Modeling interactions between visually responsive and movement related neurons in frontal eye field during saccade visual search Braden A. Purcell 1, Richard P. Heitz 1, Jeremiah Y. Cohen 1,3, Gordon D. Logan 1,2, Jeffrey D. Schall 1,2,4, Thomas J. Palmeri 1,2 Introduction Neurophysiological recordings in the frontal eye field (FEF) of awake, behaving macaque monkeys have identified cells with functionally distinct responses during visual search tasks requiring saccadic eye movement responses (Schall, 2002). One type, movement cells, exhibits a ramping of activity prior to a saccadic eye movement. A second type of FEF neuron, visual cells, shows a response to visual stimuli that evolves over time to differentiate the location of a target from that of distractors during presentation of a search array. The time when movement cell activity reaches a constant threshold has been shown to account for variability in reaction time (Hanes & Schall, 1996; Boucher et al., 2007). The relation between visual cell activity and movement cell variability, however, has not been determined. To address these, we evaluated two models designed to account for both the behavioral response time distributions and the temporal dynamics of movement cell activity, using actual recorded visual cell activity as model input. Response time (RT) is given when activity, a(t), exceeds a constant threshold, θ or – θ for a saccade to the target or to a distractor respectively, plus some ballistic movement time (t ballistic ). Note that θ and t ballistic are the same for both the easy and hard search conditions. Summary: 1. Both the absolute and accumulated difference models were able to account for variability in RT, but the best fitting parameters varied across monkeys. 2. When fit to the behavioral data, both model trajectories were able to reproduce shifts in onset of activity as recorded in FEF movement neurons. 3. While shifts in movement onset have been interpreted as support of discrete theories of information flow, we were able to simulate the same results with a continuous flow model. This work supported by AFOSR FA , NSF SBE , NEI R01-EY08890, P30-EY08126, VU ACCRE and Ingram Chair of Neuroscience. Models VTVT VDVD + - -θ-θ θ t + t ballistic = RT Absolute difference model: Accumulated difference model : VTVT VDVD + - -θ-θ θ t + t ballistic = RT ∫ Representative FEF Cells Observed Behavioral Data Easy Condition Hard Condition Trials Questions: These observations raise two questions: (1) Can visual cell activity account for variability in response times, and if so, (2) can this visual cell activity account for the variability in movement cell activity? Woodman et al. (2008) quantified several qualities of movement cell activity. Only activity onset was found to account for significant variability in reaction time. This was viewed as discrete flow of information to the movement preparation stage. Predictions of Neurophysiology Data We applied the same analyses to our simulated data to determine if the same patterns were observed in our continuous flow model. References: Schall JD. (2002) The neural selection and control of saccades by the frontal eye field. Phil Trans R Soc Lond B 357, Hanes DP, Schall JD (1996) Neural control of voluntary movement initiation. Science 274(5286), Boucher L, Palmeri TJ, Logan GD, Schall JD (2007) Inhibitory control in mind and brain: an interactive race model of countermanding saccades. Psych Rev 114(2), Woodman GF, Kang MS, Thompson K, Schall JD (2008) The effect of visual search efficiency on response preparation: neurophysiological evidence for discrete flow. Psych Sci 19(2), Department of Psychology, Vanderbilt University, Nashville, TN 3 Vanderbilt Brain Institute 2 Vanderbilt University Center for Integrative and Cognitive Neuroscience 4 Vanderbilt Vision Research Center Onset (ms) Growth rate (sp/s/ms) Reaction Time (ms) Observed: (from Woodman et al., 2008) Accumulated Difference Model: Onset (ms) Growth rate (sp/s/ms) Absolute Difference Model: Easy Hard Behavioral Paradigm Color search Motion search Averaged Model Trajectories: Monkey M Time (ms) from Target Onset Easy ConditionHard Condition Accumulated Difference Model Slow Med Fast Threshold Activity Absolute Difference Model Fits to Behavioral Data Accumulated difference model: Absolute difference model: Monkey M: Hard Condition Monkey M: Easy Condition Reaction time (ms) θ = t ballistic = ms Easy Search Hard Search Reaction Time (ms) Thick:Target in RF Thin: Distractor in RF Black: Easy Search Gray: Hard Search Vertical: mean RT Easy: 191ms Hard: 258ms Thick:Target in RF Thin: Distractor in RF Black: Easy Search Gray: Hard Search Vertical: mean RT Easy: 199ms Hard: 229ms Time (ms) from Target Onset Activity (sp/s) Visual CellMovement Cell Monkey M: Hard Condition Monkey M: Easy Condition Reaction time (ms) Reaction time (ms) θ = t ballistic = ms P(RT < t) Time (ms) from Target Onset Activity (sp/s) Easy search Hard search ΔRT ΔGR Change in growth rate: ΔRT ΔON Change in onset: