Are Irrelevant Items Actively Deleted from Visual Working Memory?: No Evidence from Repulsion Effects in Dual-Retrocue Tasks

In addition to processes associated with maintaining, manipulating, and updating to-be-remembered information for ongoing cognition, some theories suggest that working memory (WM) also involves the active deletion of irrelevant information, including items that were retained in WM, but are no longer relevant for ongoing cognition. Considerable evidence provides support for an active deletion mechanism, particularly for categorical representations (Rose et al., 2016; Fulvio & Postle, 2020; but see Bae & Luck, 2017 for contradictory evidence with line orientations). On each trial of the current task, healthy young adults maintained two line orientations in visual WM, switched attention to maintaining and recalling the orientation cued first, and then switched to recall the item cued second, at which point the uncued orientation was no-longer-relevant on the trial. The results showed that the no-longer-relevant items exerted the strongest “repulsive” bias on participants’ recall of to-be-remembered items, directly contradicting the active deletion hypothesis. We suggest that visual WM binds features like line orientations into ensemble representations, and an irrelevant feature of a bound object cannot be actively deleted--it biases recall of the target feature via repulsion. Models of WM will need to be updated to explain this dynamic phenomenon.

Publisher Correction: Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behaviour

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ensemble Perception: A Review of the Field

Ensemble perception refers to the ability of an observer to precisely estimate summary statistics of multiple objects (average, range, numerosity, etc.) at a glance. This article reviews the properties and research methodology of ensemble perception. Further, we consider the theoretical debate around mechanisms of information sampling and summary statistics calculation. One theory suggests a coarse, parallel and exhaustive mechanism, whereas another theory assumes high-precision processing of a small subsample of items to accomplish proxy statistics for the entire ensemble. We describe the evolving view of the internal ensemble representation that initially was viewed as a single magnitude (e.g., average) but later thought of as the entire feature distribution of all items. We also discuss the role of ensemble representations in various perceptual tasks. Finally, we describe potential neural correlates and neurally plausible models of ensemble perception.

Interacting Subsystems and Their Molecular Ensembles

The molecular density-partition problem is reexamined and the information-theoretic (IT) justification of the stockholder division rule is summarized. The ensemble representations of the promolecular and molecular mixed states of constituent atoms are identified and the electron probabilities in the isoelectronic stockholder atoms-in-molecules (AIM) are used to define the molecular-orbital ensembles for the bonded Hirshfeld atoms. In the pure quantum state of the whole molecular system its interacting (entangled) fragments are described by the subsystem density operators, with the subsystem physical properties being generated by the partial traces involving the fragment density matrices.

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.

Ensemble representations reveal distinct neural coding of visual working memory

We characterized the population-level neural coding of ensemble representations in visual working memory from human electroencephalography. Ensemble representations provide a unique opportunity to investigate structured representations of working memory because the visual system encodes high-order summary statistics as well as noisy sensory inputs in a hierarchical manner. Here, we consistently observe stable coding of simple features as well as the ensemble mean in frontocentral electrodes, which even correlated with behavioral indices of the ensemble across individuals. In occipitoparietal electrodes, however, we find that remembered features are dynamically coded over time, whereas neural coding of the ensemble mean is absent in the old/new judgment task. In contrast, both dynamic and stable coding are found in the continuous estimation task. Our findings suggest that the prefrontal cortex holds behaviorally relevant abstract representations while visual representations in posterior and visual areas are modulated by the task demands.