacts performance in the centroid task

In a selective centroid task, the participant views a brief cloud of items of different types-some of which are targets, the others distractors-and strives to mouse-click the centroid of the target items, ignoring the distractors. Advantages of the centroid task are that multiple target types can appear in the same display and that influence functions, which estimate the weight of each stimulus type in the cloud on the perceived centroid for each participant, can be obtained easily and efficiently. Here we document the strong, negative impact on performance that results when the participant is instructed to attend to target dots that consist of two or more levels of a single feature dimension, even when those levels differ categorically from those of the distractor dots. The results also show a smaller, but still observable decrement in performance that results when there is heterogeneity in the distractor dots.

High-capacity preconscious processing in concurrent groupings of colored dots

Grouping is a perceptual process in which a subset of stimulus components (a group) is selected for a subsequent-typically implicit-perceptual computation. Grouping is a critical precursor to segmenting objects from the background and ultimately to object recognition. Here, we study grouping by color. We present subjects with 300-ms exposures of 12 dots colored with the same but unknown identical color interspersed among 14 dots of seven different colors. To indicate grouping, subjects point-click the remembered centroid ("center of gravity") of the set of homogeneous dots, of heterogeneous dots, or of all dots. Subjects accurately judge all of these centroids. Furthermore, after a single stimulus exposure, subjects can judge both the heterogeneous and homogeneous centroids, that is, subjects simultaneously group by similarity and by dissimilarity. The centroid paradigm reveals the relative weight of each dot among targets and distractors to the underlying grouping process, offering a more detailed, quantitative description of grouping than was previously possible. A change detection experiment reveals that conscious memory contains less than two dots and their locations, whereas an ideal detector would have to perfectly process at least 15 of 26 dots to match the subjects' centroid judgments-indicating an extraordinary capacity for preconscious grouping. A different color set yielded identical results. Grouping theories that rely on predefined feature maps would fail to explain these results. Rather, the results indicate that preconscious grouping is automatic, flexible, and rapid, and a far more complex process than previously believed.

Distinguishing among potential mechanisms of singleton suppression

Previous research has revealed that people can suppress salient stimuli that might otherwise capture visual attention. The present study tests between 3 possible mechanisms of visual suppression. According to first-order feature suppression models, items are suppressed on the basis of simple feature values. According to second-order feature suppression models, items are suppressed on the basis of local discontinuities within a given feature dimension. According to global-salience suppression models, items are suppressed on the basis of their dimension-independent salience levels. The current study distinguished among these models by varying the predictability of the singleton color value. If items are suppressed by virtue of salience alone, then it should not matter whether the singleton color is predictable. However, evidence from probe processing and eye movements indicated that suppression is possible only when the color values are predictable. Moreover, the ability to suppress salient items developed gradually as participants gained experience with the feature that defined the salient distractor. These results are consistent with first-order feature suppression models, and are inconsistent with the other models of suppression. In other words, people primarily suppress salient distractors on the basis of their simple features and not on the basis of salience per se. (PsycINFO Database Record

Are hue and saturation carried in different neural channels?

Chromatic discrimination data show that a smaller physical stimulus change is required to detect a change in hue than to detect a change in saturation [Palette30, 21 (1968); Proc. R. Soc. London Ser. B283, 20160164 (2016)PRLBA40080-464910.1098/rspb.2016.0164], and, on this basis, it has been suggested that hue and saturation are carried in different neural channels [Color Space and Its Divisions: Color Order from Antiquity to the Present (Wiley, 2003), p. 311]. We used an adaptation paradigm to test explicitly for separate mechanisms, measuring hue and saturation detection thresholds before and after adaptation to hue and saturation stimuli. Within-condition adaptation did not elevate detection thresholds significantly more than between-condition adaptation. We therefore did not find psychophysical evidence for a neural channel that extracts hue thresholds more effectively than the neural channel or channels that determine saturation thresholds.