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Camille Morvan, Laurence Maloney; Suboptimal selection of initial saccade in a visual search task. Journal of Vision 2009;9(8):444. doi: 10.1167/9.8.444.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose. We investigated how the visuo-motor system plans saccades in an economic task analogous to visual search.
Methods. The subject saw two visual squares (tokens) displayed along a horizontal “base” line through the bottom of a screen 57 cm from the subject perpendicular to his line of sight. On each trial, the subject could freely saccade to any point along the base line. During the saccade, one of the two tokens would change slightly. The subjects then judged how the token changed, responding by keypress. If the subject's response were correct he received a reward. Prior to the main experiment, we mapped subjects' visual sensitivity to token change at different eccentricities. 4 subjects participated in the task.
Analyses. The key independent variable was the spacing between the two tokens. The token change was chosen to be difficult to see outside of the foveal region. If the tokens were near each other the subject could reliably identify token change from a fixation point midway between the tokens. If the tokens were far apart, then this middle strategy would lead to little reward; a stochastic strategy where the subject could saccade to one target would perform better. The key prediction of the experiment is the critical separation where the ideal subject should switch from the midpoint strategy to the stochastic strategy in order to maximize expected gain.
Results. We found that subjects were suboptimal in this task. With increasing separation of tokens, subjects switched to the stochastic strategy too soon, consistent with the possibility that they underestimate their visual acuity in periphery. As a result subjects earned only 83% of the expected gain had they switched at the correct point.
Conclusion. Even in simple displays with only two tokens and after training, subjects do not plan saccades that maximize expected gain.
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