Dynamic integration of information about salience and value for saccadic eye movements

Schütz, Alexander C.; Trommershäuser, Julia; Gegenfurtner, Karl R.

doi:10.1073/pnas.1115638109

Cited by 130 publications

(133 citation statements)

References 65 publications

Supporting

Mentioning

119

Contrasting

Order By: Relevance

“…Saccadic target selection is determined in part by the reward value of potential targets (1)(2)(3). Given the clear role of the FEF in target selection, it is surprising that few studies have explored the contribution of FEF dopamine to this behavior.…”

Section: Discussionmentioning

confidence: 99%

Dissociable dopaminergic control of saccadic target selection and its implications for reward modulation

Soltani

Noudoost

Moore

2013

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

View full text Add to dashboard Cite

To investigate mechanisms by which reward modulates target selection, we studied the behavioral effects of perturbing dopaminergic activity within the frontal eye field (FEF) of monkeys performing a saccadic choice task and simulated the effects using a plausible cortical network. We found that manipulation of FEF activity either by blocking D1 receptors (D1Rs) or by stimulating D2 receptors (D2Rs) increased the tendency to choose targets in the response field of the affected site. However, the D1R manipulation decreased the tendency to repeat choices on subsequent trials, whereas the D2R manipulation increased that tendency. Moreover, the amount of shift in target selection resulting from the two manipulations correlated in opposite ways with the baseline stochasticity of choice behavior. Our network simulation results suggest that D1Rs influence target selection mainly through their effects on the strength of inputs to the FEF and on recurrent connectivity, whereas D2Rs influence the excitability of FEF output neurons. Altogether, these results reveal dissociable dopaminergic mechanisms influencing target selection and suggest how reward can influence adaptive choice behavior via prefrontal dopamine.computational modeling | decision making | oculomotor A s the primary means of exploring the visual environment, we shift our gaze several times each second via saccadic eye movements. Where we look depends not only on the physical salience of visual stimuli, but also on their reward value (1-3). For example, while shopping, your attention may be captured differently by colored sales tags upon realizing that they indicate different discounts (e.g., yellow tags for 20% vs. red tags for 40% off). Studies of value-based behavior have established that midbrain dopamine (DA) neurons signal different aspects of reward (4) and that neurons in many cortical structures receiving dopaminergic projections represent the reward value of visual stimuli (5). The frontal eye field (FEF), the area of prefrontal cortex (PFC) most directly involved in triggering saccadic eye movements, receives inputs from structures encoding reward value (6), and these inputs might be sufficient to control value-based, saccadic choice (target selection). It is also possible that this control operates via FEF projections to caudate in the basal ganglia, where DA-mediated plasticity modulates rewarddependent target selection (7). In addition, the direct dopaminergic inputs to the FEF (8) could provide a mechanism that independently modulates target selection based on reward signals. Determining how dopamine within the FEF influences saccades is thus important for understanding the mechanism by which reward modulates the selection of visual targets for eye movements.To address these questions, we manipulated dopaminergic activity within the FEF of monkeys performing a saccadic choice task. In the task, monkeys freely selected between two identical visual targets appearing at varying temporal asynchronies. By systematically altering the target onset asy...

show abstract

Section: Discussionmentioning

confidence: 99%

Dissociable dopaminergic control of saccadic target selection and its implications for reward modulation

Soltani

Noudoost

Moore

2013

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

View full text Add to dashboard Cite

show abstract

“…Predictions based on the assumptions in LATER are borne out experimentally: Diminished supply of visual information (Reddi, Asrress, & Carpenter, 2003), reduced expectations (Carpenter & Williams, 1995), and lessened pressure to respond quickly (Reddi & Carpenter, 2000) all increase saccade latencies because they, respectively, slow the rate of rise of the decision signal (r), diminish starting activation (S 0 ), and increase decision thresholds (S T ) within the model. Reward, or more generally the expected benefit of making a saccade to a particular location, for example in terms of how much information its fixation is likely to yield, would also be expected to contribute to the LATER decision process, and a number of experiments have demonstrated that this is in fact observed (Schütz, Trommershäuser, & Gegenfurtner, 2012;Takikawa, Kawagoe, Itoh, Nakahara, & Hikosaka, 2002;Watanabe, Lauwereyns, & Hikosaka, 2003). …”

Section: Applying Later To Saccadic Decisionsmentioning

confidence: 99%

LATEST: A model of saccadic decisions in space and time.

Tatler

Brockmole

Carpenter

2017

Psychological Review

151

View full text Add to dashboard Cite

Many of our actions require visual information and for this it is important to direct the eyes to the right place at the right time. Two or three times every second, we must decide both when and where to direct our gaze. Understanding these decisions can reveal the moment-to-moment information priorities of the visual system and the strategies for information sampling employed by the brain to serve ongoing behaviour. Most theoretical frameworks and models of gaze control assume that the spatial and temporal aspects of fixation point selection depend on different mechanisms. We present a single model that can simultaneously account for both when and where we look. Underpinning this model is the theoretical assertion that each decision to move the eyes is an evaluation of the relative benefit expected from moving the eyes to a new location compared to that expected by continuing to fixate the current target. The eyes move when the evidence that favours moving to a new location outweighs that favouring staying at the present location. Our model provides not only an account of when the eyes move, but also what will be fixated.That is, an analysis of saccade timing alone enables us to predict where people look in a scene. Indeed our model accounts for fixation selection as well as (and often better than) current computational models of fixation selection in scene viewing. Much that we do requires visual information. However, the manner in which our eyes sample the environment greatly limits the information that is available to us: the small window of clear vision at the centre of gaze can only be directed to three locations or so in the environment each second. The valuable resource of high quality vision must therefore be allocated with care in order to provide the right information at the right time. Thus, understanding the mechanisms that underlie fixation allocation in space and time can tell us about the moment-to-moment information priorities of the visual system and the strategies for information sampling employed by the brain to serve ongoing behaviour. A complete understanding of eye movement behaviour must therefore encompass what determines both when and where we look. To date, attempts to decode the mechanisms underlying the temporal and spatial aspects of gaze control have remained separate in the literature. In this paper, we will argue this independence has led to a false dichotomy and instead offer the novel theoretical argument that a single decision process determines when our eyes move as well as where they move.The logic behind our proposal is straightforward yet entirely novel. The purpose of saccades is to acquire information about the outside world, and the outside world is complex, containing many potential sources of information. Questions regarding when and where to move the eyes can therefore be thought of in terms of competition between different potential sources of information in the environment. We will argue that this competition is resolved via a Bayesian-like process, in whic...

show abstract

“…Subjects use these learned associations as well as other context-based experience, such as stimulus probability, and past rewards and penalties (25)(26)(27) to hone the aim of a saccadic…”

mentioning

confidence: 99%

“…Subjects use these learned associations as well as other context-based experience, such as stimulus probability, and past rewards and penalties (25)(26)(27) to hone the aim of a saccadic eye movement. A recent review and commentary from Wolfe et al (28) explores the notion of "semantic" guidance in complex, naturalistic scenes as providing knowledge of the probability of finding a known object in a particular part of a scene.…”

mentioning

confidence: 99%

Learning where to look for a hidden target

Chukoskie

Snider

Mozer

et al. 2013

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

View full text Add to dashboard Cite

Survival depends on successfully foraging for food, for which evolution has selected diverse behaviors in different species. Humans forage not only for food, but also for information. We decide where to look over 170,000 times per day, approximately three times per wakeful second. The frequency of these saccadic eye movements belies the complexity underlying each individual choice. Experience factors into the choice of where to look and can be invoked to rapidly redirect gaze in a context and task-appropriate manner. However, remarkably little is known about how individuals learn to direct their gaze given the current context and task. We designed a task in which participants search a novel scene for a target whose location was drawn stochastically on each trial from a fixed prior distribution. The target was invisible on a blank screen, and the participants were rewarded when they fixated the hidden target location. In just a few trials, participants rapidly found the hidden targets by looking near previously rewarded locations and avoiding previously unrewarded locations. Learning trajectories were well characterized by a simple reinforcement-learning (RL) model that maintained and continually updated a reward map of locations. The RL model made further predictions concerning sensitivity to recent experience that were confirmed by the data. The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans and may extend to other forms of search such as animal foraging.ideal observer | oculomotor | reinforcement learning | saccades T he influence of evolution can be seen in foraging behaviors, which have been studied in behavioral ecology. Economic models of foraging assume that decisions are made to maximize payoff and minimize energy expenditure. For example, a bee setting off in search of flowers that are in bloom may travel kilometers to find food sources. Seeking information about an environment is an important part of foraging. Bees need to identify objects at a distance that are associated with food sources. Humans are also experts at searching for items in the world, and in learning how to find them. This study explores the problem of how humans learn where to look in the context of animal foraging.Our daily activities depend on successful search strategies for finding objects in our environment. Visual search is ubiquitous in routine tasks: finding one's car in a parking lot, house keys on a cluttered desk, or the button you wish to click on a computer interface. When searching common scene contexts for a target object, individuals rapidly glean information about where targets are typically located (1-9). This ability to use the "gist" of an image (3, 4) enables individuals to perform flexibly and efficiently in familiar environments. Add to that the predictable sequence of eye movements that occurs when some...

show abstract

Dynamic integration of information about salience and value for saccadic eye movements

Cited by 130 publications

References 65 publications

Dissociable dopaminergic control of saccadic target selection and its implications for reward modulation

Dissociable dopaminergic control of saccadic target selection and its implications for reward modulation

LATEST: A model of saccadic decisions in space and time.

Learning where to look for a hidden target

Contact Info

Product

Resources

About