In crowding, perception of a target is strongly deteriorated by nearby elements. Crowding is often explained by pooling models predicting that adding flankers increases crowding. In contrast, the centroid hypothesis proposes that adding flankers decreases crowding—“bigger is better.” In foveal vision, we have recently shown that adding flankers can increase or decrease crowding depending on whether the target groups or ungroups from the flankers. We have further shown how configural effects, such as good and global Gestalt, determine crowding. Foveal and peripheral crowding do not always reveal the same characteristics. Here, we show that the very same grouping and Gestalt results of foveal vision are also found in the periphery. These results can neither be explained by simple pooling nor by centroid models. We discuss when bigger is better and how grouping might shape crowding.

We are continuously surrounded by a noisy and ever-changing environment. Instead of analyzing all the elements in a scene, our visual system has the ability to compress an enormous amount of visual information into ensemble representations, such as perceiving a forest instead of every single tree. Still, it is unclear why such complex scenes appear to be the same from moment to moment despite fluctuations, noise, and discontinuities in retinal images. The general effects of change blindness are usually thought to stabilize scene perception, making us unaware of minor inconsistencies between scenes. Here, we propose an alternative, that stable scene perception is actively achieved by the visual system through global serial dependencies: the appearance of scene gist is sequentially dependent on the gist perceived in previous moments. To test this hypothesis, we used summary statistical information as a proxy for “gist” level, global information in a scene. We found evidence for serial dependence in summary statistical representations. Furthermore, we show that this kind of serial dependence occurs at the ensemble level, where local elements are already merged into global representations. Taken together, our results provide a mechanism through which serial dependence can promote the apparent consistency of scenes over time.

Positive sequential dependencies are phenomena in which actions, perception, decisions, and memory of features or objects are systematically biased toward visual experiences from the recent past. Among many labels, serial dependencies have been referred to as priming, sequential dependencies, sequential effects, or serial effects. Despite extensive research on the topic, the field still lacks an operational definition of what counts as serial dependence. In this meta-analysis, we review the vast literature on serial dependence and quantitatively assess its key diagnostic characteristics across several different domains of visual perception. The meta-analyses fully characterize serial dependence in orientation, face, and numerosity perception. They show that serial dependence is defined by four main kinds of tuning: serial dependence decays with time (temporal-tuning), it depends on relative spatial location (spatial-tuning), it occurs only between similar features and objects (feature-tuning), and it is modulated by attention (attentional-tuning). We also review studies of serial dependence that report single observer data, highlighting the importance of individual differences in serial dependence. Finally, we discuss a range of outstanding questions and novel research avenues that are prompted by the meta-analyses. Together, the meta-analyses provide a full characterization of serial dependence as an operationally defined family of visual phenomena, and they outline several of the key diagnostic criteria for serial dependence that should serve as guideposts for future research.