Continuous strength, population-based representations in visual working memory

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Visual working memory allows people to hold information in mind for a brief period of time so that they may use that information to perform a task (e.g., holding in mind which mug is yours as you look for it in a cluttered cabinet). This memory system is very limited. For example, we struggle to retain accurate information about just a few objects even over short timescales. The current proposal uses novel computational modeling and experimental techniques to better understand what information is stored about items in visual working memory. Importantly, the amount of information a person is able to hold in mind (i.e., in working memory) has been shown to be related to many other cognitive abilities (including intelligence), and disruptions of working memory are common in clinical disorders like attention deficit hyperactivity disorder and schizophrenia. New knowledge regarding how this information is stored and used is therefore critical for understanding the nature of important differences between individuals. Improving our understanding of the visual working memory system may also improve how we understand the execution of visual skills such as those involved in driving and other complex tasks which require simultaneous remembering and monitoring of multiple locations and objects (e.g., tracking the positions of cars in other lanes to decide whether it is safe to merge). This work may also inform the design of displays and user-interfaces by providing deeper knowledge of which visual elements are easiest (or hardest) for users to remember or navigate between. Beyond the primary research, this proposal also includes a significant outreach component, featuring both the recruitment and training of first-generation college students from underrepresented groups, as well as the development of software to allow scientists to better measure memory performance and to do so via methods that allow for recruitment of a wider, more representative sample of individuals (i.e., via internet-based experiments).This research project seeks to understand what information our working memory system holds in mind about an object. Existing theories of visual working memory commonly assume that a memory for a feature is represented by just a single value (e.g., for color memory, “I think the couch was ‘red’”). By contrast, this proposal uses computational models of continuous reproduction tasks, whereby participants must reproduce the exact color or shape of a set of items in order to test for signatures of continuous strength population-codes. The alternative conception of memory advanced by this project assumes that memory is not represented by a single value, but by an activation value for each possible value of a feature (e.g., memory for the color of the couch is not just ‘red’ but a range of values for each color). The research in this proposal tests whether people have access to a probability distribution over features, rather than simply what single feature best reflects their memory. It also tests whether the variability between items (in precision and confidence) is predictable from such representations. The new theoretical framework advanced here – which is rooted in signal detection theory – not only allows for an understanding of objective measures like memory error, but also makes predictions about memory strength and the subjective feeling of confidence associated with memory strength. The framework also shares close parallels with neural population coding models of working memory. Thus, this work makes novel connections between working memory capacity, memory precision and confidence, and provides a bridge between neural and cognitive models of working memory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.