Uncertainty is maintained and used in working memory

Representation and computation in visual working memory

The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.

I remember it now, so I'll remember it later: Working memory strength guides predictions for long-term memory performance

Judgments of learning (JOLs) are assumed to be made inferentially, based on cues. This cue-utilization approach substituted the theory that memory strength guides JOLs. The rejection of this theory ignores the existence of two memory systems: working memory (WM), which holds representations immediately accessible, and long-term memory (LTM), which is a permanent store. By manipulating and measuring WM strength, we tested a revised version of the memory-strength theory in which JOLs are guided by WM representations. In Experiment 1, participants memorized sequences of two or four colored objects, then they provided JOLs for an LTM test of these objects, and performed a WM test on the objects' colors. After learning 200 objects, the LTM test followed. Sequence-length affected WM, but not LTM performance. JOLs, however, were higher for sequences of two than for four objects and correlated higher with WM than LTM performance. We replicated these results with a simultaneous presentation of the objects (Experiment 2), in the absence of a WM test (Experiment 3), and in a word-pair task (Experiment 4). Overall, our findings are consistent with the revised memory-strength theory. WM strength should therefore be considered when examining the factors guiding JOLs.

Memory reports are biased by all relevant contents of working memory

Sensory input is inherently noisy while the world is inherently predictable. When multiple observations of the same object are available, integration of the available information necessarily increases the reliability of a world estimate. Optimal integration of multiple instances of sensory evidence has already been demonstrated during multisensory perception but could benefit unimodal perception as well. In the present study 330 participants observed a sequence of four orientations and were cued to report one of them. Reports were biased by all simultaneously memorized items that were similar and relevant to the target item, weighted by their reliability (signal-to-noise ratio). Orientations presented before and presented after the target biased report, demonstrating that the bias emerges in memory and not (exclusively) during perception or encoding. Only attended, task-relevant items biased report. We suggest that these results reflect how the visual system integrates information that is sampled from the same object at consecutive timepoints to promote perceptual stability and behavioural effectiveness in a dynamic world. We suggest that similar response biases, such as serial dependence, might be instances of a more general mechanism of working memory averaging. Data is available at https://osf.io/embcf/ .

Introspective inference counteracts perceptual distortion

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Studying the neural representations of uncertainty

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.

Challenging the fixed-criterion model of perceptual decision-making

Perceptual decision-making is often conceptualized as the process of comparing an internal decision variable to a categorical boundary or criterion. How the mind sets such a criterion has been studied from at least two perspectives. One idea is that the criterion is a fixed quantity. In work on subjective phenomenology, the notion of a fixed criterion has been proposed to explain a phenomenon called "subjective inflation"-a form of metacognitive mismatch in which observers overestimate the quality of their sensory representation in the periphery or at unattended locations. A contrasting view emerging from studies of perceptual decision-making is that the criterion adjusts to the level sensory uncertainty and is thus sensitive to variations in attention. Here, we mathematically demonstrate that previous empirical findings supporting subjective inflation are consistent with either a fixed or a flexible decision criterion. We further lay out specific task properties that are necessary to make inferences about the flexibility of the criterion: (i) a clear mapping from decision variable space to stimulus feature space and (ii) an incentive for observers to adjust their decision criterion as uncertainty changes. Recent work satisfying these requirements has demonstrated that decision criteria flexibly adjust according to uncertainty. We conclude that the fixed-criterion model of subjective inflation is poorly tenable.

How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective

Reinforcement learning and working memory are two core processes of human cognition and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here, we show that the brain networks that support them actually overlap significantly and that they are less distinct cognitive processes than often assumed. We review literature demonstrating the benefits of considering each process to explain properties of the other and highlight recent work investigating their more complex interactions. We discuss how future research in both computational and cognitive sciences can benefit from one another, suggesting that a key missing piece for artificial agents to learn to behave with more human-like efficiency is taking working memory's role in learning seriously. This review highlights the risks of neglecting the interplay between different processes when studying human behavior (in particular when considering individual differences). We emphasize the importance of investigating these dynamics to build a comprehensive understanding of human cognition.

Joint representation of working memory and uncertainty in human cortex

Neural representations of visual working memory (VWM) are noisy, and thus, decisions based on VWM are inevitably subject to uncertainty. However, the mechanisms by which the brain simultaneously represents the content and uncertainty of memory remain largely unknown. Here, inspired by the theory of probabilistic population codes, we test the hypothesis that the human brain represents an item maintained in VWM as a probability distribution over stimulus feature space, thereby capturing both its content and uncertainty. We used a neural generative model to decode probability distributions over memorized locations from fMRI activation patterns. We found that the mean of the probability distribution decoded from retinotopic cortical areas predicted memory reports on a trial-by-trial basis. Moreover, in several of the same mid-dorsal stream areas, the spread of the distribution predicted subjective trial-by-trial uncertainty judgments. These results provide evidence that VWM content and uncertainty are jointly represented by probabilistic neural codes.