TEMPLATE DESIGN © 2007 www.PosterPresentations.com Scaling of anticipatory smooth pursuit eye movements with target Scaling of anticipatory smooth pursuit eye movements with target speed probability speed probability David Souto, Anna Montagnini & Guillaume S. Masson david.souto@pse.unige.ch or anna.montagnini@incm.cnrs-mrs.fr Background Disagreement with previous models References Direction randomization Anticipatory smooth pursuit permits tracking of expected target trajectories with little delay. It can be a mirror of cognitive expectations, but It is still unclear how these expectations are built. Pursuit anticipation scales with several target parameters in an adaptive way [1]. This is compatible, for instance, with a switch between expectation states after one or several wrong guesses [2]. Alternatively, the underlying mechanism could rely on a global subjective probability estimation. Goals – clarify the nature of the expectations that drive anticipatory pursuit z by examining effects of velocity and direction randomization across blocks of different probability z by looking at trial history effects Markov two-states model [2]: Anticipatory pursuit may reflect alternation between two mutually exclusive states of expectation (high vs. low velocity or left vs. rightward). This model predicts a bimodal distribution of anticipatory pursuit for intermediate values of p (black curve) 3 human subjects participated in two experiments. Eye movements were recorded with a scleral search coil. Each probability block comprised 250-500 trials with two possible values of target speed (Exp. 1) or direction (Exp. 2). The probability p of the highest speed (or of the right direction) to be presented was varied from 0 to 100% across blocks. VSS 2008 @ Naples, Florida Data analysis Direction rand. Trial-history effects Speed randomization Conclusion Speed rand. 5 °/s 15 °/s V80 300-450 300ms gap fixation (LED) 500 ms P (V=15) ={0 -100%} P (Right) = {0 -100%} 15 °/s t [ms] Probability bias http://www.unige.ch/fapse/cognition/souto [1] Heinen, S. J., Badler, J. B., & Ting, W. (2005). J Vis, 5(6), 493-503. [2] Kowler, E., Martins, A. J., & Pavel, M. (1984). Vis Res, 24(3), 197-210. Effects of the n th previous trial on the current trial, for a 5 (upper panels) or 15 deg/sec target motion. Error-bars = C.I. Effects increase with scarcity of stimulus Sequence effects Speed rand. Direction rand. Distribution Speed rand. Direction rand. z Scaling of anticipatory speed with probability: favoring low speeds for speed randomization, nearly linear for direction randomization z Large effects of the last 2-4 trials. Two wrong expectations are not enough to turn off anticipations in biased blocks. z Anticipation may better be explained by a continuous accumulation-of-evidence model z Coming soon: quantitative predictions Scaling with block probability Anticipatory velocity (V80), latency and mean anticipatory acceleration scale with probability bias Anticipatory velocity (V80) scales linearly with probability bias. Anticipatory pursuit onset and mean acceleration scale with absolute anticipatory velocity. anticipatory pursuit onset p(Left) = .75 Effect of n th previous trial on current trial when it was a leftward or rightward target. Error-bars = C.I. Anticipatory velocity (V80) has a unimodal distribution which shifts with p both for speed and direction randomization velocity (deg/sec) 2 4 6 8 10 -1 -0.5 0 0.5 1 trials back 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 p = .10 p = .50 -1 -0.5 0 0.5 1 p = .25 p = .75 p = .90 p(V=15) n-trial = 5 deg/sec p = .10 p = .50 p = .25 p = .75 p = .90 n-trial = 15 deg/sec Observer AM -0.5 0 0.5 1 1.5 2 4 6 8 10 -1.5 -1 -0.5 0 0.5 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 p(Right) n-trial = right Observer AM n-trial = left trials back velocity (deg/sec) p = .10 p = .50 p = .25 p = .75 p = .90 p = .10 p = .50 p = .25 p = .75 p = .90 36.535 500 ms Our experimental results (red curves) disagree with this prediction: an alternative model could better predict the shift of the unimodal distribution with p (e.g. a continuous accumulation-of-evidence model) p(Right) = .25 -15 -10 -5 0 5 10 15 0 0.2 0.4 0.6 0.8 1 anticipatory velocity at V80 (deg/s) P(X<x) 0 10 25 50 75 90 100 P(Right) AM -8 -6 -4 -2 0 2 4 6 8 anticipatory velocity at V80 (deg/s) -250 -200 -150 -100 -50 0 anticipatory pursuit onset (ms) 0 25 50 75 100 0 5 10 15 20 25 30 anticipatory acceleration at V80 (deg/s) 0 25 50 75 100 0 25 50 75 100 Probability of Right-direction (%) AM DS AR AM DS AR AM DS AR 25 50 75 -8 -4 0 4 8 25 50 75 25 50 75 25 50 75 0 2 4 6 8 10 P(V=15) 25 50 75 25 50 75 v5-v5 sequence v15-v15 sequence LL sequence RR sequence AM DS GM AM DS AR P(Right) ant. vel (V80) [deg/s] ant. vel (V80) [deg/s] -10 -5 0 5 10 0 0.2 0.4 0.6 p =0 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 0 0.2 0.4 0.6 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 0 0.2 0.4 0.6 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 -10 -5 0 5 10 ant. vel. at v80 (deg/s) FSMM model prediction experimental data DS AM AR p = 10 p = 25 p(Right) p= 75 p= 90 p = 100 ant. vel. at v80 (deg/s) ant. vel. at v80 (deg/s) p = 50 density Pursuit task -5 0 5 10 15 0 0.2 0.4 0.6 0.8 1 0 10 25 50 75 90 100 P(V=15) anticipatory velocity at V80 (deg/s) P(X<x) AM 0 2 4 6 8 10 -250 -200 -150 -100 -50 0 20 40 60 80 100 120 AM DS GM AM DS GM AM DS GM anticipatory velocity at V80 (deg/s) anticipatory pursuit onset (ms) anticipatory acceleration at V80 (deg/s) Probability of High Velocity (%) 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100