2008/05/20 by Maria M. Robinson, Timothy F. Brady, 洋志 荒井 +8 · 9 citations
Computer Science · Medicine · Neuroscience · Psychology · #Artificial intelligence #Cognition #Computational model #Computer science #Ensemble forecasting #Feature (linguistics) #Leverage (statistics) #Machine learning #Medicine #Multiple Models #Neural dynamics and brain function #Pediatric surgery #Pediatrics #Perception #Psychology #Set (abstract data type) #Sick child #Stoma care and complications #Surgery #Task (project management) #Visual Attention and Saliency Detection #Visual perception and processing mechanisms
paper · open access · doi:10.1038/s41562-023-01602-z
published in 日本小児外科学会雑誌 44(3), 493-1651 (Nature Portfolio)
openalex publication_date 2008/05/20 · openalex created_date 2016/06/24 · openalex updated_date 2026/06/24
Ensemble perception is a process by which we summarize complex scenes. Despite the importance of ensemble perception to everyday cognition, there are few computational models that provide a formal account of this process. Here we develop and test a model in which ensemble representations reflect the global sum of activation signals across all individual items. We leverage this set of minimal assumptions to formally connect a model of memory for individual items to ensembles. We compare our ensemble model against a set of alternative models in five experiments. Our approach uses performance on a visual memory task for individual items to generate zero-free-parameter predictions of interindividual and intraindividual differences in performance on an ensemble continuous-report task. Our top-down modelling approach formally unifies models of memory for individual items and ensembles and opens a venue for building and comparing models of distinct memory processes and representations.