Диссертации, представленные на защиту и подготовленные в НИУ ВШЭ

The dissertation presents a collection of three articles on the general issue of the representational format in visual working memory. There is a big debate on the question: what are units of visual working memory? We found that not objects or features are visual working memory units but hierarchically organized features bundles (Markov, Tiurina, & Utochkin, 2019). Another study demonstrated that it could also be applied to real-world objects, which representations in visual working memory are not totally holistic (Markov, Utochkin, & Brady, 2021). Finally, we showed that the retrieval of information from visual working memory relies on noisy familiarity signals and highly depends on the task (Markov & Utochkin, 2022). Thus, overall, our findings suggest that visual working memory representations are flexible, hierarchical, and highly dependent on the current task.

The dissertation presents a collection of seven articles on the general issue of the representational format underlying an ability to perceive multiple objects and to store them in working and long-term memory. Representing large sets of things is supposed to be a computationally demanding problem because of fundamental limitation of deep object processing. The basic framework presented in the dissertation suggests that the visual system can recover object information from sparse and compressed representations that do not necessary require full access to deep object processing (e.g. focused attention). Overall, three hierarchically organized types of representations are discussed: (1) features - separable object properties such as color or size, separate image parts, etc; (2) genuine whole objects, and (3) ensembles - global statistical summaries of multiple object features, such as average speed or size variance. Features are shown to play an important role in organizing visual experience: For example, complex everyday objects can be decomposed to independent semantically meaningful features in long-term memory (Utochkin & Brady, 2020a). On the other hand, ensemble representations can give easy access to quite rich representations of multiple objects without need to know everything about each individual object (Khvostov & Utochkin, 2019; Tiurina & Utochkin, 2019) and can be used in a number of visual tasks including categorization, visual search, texture discrimination (Utochkin, 2015; Utochkin & Yurevich, 2016; Utochkin, Khvostov, & Stakina, 2018), and in working memory encoding and storage (Utochkin & Brady, 2020b).