Categorical distinctiveness constrains the labeling benefit in visual working memory

Alessandra S Souza,Clara Overkott,Marta Matyja

doi:10.1016/j.jml.2021.104242

Abstract

• Visual working memory (VWM) has a limited capacity to store visual information. • Labels categorize the visual stimuli, increasing categorical representations in VWM. • Labeling also increases continuous information about the stimulus in VWM. • The increase in continuous information depends on the distinctiveness of the labels. • Labels that are used to categorize many stimuli, reduce memory precision. Describing our visual experiences improves their retention in visual working memory, yielding a labeling benefit. Labels vary, however, in categorical distinctiveness: they can be applied broadly or narrowly to categorize stimuli. Does categorical distinctiveness constrain the labeling benefit? Here, we varied the number of terms used to label continuously varying colors (Experiment 1) and shapes (Experiment 2). Participants memorized four items, and later recalled them using a continuous color or shape wheel. During study, participants articulated “bababa” or labeled the items with two, four, or their preferred term. Recall error decreased with increases in the number of labels. Mixture modeling showed that labeling increased the probability of recall. Memory precision, however, varied with categorical distinctiveness: broad labels reduced precision, whereas categorically distinct labels increased precision compared to no-labels. In sum, in-the-moment labeling activates categorical knowledge that facilitates the storage of visual details. Data and analysis scripts are available at: https://osf.io/mqg4k/

Highlights

Describing our visual experiences improves their retention in visual working memory, yielding a labeling benefit
We will delineate hypotheses regarding the role of categorical distinctiveness in visual working memory
These models allow some responses to be based on categorical representations, whereas other responses are assumed to be based on continuous representations of the exact feature value experienced (Bae et al, 2015; Cibelli et al, 2016; Hardman et al, 2017; Pratte et al, 2017). These studies have shown that a substantial amount of responses in visual working memory tasks are influenced by the prior knowledge of the participants, as reflected in well-learned feature categories

Summary

Introduction

Describing our visual experiences improves their retention in visual working memory, yielding a labeling benefit. More recently researchers have extended the mixture models used to assess performance in visual working memory tasks to account for the influence of categorical knowledge In essence, these models allow some responses to be based on categorical representations (e.g., red vs green), whereas other responses are assumed to be based on continuous (fine-grained) representations of the exact feature value experienced (Bae et al, 2015; Cibelli et al, 2016; Hardman et al, 2017; Pratte et al, 2017). These studies have shown that a substantial amount of responses in visual working memory tasks are influenced by the prior knowledge of the participants, as reflected in well-learned feature categories

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