Active sensing in the categorization of visual patterns

Yang, Scott Cheng‐Hsin; Lengyel, Máté; Wolpert, Daniel M.

doi:10.7554/elife.12215

Cited by 95 publications

(133 citation statements)

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“…In many conditions, the postsampling decisions may be quite complex (2,41), and their rewards may be ambiguous or fully unknown [as in curiosity-based exploration (6)]. In such complex conditions, the brain may derive a significant advantage from computing decision variables over shorter time scales, related to reducing the uncertainty of a proximate action.…”

Section: Discussionmentioning

confidence: 99%

“…), making categorization decisions (which is the most informative feature?) (1,2), and guiding skilled actions (what should I keep my eyes on while driving?). Despite the ubiquity and significance of active sampling mechanisms, few studies have been devoted to understanding these mechanisms and their importance for decision theories.…”

mentioning

confidence: 99%

“…Behavioral and computational studies suggest that a full explanation of instrumental sampling behaviors requires considering not only rewards but also bona fide informational factors (2,(7)(8)(9)13). However, we lack behavioral paradigms that can be used with animals and can clearly dissociate neuronal signals of instrumental information from those of expected reward gains, or furnish an understanding of neural signals related to these factors.…”

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confidence: 99%

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Parietal neurons encode expected gains in instrumental information

Foley

Kelly

Mhatre

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain's information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons' visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues.information sampling | saccades | attention | decisions | reward I n natural behavior, animals have access to multiple sources of information, but few of these sources are relevant for learning or action. Making good decisions therefore entails not only the selection of the ultimate action but, more primarily, the decision of which source of information to sample. Decisions about information sampling are central for tasks as diverse as making a medical diagnosis (which is the best test to prescribe?), making categorization decisions (which is the most informative feature?) (1, 2), and guiding skilled actions (what should I keep my eyes on while driving?). Despite the ubiquity and significance of active sampling mechanisms, few studies have been devoted to understanding these mechanisms and their importance for decision theories. Evidence accumulation has been extensively examined in decision research (3) but has been portrayed as a passive process, in the sense that decision makers rely on predetermined (experimenter selected) sources of information but cannot themselves determine which source to consult to guide a future action.Recent studies begin to shed light on this question by showing that animals (including pigeons, monkeys, and humans) prefer to observe cues that are predictive rather than nonpredictive about a future reward, and that the value of informative cues is encoded in the orbital frontal cortex and midbrain dopamine (DA) cells (4, 5). These investigations, however, have been limited to noninstrumental contexts in which animals seek to obtain information about a reward merely in order to know, but cannot act based on the information. Very little is known about the much more common scenario in which animals sample instrumental information to make decisions and guide future actions (6).Understanding instrumental sampling poses...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Parietal neurons encode expected gains in instrumental information

Foley

Kelly

Mhatre

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Hence, it is about using a spatial configuration (a predictable structure) to efficiently sample informative parts and eliminating non-diagnostic information. This is nicely illustrated in heat maps of eye movements, showing how people ''forage" information in object-specific trajectories (Yang, Lengyel, & Wolpert, 2016). Friston, Adams, Perrinet, and Breakspear (2012) model these saccades based on expected precisions, given certain predictive models (e.g., for the presence of a face).…”

Section: Salience In Contextmentioning

confidence: 99%

Disentangling signal and noise in autism spectrum disorder

Cruys

Hallen

Wagemans

2017

Brain and Cognition

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a b s t r a c tPredictive coding has recently been welcomed as a fruitful framework to understand autism spectrum disorder. Starting from an account centered on deficient differential weighting of prediction errors (based in so-called precision estimation), we illustrate that individuals with autism have particular difficulties with separating signal from noise, across different tasks. Specifically, we discuss how deficient precision-setting is detrimental for learning in unstable environments, for context-dependent assignment of salience to inputs, and for robustness in perception, as illustrated in coherent motion paradigms.

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“…Its exquisite adaptability can be appreciated by considering the different factors influencing gaze, including low-level image features (6), scene gist (7) and scene semantics (8), task constraints (9), and extrinsic rewards (10); also, combinations of factors have been investigated (11). Studies have cast doubt on the optimality of spatial gaze selection (12)(13)(14), but computational models of the spatial selection have in part established that humans are close to optimal in targeting locations in the visual scene, as in visual search for visible (15) and invisible targets (16) as well as face recognition (17) and pattern classification (18). Although these studies provide insights into where humans look in a visual scene, the ability of managing complex tasks cannot be explained solely by optimal spatial gaze selection.…”

mentioning

confidence: 99%

Learning rational temporal eye movement strategies

Hoppe

Rothkopf

2016

Proc. Natl. Acad. Sci. U.S.A.

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During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is highly efficient, as quantified by computational models of human gaze shifts in visual search and face recognition tasks. However, when we shift gaze is mostly unknown despite its fundamental importance for survival in a dynamic world. It has been suggested that during naturalistic visuomotor behavior gaze deployment is coordinated with taskrelevant events, often predictive of future events, and studies in sportsmen suggest that timing of eye movements is learned. Here we establish that humans efficiently learn to adjust the timing of eye movements in response to environmental regularities when monitoring locations in the visual scene to detect probabilistically occurring events. To detect the events humans adopt strategies that can be understood through a computational model that includes perceptual and acting uncertainties, a minimal processing time, and, crucially, the intrinsic costs of gaze behavior. Thus, subjects traded off event detection rate with behavioral costs of carrying out eye movements. Remarkably, based on this rational bounded actor model the time course of learning the gaze strategies is fully explained by an optimal Bayesian learner with humans' characteristic uncertainty in time estimation, the wellknown scalar law of biological timing. Taken together, these findings establish that the human visual system is highly efficient in learning temporal regularities in the environment and that it can use these regularities to control the timing of eye movements to detect behaviorally relevant events.eye movements | computational modeling | decision making | learning | visual attention W e live in a dynamic and ever-changing environment. To avoid missing crucial events, we need to constantly use new sensory information to monitor our surroundings. However, the fraction of the visual environment that can be perceived at a given moment is limited by the placement of the eyes and the arrangement of the receptor cells within the eyes (1). Thus, continuously monitoring environmental locations, even when we know which regions in space contain relevant information, is unfeasible. Instead, we actively explore by targeting the visual apparatus toward regions of interest using proper movements of the eyes, head, and body (2-5). This constitutes a fundamental computational problem requiring humans to decide sequentially when to look where. Solving this problem arguably has been crucial to our survival from the early human hunters pursuing a herd of prey and avoiding predators to the modern human navigating a crowded sidewalk and crossing a busy road.So far, the emphasis of most studies on eye movements has been on spatial gaze selection. Its exquisite adaptability can be appreciated by considering the different factors influencing gaze, including low-level image features (6), scene gist (7) and scene semantics (8), task constraints (9), and extrinsic rewards (10); al...

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Active sensing in the categorization of visual patterns

Cited by 95 publications

References 44 publications

Parietal neurons encode expected gains in instrumental information

Parietal neurons encode expected gains in instrumental information

Disentangling signal and noise in autism spectrum disorder

Learning rational temporal eye movement strategies

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