In the Light of Evolution Volume VII: The Human Mental Machinery (2014) / Chapter Skim
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14 Learning Where to Look for a Hidden Target--Leanne Chukoskie, Joseph Snider, Michael C. Mozer, Richard J. Krauzlis, and Terrence J. Sejnowski
14 Learning Where to Look for a Hidden Target--Leanne Chukoskie, Joseph Snider, Michael C. Mozer, Richard J. Krauzlis, and Terrence J. Sejnowski
Pages 243-262
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From page 243... ...
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.
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. Surprisingly, however, little research has been directed to how individuals learn to direct gaze in a context- and task-appropriate manner in novel environments.
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have also shown that in addition to scene gist itself, learned spatial associations guide eye movements during search. Subjects use these learned associations as well as other contextbased experience, such as stimulus probability, and past rewards and penalties (Geng and Behrmann, 2005; Stritzke and Trommershäuser, 2007; Schütz et al., 2012)
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(C) A representation of the screen is superimposed with the hidden target distribution that is learned over the session as well as sample eye traces from three trials for participant M
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The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory assuming perfect knowledge of the static target distribution and independently chosen fixations. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans.
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and its surroundings, were available to guide the movement. To understand how participants learn where to look in a novel scene or context where no relationship exists between visual targets and associated rewards or penalties, we designed a search task in which participants were rewarded for finding a hidden target, similar to the scenario encountered by a foraging animal (Fig.
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Taking trials 31–60 to reflect asymptotic behavior, we examined the efficiency of human search in comparison with a theoretical optimum. An ideal observer was derived for the Hidden Target Search Task assuming that fixations are independent of one another and that the target distribution is known, and the expected number of trials is minimized.
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250 / Leanne Chukoskie et al.
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The dashed line is the ideal-observer theoretical optimum in each case, assuming perfect knowledge of the target distribution.
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The cost in terms of extra saccades for nonoptimal search spreads (away from the minimum) was higher for the larger target distributions, and the comparatively shallow rise for search spreads above optimal meant that if subjects were to err, then they should tend toward larger spreads.
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A wide range of statistical measures quite distinct from the training criterion was used to compare human and model performance: mean distance from target centroid, SD of the distribution of eye movements, and the median number of fixations (Fig.
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versus the permuted intertrial distance (the distance between the final fixation on a trial and the first fixation of another randomly drawn trial)
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. Very small "fixational" eye movements compose the left side of the plot and large larger saccadic jumps on the right for three different sizes of target distribution.
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. In our hidden target search task, participants explored a novel environment and quickly learned to align their fixations with the region of space over which invisible targets were probabilistically distributed.
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: Recent targets influence subsequent behavior, even after the searcher has seemingly learned the target distribution, as reflected in asymptotic performance. Sequential dependencies were predicted by the RL model, which generated behavior remarkably close to that of the participants as a group, and also captured individual idiosyncrasies (Reinforcement Learning Model)
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These results provide a neurophysiological basis for understanding how experience is learned and consolidated in the service of the saccades we make to gather information about our environment about three times each second. CONCLUSIONS In our eye-movement search task, subjects learned to choose saccade goals based on prior experience of reward that is divorced from specific visual features in a novel scene.
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From page 259... ...
We took advantage of the fact that the goal of saccadic eye movements is to obtain information about the world and asked human participants to "conduct an eye movement search to find a rewarded target location as quickly as possible." Participants were also told that they would learn more about the rewarded targets as the session progressed and that they should try to find the rewarded target location as quickly as possible. The rewarded targets had no visual representation on the screen and were thus invisible to the subject.
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Eye movements were analyzed offline in MATLAB. We detected saccades and blinks by using a conservative velocity threshold (40°/s with a 5-ms shoulder after each saccade)
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Grant SBE 0542013 to the Temporal Dynamics of Learning Center, an NSF Science of Learning Center grant (to L.C., J.S., M.C.M., and T.J.S.) , a Blasker Rose-Miah grant from the San Diego Foundation (to L.C.)
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