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Michael Dieciuc, Walter Boot; Capturing the response dynamics of attention capture with mouse tracking. Journal of Vision 2018;18(10):456. doi: https://doi.org/10.1167/18.10.456.
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© ARVO (1962-2015); The Authors (2016-present)
Most studies that have investigated attention capture have done so looking at response-times, eye movements, and EEG measures. In contrast, the current study used mouse-tracking, a continuous online measure of response dynamics, to investigate attention capture and the mouse tracking metrics most sensitive to capture. Participants completed a variant of the oculomotor capture paradigm, where instead of making an eye movement to the target, participants used their mouse to click on it as quickly as possible. Participants were shown a screen with 4 green boxes after which 3 of them turned white. Participants clicked on the remaining green box (their target). On half the trials, a new white box appeared on the screen (onset) that was either on the same side as the target or on the opposite side. Overall, our results demonstrated that abrupt onsets influenced trajectories. Crucially, while there were differences in area under the curve (F(2, 48) =12.3, p < .001, n2p = .34), there were no differences in RT (F(2, 48) =1.69, p = .2, n2p = .07) or initiation time (F(2, 48) =.135, p = .87, n2p < .001). Relative to trials where there were no onsets, onsets on the same side as the target facilitated trajectories (more direct paths, t(24) = 2.1, p =.049) whereas onsets on the opposite side of the target interfered with trajectories (less direct paths, t(24) = 4.2, p < .001). Given mouse-tracking's continuous and high-density measure of behavior, these results suggest that response dynamics might be a more sensitive measure of attention capture than relatively discrete and ballistic measures like response time or saccades. Continuously measuring response dynamics as they unfold over time and space may ultimately be helpful in disentangling the long-standing debate regarding the nature of attention capture.
Meeting abstract presented at VSS 2018
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