Categorically distinct subsets allow flexible memory selection in hybrid search.

In many everyday situations, we search our visual surroundings for any one of many memorized items held in memory, a process termed . In some cases, only a portion of the memorized mental list is relevant within a specific visual context, thus, restricting memory search to the relevant subset would be desirable. Previous research had shown that participants largely fail to "partition" memory into several distinct subsets, on a trial-by-trial basis.

A signal-detection account of item-based and ensemble-based visual change detection: A reply to Harrison, McMaster, and Bays.

Growing empirical evidence shows that ensemble information (e.g., the average feature or feature variance of a set of objects) affects visual working memory for individual items.

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.

A population response model of ensemble perception.

Ensemble representations have been considered as one of the strategies that the visual system adopts to cope with its limited capacity. Thus, they include various statistical summaries such as mean, variance, and distributional properties and are formed over many stages of visual processing. The present study proposes a population-coding model of ensemble perception to provide a theoretical and computational framework for these various facets of ensemble perception.

Ensemble averaging: What can we learn from skewed feature distributions?

Many studies have shown that observers can accurately estimate the average feature of a group of objects. However, the way the visual system relies on the information from each individual item is still under debate. Some models suggest some or all items sampled and averaged arithmetically.

Effects of item distinctiveness on the retrieval of objects and object-location bindings from visual working memory.

Visual working memory (VWM) is prone to interference from stored items competing for its limited capacity. Distinctiveness or similarity of the items is acknowledged to affect this competition, such that poor item distinctiveness causes a failure to discriminate between items sharing common features. In three experiments, we studied how the distinctiveness of studied real-world objects (i.

Real-world objects are not stored in holistic representations in visual working memory.

When storing multiple objects in visual working memory, observers sometimes misattribute perceived features to incorrect locations or objects. These misattributions are called binding errors (or swaps) and have been previously demonstrated mostly in simple objects whose features are easy to encode independently and arbitrarily chosen, like colors and orientations. Here, we tested whether similar swaps can occur with real-world objects, where the connection between features is meaningful rather than arbitrary.

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.

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.

Roles of saliency and set size in ensemble averaging.

Ensemble statistics are often thought of as a reliable impression of numerous items despite limited capacities to consciously represent each individual. However, whether all items equally contribute to ensemble summaries (e.g.

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.

Individual representations in visual working memory inherit ensemble properties.

Prevailing theories of visual working memory assume that each encoded item is stored or forgotten as a separate unit independent from other items. Here, we show that items are not independent and that the recalled orientation of an individual item is strongly influenced by the summary statistical representation of all items (ensemble representation). We find that not only is memory for an individual orientation substantially biased toward the mean orientation, but the precision of memory for an individual item also closely tracks the precision with which people store the mean orientation (which is, in turn, correlated with the physical range of orientations).

Entities also require relational coding and binding.

Although Bastin et al. propose a useful model for thinking about the structure of memory and memory deficits, their distinction between entities and relational encoding is incompatible with data showing that even individual objects - prototypical "entities" - are made up of distinct features which require binding. Thus, "entity" and "relational" brain regions may need to solve fundamentally the same problems.

Independent storage of different features of real-world objects in long-term memory.

People can store thousands of real-world objects in visual long-term memory with high precision. But are these objects stored as unitary, bound entities, as often assumed, or as bundles of separable features? We tested this in several experiments. In the first series of studies, participants were instructed to remember specific exemplars of real-world objects presented in a particular state (e.

Independent and parallel visual processing of ensemble statistics: Evidence from dual tasks.

The visual system can represent multiple objects in a compressed form of ensemble summary statistics (such as object numerosity, mean, and feature variance/range). Yet the relationships between the different types of visual statistics remain relatively unclear. Here, we tested whether two summaries (mean and numerosity, or mean and range) are calculated independently from each other and in parallel.

Different features are stored independently in visual working memory but mediated by object-based representations.

The question of whether visual working memory (VWM) stores individual features or bound objects as basic units is actively debated. Evidence exists for both feature-based and object-based storages, as well as hierarchically organized representations maintaining both types of information at different levels. One argument for feature-based storage is that features belonging to different dimensions (e.

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.

Ensemble perception in depth: Correct size-distance rescaling of multiple objects before averaging.

Previous studies have shown that people are good at rapidly estimating ensemble summary statistics, such as the mean size of multiple objects. In the present study, we tested whether these average estimates are based on "raw" retinal representations (proximal sizes) or on how items should appear based on context, such as the viewing distance (distal sizes). In our experiments, observers adjusted the mean size of multiple objects presented at various apparent distances through a stereoscope.

Continuous to discrete: Ensemble-based segmentation in the perception of multiple feature conjunctions.

Although objects around us vary in a number of continuous dimensions (color, size, orientation, etc.), we tend to perceive the objects using more discrete, categorical descriptions (e.g.

Visual search for changes in scenes creates long-term, incidental memory traces.

Humans are very good at remembering large numbers of scenes over substantial periods of time. But how good are they at remembering changes to scenes? In this study, we tested scene memory and change detection two weeks after initial scene learning. In Experiments 1-3, scenes were learned incidentally during visual search for change.

The numerosity and mean size of multiple objects are perceived independently and in parallel.

It is well documented that people are good at the rapid representation of multiple objects in the form of ensemble summary statistics of different types (numerosity, the average feature, the variance of features, etc.). However, there is not enough clarity regarding the links between statistical domains.

Binocularity and visual search-Revisited.

Binocular rivalry is a phenomenon of visual competition in which perception alternates between two monocular images. When two eye's images only differ in luminance, observers may perceive shininess, a form of rivalry called binocular luster. Does dichoptic information guide attention in visual search? Wolfe and Franzel (Perception & Psychophysics, 44(1), 81-93, 1988) reported that rivalry could guide attention only weakly, but that luster (shininess) "popped out," producing very shallow Reaction Time (RT) × Set Size functions.

Similarity and heterogeneity effects in visual search are mediated by "segmentability".

The heterogeneity of our visual environment typically reduces the speed with which a singleton target can be found. Visual search theories explain this phenomenon via nontarget similarities and dissimilarities that affect grouping, perceptual noise, and so forth. In this study, we show that increasing the heterogeneity of a display can facilitate rather than inhibit visual search for size and orientation singletons when heterogeneous features smoothly fill the transition between highly distinguishable nontargets.

Ensemble summary statistics as a basis for rapid visual categorization.

Ensemble summary statistics represent multiple objects on the high level of abstraction-that is, without representing individual features and ignoring spatial organization. This makes them especially useful for the rapid visual categorization of multiple objects of different types that are intermixed in space. Rapid categorization implies our ability to judge at one brief glance whether all visible objects represent different types or just variants of one type.

Parallel averaging of size is possible but range-limited: a reply to Marchant, Simons, and De Fockert.

In their recent paper, Marchant, Simons, and De Fockert (2013) claimed that the ability to average between multiple items of different sizes is limited by small samples of arbitrarily attended members of a set. This claim is based on a finding that observers are good at representing the average when an ensemble includes only two sizes distributed among all items (regular sets), but their performance gets worse when the number of sizes increases with the number of items (irregular sets). We argue that an important factor not considered by Marchant et al.