The goal of this work is to gain a fundamental, quantitative understanding of the mechanisms of visual short- term memory (VSTM) in health. Deficits in VSTM are found in numerous disorders, including visual neglect, parietal and frontal lobe damage, attention deficit/hyperactivity disorder, and schizophrenia. A better characterization of VSTM loss may point the direction of therapy tasks to help restore some of the loss. This research proposal relies in an essential way on integrating psychophysics with neuroscience. The leading class of VSTM models asserts that VSTM is a noiseless storage with a fixed, limited capacity of about 4 items. Extra items, if any, will not be remembered. We propose an alternative theory that casts the limitations of VSTM in terms of the neural mechanisms of low-level vision. Sensory information comes with uncertainty, in part due to neural variability. In simple perceptual tasks like cue combination, it is well known that humans perform probabilistic inference to optimize performance under such uncertainty. Applying the same concepts to VSTM, we postulate that: 1) uncertainty increases with set size due to a neural constraint;2) the brain performs probabilistic inference on uncertain inputs. We call this the uncertainty model.
Aim 1 : To test whether the uncertainty model or fixed-capacity models better explain delayed estimation performance. Subjects estimate the identity of a remembered item. We will use two distinct tasks to measure subjects'uncertainty as a function of set size. We will test the hypothesis that VSTM is limited not by a fixed capacity, but by a constraint on neural resources which are distributed continuously among items.
Aim 2 : To test whether the uncertainty model or fixed-capacity models better explain change detection performance. Change detection is a leading paradigm for studying VSTM. We will test the hypothesis that observers optimally detect changes under uncertainty by computing the probability of a change given the noisy observations (probabilistic inference), in analogy to low-level visual tasks.
Aim 3 : To test the hypothesis that human observers optimally integrate likelihoods and priors in change detection. An optimal observer uses knowledge of uncertainty on an item-to-item and trial-to-trial basis in downstream computation. To test whether humans do this in change detection, we will vary either the likelihood or a prior, at fixed set size, by manipulating contrast and overall task statistics, respectively.
Aim 4 : To model the neural basis of visual change detection. Informed by the experimental findings in Aims 1-3, we will construct a behaviorally constrained neural network for change detection. We will use the theoretical framework of probabilistic population coding. The resulting network will be entirely based on the uncertainty model but exhibit the appearance of a capacity limit. It will serve as a basis for physiological tests.

Public Health Relevance

Deficits in visual short-term memory are found in many forms of brain damage and disease, including visual neglect, parietal and frontal lesions, attention deficit/hyperactivity disorder, and Alzheimer's disease. Here, we propose to better characterize, through experiment and theory, the behavioral and neural mechanisms underlying visual short-term memory, with the eventual goal of improving the diagnosis and treatment of these disorders.

National Institute of Health (NIH)
National Eye Institute (NEI)
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Central Visual Processing Study Section (CVP)
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Wiggs, Cheri
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New York University
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van den Berg, Ronald; Ma, Wei Ji (2014) "Plateau"-related summary statistics are uninformative for comparing working memory models. Atten Percept Psychophys 76:2117-35
van den Berg, Ronald; Awh, Edward; Ma, Wei Ji (2014) Factorial comparison of working memory models. Psychol Rev 121:124-49
Ma, Wei Ji; Husain, Masud; Bays, Paul M (2014) Changing concepts of working memory. Nat Neurosci 17:347-56
Mazyar, Helga; van den Berg, Ronald; Seilheimer, Robert L et al. (2013) Independence is elusive: set size effects on encoding precision in visual search. J Vis 13:
Keshvari, Shaiyan; van den Berg, Ronald; Ma, Wei Ji (2013) No evidence for an item limit in change detection. PLoS Comput Biol 9:e1002927
Pouget, Alexandre; Beck, Jeffrey M; Ma, Wei Ji et al. (2013) Probabilistic brains: knowns and unknowns. Nat Neurosci 16:1170-8
van den Berg, Ronald; Vogel, Michael; Josic, Kresimir et al. (2012) Optimal inference of sameness. Proc Natl Acad Sci U S A 109:3178-83
Beck, Jeffrey M; Ma, Wei Ji; Pitkow, Xaq et al. (2012) Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74:30-9
Wright, Anthony A; Katz, Jeffrey S; Ma, Wei Ji (2012) How to be proactive about interference: lessons from animal memory. Psychol Sci 23:453-8
Elmore, L Caitlin; Ma, Wei Ji; Magnotti, John F et al. (2011) Visual short-term memory compared in rhesus monkeys and humans. Curr Biol 21:975-9

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