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• W2413302958 abstract "•In free-viewing monkeys, orbitofrontal neurons signal the distance of gaze from a cue•The distance signal is nearly as strong as the signal representing cue value•In some cells, value signals increase when subjects fixate on the cue•Representation of gaze distance persists across two distinct task phases In the natural world, monkeys and humans judge the economic value of numerous competing stimuli by moving their gaze from one object to another, in a rapid series of eye movements. This suggests that the primate brain processes value serially, and that value-coding neurons may be modulated by changes in gaze. To test this hypothesis, we presented monkeys with value-associated visual cues and took the unusual step of allowing unrestricted free viewing while we recorded neurons in the orbitofrontal cortex (OFC). By leveraging natural gaze patterns, we found that a large proportion of OFC cells encode gaze location and, that in some cells, value coding is amplified when subjects fixate near the cue. These findings provide the first cellular-level mechanism for previously documented behavioral effects of gaze on valuation and suggest a major role for gaze in neural mechanisms of valuation and decision-making under ecologically realistic conditions. In the natural world, monkeys and humans judge the economic value of numerous competing stimuli by moving their gaze from one object to another, in a rapid series of eye movements. This suggests that the primate brain processes value serially, and that value-coding neurons may be modulated by changes in gaze. To test this hypothesis, we presented monkeys with value-associated visual cues and took the unusual step of allowing unrestricted free viewing while we recorded neurons in the orbitofrontal cortex (OFC). By leveraging natural gaze patterns, we found that a large proportion of OFC cells encode gaze location and, that in some cells, value coding is amplified when subjects fixate near the cue. These findings provide the first cellular-level mechanism for previously documented behavioral effects of gaze on valuation and suggest a major role for gaze in neural mechanisms of valuation and decision-making under ecologically realistic conditions. One of the most important tasks that an organism performs is judging the economic value—the potential for reward or punishment—associated with the stimuli in its environment. This is a difficult task in natural settings, in which many stimuli need to be accurately evaluated. One way that organisms address this problem is by evaluating stimuli serially. In primates, this is done through saccadic eye movements: by shifting gaze between objects, primates can focus their perceptual and cognitive resources on one stimulus at a time (Bichot et al., 2005Bichot N.P. Rossi A.F. Desimone R. Parallel and serial neural mechanisms for visual search in macaque area V4.Science. 2005; 308: 529-534Crossref PubMed Scopus (487) Google Scholar, DiCarlo and Maunsell, 2000DiCarlo J.J. Maunsell J.H.R. Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing.Nat. Neurosci. 2000; 3: 814-821Crossref PubMed Scopus (68) Google Scholar, Mazer and Gallant, 2003Mazer J.A. Gallant J.L. Goal-related activity in V4 during free viewing visual search. Evidence for a ventral stream visual salience map.Neuron. 2003; 40: 1241-1250Abstract Full Text Full Text PDF PubMed Scopus (197) Google Scholar, Sheinberg and Logothetis, 2001Sheinberg D.L. Logothetis N.K. Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision.J. Neurosci. 2001; 21: 1340-1350Crossref PubMed Google Scholar). A logical hypothesis, therefore, is that when primates judge the value of visual objects in natural settings, they recruit their valuation circuitry in a serial fashion, according to the location of gaze. Furthermore, this suggests that to understand ecologically realistic decisions in primates, it is critical to understand how neural valuation circuitry is influenced by gaze. While several neural mechanisms exist for encoding the value of visible objects (Kennerley et al., 2011Kennerley S.W. Behrens T.E.J. Wallis J.D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons.Nat. Neurosci. 2011; 14: 1581-1589Crossref PubMed Scopus (318) Google Scholar, Padoa-Schioppa and Assad, 2006Padoa-Schioppa C. Assad J.A. Neurons in the orbitofrontal cortex encode economic value.Nature. 2006; 441: 223-226Crossref PubMed Scopus (1072) Google Scholar, Platt and Glimcher, 1999Platt M.L. Glimcher P.W. Neural correlates of decision variables in parietal cortex.Nature. 1999; 400: 233-238Crossref PubMed Scopus (1219) Google Scholar, Roesch and Olson, 2004Roesch M.R. Olson C.R. Neuronal activity related to reward value and motivation in primate frontal cortex.Science. 2004; 304: 307-310Crossref PubMed Scopus (417) Google Scholar, Thorpe et al., 1983Thorpe S.J. Rolls E.T. Maddison S. The orbitofrontal cortex: neuronal activity in the behaving monkey.Exp. Brain Res. 1983; 49: 93-115Crossref PubMed Scopus (671) Google Scholar, Yasuda et al., 2012Yasuda M. Yamamoto S. Hikosaka O. Robust representation of stable object values in the oculomotor Basal Ganglia.J. Neurosci. 2012; 32: 16917-16932Crossref PubMed Scopus (71) Google Scholar), little is known about how value-coding neurons modulate their firing when subjects move their gaze from one object to the next, as value signals in primates are usually measured in the near-absence of eye movements. In fact, most primate behavioral tasks suppress natural eye movements by requiring prolonged fixation of gaze at a single location. And in cases where gaze was not subject to strict control, there has been no analysis—or even discussion—of how value signals might relate to gaze (e.g., Bouret and Richmond, 2010Bouret S. Richmond B.J. Ventromedial and orbital prefrontal neurons differentially encode internally and externally driven motivational values in monkeys.J. Neurosci. 2010; 30: 8591-8601Crossref PubMed Scopus (146) Google Scholar, Strait et al., 2014Strait C.E. Blanchard T.C. Hayden B.Y. Reward value comparison via mutual inhibition in ventromedial prefrontal cortex.Neuron. 2014; 82: 1357-1366Abstract Full Text Full Text PDF PubMed Scopus (173) Google Scholar, Thorpe et al., 1983Thorpe S.J. Rolls E.T. Maddison S. The orbitofrontal cortex: neuronal activity in the behaving monkey.Exp. Brain Res. 1983; 49: 93-115Crossref PubMed Scopus (671) Google Scholar, Tremblay and Schultz, 1999Tremblay L. Schultz W. Relative reward preference in primate orbitofrontal cortex.Nature. 1999; 398: 704-708Crossref PubMed Scopus (1040) Google Scholar). In contrast, several recent behavioral studies in humans have shown that simple economic decisions are influenced by fluctuations in gaze location during the choice process (Armel et al., 2008Armel K.C. Beaumel A. Rangel A. Biasing simple choices by manipulating relative visual attention.Judgm. Decis. Mak. 2008; 3: 396Crossref Google Scholar, Krajbich et al., 2012Krajbich I. Lu D. Camerer C. Rangel A. The attentional drift-diffusion model extends to simple purchasing decisions.Front. Psychol. 2012; 3: 193Crossref PubMed Scopus (192) Google Scholar, Krajbich et al., 2010Krajbich I. Armel C. Rangel A. Visual fixations and the computation and comparison of value in simple choice.Nat. Neurosci. 2010; 13: 1292-1298Crossref PubMed Scopus (780) Google Scholar, Krajbich and Rangel, 2011Krajbich I. Rangel A. Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions.Proc. Natl. Acad. Sci. USA. 2011; 108: 13852-13857Crossref PubMed Scopus (406) Google Scholar, Shimojo et al., 2003Shimojo S. Simion C. Shimojo E. Scheier C. Gaze bias both reflects and influences preference.Nat. Neurosci. 2003; 6: 1317-1322Crossref PubMed Scopus (582) Google Scholar, Towal et al., 2013Towal R.B. Mormann M. Koch C. Simultaneous modeling of visual saliency and value computation improves predictions of economic choice.Proc. Natl. Acad. Sci. USA. 2013; 110: E3858-E3867Crossref PubMed Scopus (154) Google Scholar, Vaidya and Fellows, 2015Vaidya A.R. Fellows L.K. Testing necessary regional frontal contributions to value assessment and fixation-based updating.Nat. Commun. 2015; 6: 10120Crossref PubMed Scopus (34) Google Scholar). Specifically, subjects are more likely to choose an item if they fixate on that item longer than the alternatives. While the underlying neural mechanism is unknown, computational models suggest that the effects of fixation on choice are best explained by a value signal that is modulated by the location of gaze. In these models, choices are made by comparing and sequentially integrating over time the instantaneous value of the available items. As subjects shift their fixation between items, at any given instant the value of the fixated item is amplified relative to the unfixated ones, biasing the integration process in its favor, producing a choice bias for the items fixated longer overall. Using fMRI in humans, Lim et al., 2011Lim S.-L. O’Doherty J.P. Rangel A. The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention.J. Neurosci. 2011; 31: 13214-13223Crossref PubMed Scopus (216) Google Scholar tested this hypothesis by asking if changes in fixation target could modulate the decision value signals in ventromedial prefrontal cortex (vmPFC, see Basten et al., 2010Basten U. Biele G. Heekeren H.R. Fiebach C.J. How the brain integrates costs and benefits during decision making.Proc. Natl. Acad. Sci. USA. 2010; 107: 21767-21772Crossref PubMed Scopus (283) Google Scholar, Hare et al., 2009Hare T.A. Camerer C.F. Rangel A. Self-control in decision-making involves modulation of the vmPFC valuation system.Science. 2009; 324: 646-648Crossref PubMed Scopus (1319) Google Scholar, Kable and Glimcher, 2007Kable J.W. Glimcher P.W. The neural correlates of subjective value during intertemporal choice.Nat. Neurosci. 2007; 10: 1625-1633Crossref PubMed Scopus (1296) Google Scholar). They found that vmPFC value signals were positively correlated with the value of the currently fixated object and negatively correlated with the unfixated object’s value. While these results suggest that value signals are modulated by gaze, they leave many open questions, which the current study begins to address. First, with its limited spatial and temporal resolution, fMRI cannot show whether gaze modulates value signals at the natural functional unit of the nervous system (single neurons) and at the millisecond timescale of natural free viewing. Second, the gaze studies discussed above have focused on binary choice situations, yet it is possible that gaze modulates value signals in any situation in which value is relevant, not only when facing an explicit choice. Third, an effect of gaze on value representations has not been demonstrated in non-human primates. To address these questions simultaneously, we use a behavioral task in which monkeys viewed reward-associated visual cues with no eye movement restrictions (free viewing), while we recorded single and multi-unit neural activity in a region known to express robust value signals for visual objects, the orbitofrontal cortex (OFC) (Abe and Lee, 2011Abe H. Lee D. Distributed coding of actual and hypothetical outcomes in the orbital and dorsolateral prefrontal cortex.Neuron. 2011; 70: 731-741Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar, Morrison and Salzman, 2009Morrison S.E. Salzman C.D. The convergence of information about rewarding and aversive stimuli in single neurons.J. Neurosci. 2009; 29: 11471-11483Crossref PubMed Scopus (150) Google Scholar, Padoa-Schioppa and Assad, 2006Padoa-Schioppa C. Assad J.A. Neurons in the orbitofrontal cortex encode economic value.Nature. 2006; 441: 223-226Crossref PubMed Scopus (1072) Google Scholar, Roesch and Olson, 2004Roesch M.R. Olson C.R. Neuronal activity related to reward value and motivation in primate frontal cortex.Science. 2004; 304: 307-310Crossref PubMed Scopus (417) Google Scholar, Rolls, 2015Rolls E.T. Taste, olfactory, and food reward value processing in the brain.Prog. Neurobiol. 2015; 127-128: 64-90Crossref PubMed Scopus (163) Google Scholar, Tremblay and Schultz, 1999Tremblay L. Schultz W. Relative reward preference in primate orbitofrontal cortex.Nature. 1999; 398: 704-708Crossref PubMed Scopus (1040) Google Scholar, Wallis and Miller, 2003Wallis J.D. Miller E.K. Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task.Eur. J. Neurosci. 2003; 18: 2069-2081Crossref PubMed Scopus (489) Google Scholar). This task explicitly manipulates the expectation of value, but, unconventionally, allows for natural gaze behavior, producing rich variation in gaze location that we then exploit to assess the effect of gaze on value coding. Importantly, reward delivery in the task did not depend on gaze behavior, meaning that any effects of gaze on value-related neural activity was not confounded by the operant demands of the task. We found strong modulation of value coding by gaze, including cells in which value signals became amplified as the fixation drew close to the cue. Overall, the encoding of fixation location was nearly as strong as the encoding of value in the OFC population, a surprising observation given the predominance of value-coding accounts of OFC in the literature (Rolls, 2015Rolls E.T. Taste, olfactory, and food reward value processing in the brain.Prog. Neurobiol. 2015; 127-128: 64-90Crossref PubMed Scopus (163) Google Scholar). Taken together, these findings provide, (1) novel insight into the dynamic coding of value during free viewing, (2) evidence for a key element of computational models that account for the effects of fixation on behavioral choice, and (3) a link between the dynamics of frontal lobe value signals at the areal and cellular levels (human fMRI and monkey electrophysiology, respectively). Every experimental session had two phases: an initial conditioning phase followed by neural data collection. During the conditioning phase, monkeys were trained to associate three distinct color cues with three different volumes of juice (large = ∼3 drops, small = 1 drop, and no reward = 0 drops), using a form of Pavlovian conditioning (Figure 1). New, randomly chosen colors were used in every session, and the monkeys performed the conditioning trials until they had learned the cue-reward association, indicated by different licking responses for all three cues (see Experimental Procedures). Figure 1B shows licking responses after conditioning was complete. Neurons were isolated and data collection began immediately after behavioral conditioning. The trial structure during both conditioning and data collection was identical (Figure 1A): Each trial began with a brief period of enforced gaze upon a fixation point (FP) placed at one of two locations in the screen. Then, a conditioned cue (chosen randomly) appeared at the center of the fixation window and fixation control was immediately released, allowing the monkey to move his gaze for the 4 s duration of the trial. At 4 s after cue onset, the predicted amount of juice (if any) was delivered and the trial ended. Eye position was monitored during the trial, but had no consequence on the outcome. Importantly, we only collected spike data after the cue-reward associations had been learned and licking behavior had stabilized. Thus, none of the results described here include data from the initial conditioning phase. Figure 2 shows the location of fixations during the free-viewing period, using data from 0.5 to 3.75 s after cue onset. We focus on this period to minimize the impact of the cue onset or the juice delivery on the analysis. Figure 2A depicts the eye position in a sample trial, which starts at the cue location, visits several locations on the screen (including the cue), and ends at the cue just before juice delivery. Figure 2B illustrates the distribution of fixations across the study. Both animals were more likely to fixate on the cue than anywhere else, regardless of cue identity (frequency at center grid square was significantly greater than at the square with next-highest frequency, p < 8 × 10−7 by Wilcoxon rank-sum test, for all six images in Figure 2B). Importantly, this is not driven by the fact that the cue is first presented at the initial FP, because the subjects almost always moved their eyes away from the cue within the first 0.5 s after cue onset (see Figure 3A for an example), and all data before 0.5 s were excluded from this analysis. Qualitatively, Figure 2B also shows that non-cue fixations were distributed widely, but tended to fall below the cue location for monkey 1 and toward the right edge of the monitor for monkey 2. Finally, the average likelihood of fixating the cue was not monotonically related to the size of the juice reward: the cue indicating a small reward was fixated more often than either the large or no reward cues by both monkey 1 (p < 9 × 10−7 for both small versus no reward and small versus large, Wilcoxon rank-sum test) and monkey 2 (p < 2 × 10−5 for both comparisons).Figure 3Value and Fixation Location Encoding in a Single NeuronShow full captionAll images show the same single neuron. In (A)–(C), data are aligned to cue onset at t = 0 s and continue through reward delivery at t = 4 s.(A) Eye position and neural data in a single trial: the thick black line gives the distance of gaze from the cue, and the raster with black tick marks (below x axis) shows the spikes of a single cell. The gray shading in the raster shows when the eyes were within 5 degrees of the cue center.(B) Rasters showing spiking on 15 trials for three cues indicating different reward volumes. The top raster line is the trial in (A). The gray shading indicates eyes <5 degrees from cue center.(C) Average firing across all trials; shaded area shows SEM. The gray horizontal line shows the time range used for subsequent analyses (Experimental Procedures).(D) The left peri-stimulus time histogram (PSTH) shows average firing time-locked to fixations near the cues (<3 degrees), and the right shows firing time-locked to fixations away (>10 degrees); shaded areas show SEM. The dotted line in each PSTH indicates average eye position over all trials (right axis scale), and the solid gray box indicates the post-fixation analysis window used for this neuron to generate Figure 4A. See also Figure S1. The dots and squares above the PSTHs are illustrations, not the actual fixation data.View Large Image Figure ViewerDownload Hi-res image Download (PPT) All images show the same single neuron. In (A)–(C), data are aligned to cue onset at t = 0 s and continue through reward delivery at t = 4 s. (A) Eye position and neural data in a single trial: the thick black line gives the distance of gaze from the cue, and the raster with black tick marks (below x axis) shows the spikes of a single cell. The gray shading in the raster shows when the eyes were within 5 degrees of the cue center. (B) Rasters showing spiking on 15 trials for three cues indicating different reward volumes. The top raster line is the trial in (A). The gray shading indicates eyes <5 degrees from cue center. (C) Average firing across all trials; shaded area shows SEM. The gray horizontal line shows the time range used for subsequent analyses (Experimental Procedures). (D) The left peri-stimulus time histogram (PSTH) shows average firing time-locked to fixations near the cues (<3 degrees), and the right shows firing time-locked to fixations away (>10 degrees); shaded areas show SEM. The dotted line in each PSTH indicates average eye position over all trials (right axis scale), and the solid gray box indicates the post-fixation analysis window used for this neuron to generate Figure 4A. See also Figure S1. The dots and squares above the PSTHs are illustrations, not the actual fixation data. Together, these results show that the fixations fluctuated widely across the screen, which is necessary for the analyses below. We leveraged the rich, natural variability in fixation location to address the key question of our study: how fixation location influenced value signals in OFC neurons. We recorded from 176 single neurons and 107 multi-unit signals (total 283, see Experimental Procedures; Figures 1C and 1D). When discussing individual neural responses, we use the terms “single unit” and “multi-unit signal” as appropriate; when referring to group-level data, we use the terms “cells” or “neurons,” which encompass both single unit and multi-unit signals. Below, we present examples of the main form of gaze modulation in the OFC, which is the encoding of fixation distance from the cue. Then, we show that this distance signal is widespread at the population level and unlikely to be due to encoding of other gaze-related variables. Finally, we show that gaze distance and value signals overlap in many cells, including in a subset of cells with value signals that are greatest when subjects fixate on the cue. Figure 3A depicts eye position and the firing of an identified single unit over one trial. In this trial, the cell fired more after fixations near the cue (gray bars in raster below x axis), but fired less following fixations away from the cue. Critically, this location-dependent modulation was strong when the “no reward” cue was shown (Figure 3B, top rows), but was weak or absent when the “small” or “large” reward was shown (Figure 3B, middle and bottom rows). Average firing rates time-locked to cue onset in each trial (Figure 3C) show that this cell fires most for the no reward cue and least for large, throughout nearly the whole trial (Figure 3C). However, trial-averaged data obscure the effect of fixation location on firing, because fixation patterns were unique in every trial. In Figure 3D, therefore, we replot these data to show firing time-locked to fixation onset, using fixations that began between 0.50 and 3.75 s after cue onset (Figure 3C, “fixations eligible for analysis,” see Experimental Procedures). Referenced to fixation onset, activity is clearly modulated by both value and fixation location: fixations near the no reward cue were followed by a burst of firing, but fixations onto the other cues elicited little or no response (Figure 3D, left). Thus, the cell’s value code—its differentiation between the cues—depends on fixation distance: it is strong following fixations onto the cue, but is weak following fixations away (Figure 3D, right). To summarize value and fixation distance encoding in this single unit, we segmented the eye position data into saccade and fixation epochs, extracted the firing in a 200 ms window following the onset of each fixation (see Experimental Procedures; Figure S1), and plotted this “fixation-evoked” firing as a function of cue value and the distance of fixation from the cue. This plot, in Figure 4A, shows the interaction between value and distance encoding exhibited by this cell: value coding is maximal when fixations land near the cue. Figures 4B–4D show three additional examples of value and fixation encoding, with patterns distinct from the cell in Figure 4A. The multi-unit signal in Figure 4B also encodes an interaction between value and location, but with the opposite effect from the cell in Figure 4A: firing does not distinguish between the cues following on-cue fixations, but does following fixations away. The multi-unit signal in Figure 4C and single unit in Figure 4D encode both value and fixation location, but in an additive, not interactive, manner. Both distinguish between the cues, and at the same time, they modulate their overall activity level depending on gaze: one fires more overall for near-to-cue fixations (Figure 4C), while the other fires more for fixations away (Figure 4D). In contrast to these four examples with both value and fixation location effects, cells that only encode either value or distance alone yield very different firing patterns, illustrated in Figures 5H–5J. We now look beyond individual examples to the population of recorded neurons (n = 283) to ask how often OFC cells encode fixation location, especially in comparison to the value signals for which this region is known, and to ask how often both value and location are encoded by individual cells. We fit for every cell (single unit or multi-unit signal) a linear model that explains firing as a function of three variables: cue value, distance of fixation from the cue, and the value-by-distance interaction (here, cue value is its associated reward volume, because the cue-reward association as well as reward timing and probability remain constant throughout the session). We then ask how many cells show statistically significant effects of value or gaze alone, and, critically, how many show both effects, or show an interaction between value and gaze (as in Figures 3, 4A, and 4B). We also address the encoding of gaze-related variables other than fixation distance. Table 1 shows the percentage of OFC neurons with significant effects of cue value, fixation distance from the cue, and the value-by-distance interaction in the generalized linear models (GLM). Many cells had significant effects of cue value, consistent with prior observations (Morrison and Salzman, 2009Morrison S.E. Salzman C.D. The convergence of information about rewarding and aversive stimuli in single neurons.J. Neurosci. 2009; 29: 11471-11483Crossref PubMed Scopus (150) Google Scholar, Rolls, 2015Rolls E.T. Taste, olfactory, and food reward value processing in the brain.Prog. Neurobiol. 2015; 127-128: 64-90Crossref PubMed Scopus (163) Google Scholar). Critically, nearly as many were significantly modulated by fixation distance or the value-by-distance interaction. The number of cells encoding these fixation variables reached its maximum around 200 ms after fixation onset, consistent with the typical visual response latency in OFC (Figure S2). For all variables, the number of neurons with significant effects far exceeded chance levels (p < 0.001), established by a permutation test (Table 1, right column). In Table S1, we further explore these results, showing: (1) results were similar for the two subjects, with the exception that monkey 2 had fewer significant effects of value and gaze distance. (2) Results were similar for single and multi-unit signals. And (3) results were similar when the GLM was performed using the same post-fixation firing window in all cells (rather than the cell-specific windows used in the main analysis, see Experimental Procedures).Table 1Significant Effects in GLMPercent of Neurons with Effects at p < 0.05aThe percentage of neurons (out of 283) significantly modulated by variables in a GLM (Equation 1). The maximum percentages expected by chance (right column) were determined by finding the maximum percentage of significant effects of any single variable within 1,000 randomly permuted data sets (see Experimental Procedures). Corrected p values were obtained with Holm’s variant of the Bonferroni correction.RegressorsMax Expected by Chance (All Variables)ValueDistanceValue by DistanceUncorrected59.453.727.99.8Corrected36.030.78.82.5See also Table S1 and Figure S4.a The percentage of neurons (out of 283) significantly modulated by variables in a GLM (Equation 1). The maximum percentages expected by chance (right column) were determined by finding the maximum percentage of significant effects of any single variable within 1,000 randomly permuted data sets (see Experimental Procedures). Corrected p values were obtained with Holm’s variant of the Bonferroni correction. Open table in a new tab See also Table S1 and Figure S4. We noted that some cells began encoding cue value within ∼100 ms of cue onset (Figure 3C). This early value encoding is perhaps the best characterized response in prior OFC studies (Morrison and Salzman, 2009Morrison S.E. Salzman C.D. The convergence of information about rewarding and aversive stimuli in single neurons.J. Neurosci. 2009; 29: 11471-11483Crossref PubMed Scopus (150) Google Scholar, Padoa-Schioppa and Assad, 2006Padoa-Schioppa C. Assad J.A. Neurons in the orbitofrontal cortex encode economic value.Nature. 2006; 441: 223-226Crossref PubMed Scopus (1072) Google Scholar, Roesch and Olson, 2004Roesch M.R. Olson C.R. Neuronal activity related to reward value and motivation in primate frontal cortex.Science. 2004; 304: 307-310Crossref PubMed Scopus (417) Google Scholar, Thorpe et al., 1983Thorpe S.J. Rolls E.T. Maddison S. The orbitofrontal cortex: neuronal activity in the behaving monkey.Exp. Brain Res. 1983; 49: 93-115Crossref PubMed Scopus (671) Google Scholar, Tremblay and Schultz, 1999Tremblay L. Schultz W. Relative reward preference in primate orbitofrontal cortex.Nature. 1999; 398: 704-708Crossref PubMed Scopus (1040) Google Scholar, Wallis and Miller, 2003Wallis J.D. Miller E.K. Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task.Eur. J. Neurosci. 2003; 18: 2069-2081Crossref PubMed Scopus (489) Google Scholar). We asked how similar this “early” value signal was to the value signal measured in the main GLM, which used only data 0.5 s post-cue. First, we measured firing 50–500 ms after cue onset and fit a linear model with cue value as the only variable; 32% of neurons had a significant effect of value (p < 0.05, corrected). We then compared the beta co-efficients for value from this early GLM to the co-efficients for value obtained during the cue vi" @default.
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• W2413302958 title "Orbitofrontal Cortex Value Signals Depend on Fixation Location during Free Viewing" @default.
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• W2413302958 doi "https://doi.org/10.1016/j.neuron.2016.04.045" @default.
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