Memories of Visual Events Can Be Formed Without Specific Spatial Coordinates

Why does visual working memory ability improve with age: More objects, more feature detail, or both? A registered report

We investigated how visual working memory (WM) develops with age across the early elementary school period (6-7 years), early adolescence (11-13 years), and early adulthood (18-25 years). The work focuses on changes in two parameters: the number of objects retained at least in part, and the amount of feature-detail remembered for such objects. Some evidence suggests that, while infants can remember up to three objects, much like adults, young children only remember around two objects. This curious, nonmonotonic trajectory might be explained by differences in the level of feature-detail required for successful performance in infant versus child/adult memory paradigms. Here, we examined if changes in one of two parameters (the number of objects, and the amount of detail retained for each object) or both of them together can explain the development of visual WM ability as children grow older. To test it, we varied the amount of feature-detail participants need to retain. In the baseline condition, participants saw an array of objects and simply were to indicate whether an object was present in a probed location or not. This phase begun with a titration procedure to adjust each individual's array size to yield about 80% correct. In other conditions, we tested memory of not only location but also additional features of the objects (color, and sometimes also orientation). Our results suggest that capacity growth across ages is expressed by both improved location-memory (whether there was an object in a location) and feature completeness of object representations.

An interference model for visual working memory: Applications to the change detection task

Most studies of visual-working memory employ one of two experimental paradigms: change-detection or continuous-stimulus reproduction. In this study, we extended the Interference Model (IM; Oberauer & Lin, 2017), which was designed for continuous reproduction, to the single-probe change-detection task. In continuous reproduction, participants occasionally report the non-target items instead of the target. The presence of non-target response is predicted by the Interference Model, which relies in part on the interference of non-target items to explain the set-size effect. By presenting a probe matching a non-target item, we can investigate the amount of interference from non-target items in change detection. As predicted by the Interference Model, we observed poorer performance in rejecting a probe matching a non-target item compared to a new probe (i.e., a cost due to intrusions from non-targets). We fitted the IM along with the Variable Precision, the Slot-Averaging, and the Neural-Population model to the data from two change-detection experiments. The models were equipped with a Bayesian decision rule based on the one used in Keshvari, van den Berg, and Ma (2013). The Interference Model and the Neural-Population model successfully predicted the set-size effect and the non-target intrusion cost, whereas the Variable Precision (VP) and Slot-Averaging (SA) models failed to predict the intrusion cost at all. Even with additional assumptions enabling VP and SA to produce intrusion costs, the IM still performed better than the competing models quantitatively.