Abstract

Previous work has demonstrated that humans select visuomotor strategies maximizing expected gain during speeded hand movements under risk; see, e.g., [Trends Cogn. Sci. 12, 291 (2008) ]; [ Glimcher et al., eds., Neuroeconomics: Decision Making and the Brain (Elsevier, 2008), p. 95 ]. Here we report a similar study in which we recorded saccadic eye movements in a saccadic decision task in which monetary rewards and losses were associated with the final position of the eye movement. Saccades into a color-coded target region won points; saccades into a partially overlapping or abutting penalty region could yield a loss. The points won during the experiment were converted into a small monetary bonus at the end of the experiment. We compared participants’ winnings to the score of an optimal observer maximizing expected gain that was calculated based on each participant’s saccadic endpoint variability, similar to a recent model of optimal movement planning under risk [J. Opt. Soc. Am. A 20, 1419 (2003) ]; [Spatial Vis. 16, 255 (2003) ]. We used three different experimental paradigms with different interstimulus intervals (Gap, No Gap, and Overlap) to manipulate saccadic latencies and a fourth experiment (Memory) with a prolonged 500ms delay period. Our results show that our subjects took the reward information, as specified by the different penalties, into account when making saccades and fixated onto or very close to the target region and less into the penalty region. However, the selected strategies differed significantly from optimal strategies maximizing expected gain in conditions when the magnitude of reward or penalty was changed. Furthermore, scores were notably affected by stimulus saliency. They were higher when the target region was filled and the penalty region outlined by a thin line, as compared to conditions in which the target was indicated by a less salient stimulus. Scores were particularly poor in trials with the shortest latencies (120140ms) mostly obtained in the Gap paradigm. At longer latencies scores improved considerably for latencies longer than 160ms. This was in line with an improvement in accuracy for single targets up to 160ms. Our results indicate that processing both of reward information and of stimulus saliency affect the programming of saccades, with a dominating contribution of stimulus saliency for eye movements with faster latencies.

© 2009 Optical Society of America

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2009 (1)

C. Morvan and L. T. Maloney, “Suboptimal selection of initial saccade in a visual search task,” J. Vision 9, 444 (2009) (conference abstract).
[CrossRef]

2008 (5)

J. Trommershäuser, L. T. Maloney, and M. S. Landy, “Decision making, movement planning, and statistical secision theory,” Trends Cog. Sci. 12, 291-297 (2008).
[CrossRef]

B. J. White, M. Stritzke, and K. R. Gegenfurtner, “Saccadic facilitation in natural backgrounds,” Curr. Biol. 18, 124-128 (2008).
[CrossRef] [PubMed]

W. Einhäuser, U. Rutishauser, and C. Koch, “Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli,” J. Vision 8, 1-19 (2008).
[CrossRef]

J. Najemnik and W. S. Geisler, “Eye movement statistics in humans are consistent with an optimal search strategy,” J. Vision 8, 1-14 (2008).
[CrossRef]

S. Fuller, R. Z. Rodrigez, and M. Carrasco, “Apparent contrast differs across the vertical meridian: visual and attentional factors,” J. Vision 8, 16.1-16 (2008).
[CrossRef]

2007 (6)

B. W. Tatler, N. J. Wade, and K. Kaulard, “Examining art: Dissociating pattern and perceptual influences on oculomotor behaviour,” Spatial Vis. 21, 165-184 (2007).
[CrossRef]

C. A. Rothkopf, D. H. Ballard, and M. M. Hayhoe, “Task and context determine where you look,” J. Vision 7, 16.1-20 (2007).
[CrossRef]

M. Stritzke and J. Trommershäuser, “Eye movements during rapid pointing under risk,” Vision Res. 47, 2000-2009 (2007).
[CrossRef] [PubMed]

D. M. Milstein and M. C. Dorris, “The influence of expected value on saccadic preparation,” J. Neurosci. 27, 4810-4818 (2007).
[CrossRef] [PubMed]

R. J. van Beers, “The sources of variability in saccadic eye movements,” J. Neurosci. 27, 8757-8770 (2007).
[CrossRef] [PubMed]

J. A. Droll, K. Gigone, and M. M. Hayhoe, “Learning where to direct gaze during change detection,” J. Vision 7, 6.1-6.12 (2007).
[CrossRef]

2006 (8)

D. Panchuk and J. N. Vickers, “Gaze behaviors of goaltenders under spatial-temporal constraints,” Hum. Mov. Sci. 25, 733-752 (2006).
[CrossRef] [PubMed]

W. Schultz, “Behavioral theories and the neurophysiology of reward,” Annu. Rev. Psychol. 57, 87-115 (2006).
[CrossRef]

M. P. Eckstein, B. A. Drescher, and S. S. Shimozaki, “Attentional cues in real scenes, saccadic targeting, and Bayesian priors,” Psychonomic Sci. 17, 973-980 (2006).
[CrossRef]

W. S. Geisler, J. S. Perry, and J. Najemnik, “Visual search: the role of peripheral information measured using gaze-contingent displays,” J. Vision 6, 858-873 (2006).
[CrossRef]

M. B. Neider and G. J. Zelinsky, “Scene context guides eye movements during visual search,” Vision Res. 46, 614-621 (2006).
[CrossRef]

A. Torralba, A. Oliva, M. S. Castelhano, and J. M. Henderson, “Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search,” Psychol. Rev. 113, 766-786 (2006).
[CrossRef] [PubMed]

J. Trommershäuser, M. S. Landy, and L. T. Maloney, “Humans rapidly estimate expected gain in movement planning,” Psychol. Sci. 17, 981-988 (2006).
[CrossRef] [PubMed]

J. Trommershäuser, J. Mattis, L. T. Maloney, and M. S. Landy, “Limits to human movement planning with delayed and unpredictable onset of needed information,” Exp. Brain Res. 175, 276-284 (2006).
[CrossRef] [PubMed]

2005 (3)

J. Trommershäuser, S. Gepshtein, L. T. Maloney, M. S. Landy, and M. S. Banks, “Optimal compensation for changes in task-relevant movement variability,” J. Neurosci. 25, 7169-7178 (2005).
[CrossRef] [PubMed]

L. P. Sugrue, G. S. Corrado, and W. T. Newsome, “Choosing the greater of two goods: neural currencies for valuation and decision making,” Nat. Rev. Neurosci. 6, 363-375 (2005).
[CrossRef] [PubMed]

J. Najemnik and W. S. Geisler, “Optimal eye movement strategies in visual search,” Nature 343, 387-390 (2005).
[CrossRef]

2004 (4)

S. Musallam, B. D. Corneil, B. Greger, H. Scherberger, and R. A. Andersen, “Cognitive control signals for neural prosthetics,” Science 305, 258-262 (2004).
[CrossRef] [PubMed]

L. P. Sugrue, G. S. Corrado, and W. T. Newsome, “Matching behavior and the representation of value in the parietal cortex,” Science 304, 1782-1787 (2004).
[CrossRef] [PubMed]

R. J. Krauzlis, D. Liston, and C. D. Carello, “Target selection and the superior colliculus: goals, choices and hypotheses,” Vision Res. 44, 1445-1451 (2004).
[CrossRef] [PubMed]

J. A. Saunders and D. C. Knill, “Visual feedback control of hand movements,” J. Neurosci. 24, 3223-3234 (2004).
[CrossRef] [PubMed]

2003 (3)

J. W. Bisley and M. E. Goldberg, “Neuronal activity in the lateral intraparietal area and spatial attention,” Science 299, 81-85 (2003).
[CrossRef] [PubMed]

J. Trommershäuser, L. T. Maloney, and M. S. Landy, “Statistical decision theory and trade-offs in the control of motor response,” Spatial Vis. 16, 255-275 (2003).
[CrossRef]

J. Trommershäuser, L. T. Maloney, and M. S. Landy, “Statistical decision theory and the selection of rapid, goal-directed movements,” J. Opt. Soc. Am. A 20, 1419-1433 (2003).
[CrossRef]

2002 (4)

P. L. Gribble, S. Everling, K. Ford, and A. Mattar, “Hand-eye coordination for rapid pointing movements,” Exp. Brain Res. 145, 372-382 (2002).
[CrossRef] [PubMed]

R. A. Andersen and C. A. Buneo, “Intentional maps in posterior parietal cortex,” Annu. Rev. Neurosci. 25, 189-220 (2002).
[CrossRef] [PubMed]

J. D. Roitman and M. N. Shadlen, “Response of neurons in the lateral intra-parietal area during a combined visual discrimination reaction time task,” J. Neurosci. 22, 9475-9489 (2002).
[PubMed]

D. P. Munoz, “Commentary: Saccadic eye movements: overview of neural circuitry,” Prog. Brain Res. 140, 89-96 (2002).
[CrossRef]

2001 (3)

P. W. Glimcher, “Making choices: the neurophysiology of visual-saccadic decision making,” Trends Neurosci. 24, 654-659 (2001).
[CrossRef] [PubMed]

G. Binsted, R. Chua, W. Helsen, and D. Elliott, “Eye-hand coordination in goal-directed aiming,” Hum. Mov. Sci. 20, 563-585 (2001).
[CrossRef] [PubMed]

L. Itti and C. Koch, “Computational modelling of visual attention,” Nat. Rev. Neurosci. 2, 194-203 (2001).
[CrossRef] [PubMed]

2000 (3)

L. Itti and C. Koch, “A saliency-based search mechanism for overt and covert shifts of visual attention,” Vision Res. 40, 1489-1506 (2000).
[CrossRef] [PubMed]

M. F. Land and P. McLeod, “From eye movements to actions: how batsmen hit the ball,” Nat. Neurosci. 3, 1340-1345 (2000).
[CrossRef] [PubMed]

D. Vishwanath, E. Kowler, and J. Feldman, “Saccadic localization of occluded targets,” Vision Res. 40, 2797-2811 (2000).
[CrossRef] [PubMed]

1999 (3)

J. M. Findlay and R. Walker, “A model of saccade generation based on parallel processing and competitive inhibition,” Behav. Brain Sci. 22, 661-674 (1999).
[CrossRef]

M. L. Platt and P. W. Glimcher, “Neural correlates of decision variables in parietal cortex,” Nature 400, 233-238 (1999).
[CrossRef] [PubMed]

C. L. Colby and M. E. Goldberg, “Space and attention in parietal cortex,” Annu. Rev. Neurosci. 22, 319-349 (1999).
[CrossRef] [PubMed]

1998 (1)

H. Weber, N. Dürr, and B. Fischer, “Effects of pre-cues on voluntary and reflexive saccade generation. II. Pro-cues for anti-saccades,” Exp. Brain Res. 120, 417-431 (1998).
[CrossRef] [PubMed]

1996 (2)

C. L. Colby, J. R. Duhamel, and M. E. Goldberg, “Visual, presaccadic and cognitive activation of single neurons in monkey lateral intraparietal area,” J. Neurophysiol. 76, 2841-2852 (1996).
[PubMed]

D. Whitaker, P. V. McGraw, I. Pacey, and B. T. Barrett, “Centroid analysis predicts visual localization of first- and second-order stimuli,” Vision Res. 36, 2957-2970 (1996).
[CrossRef] [PubMed]

1994 (1)

J. M. White, D. L. Sparks, and T. R. Stanford, “Saccades to remembered target locations: an analysis of systematic and variable errors,” Vision Res. 34, 79-92 (1994).
[CrossRef] [PubMed]

1993 (2)

D. Elliott, “Use of visual feedback during radpi aiming at a moving target,” Percept. Mot. Skills 76, 690 (1993).
[CrossRef] [PubMed]

A. Kingstone and R. M. Klein, “Visual offsets facilitate saccadic latency: does predisengagement of visuospatial attention mediate this gap effect?” J. Exp. Psychol. 19, 1251-1265 (1993).

1989 (1)

A. J. van Opstal and J. A. van Gisbergen, “Scatter in the metrics of saccades and properties of the collicular motor map,” Vision Res. 29, 1183-1196 (1989).
[CrossRef] [PubMed]

1980 (1)

L. E. Ross and S. M. Ross, “Saccade latency and warning signals: stimulus onset, offset, and change as warning events,” Percept. Psychophys. 27, 251-257 (1980).
[CrossRef] [PubMed]

1967 (1)

1903 (1)

R. Dodge, “Five types of eye movement in the horizontal meridian plane of the field of regard,” Ann. Inst. Henri Poincare 8, 307-329 (1903).

1878 (1)

L. E. Javal, “Essai sur la physiologie de la lecture,” Ann. Ocul. (Paris) 70, 97-117 (1878).

Andersen, R. A.

S. Musallam, B. D. Corneil, B. Greger, H. Scherberger, and R. A. Andersen, “Cognitive control signals for neural prosthetics,” Science 305, 258-262 (2004).
[CrossRef] [PubMed]

R. A. Andersen and C. A. Buneo, “Intentional maps in posterior parietal cortex,” Annu. Rev. Neurosci. 25, 189-220 (2002).
[CrossRef] [PubMed]

Ballard, D. H.

C. A. Rothkopf, D. H. Ballard, and M. M. Hayhoe, “Task and context determine where you look,” J. Vision 7, 16.1-20 (2007).
[CrossRef]

Banks, M. S.

J. Trommershäuser, S. Gepshtein, L. T. Maloney, M. S. Landy, and M. S. Banks, “Optimal compensation for changes in task-relevant movement variability,” J. Neurosci. 25, 7169-7178 (2005).
[CrossRef] [PubMed]

Barrett, B. T.

D. Whitaker, P. V. McGraw, I. Pacey, and B. T. Barrett, “Centroid analysis predicts visual localization of first- and second-order stimuli,” Vision Res. 36, 2957-2970 (1996).
[CrossRef] [PubMed]

Binsted, G.

G. Binsted, R. Chua, W. Helsen, and D. Elliott, “Eye-hand coordination in goal-directed aiming,” Hum. Mov. Sci. 20, 563-585 (2001).
[CrossRef] [PubMed]

Bisley, J. W.

J. W. Bisley and M. E. Goldberg, “Neuronal activity in the lateral intraparietal area and spatial attention,” Science 299, 81-85 (2003).
[CrossRef] [PubMed]

Buneo, C. A.

R. A. Andersen and C. A. Buneo, “Intentional maps in posterior parietal cortex,” Annu. Rev. Neurosci. 25, 189-220 (2002).
[CrossRef] [PubMed]

Carello, C. D.

R. J. Krauzlis, D. Liston, and C. D. Carello, “Target selection and the superior colliculus: goals, choices and hypotheses,” Vision Res. 44, 1445-1451 (2004).
[CrossRef] [PubMed]

Carpenter, R. H. S.

R. H. S. Carpenter, Movements of the Eyes (Pion, 1988).

Carrasco, M.

S. Fuller, R. Z. Rodrigez, and M. Carrasco, “Apparent contrast differs across the vertical meridian: visual and attentional factors,” J. Vision 8, 16.1-16 (2008).
[CrossRef]

Castelhano, M. S.

A. Torralba, A. Oliva, M. S. Castelhano, and J. M. Henderson, “Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search,” Psychol. Rev. 113, 766-786 (2006).
[CrossRef] [PubMed]

Chua, R.

G. Binsted, R. Chua, W. Helsen, and D. Elliott, “Eye-hand coordination in goal-directed aiming,” Hum. Mov. Sci. 20, 563-585 (2001).
[CrossRef] [PubMed]

Colby, C. L.

C. L. Colby and M. E. Goldberg, “Space and attention in parietal cortex,” Annu. Rev. Neurosci. 22, 319-349 (1999).
[CrossRef] [PubMed]

C. L. Colby, J. R. Duhamel, and M. E. Goldberg, “Visual, presaccadic and cognitive activation of single neurons in monkey lateral intraparietal area,” J. Neurophysiol. 76, 2841-2852 (1996).
[PubMed]

Corneil, B. D.

S. Musallam, B. D. Corneil, B. Greger, H. Scherberger, and R. A. Andersen, “Cognitive control signals for neural prosthetics,” Science 305, 258-262 (2004).
[CrossRef] [PubMed]

Corrado, G. S.

L. P. Sugrue, G. S. Corrado, and W. T. Newsome, “Choosing the greater of two goods: neural currencies for valuation and decision making,” Nat. Rev. Neurosci. 6, 363-375 (2005).
[CrossRef] [PubMed]

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Figures (7)

Fig. 1
Fig. 1

Stimulus configuration. Stimuli were presented randomly within an annulus 4°–6° ( 36 54 mm ) eccentric from initial fixation that was in the center of the screen. The circles were presented adjacent to each other or overlapped by 0.5° ( 4.5 mm ; far or near spatial condition). The orientation of the stimulus configuration was such that the distance from initial fixation to the two circle centers was the same. The penalty circle could appear either clockwise or counterclockwise with respect to the target circle.

Fig. 2
Fig. 2

Trial procedure. The display of a fixation cross at screen center indicated the start of the trial in all four experiments. The participant was required to fixate accurately and then press a button. A saccade into the target circle gained 100 points. The penalty for making a saccade into the penalty circle was constant within a block and could amount to a loss of 0 or 500 points. If the first saccade ended in the region where target and penalty circles overlapped, the reward and penalty were combined; if the participants’ saccade landed in neither of the circles, no reward or loss was issued.

Fig. 3
Fig. 3

Typical distribution of saccadic endpoints. Data pooled across participants, far spatial configuration, target filled, penalty 500 points. Trials were sorted by latency and grouped into sextiles. The gray dots indicate saccadic endpoints from the sextile with shortest latencies; the black dots indicate saccadic endpoints from the sextile with longest latencies. Circles denote the mean of a sextile. The large gray circle denotes the target area; the hollow black circle denotes the penalty area. Upper left panel, data from experiment 1 (Gap); upper right panel, data from experiment 2 (No Gap); lower left panel, data from experiment 3 (Overlap); Lower right panel, data from experiment 4 (Memory).

Fig. 4
Fig. 4

Endpoint distance to target center. Distance between mean saccadic endpoints and the target center as a function of saccadic latency. Data were grouped into sextiles. Different symbols denote different experiments; the solid lines/gray symbols show data from the far spatial condition; the dashed lines/open symbols show data from the near spatial condition. Data pooled across participants, penalty conditions, and saliency conditions. Error bars denote ± 1 SEM.

Fig. 5
Fig. 5

Target and penalty hit frequency. Percentage of hits into the target (gray circle symbols), penalty (gray squares) and background area (black circle symbols) as a function of saccadic latency for three saliency conditions (left panels, target filled; center panels, circles; right panels, penalty filled). Data were pooled across participants and penalty conditions and grouped into sextiles. The solid lines indicate data from the near spatial condition, the dashed lines indicate data from the far spatial condition. (a) Gap experiment, (b) No Gap experiment, (c) Overlap experiment, (d) Memory experiment. Error bars denote ± 1 SEM. Please note the difference in scaling of the x axis.

Fig. 6
Fig. 6

Comparison of normalized average score across experiments as function of saccadic latency. Comparison of efficiency (actual scores divided by optimal scores) for penalty 0 and penalty 500 conditions as a function of saccadic latency. Data were grouped into sextiles. Different symbols denote different experiments. Upper panel, penalty 0 condition; lower panel, penalty 500 condition. Data pooled across participants, spatial conditions, and saliency conditions. Error bars denote ± 1 SEM. Please note the difference in scaling of the y axis.

Fig. 7
Fig. 7

Comparison of behavioral results with the predictions for the optimal eye movement planner. Upper panel, comparison of average scores as function of saccadic latency (penalty 500 condition, near and far condition, gap condition); lower panel, comparison of shift in mean saccadic end point in horizontal direction as function of saccadic latency (penalty 500 condition, near and far condition, gap condition). Data pooled across six participants, shown separately for the three saliency conditions; model predictions computed based on pooled, averaged variability estimates (pooled across six subjects).

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

Γ ( S ) = i = 1 2 G i P ( R i | S ) ,
p ( x , y | x ¯ , y ¯ , σ x , σ y ) = 1 2 π σ x σ y exp [ ( x x ¯ ) 2 2 σ x 2 ] exp [ ( y y ¯ ) 2 2 σ y 2 ] .
P ( R i | x ¯ , y ¯ , σ x , σ y ) = d x d y p ( x , y | x ¯ , y ¯ , σ x , σ y ) d x d y .
Γ ( x ¯ , y ¯ ) = i = 1 2 G i P ( R i | x ¯ , y ¯ , σ x , σ y ) .

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