Vol 439 | 16 February 2006 | doi : 10.1038 / nature04490 LETTERS The primate amygdala represents the positive and negative value of visual stimuli during learning Joseph J. Paton1 * , Marina A. Belova1 * , Sara E. Morrison1 & C. Daniel Salzman1 ,2,3 Visual stimuli can acquire positive or negative value through their association with rewards and punishments , a process called reinforcement learning . Although we now know a great deal about how the brain analyses visual information , we know little about how visual representations become linked with values . To study this process , we turned to the amygdala , a brain structure implicated in reinforcement learning1 5 . We recorded the activity of individual amygdala neurons in monkeys while abstract images acquired either positive or negative value through conditioning . After monkeys had learned the initial associations , we reversed image value assignments . We examined neural responses in relation to these reversals in order to estimate the relative contribution to neural activity of the sensory properties of images and their conditioned values . Here we show that changes in the values of images modulate neural activity , and that this modulation occurs rapidly enough to account for , and correlates with , monkeys ' learning . Furthermore , distinct populations of neurons encode the positive and negative values of visual stimuli . Behavioural and physiological responses to visual stimuli may therefore be based in part on the plastic representation of value provided by the amygdala . The complex anatomical connections of the amygdala , a collection of nuclei located deep in the medial temporal lobe , make it a prime candidate for providing a representation of the value of visual stimuli . The amygdala receives inputs from the visual system and from other sensory systems that represent reinforcing stimuli6 8 . In addition , the amygdala is likely to receive error signals that represent stimuli in relation to expectations and that may be essential in creating an updated representation of value . The source of error signals for aversive learning has not been identified ; however , midbrain dopamine neurons might supply such error signals for appetitive learning9 . These signals could influence amygdala neural responses either directly7 or indirectly through other brain structures such as the prefrontal cortex6 ,10 . The convergence of information from each of these input pathways , perhaps combined with processing that occurs through intrinsic connections within the amygdala , may form a representation of visual stimulus value . Unlike the anatomy of the amygdala , the physiological properties of amygdala neurons -- especially with respect to learning the value of visual stimuli -- remain poorly understood . We therefore recorded the activity of single amygdala neurons while monkeys learned the positive or negative value of new , abstract images during a trace-conditioning procedure11 -LRB- Fig. 1a -RRB- . In this task , a brief time interval -LRB- the trace interval -RRB- separated the presentation of a conditioned stimulus -LRB- CS -RRB- from an unconditioned stimulus -LRB- US -RRB- . In each experiment , we assigned every CS a value : positive , negative or non-reinforced . After the image viewing and trace intervals , we delivered a liquid reward -LRB- after positive images -RRB- , an aversive air-puff directed at the face -LRB- after negative images -RRB- or nothing -LRB- after nonreinforced images -RRB- . The monkeys learned to associate each image with its value : during the trace interval they licked at a spout in anticipation of a liquid reward , or closed their eyes -- a defensive behaviour -- in anticipation of an air-puff . -LRB- We refer to the monkeys ' eye closures as blinks , but the eyes typically stayed closed for longer than a blink , suggesting that this behaviour was purposeful . -RRB- After the monkeys had learned the associations , without warning we reversed the values of the positive and negative images , and the monkeys learned the new values . Unlike a task in which subjects learn to avoid aversive USs , in our task , CSs predicted USs with 100 % certainty . We tested every neuron during both appetitive and aversive conditioning for the same CS , allowing us to determine whether distinct amygdala neurons respond preferentially to positive or negative value for each CS . We report that amygdala neurons encode -- in a flexible manner and in separate populations of neurons -- the positive or negative value of conditioned images . Furthermore , these signals predict when monkeys learn , and develop rapidly enough to account for learning . Anticipatory licking and blinking behaviour provided a measure of monkeys ' learning about the positive and negative association of images . In the experiment shown in Fig. 2a , b , licking response rates were greater when an image was positive than when the same image was negative . Blinking responses showed the opposite trend . We used a change-point test to identify when the response rate changed significantly in relation to a value reversal -LRB- P , 0.05 -RRB- , represented graphically by a change in slope of the cumulative record of responses12 . For image 1 , the licking rate decreased significantly at trial 52 and blinking rate increased at trial 48 -LRB- 22 and 18 trials after reversal , respectively -RRB- . Because the monkey still licked sporadically when image 2 was negative , the change-point algorithm identified trial 29 as when licking began to increase , slightly before the reversal . Blinking decreased significantly starting at trial 35 . The primary objectives of our study were -LRB- 1 -RRB- to determine whether positive and negative conditioned visual stimuli engage the same or different populations of amygdala neurons , -LRB- 2 -RRB- to compare quantitatively the time course of how neural activity and behaviour change upon image value changes , and -LRB- 3 -RRB- to characterize the relative contributions of conditioned value and image identity to neuronal responses . In each of two monkeys -LRB- monkey V and monkey P -RRB- , we used magnetic resonance imaging -LRB- MRI -RRB- to localize our recording electrode to the amygdala -LRB- Fig. 1b d -RRB- . We obtained complete data sets during monkeys ' performance of initial and reversal learning for 196 neurons . To determine the extent to which amygdala neural activity reflected the conditioned values of images , or the images ' sensory properties , we examined neural activity in relation to image value reversals . For the amygdala neuron recorded during the behaviour depicted in Fig. 2a , b , neural activity during the trace 1 Center for Neurobiology and Behavior , 2W . M. Keck Center on Brain Plasticity and Cognition , and 3Department of Psychiatry , Columbia University , 1051 Riverside Drive , Unit 87 , New York , New York 10032 , USA . * These authors contributed equally to this work . 2006 Nature Publishing Group 865 LETTERS NATURE | Vol 439 | 16 February 2006 interval was higher when images had a positive value than when the same images had a negative value -LRB- Fig. 2c f -RRB- , typical of a valuecoding neuron . Other cells showed value-coding during the visual stimulus interval -LRB- see Supplementary Note 1 and Supplementary Fig. 1 -RRB- . To characterize how quickly neural activity changed in relation to reversal of image value , we applied the change-point test12 to trace interval neural responses -LRB- Fig. 2g , h -RRB- . For image 1 , neural activity began to decrease at trial 38 -LRB- 8 trials after reversal -RRB- , before either licking or blinking behaviour changed . For image 2 , neural activity increased at trial 33 , preceding the change in blinking behaviour . Overall , 100/196 -LRB- 51 % -RRB- of neurons changed activity in relation to value during the visual stimulus and/or trace intervals . For the cell shown in Fig. 2 , a two-way analysis of variance -LRB- ANOVA -RRB- verified the influence of image value on neural responses -LRB- 65 % of variance accounted for by image value , P , 0.05 -RRB- , as well as a much smaller effect of image identity -LRB- 8 % of variance accounted for by image identity , P , 0.05 -RRB- . Across our population of neurons , image value often accounted for a statistically significant percentage of the variance -LRB- Fig. 3a , b ; P , 0.05 , two-way ANOVA -RRB- . In addition , image identity had a significant effect in many cells , often overlapping with the representation of value at the single-cell level . The inset histograms show the difference in the strength of identity and value coding for all neurons with a significant effect of image identity , value or both factors . The two distributions are significantly different , with the trace interval showing significantly more value coding compared to image identity coding than the visual stimulus interval -LRB- P , 1025 , Wilcoxon test ; visual stimulus median difference 21.2 ; trace median difference 11.8 -RRB- . Thus , the representation of image identity weakens substantially after an image disappears , and neural activity then predominantly reflects the learned value of images . To further characterize the representation of value in the amygdala , we determined quantitatively whether neurons responded more strongly when an image had a positive or negative value . We used a receiver operating characteristic -LRB- ROC -RRB- analysis13 to estimate the extent to which activity before and after an identified change point differed -LRB- Fig. 3c -RRB- . Each data point from this analysis reflects the change in activity for a single image in a single interval . By convention , we used the negative value responses as the reference distribution , so ROC values .0.5 indicated cells that responded more when an image was positive than when the same image was negative . All ROC values except two were significantly different from 0.5 -LRB- P , 0.05 , permutation test -RRB- . The bi-modally distributed data indicate that some amygdala neurons prefer negative conditioned stimuli , and others prefer positive conditioned stimuli -LRB- see Supplementary Note 2 -RRB- . The activity of neurons encoding positive and negative values was not related to motor responses -LRB- licking and blinking ; see Supplementary Figs 3 , 4 -RRB- . Moreover , the development of value coding was not an artefact of using different modalities for our reinforcing stimuli -LRB- although the air-puff and liquid reward both have auditory and somatosensory components -RRB- . Many neurons encoding positive image value responded to air-puffs , and many neurons whose activity reflected negative image value responded to rewards -LRB- Table 1 and Supplementary Fig. 5 -RRB- . Thus , neurons encoding value received information about both kinds of reinforcing stimuli . Finally , neurons representing positive and negative values did not demonstrate clear anatomical clustering -LRB- Fig. 1e , f -RRB- . We next characterized how neurons represent value across time during trials . An ROC analysis applied to consecutive overlapping time-windows of 100 ms -LRB- advanced in steps of 20 ms -RRB- , revealed that different neurons encoded image value during different overlapping time segments -LRB- Fig. 3d -RRB- . Some neurons represented value exclusively during the visual stimulus or trace intervals , but other cells sustained Figure 1 | Task and brain MRI . a , Sequence of events for the three trial types . Top and bottom squares , image values reverse . Middle square , image always non-reinforced . b , Coronal MRI acquired with a two-dimensional -LRB- 2D -RRB- spoiled gradient recalled acquisition -LRB- SPGR -RRB- sequence in monkey V , showing the artefact from a tungsten microelectrode dorsal to the amygdala -LRB- outlined in white -RRB- . c f , Coronal MRI with 2D inversion recovery -LRB- IR -RRB- sequence -LRB- c , arrows point to the electrode -RRB- . Magnified images show the probable border of the lateral nucleus -LRB- arrow in d -RRB- and recording site 866 locations -LRB- slice in f is immediately posterior to e -RRB- . We collapsed recording sites spanning 2 mm in the anterior posterior dimension onto each image slice , in many cases resulting in the superposition of multiple cells with different properties -LRB- see key above f for symbols denoting properties ; ` ' denotes positive value-coding , ` 2 ' denotes negative value-coding , and ` no ' symbol indicates no value-coding -RRB- . Recording sites from monkey P occurred in an overlapping region of the amygdala . 2006 Nature Publishing Group NATURE | Vol 439 | 16 February 2006 LETTERS Figure 2 | Behaviour and neural activity from a single amygdala neuron during learning . a , b , Cumulative -LRB- curves -RRB- and trial-by-trial -LRB- tick marks -RRB- measures of licking -LRB- red -RRB- and blinking -LRB- blue -RRB- , plotted as a function of trial number for images 1 and 2 . Black dots indicate change points . Vertical green lines indicate value reversals . c f , Rasters and peri-stimulus time histograms -LRB- PSTHs -RRB- , truncated at US delivery , for the amygdala cell recorded during the same experiment . Each row of dots represents the timing of action potentials during one trial . PSTHs sum activity across trials and were smoothed by taking a 10-ms moving average of activity . Blue ticks indicate fixation point onset . Red ticks indicate visual stimulus onset/offset . g , h , Spike count and cumulative spike count during the trace interval , plotted as a function of trial number for images 1 and 2 . Red dots indicate change points . a representation across some or all of both intervals . Across the population of neurons , signals representing value developed rapidly after visual stimulus onset and were maintained through the trace interval -LRB- Fig. 3d ; for positive-coding cells , the first bin significantly greater than 0.5 occurred 80 180 ms after visual stimulus onset , P , 0.05 by t-test ; for negative-coding cells , the first significant bin was 120 220 ms after visual stimulus onset -RRB- . This temporally extended representation of value may be useful for the development of adaptive behaviour -- such as licking and blinking -- that anticipates reinforcement . Furthermore , the representation of value over time could have an important role in reinforcement learning : computations of error signals may depend on a comparison of neural signals that represent value as a function of time9 . If decisions to lick or blink in anticipation of a reward or punishment are based in part on the representation of value we describe in the amygdala , then , on average , neural activity should change at the same time as learning occurs . To compare behavioural learning of the image value reversals to changes in neural activity , we plotted the change-point for neural activity against the trial when behaviour changed -LRB- Fig. 4a , b ; each data point compares a neural activity change point with either a blinking or licking change point ; the distributions of licking and blinking change points were not significantly different from one another ; P . 0.25 , t-test -RRB- . Learning was significantly correlated with changes in neural activity during both the visual stimulus and trace intervals -LRB- visual stimulus interval : monkey V , P 0.02 , r 2 0.06 ; monkey P , P , 1025 , r 2 0.44 ; trace interval : monkey V , P , 1025 , r 2 0.40 ; monkey P , P , 1025 , r 2 0.32 -RRB- . The difference between neural and behavioural change points was near zero -LRB- Fig. 4 histograms ; monkey V : visual stimulus mean difference 1.4 trials , trace mean difference 20.8 trials ; monkey P : visual stimulus mean difference 20.5 trials , trace mean difference 0.3 trials -RRB- . For all comparisons , the onsets of changes in behavioural and neural responses were statistically indistinguishable -LRB- paired t-test , P . 0.05 -RRB- . In addition , the distributions comparing change points during the visual stimulus and trace intervals were not significantly different -LRB- P . 0.1 , t-test -RRB- . These results indicate that amygdala neurons can rapidly adjust their activity to reflect image value ; these changes in activity both predict when monkeys learn and develop , on average , as fast as monkeys learn . The fact that some cells change their activity more rapidly than monkeys learn suggests that monkeys can not selectively ` listen ' to these neurons , or they would demonstrate faster learning . Learning may be better accounted for if monkeys must evaluate signals from the population of neurons , which includes neurons that change their activity more slowly . The data presented so far compare the onset of changes in behavioural and neural responses . We next compared the time course of average neural and behavioural responses across the 100 neurons encoding image value -LRB- as identified by the change-point test -RRB- . We normalized and then averaged neural activity and behaviour from the 20 trials before and after the value reversal of each image . To describe the behavioural and neural changes around reversals , we fitted the behavioural and neural data with sigmoid functions -LRB- Fig. 4c , see Supplementary Fig. 6 -RRB- . The 95 % prediction intervals for these functions overlapped , indicating that the trajectories of changes in behavioural responses and neural activity were similar under this model . In contrast , the same analysis on the non-reinforced images for the same cells showed no change in activity or behaviour in relation to the reversal of the positive and negative images -LRB- Fig. 4d -RRB- . These data demonstrate that , on average , neurons begin to change their activity within very few trials of a change in image value , and Table 1 | Responses to reinforcing stimuli in amygdala neurons that code value Excitatory response to air-puff Inhibitory response to air-puff Excitatory response to reward Inhibitory response to reward No response to reward or air-puff Cells coding positive CS value -LRB- n 61 -RRB- Cells coding negative CS value -LRB- n 39 -RRB- 34 18 12 8 23 11 6 7 9 7 Excitatory and inhibitory responses to air-puffs and rewards for all cells encoding the positive or negative value of images . The number of cells adds up to more than the number of valuecoding neurons because many cells were modulated by both air-puff and reward . 2006 Nature Publishing Group 867 LETTERS NATURE | Vol 439 | 16 February 2006 Figure 3 | Amygdala neurons encode the positive and negative value of visual stimuli . a , b , Image identity coding index -LRB- II_CI -RRB- plotted against image value coding index -LRB- IV_CI -RRB- for the visual stimulus -LRB- VS -RRB- and trace intervals . Green , blue and red dots , P , 0.05 for II_CI , IV_CI or both indices , respectively -LRB- two-way ANOVA -RRB- . Yellow dots , not significant . Inset histograms show II_CI subtracted from IV_CI for non-yellow data points . See Supplementary Fig. 9 . c , Encoding of positive and negative value shown by ROC analysis . d , Development of value signals as a function of time . Each row in the colour map represents value coding for a neuron during presentation of a single image , with positive and negative cell rows sorted in opposite order according to the first post-visual-stimulus data point significantly different from 0.5 -LRB- P , 0.05 , permutation test -RRB- . White curves show mean ROC values across the positive - and negative-coding populations . Time 0 is the start of the bin spanning from 0 99 ms after visual stimulus onset . that -- across the population of neurons -- the rate of changes in activity closely tracks behavioural learning . The rapid appearance of a representation of visual stimulus value in the amygdala raises the possibility that this representation is formed locally . If a representation of value is first formed elsewhere in the brain , it would have to provide signals that represent value at earlier time points within trials , and activity would have to change even more rapidly across trials compared to what we observe in the amygdala . Moreover , the correlation between changes in amygdala activity and behaviour strongly suggests that the amygdala representation is used by the monkey , regardless of its source . Nonetheless , other brain structures such as the orbitofrontal cortex probably have critical roles in learning on our task . The amygdala seems to update its representation as quickly as the orbitofrontal cortex changes its Figure 4 | The relationship between changes in neural activity and behavioural responses . a , b , Onset of changes in neural activity plotted against the onset of changes in licking or blinking for visual stimulus -LRB- VS -RRB- and trace interval activity . Histograms show the difference between neural and behavioural change points . Blue , data and regression lines for monkey V ; red , data and regression lines for monkey P. c , Average normalized neural activity and behavioural responses plotted as a function of trial number relative to the reversal in image value . Shaded regions indicate 95 % prediction intervals for best-fit Weibull functions . d , Similar representation as in c , applied to the non-reinforced images . Shaded regions show s.e.m. of data points . 868 activity during reversal learning -LRB- using a different task , in which monkeys learn to avoid aversive stimuli , and different analytic methods -RRB- 14 . Other studies that have not used aversive stimuli also suggest that the orbitofrontal cortex provides a representation of reward value15 17 . Understanding the complex interactions between the amygdala and orbitofrontal cortex -LRB- and other structures -RRB- will require substantial investigative effort in order to tease apart the distinct contributions of each brain area to reinforcement learning . In rodents , some evidence suggests that the physiological properties of amygdala and orbitofrontal neurons are interdependent18 ,19 . Human and non-human primate behaviour is strongly influenced by visual processing and , therefore , the assignment of value to visual stimuli is a fundamental process that underlies many of our actions . In lower mammals , previous studies have investigated amygdala neurophysiology in relation to learning ; however , these studies have used auditory or olfactory conditioned stimuli , and tasks that either do not include appetitive conditioning or that involve learning to avoid an aversive unconditioned stimulus1 ,20 . The primate amygdala has extensive interconnections with the visual cortex21 , and previous lesion studies in monkeys and neuroimaging studies in humans have suggested that the amygdala is involved in updating a representation of the value of visual stimuli3 ,22,23 -LRB- see Supplementary Note 3 -RRB- . Previous neurophysiological recordings in monkey amygdala have used different methodological procedures than those here , and provide conflicting data regarding whether the amygdala contains an updated representation of visual stimulus value24 26 . Our data establish that distinct amygdala neurons encode the positive and negative learned value of images . The representation of value is often linked to image selectivity initially , but this representation becomes transformed over time so that value , and not image selectivity , is predominantly encoded after visual stimuli disappear . Signals that reflect value develop rapidly , and evolve as quickly , on average , as monkeys learn . Furthermore , the timing of changes in amygdala activity predicts when monkeys learn . Thus , in principle , decisions to either lick or blink during performance of our task could be based on the plastic representation of visual stimulus value provided by the amygdala . Reinforcement learning , a process by which sensory stimuli become associated with positive or negative values , is intimately related to physiological and behavioural responses thought to represent emotions , because such responses frequently occur as a reaction to sensory stimuli endowed with value . Amygdala neural signals that reflect CS value may influence a variety of interconnected brain structures -- including the ventral striatum , prefrontal cortex , sensory cortices , the hippocampus and rhinal cortices , and subcortical brain areas -- involved in aspects of emotional responses and reinforcement learning . Different amygdala nuclei have complex 2006 Nature Publishing Group NATURE | Vol 439 | 16 February 2006 LETTERS patterns of connectivity with each of these structures6 ,7,10,21,27 , suggesting that neural signals in the amygdala may be used for very different purposes depending upon their anatomical destination . For example , signals that reflect CS value may influence attention28 or computations of error signals9 through projections from the amygdala central nucleus to the ventral tegmental area and substantia nigra29 . In addition , value signals could influence behavioural and physiological responses to visual stimuli through other subcortical projections from the central nucleus30 , and through projections from the basal and accessory basal nuclei to the prefrontal cortex and ventral striatum6 ,7,10 . The amygdala neural signals we describe -- which reflect the learned value of visual stimuli -- are therefore only one component of the intricate neural circuitry that mediates reinforcement learning and our emotional lives . To understand how this circuitry operates , future studies must identify how dynamic interactions between the amygdala and interconnected brain structures function to support a wide range of emotional and cognitive behaviours . METHODS Behavioural task . In every experiment , monkeys learned the positive , negative or non-reinforced value of three new images through a trace-conditioning procedure . In individual trials , monkeys centred their gaze at a fixation point -LRB- FP -RRB- for 1 s and viewed an image for 300 or 350 ms , determined empirically for each monkey to minimize broken fixation behaviour -LRB- see Supplementary Fig. 7 -RRB- . After a 1,500-ms trace interval during which monkeys were free to move their eyes , we delivered a US -LRB- for positive images , a 0.2 0.9 ml liquid reward ; for negative images , a 50 100 ms , 40 60 psi aversive air-puff directed at the face ; for non-reinforced images , nothing -RRB- . We reversed image value assignments without warning at least 8 trials after monkeys learned the initial contingencies , as assessed by viewing licking and blinking data during the experiment . All trials were pseudorandomly interleaved . Images were fractal patterns constructed with a custom-written Matlab -LRB- Mathworks -RRB- program . Images had no inherent value and were easily distinguishable from each other and from recently shown images on previous days . Data collection . We recorded neural activity from the right amygdala of two rhesus monkeys -LRB- Macaca mulatta -RRB- weighing 4 10 kg . All animal procedures conformed to NIH guidelines and were approved by the Institutional Animal Care and Use Committees at New York State Psychiatric Institute and Columbia University . We positioned recording chambers based on anatomical information acquired using MRI . Conventional metal microelectrodes -LRB- 1 4 -RRB- -LRB- FHC Instruments -RRB- were individually controlled and advanced into the brain through durapuncturing guide tubes using either a motor-controlled hydraulic microdrive -LRB- Narishige -RRB- or a motorized multi-electrode drive -LRB- NAN -RRB- . Signal amplification , filtering , digitizing of spike waveforms , and spike-sorting using a principal component analysis platform -LRB- online , with offline verification -RRB- were accomplished using the Plexon system . While we searched for neurons , monkeys performed either no task or a fixation task without images present . We studied all well-isolated neurons -LRB- 196 neurons in total : 92 from monkey V , 104 from monkey P -RRB- . We reconstructed recording sites using MRI combined with laboratory notes . Neurons were located in the middle 50 % of the anterior posterior extent of the amygdala , in the lateral 75 % of the medial lateral extent , and in the dorsal 75 % of the dorsal ventral extent . All neurons were probably located in either the basal , accessory basal , lateral or central nucleus , or the intercalated masses . Licking was measured by the interruption of an infrared beam by the tongue extending to the reward tube , and blinking by the loss in signal from an infrared eye tracker -LRB- ASL -RRB- , which corresponded to blinks as verified by a video camera feed . Licks or blinks occurring during the 500 ms preceding US delivery were scored as responses . Data analysis . Based on an analysis of visual response latency -LRB- see Supplementary Fig. 8 -RRB- , we divided the trial into visual stimulus -LRB- 90 ms after image appearance until 90 ms after image disappearance -RRB- and trace -LRB- 90 ms after image disappearance until US delivery -RRB- intervals . Our data analysis had four principal goals . -LRB- 1 -RRB- To find the trial closest to image value reversals at which neural and behavioural -LRB- licking , blinking -RRB- responses began to change , we used a change-point test12 -LRB- P , 0.05 , correcting for multiple comparisons -RRB- . -LRB- 2 -RRB- To characterize the relative contribution of image identity -LRB- that is , the sensory properties of images -RRB- and value on neural activity , we performed a two-way ANOVA . We defined three indices , corresponding to the percentage of variance accounted for by image identity , image value and their interaction -LRB- image identity , image value , and image value identity interaction coding indices -RRB- . -LRB- 3 -RRB- To determine whether amygdala neurons responded more when an image had a positive or negative value , we compared spike counts from the 20 trials on either side of an identified change point using a receiver operating characteristic -LRB- ROC -RRB- analysis13 on neurons categorized as value-coding -LRB- 209 intervals from 100 cells coding image value during the visual stimulus , trace or both intervals ; 72/100 cells changed activity in opposite directions for both images upon reversal ; 28/100 neurons changed responses for one image , but two-way ANOVA showed significant effects of image value -RRB- . We also characterized the development within trials of signals representing value , computing an ROC value in 100-ms windows moved in 20-ms steps across the trial -LRB- performed on 172 image/cell combinations from the 100 value-coding cells -RRB- . -LRB- 4 -RRB- To compare the trajectories of changes in neural and behavioural responses , we normalized and averaged neural activity and behaviour from each of the 20 trials before and after image value reversal across the 100 value-coding neurons . We fitted the behavioural and neural data with a Weibull function : f x u 1 2 l 2 u exp 2x = a b 1 which modelled normalized response as a function of trial number . We performed a similar normalization and averaging procedure for non-reinforced trials , analysing the 20 trials before and after the trial that the other images reversed . Details of all methods are described further in Supplementary Methods . Received 20 September ; accepted 25 November 2005 . 1 . 2 . 3 . 4 . 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Newsome for mentoring during a career development award to C.D.S. . This work was supported by the Keck foundation , grants from the NIMH , and the Klingenstein , Sloan and NARSAD foundations , and by a Charles E. Culpeper Scholarship award from Goldman Philanthropic Partnerships to C.D.S. J.J.P. received support from NICHD and NEI institutional training grants . S.E.M. received support from an NSF graduate research fellowship . Author Contributions J.J.P. and M.A.B. performed all experiments and conducted data analyses . S.E.M. performed some of the data analyses and contributed to many discussions . Experiments were designed and implemented in the laboratory of C.D.S. Author Information Reprints and permissions information is available at npg.nature.com/reprintsandpermissions . The authors declare no competing financial interests . Correspondence and requests for materials should be addressed to C.D.S. -LRB- cds2005@columbia.edu -RRB- . Supplementary Information is linked to the online version of the paper at www.nature.com/nature . 870 2006 Nature Publishing Group

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reinforcement reward monkey punishment aversive amygdala