Attention, Learning, and the Value of Information

Gottlieb, Jacqueline

doi:10.1016/j.neuron.2012.09.034

Cited by 215 publications

(220 citation statements)

References 105 publications

(156 reference statements)

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“…Third, intersaccadic interval statistics were very close to the ones observed in naturalistic settings according to previous literature (34)(35)(36)(37). Overall, the found concepts contribute uniquely to our understanding of human visual behavior and should inform our ideas about neuronal mechanisms underlying attentional shifts (47), conceptualized as sequential decisions. The results of this study may similarly be relevant for the understanding of learning optimal sensory strategies in other modalities and the methodologies can fruitfully be applied there, too.…”

Section: Discussionsupporting

confidence: 81%

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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Section: Discussionsupporting

confidence: 81%

Learning rational temporal eye movement strategies

Hoppe

Rothkopf

2016

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

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“…This leads to the more general point that these experiments, like others (for reviews, see: Awh et al, 2012;Gottlieb, 2012), indicate that biased processing (or selective attention) is the outcome of a complex dynamical system with potentially multiple pathways. This is congruent with theoretical frameworks postulating that neural activity in a unified topographic map of objects charts the relative behavioural relevance assigned to each object (Awh et al, 2012;Bisley & Goldberg, 2010;Fecteau & Munoz, 2006;Gottlieb, 2012), and the allocation of attention and executed actions are direct read-outs of this topographic map (Baldauf & Deubel, 2010;Cisek & Kalaska, 2010).…”

Section: Discussionmentioning

confidence: 71%

“…An important and often overlooked source of stimulus processing bias is its "selection history" (Awh et al, 2012) or "attention for action" (Gottlieb, 2012). That is, the physical act of choosing or acting on a specific object is enough for it to be preferentially processed in subsequent encounters.…”

mentioning

confidence: 99%

Separating value from selection frequency in rapid reaching biases to visual targets

2014

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Stimuli associated with positive rewards in one task often receive preferential processing in a subsequent task, even when those associations are no longer relevant. Here we use a rapid reaching task to investigate these biases. In Experiment 1 we first replicated the learning procedure of Raymond and O'Brien (2009), for a set of arbitrary shapes that varied in value (positive, negative) and probability (20%, 80%). In a subsequent task, participants rapidly reached toward one of two shapes, except now the previously learned associations were irrelevant. As in the previous studies, we found significant reach biases toward shapes previously associated with a high probable, positive outcome. Unexpectedly, we also found a bias toward shapes previously associated with a low probable, negative outcome. Closer inspection of the learning task revealed a potential second factor that might account for these results; since a low probable negative shape was always paired with a high probable negative shape, it was selected with disproportionate frequency. To assess how selection frequency and reward value might both contribute to reaching biases we performed a second experiment. The results of this experiment at a group level replicated the reach-bias toward positively rewarding stimuli, but also revealed a separate bias toward stimuli that had been more frequently selected. At the level of individual participants, we observed a variety of preference profiles, with some participants biased primarily by reward value, others by frequency, and a few actually biased away from both highly rewarding and high frequency targets. These findings highlight that: (1) rapid reaching provides a sensitive readout of preferential processing; (2) target reward value and target selection frequency are

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“…A vast literature shows that stimuli associated with positive incentives automatically gain advantage in perceptual processing under various conditions: without conscious processing of the visual stimuli and reward contingencies (Seitz, Kim, & Watanabe, 2009), when the stimuli are not salient on a mere physical level (Gottlieb, 2012) or even when deliberately attending rewardrelated stimulus characteristics is counterproductive (Hickey et al, 2010). Nonetheless, negative events and consequences of actions are thought to be even more powerful than positive ones in shaping various psychological processes, including perception and attention (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001;Cacioppo & Gardner, 1999); moreover, stimuli with negative motivational value are known to profoundly impact early perceptual processes (Pourtois, Schettino, & Vuilleumier, 2013;Phelps, Ling, & Carrasco, 2006).…”

mentioning

confidence: 99%