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• W2155239581 abstract "Proper timing is a critical aspect of motor learning. We report a relationship between a representation of time and an expression of learned timing in neurons in the smooth eye movement region of the frontal eye fields (FEFSEM). During prelearning pursuit of target motion at a constant velocity, each FEFSEM neuron is most active at a distinct time relative to the onset of pursuit tracking. In response to an instructive change in target direction, a neuron expresses the most learning when the instruction occurs near the time of its maximal participation in prelearning pursuit. Different neurons are most active, and undergo the most learning, at distinct times during pursuit. We suggest that the representation of time in the FEFSEM drives learning that is temporally linked to an instructive change in target motion, and that this may be a general function of motor areas of the cortex. Proper timing is a critical aspect of motor learning. We report a relationship between a representation of time and an expression of learned timing in neurons in the smooth eye movement region of the frontal eye fields (FEFSEM). During prelearning pursuit of target motion at a constant velocity, each FEFSEM neuron is most active at a distinct time relative to the onset of pursuit tracking. In response to an instructive change in target direction, a neuron expresses the most learning when the instruction occurs near the time of its maximal participation in prelearning pursuit. Different neurons are most active, and undergo the most learning, at distinct times during pursuit. We suggest that the representation of time in the FEFSEM drives learning that is temporally linked to an instructive change in target motion, and that this may be a general function of motor areas of the cortex. FEFSEM neurons show changes in firing during temporally precise pursuit learning Learning is biggest in neurons that prefer the onset time of the instructive signal A shift in the timing of the instruction promotes learning in different neurons The learned neural response does not merely reflect the altered eye movement Young children jumping rope soon learn the importance of timing: jumping too early or too late can be as bad as failing to jump at all. Precise timing is critical to all aspects of motor control at levels ranging from the coordination of joints and muscles during simple reflexive movements to the acquisition of complex skills such as playing a musical instrument. Indeed, timing is so important for motor control that it can be learned. There now are multiple demonstrations that the motor system can learn not just what to do but also when to do it (Mauk and Ruiz, 1992Mauk M.D. Ruiz B.P. Learning-dependent timing of Pavlovian eyelid responses: Differential conditioning using multiple interstimulus intervals.Behav. Neurosci. 1992; 106: 666-681Crossref PubMed Scopus (95) Google Scholar, Medina et al., 2005Medina J.F. Carey M.R. Lisberger S.G. The representation of time for motor learning.Neuron. 2005; 45: 157-167Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar, de Hemptinne et al., 2007de Hemptinne C. Nozaradan S. Duvivier Q. Lefèvre P. Missal M. How do primates anticipate uncertain future events?.J. Neurosci. 2007; 27: 4334-4341Crossref PubMed Scopus (32) Google Scholar, Doyon et al., 2009Doyon J. Bellec P. Amsel R. Penhune V. Monchi O. Carrier J. Lehéricy S. Benali H. Contributions of the basal ganglia and functionally related brain structures to motor learning.Behav. Brain Res. 2009; 199: 61-75Crossref PubMed Scopus (444) Google Scholar). In the smooth pursuit system, repeated presentations of a precisely timed instructive change in the direction of a moving target elicits a learned smooth pursuit eye movement that peaks near the time when the instructive motion is expected to occur (Medina et al., 2005Medina J.F. Carey M.R. Lisberger S.G. The representation of time for motor learning.Neuron. 2005; 45: 157-167Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar, Carey et al., 2005Carey M.R. Medina J.F. Lisberger S.G. Instructive signals for motor learning from visual cortical area MT.Nat. Neurosci. 2005; 8: 813-819Crossref PubMed Scopus (36) Google Scholar). The ability to learn timing in motor control requires a representation of time during movements. The most relevant temporal signals for motor control are typically on the order of tens to hundreds of milliseconds (Buonomano and Karmarkar, 2002Buonomano D.V. Karmarkar U.R. How do we tell time?.Neuroscientist. 2002; 8: 42-51Crossref PubMed Scopus (145) Google Scholar, Mauk and Buonomano, 2004Mauk M.D. Buonomano D.V. The neural basis of temporal processing.Annu. Rev. Neurosci. 2004; 27: 307-340Crossref PubMed Scopus (632) Google Scholar). In eyelid conditioning and smooth pursuit eye movements, learning is largest for an instructive signal that occurs in the range from 200–400 ms after the onset of a conditioned stimulus that references time (Mauk and Ruiz, 1992Mauk M.D. Ruiz B.P. Learning-dependent timing of Pavlovian eyelid responses: Differential conditioning using multiple interstimulus intervals.Behav. Neurosci. 1992; 106: 666-681Crossref PubMed Scopus (95) Google Scholar, Medina et al., 2005Medina J.F. Carey M.R. Lisberger S.G. The representation of time for motor learning.Neuron. 2005; 45: 157-167Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar). Possible timing signals have been observed via imaging or electrophysiological studies throughout the brain, for example in the basal ganglia (Rao et al., 2001Rao S.M. Mayer A.R. Harrington D.L. The evolution of brain activation during temporal processing.Nat. Neurosci. 2001; 4: 317-323Crossref PubMed Scopus (662) Google Scholar, Chiba et al., 2008Chiba A. Oshio K. Inase M. Striatal neurons encoded temporal information in duration discrimination task.Exp. Brain Res. 2008; 186: 671-676Crossref PubMed Scopus (31) Google Scholar, Jin et al., 2009Jin D.Z. Fujii N. Graybiel A.M. Neural representation of time in cortico-basal ganglia circuits.Proc. Natl. Acad. Sci. USA. 2009; 106: 19156-19161Crossref PubMed Scopus (144) Google Scholar), the cerebellum (Lewis and Miall, 2003Lewis P.A. Miall R.C. Brain activation patterns during measurement of sub- and supra-second intervals.Neuropsychologia. 2003; 41: 1583-1592Crossref PubMed Scopus (311) Google Scholar, Smith et al., 2003Smith A. Taylor E. Lidzba K. Rubia K. A right hemispheric frontocerebellar network for time discrimination of several hundreds of milliseconds.Neuroimage. 2003; 20: 344-350Crossref PubMed Scopus (108) Google Scholar), the prefrontal cortex (Sakurai et al., 2004Sakurai Y. Takahashi S. Inoue M. Stimulus duration in working memory is represented by neuronal activity in the monkey prefrontal cortex.Eur. J. Neurosci. 2004; 20: 1069-1080Crossref PubMed Scopus (48) Google Scholar, Oshio et al., 2006Oshio K. Chiba A. Inase M. Delay period activity of monkey prefrontal neurones during duration-discrimination task.Eur. J. Neurosci. 2006; 23: 2779-2790Crossref PubMed Scopus (27) Google Scholar, Jin et al., 2009Jin D.Z. Fujii N. Graybiel A.M. Neural representation of time in cortico-basal ganglia circuits.Proc. Natl. Acad. Sci. USA. 2009; 106: 19156-19161Crossref PubMed Scopus (144) Google Scholar), the supplementary motor cortex (Shih et al., 2009Shih L.Y. Kuo W.J. Yeh T.C. Tzeng O.J. Hsieh J.C. Common neural mechanisms for explicit timing in the sub-second range.Neuroreport. 2009; 20: 897-901Crossref PubMed Scopus (33) Google Scholar, Onoe et al., 2001Onoe H. Komori M. Onoe K. Takechi H. Tsukada H. Watanabe Y. Cortical networks recruited for time perception: A monkey positron emission tomography (PET) study.Neuroimage. 2001; 13: 37-45Crossref PubMed Scopus (112) Google Scholar), and the parietal cortex (Leon and Shadlen, 2003Leon M.I. Shadlen M.N. Representation of time by neurons in the posterior parietal cortex of the macaque.Neuron. 2003; 38: 317-327Abstract Full Text Full Text PDF PubMed Scopus (430) Google Scholar). The next step is to establish a link between a representation of time and a neural expression of learning. A prior paper from our laboratory reported a representation of time in the smooth eye movement region of the frontal eye fields (FEFSEM) (Schoppik et al., 2008Schoppik D. Nagel K.I. Lisberger S.G. Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses.Neuron. 2008; 58: 248-260Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar). Each neuron in the FEFSEM reaches its maximal firing rate at a particular time during pursuit, and the peak responses of the full population tile the entire duration of pursuit. Thus, the representation of smooth pursuit in the FEFSEM is such that each neuron primarily contributes to a particular moment in the eye movement. In contrast, most of the brain regions in the pursuit circuit have stereotyped responses as a function of time during pursuit. Neurons in middle temporal visual area (MT) tend to have transient responses that are driven by, and time-locked to, the visual motion signals caused by the initial target motion (Newsome et al., 1988Newsome W.T. Wurtz R.H. Komatsu H. Relation of cortical areas MT and MST to pursuit eye movements. II. Differentiation of retinal from extraretinal inputs.J. Neurophysiol. 1988; 60: 604-620PubMed Google Scholar). Similarly, Purkinje cells in the cerebellar flocculus show transient responses that are well timed to the onset of target motion, followed by sustained responses that are monotonically related to the smooth eye velocity (Stone and Lisberger, 1990Stone L.S. Lisberger S.G. Visual responses of Purkinje cells in the cerebellar flocculus during smooth-pursuit eye movements in monkeys. I. Simple spikes.J. Neurophysiol. 1990; 63: 1241-1261PubMed Google Scholar, Krauzlis and Lisberger, 1994Krauzlis R.J. Lisberger S.G. Simple spike responses of gaze velocity Purkinje cells in the floccular lobe of the monkey during the onset and offset of pursuit eye movements.J. Neurophysiol. 1994; 72: 2045-2050PubMed Google Scholar). The unique, temporally-selective representation of pursuit in the FEFSEM raises the possibility we tested here, that this cortical area plays a temporally specific role in the modulation of pursuit through learning. We recorded changes in the responses of FEFSEM neurons during pursuit learning induced by a precisely timed instructive change in target direction to ask whether the learned eye movement would be driven selectively by neurons that contribute to pursuit around the time of the instruction. In agreement with this prediction, we found that the magnitude of learning in any given neuron is correlated with how strongly the same neuron would have responded (during prelearning pursuit) at the time of the instructive change in target trajectory. We suggest that the representation of time within the FEFSEM may be harnessed to guide the temporal specificity of pursuit learning and that temporally specific modulation of motor behavior could be a general function of the motor regions of the cerebral cortex. We recorded from 100 FEFSEM neurons in two monkeys during directional smooth pursuit learning. The neurons we selected for investigation responded vigorously during pursuit prior to learning and were tuned for the direction of pursuit. In the prelearning behavioral block, we characterized the direction tuning of each FEFSEM neuron by measuring its mean firing rate during pursuit in each of eight directions spaced 45° apart. The neuron in Figure 1A responded most strongly for pursuit that was upward or obliquely up and left and therefore had a preferred direction between 90° and 135°. The neuron was only weakly active for purely horizontal pursuit to the right or left. The tuning of the neuron under study specified the direction parameters of the learning experiment (see schematic in Figure 1B). We chose the learning direction to be the cardinal direction closest to the neuron's preferred direction: 90° in Figure 1. The cardinal axis orthogonal to the learning direction defined the probe and control directions: 360° and 180° in Figure 1. Each learning experiment began with a baseline block of trials that used step-ramp target motions in the probe and the control direction to establish the baseline pursuit response prior to learning. After the monkey fixated a stationary central target, the target stepped 2° or 3° in one direction and ramped immediately in the opposite direction at 20°/s (Figure 1H). For the probe trials in Figures 1F and 1H, the mean horizontal eye velocity was zero for almost 100 ms after target motion onset, accelerated to the right for 100 to 200 ms, and then approximated the target speed of 20°/s for the remainder of the 750 ms target motion. Vertical target velocity was zero throughout the trial as was the mean vertical eye velocity prior to learning. The subsequent learning block introduced learning trials that started like probe trials with a step-ramp of target motion in the probe direction but underwent a predictable change in target direction at a fixed time. In Figures 1E and 1G, the initial 20°/s ramp motion took the target to the right. After 250 ms, an upward motion at 30°/s began so that the target moved up and to the right for 500 ms. The direction of the added component of target motion defines the learning direction; the 250 ms delay between the onset of target motion and the change in target direction defines the instruction time. Both the learning direction and instruction time were fixed for a given learning experiment. Learning trials comprised 45% of the trials in a learning block. The remaining 55% consisted of control trials (45%) and probe trials (10%), which were identical to the control and probe trials in the baseline block. The average vertical eye velocity from the learning trials (Figure 1E, lower, red traces) shows a small upward deflection that starts before the instructive change in target direction and represents the learned response. The initial, early response is followed by a later, more abrupt, “visually-driven” change in eye velocity that is the immediate consequence of the instructive upward target motion. The learned response is not present in the first few learning trials but grows rapidly and asymptotes after about 20–40 learning trials. This early, upward response reflects behavioral learning because it (1) precedes the onset of the instructive stimulus, and (2) occurs in the infrequent probe trials interspersed in the learning block even though they lack an instructive change in target motion (Figure 1F, lower, blue trace). As reported before, the peak of the learned vertical eye velocity deflection in the probe trials coincides with the instruction time (Medina et al., 2005Medina J.F. Carey M.R. Lisberger S.G. The representation of time for motor learning.Neuron. 2005; 45: 157-167Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar). Our learning paradigm elicits robust, but short-term behavioral changes. For any given learning experiment, behavioral learning was quantified as the difference in mean eye velocity between the learning trials and the baseline probe trials integrated across 100 to 320 ms (Figure 1E, gray shaded region). Integrating eye velocity yields the change in eye position. Behavioral learning averaged 0.8° in Monkey G (standard deviation [SD]: 0.2°; range: 0.4° to 1.2°) and 2.1° in Monkey S (SD: 0.7°; range: 0.7° to 4.5°) and was significantly different from zero in all experiments (Mann-Whitney U test: p < 0.001). Residual behavioral learning did not persist across learning experiments; the mean eye velocity measured in the sessions following training on a particular learning direction was not significantly different from the mean eye velocity in the sessions following learning in the opposite direction (Monkey G: p = 0.80, Monkey S: p = 0.88, Mann-Whitney U test). The rate of behavioral learning also did not vary as the study progressed. Behavioral changes continued to reach a plateau after about 20 to 40 learning trials. We conclude that learning proceeded anew for each experiment so that we could pool neural data across recording sessions to assess the effect of directional pursuit learning on the activity of the population of neurons in the FEFSEM. The example neuron in Figure 1 produced only a few spikes during the baseline block probe trials (Figure 1D, black raster) because the probe direction was orthogonal to the neuron's preferred direction. During learning trials, the neuron produced the expected vigorous response to the visually-driven eye movement in the learning direction and also acquired a small learned response that appeared before the instructive change in target direction (Figure 1C, red raster). The learned neural response also appeared in probe trials during the later part of the learning block (Figure 1D, blue raster) and, like the learned eye velocity, began before the time when the instructive change in target direction would have occurred in learning trials. Different neurons expressed varying degrees of learning. The two neurons whose responses appear in Figure 2 were recorded on different days with strong behavioral learning that reached almost 4°/s by the time of the instructive change in target direction in both experiments (Figures 2C and 2D). However, neuron #1 exhibited a large learned change in mean firing rate, while neuron #2 did not. Neuron #2 did respond strongly to the instructive change in target direction but only after the visual latency of 70 ms typically found in the FEFSEM (Figure 2B, red trace; Gottlieb et al., 1994Gottlieb J.P. MacAvoy M.G. Bruce C.J. Neural responses related to smooth-pursuit eye movements and their correspondence with electrically elicited smooth eye movements in the primate frontal eye field.J. Neurophysiol. 1994; 72: 1634-1653PubMed Google Scholar). The learned change in firing rate, when present, had several important features. First, it appeared in temporal register with the learned change in eye velocity in the interval preceding the visual input caused by the instructive target motion. Second, it was present in the probe trials in the learning block (Figure 2A, blue trace) and had a transient time course that peaked near the instruction time. Third, it appeared during target motion in a direction that did not evoke much neural activity before learning, as seen by comparison of the blue and black traces in Figure 2A. Therefore, the learned firing rate is related to the acquisition of a vertical response to the horizontal target motion and not to the horizontal eye movement itself, which changed very little as a consequence of learning (Figure 1F, top). Figure 2 shows an important feature of the data that motivated our analysis procedures. The averages of both eye velocity and firing rate followed the same trajectory during learning trials and the interleaved probe trials, up to about 70 ms after the instruction time (Figure 2). Thereafter, the mean eye velocity and firing rate in the learning trials, but not the probe trials, showed large visually-driven reactions to the instructive change in target direction. The sequence of identical responses followed by divergence due to the visual stimulus is expected because the learning and probe trials were interleaved randomly. It allowed us to assess neural changes related purely to learning from the more frequent learning trials in the 220 ms interval from 100 ms after the onset of target motion to 70 ms after the instruction time. We showed in Figure 2 that the size of the learned response could be very different across FEFSEM neurons even when the concomitant behavioral changes were similar. Only 35% of neurons (15/55 in Monkey G, 20/45 in Monkey S) exhibited a significant learned change in firing rate (Mann-Whitney U test: p < 0.001). All neurons with statistically significant changes in firing rate showed increases in activity as a result of learning. Because the firing rate in the preceding fixation period almost always remained stable in spite of learning, we argue that the neural changes in the analysis interval probably are due to learning and not to fatigue, decreases in motivation, or recording instabilities. Finally, learning did not affect eye velocity during control trials and only five neurons showed significant changes in firing rate during the control trials from the baseline and learning blocks: 4/55 in Monkey G, 1/45 in Monkey S. Excluding neurons with significant changes in response amplitude during pursuit in the control direction did not alter any of our conclusions. Each neuron's response during pursuit of a ramp target motion at constant velocity showed a distinct and repeatable trajectory as a function of time (e.g., Figure 3A ). The smoothed firing rate for this FEFSEM neuron increased rapidly after the onset of pursuit, peaked approximately 340 ms after the onset of target motion, and declined gradually thereafter. We defined the neural preference for a particular time during the pursuit trial as the firing rate at that time normalized for the peak firing rate. At 250 ms after the onset of target motion (intersection of dashed lines), this particular neuron had a neural preference of 0.7, indicating that it fired at 70% of its maximum. The neuron's preferred time was 340 ms after the onset of target motion. We measured neural preference from data acquired in the prelearning pursuit block using step-ramp target motion in the direction subsequently chosen to be the learning direction. The preferred time varied widely across the full sample of FEFSEM neurons. In Figure 3B, each row uses color to depict the neural preference for a single FEFSEM neuron as a function of time. Neurons are ordered by the latency to 95% of their peak response. The narrowness of the red diagonal band indicates that the time of maximal neural activity is well defined, and its distribution across the full duration of the pursuit movement indicates that the population of FEFSEM neurons shows a wide range of preferred times. Thus, individual neurons are most active during limited distinct temporal chunks of the eye movement, only a fraction of the population is close to maximal response at any given time, and the population of FEFSEM neurons encodes all times throughout the entire movement. In our sample, preferred times were fairly evenly distributed across the full pursuit movement duration, with some preponderance of neurons that preferred the initiation of pursuit, from 100 to 200 ms after the onset of target motion (Figure 3C). Much of the variation in the magnitude of learning across neurons was related to the wide range of neural preferences at the time of the instructive change in target direction. When we plotted the size of the mean learned response in each neuron as a function of its neural preference for the instruction time of 250 ms (Figure 3E), we obtained positive correlations that were statistically significant in both monkeys (Monkey G: r = 0.50, p < 0.0001; Monkey S: r = 0.58, p < 0.0001). Figure 3E uses the mean response averaged across all learning trials as an index of the magnitude of learning, but we obtained similar correlations when we estimated the magnitude of learning from the first or last 40 learning trials within each learning block. Figure 3E shows the relationship between the neural preference at the single time of 250 ms during prelearning pursuit and the magnitude of neural learning. For this one time point, the correlation coefficients were quite high. To judge the importance of neural preference at the time of the instructive change in target direction in determining the neuron's susceptibility to learning, we performed the same analysis shown in Figure 3E, except that we varied systematically the time used to obtain neural preference from 0 to 750 ms relative to the onset of target motion, and computed the correlation between neural preference at each time and the magnitude of neural learning for instructions delivered at 250 ms. For each monkey (Figure 3F), the size of learning across our sample of FEFSEM neurons showed the highest correlation with the neural preference near 250 ms, the time of instruction, and lower correlations with neural preference at earlier or later times. Thus, learning with an instruction time of 250 ms engages neurons that specifically prefer 250 ms. The temporally-selective relationship between neural preference and the magnitude of neural learning in Figure 3F provides evidence that the distributed representation of time within the FEFSEM may be used to regulate the temporal specificity of pursuit learning. As an alternate way to examine the relationship between the amount of neural learning in an FEFSEM neuron and its temporal preference during pursuit, we plotted the magnitude of neural learning as a function of the difference between the neuron's preferred time and 250 ms (Figure 3D). There is considerable scatter in the plot, but for the population as a whole learning is largest in neurons with preferred times close to 250 ms, and is smaller in neurons with earlier or later preferred times. A small subpopulation of neurons exhibited negative learned responses, but the preferred times of these neurons were evenly distributed before and after the instruction time. The size of neural learning also was positively correlated with the size of the learned eye velocity and the opponent response of the neuron, defined as the difference in mean firing rate between prelearning pursuit in the probe direction versus in the learning direction, measured in the interval from 100 to 320 ms after the onset of target motion. Partial correlation analysis (Table 1) revealed that a strong correlation between the magnitude of neural learning and the neural preference for 250 ms persisted even when the correlations with the other variables were taken into account. The size of the opponent response during prelearning pursuit was not a statistically significant predictor of the magnitude of learning. Not surprisingly, the magnitude of the learned eye velocity was a strong predictor of the magnitude of neural learning in Monkey S, who had wider variation in the size of his behavioral learning.Table 1Partial Correlation Coefficients between the Magnitude of Neural Learning and other Neural or Behavioral ParametersMonkey GMonkey SCorrelation, SignificanceMean [Range]Correlation, SignificanceMean [Range]Neural Preference for 250 ms0.43, p = 0.0010.63 , [0.06 to 0.98]0.36, p = 0.020.50, [0.01 to 0.98]Opponent Firing Rate0.22, p = 0.1119.2 spikes/s, [0.6 to 88.7]0.27, p = 0.0819.0 spikes/s, [−1.9 to 77.1]Behavioral Learning0.18, p = 0.190.8°, [0.4 to 1.2]0.47, p = 0.012.1°, [0.7 to 4.5]For assistance in interpreting the correlations, the table also shows the mean and range of each variable in the two monkeys. Open table in a new tab For assistance in interpreting the correlations, the table also shows the mean and range of each variable in the two monkeys. We now ask whether the magnitude of neural learning varies systematically within an individual neuron when we alter the instruction time. The same neuron was exposed to two learning experiments featuring different instruction times associated with disparate neural preferences. The results in Figure 3 predict that the example neuron in Figure 4A should show larger learning for an instruction time of 150 ms, when its neural preference was 1.0, versus an instruction time of 250 ms, when its neural preference was 0.6. The prediction was borne out by performing two different learning experiments with instruction times of 250 and 150 ms, respectively. The amount of neural learning was greater when the instruction time was 150 ms (Figure 4B, top), even though the learned change in eye velocity was somewhat larger when the instruction time was 250 ms (Figure 4B, bottom). We studied the activity of 31 neurons (11 in Monkey G, 20 in Monkey S) during two sequential learning experiments that were identical in all respects except the instruction time. The instruction time for one experiment was always 250 ms; the instruction time for the other experiment was chosen among 150 ms, 350 ms, or 450 ms. We sorted the 31 neurons into two groups based on whether their neural preference for 250 ms was larger or smaller than for the other instruction time. Then, we computed the size of learning for a 250 ms instruction time minus that for the other instruction time. These values would be positive or negative depending on whether neural learning was larger or smaller when the instruction occurred at 250 ms. Neurons with larger preferences for 250 ms showed more learning for an instruction time of 250 ms than for the other instruction time, while neurons with larger preferences for the other instruction time showed less learning for an instruction time of 250 ms, results that were confirmed statistically (Figure 4C; Monkey G: p = 0.01; Monkey S: p = 0.01; Mann-Whitney U test). The magnitude of neural learning did not depend significantly on alternative explanatory variables, such as the disparity in the sizes of the mean learned behavior elicited by the two instruction times (Monkey G: p = 0.76; Monkey S: p = 0.88), or the order of presentation of the two instruction times (Monkey G: p = 0.24; Monkey S: p = 0.28). Finally, the magnitude of neural learning produced with the most frequently used other instruction time, 150 ms, was correlated much better with neural preference for 150 ms (Monkey G: r = 0.61, p = 0.11, 8 neurons; Monkey S: r = 0.75, p = 0.001, 15 neurons), than with neural preference for 250 ms (Monkey G: r = 0.075; Monkey S: r = 0.31). In conclusion, we have demonstrated that pursuit learning with specific timing requir" @default.
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• W2155239581 date "2011-01-01" @default.
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• W2155239581 title "Learned Timing of Motor Behavior in the Smooth Eye Movement Region of the Frontal Eye Fields" @default.
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• W2155239581 doi "https://doi.org/10.1016/j.neuron.2010.11.043" @default.
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