Scaling models of visual working memory to natural images.

ABSTRACT: Over the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g., the color or orientation of a simple object). Our work has two aims. The first is to build models that explain people's memory errors in continuous report tasks with natural images. Here, we use image generation algorithms to generate continuously varying response alternatives that differ from the stimulus image in natural and complex ways, in order to capture the richness of people's stored representations. The second aim is to determine whether models that do a good job of explaining memory errors with

SUBMITTER: Bates CJ 

PROVIDER: S-EPMC11332237 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature