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Supplementary Motor Area Encodes Reward Expectancy in Eye-Movement Tasks

¹Computation and Neural Systems and ²Division of Biology, California Institute of Technology, Pasadena, California

Submitted 10 January 2005; accepted in final form 13 April 2005

	ABSTRACT

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Neural activity signifying the expectation of reward has been found recently in many parts of the brain, including midbrain and cortical structures. These signals can facilitate goal-directed behavior or the learning of new skills based on reinforcements. Here we show that neurons in the supplementary motor area (SMA), an area concerned with movements of the body and limbs, also carry a reward expectancy signal in the postsaccadic period of oculomotor tasks. While the monkeys performed blocks of memory-guided and object-based saccades, the neurons discharged a burst after a 200-ms delay following the target-acquiring saccade in the memory task but often fired concurrently with the target-acquiring saccade in the object task. The hypothesis that this postsaccadic bursting activity reflects the expectation of a reward was tested with a series of manipulations to the memory-guided saccade task. It was found that although the timing of the bursting activity corresponds to a visual feedback stimulus, the visual feedback is not required for the neurons to discharge a burst. Second, blocks of no-reward trials reveal an extinction of the bursting activity as the monkeys come to understand that they would not be rewarded for properly generated saccades. Finally, the delivery of unexpected rewards confirmed that in many of the neurons, the activity is not related to a motor plan to acquire the reward (e.g., licking). Thus we conclude that reward expectancy is represented by the activity of SMA neurons, even in the context of an oculomotor task. These results suggest that the reward expectancy signal is broadcast over a large extent of motor cortex, and may facilitate the learning of new, coordinated behavior between different body parts.

	INTRODUCTION

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There has been substantial progress in recent years on the identification and characterization of the network of brain areas that are involved in the processing of reward. Reward expectancy signals have been found in many cortical areas such as the medialfrontal (Matsumoto et al. 2003; Shidara and Richmond 2002), dorsolateral prefrontal (Barraclough et al. 2004; Kobayashi et al. 2002), orbitofrontal (Hikosaka and Watanabe 2000; Tremblay and Schultz 2000), and parietal cortices (Musallam et al. 2004; Platt and Glimcher 1999; Sugrue et al. 2004). Subcortical regions expressing reward expectancy include the caudate (Watanabe et al. 2003), striatum (Cromwell and Schultz 2003; Hassani et al. 2001; Tremblay et al. 1998), superior colliculus (Ikeda and Hikosaka 2003), and midbrain dopamine neurons (Satoh et al. 2003; Schultz et al. 1997). Reward-related signals have also been found in the dorsomedial frontal cortex (DMFC), an anatomical region that includes our current area of interest, the supplementary motor area (SMA).

The DMFC has been shown to participate in volitional (Schlag and Schlag-Rey 1985) or goal-oriented motor acts (Mann et al. 1988). It contains at least three well-studied motor-representation areas that are thought to be involved in higher-order control of behavior: the SEF (Schlag and Schlag-Rey 1985, 1987), the SMA (Luppino et al. 1991; Matsuzaka et al. 1992) and the presupplementary motor area (pre-SMA) (Fujii et al. 2002; Nakamura et al. 1998; Shima and Tanji 2000). These three motor areas of DMFC can be distinguished based on anatomical connectivity (Luppino et al. 1993; Parthasarathy et al. 1992) and physiological responsivity (Matsuzaka et al. 1992). The SMA and pre-SMA are located in the DMFC on and above the medial wall in the frontal lobe. An orofacial region occupies the rostral end of the SMA, and further rostral is the pre-SMA. Intracortical microstimulation evokes movements in both areas, though the movements evoked in the pre-SMA require longer trains of pulses that produce more complex movements (Fujii et al. 2002). The pre-SMA has been implicated in planned motor acts (Matsuzaka and Tanji 1996) and the acquisition (Nakamura et al. 1998), planning, and regulating (Shima and Tanji 2000) of sequential procedures. Additionally, pre-SMA neurons respond more often to visual stimulation compared with SMA neurons. The SMA consists of a rostrocaudal progression of orofacial, forelimb, and hindlimb movement representations (Mitz and Wise 1987). Lateral to the SMA, microstimulation will evoke eye movements. This area is defined as SEF (Fujii et al. 2002). Several studies have shown that electrical microstimulation at low currents (<50 µA and sometimes as low at 10 µA) will elicit saccades in SEF (Chen and Wise 1995; Fujii et al. 1995; Mann et al. 1988; Russo and Bruce 1993; Tehovnik and Sommer 1996).

Three recent studies have explicitly connected the SEF to reward variables. Amador et al. (2000) discovered reward-predicting and -detecting neuronal activity in SEF. Schall and colleagues used the countermanding task to characterize three different types of neurons in the SEF—error, conflict, and reinforcement neurons—and suggested that these could serve a performance monitoring function (Stuphorn et al. 2000). Roesch and Olson (2003, 2004) found modulations of neural activity in response to both reward and punishment and concluded that these modulations during the early stages of the trials correlate with the motivation and not reward expectation. In this study, we present neural activity reflecting reward expectation during a later stage of the trials, specifically after the monkey performs the instructed behavior. Our findings are similar to the reports of Amador and colleagues and Stuphorn and colleagues; however, the reward expectancy signal wedescribe is found in the SMA, whereas these other studies were recording from nearby SEF. Taken together, these results suggest that a reward expectancy signal may be present throughout the DMFC.

In this study, we present evidence that a reward-expectancy signal is expressed in the neural activity of the SMA during the performance of an oculomotor task. The signal is not related to the metric of the eye movement. Rather it encodes expectation of the reward after the successful completion of the instructed behavior. The results presented here began as a discovery during a project that was originally intended to investigate the contribution of the SEF to saccades to objects. Some early recordings in the SMA uncovered a postsaccadic bursting activity that we hypothesized might be related to the expectation of reward, and experiments devised during the course of the project confirmed this hypothesis. While future studies of reward expectancy in SMA might use tasks that are specifically designed to investigate reward variables, with the two oculomotor tasks employed here we are able to establish two novel findings. First, we show that a reward expectancy signal is present in the SMA, an area that has been thought to be concerned only with movements of the body and limbs, during an oculomotor task. Second, we show the coupling of the signal’s onset time with a secondary reinforcer. These findings suggest a general learning mechanism that would reinforce all motor representations in DMFC that are active just before the animal can expect to receive a reward. A preliminary account of this study has appeared previously (Campos et al. 2003).

	METHODS

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Studies were performed on two behaving, male rhesus monkeys (Macaca mulatta). Each was chronically fitted with a stainless steel head post for head immobilization and a recording chamber over a craniotomy for electrode insertions. All procedures were approved by the Caltech Institutional Animal Care and Use Committee.

Stimuli and tasks

Monkeys were seated in a dimly lit room, 42 cm from a tangent screen. Stimuli were rear-projected with 800 x 600 resolution and a refresh rate of 72 Hz using a custom-built software display client with OpenGL. Task logic was controlled by National Instruments real-time LabView software.

Two eye-movement tasks were used: a memory-guided saccade task and an object-based saccade task. In both tasks, the monkey was instructed to perform a saccade from a central fixation point to 1 of 43 targets placed at regular intervals to cover the entire visual field out to 17° of visual angle in every direction from central fixation.

In the memory-guided saccade task (Fig. 1A), monkeys were required to maintain central fixation while a peripheral target was briefly flashed, wait until the central fixation point extinguished, and then saccade to the remembered location. After successfully holding fixation at the target location, the target re-appeared to provide visual feedback of the correct eye position. The monkey then had to maintain fixation on the visible target for an additional interval of 250 before receiving a juice reward of 0.2 ml.

View larger version (17K): [in this window] [in a new window]   FIG. 1. Time course of oculomotor tasks. Progression of tasks are shown in successive panels from top left to bottom right. In the memory-guided saccade task (A), the monkey is required to acquire a central fixation point at the start of the trial. After a variable delay, a cue is briefly flashed at 1 of 43 targets in the periphery. The possible targets cover the entire visual field out to 17°. Following a hold interval the fixation point is extinguished, and the monkey is required to saccade to the remembered target location and fixate there. After 250 ms, the target reappears, and then following an additional 250-ms fixation, the animal is rewarded with a drop of juice. In the object-based saccade task (B), the monkey begins the trial by acquiring a central fixation point. An object appears over the fixation point, and after a delay, 1 side of the object is briefly cued. Following a hold period the object is extinguished and immediately reappears in a new location. The monkey is then required to saccade to the cued portion of the object and fixate there for 250 ms before receiving a juice reward.

The memory-guided and object-based saccade tasks were designed to investigate the neural computations supporting object-based saccades; however, the important difference between the tasks for the purposes of this study was actually what happened after the saccade was completed. In the object task, the target was visible at the time of the saccade, and the monkey could acquire a visible target. In the memory task, the target reappeared 250 ms after the saccade to the remembered location. Thus in the object task the animals received earlier feedback from a secondary reinforcement stimulus.

In a recording session, a block of memory-guided saccades preceded a block of object based saccades. The memory-guided saccade block consisted of three correct saccades to each location. The object-based saccade block consisted of 12 correct saccades to each location. Control trials were performed during the memory-guided saccade block at the discretion of the experimenter.

Recording procedure

Neurons were accessed on vertical penetrations with glass-coated platinum-iridium electrodes (Fred Haer). The electrodes were advanced with a Fred Haer or Narashige microdrive system through a blunt stainless steel guide tube pressed against the dura. Neurons were generally found 1–3 mm beneath the exterior of the dura.

Waveforms were amplified and isolated on-line with a commercial hardware and software package (Plexon). Cell activity was monitored with custom built on-line data visualization software written in Matlab.

Data analysis

Bursting activity was identified using a burst detection algorithm (Hanes et al. 1995; Thompson et al. 1996). Bursts were initially detected by a threshold crossing of a surprise index (SI), which is the negative of the log of a calculated significance level. The significance level describes the likelihood that the observed number of spikes would occur in a given interval, considering the average firing rate of the cell, based on the assumption that the inter-spike intervals follow a Poisson distribution. The significance level used to calculate the threshold for the SI was 0.01. The mean of the Poisson distribution was calculated as the number of spikes in the trial divided by the duration of the trial. Because the mean can change from trial to trial, the algorithm assumes stationarity only over the duration of a single trial, and the threshold will adapt to changes in the baseline firing rate of the neuron over time. After the initial threshold crossing, the beginning and end of the burst were precisely identified, and multiple bursts could be identified in a single spike train (Thompson et al. 1996).

For ANOVA of firing activity in task intervals, the intervals were defined as follows. The baseline period was the interval between the acquisition of the fixation point and the cue appearance. The cue period was the interval that the cue was visible, and the memory period was the interval between the cue disappearance and the fixation point disappearance (the signal for the monkey to make the saccade). The saccade period was the 200-ms interval preceding the acquisition of the target, and the postsaccadic period was the interval from the target acquisition until the delivery of reward. All intervals were defined by these same events in both the memory and object-based tasks. The duration of the postsaccadic interval was 500 ms in the memory task, and 250 ms in the object-based saccade task.

Electrical stimulation

A BAK instruments stimulator was used to deliver biphasic currents at 330 Hz of typically <200 µA in 100- to 500-ms trains through the recording electrodes.

Electromyography

Electromyographic (EMG) recordings were performed in one monkey with a World Precision Instruments (DAM 80) AC/DC amplifier, and paired hook-wire electrodes (44 ga x 100 mm) from Viasys healthcare.

MR imaging

Magnetic resonance (MR) imaging was performed at the Caltech Brain Imaging Center on a 3 T Siemens Trio. Anatomical images were acquired sagittally with 0.7 mm slice thickness using an in plane field of view of 168 x 168 mm on a 256 x 256 base matrix, yielding a final native voxel resolution of 0.656 x 0.656 x 0.7 mm. These images were realigned via multi-planar reformat to recording chamber landmarks using Siemens Syngo software (version MR 2003T DHHS.) This rotated volume was resliced at 0.7 mm spacing along the z axis of the chamber and visualized using the AFNI software package (Cox 1996).

	RESULTS

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With a series of single-electrode penetrations, 173 cells were recorded in both tasks from two monkeys (monkey S: 100; monkey R: 73). According to ANOVA of baseline firing rates versus the postsaccadic interval, 50 (S: 34; R: 16) neurons demonstrated a significant modulation in the postsaccadic interval, with 17 (S: 9; R: 8) of these modulated in the postsaccadic interval exclusively. Many of the neurons were also active during task periods. According to ANOVA of baseline firing rates versus cue, memory, and saccade intervals 84 (S: 55; R: 29) of the recorded cells were significantly (P < 10^–5) modulated during at least one of these intervals in both tasks. A breakdown of neurons with significant modulations for the individual periods of the memory saccade task (cue: 23, memory: 51, saccade: 71), show that there was substantial activity present in all task intervals, however, this activity was generally not spatially tuned. Summary cell count information is provided in Table 1, along with results of control experiments; see Table 2 and DISCUSSION for activity during task intervals.

The sites of all of the electrode penetrations included in this study are superimposed on axial MRI scans in Fig. 2 (A and B). While recordings were taken on the surface of cortex, MRI sections for anatomical localization were chosen at a depth appropriate to clearly show the locations of the penetrations relative to surrounding sulci.

View larger version (77K): [in this window] [in a new window]   FIG. 2. Sites of neural recording. Projections of chamber walls are indicated with a blue circle superimposed on axial MRI scans of Monkey S (A) and R (B). Anatomical landmarks are of the arcuate sulci (AS), principal sulci (PS) and central sulci (CS). Recording sites that yielded reward interval activity are shown as red dots, and the remaining recording sites are blue. Averaged output of all recorded neurons (C and D) shows the average firing rate for all recorded neurons for each monkey aligned on the target acquire event of memory saccade trials.

While much of SMA is in F3 on the mesial surface, there is also a portion of F3 on the dorsal surface, within 3 mm of the midline that is also considered SMA proper (Luppino et al. 1991; Matsuzaka et al. 1992). The neurons of interest in this report, indicated in red on the axial slices in Fig. 2, mostly cluster within this distance to the left (monkey’s right) of the midline for monkey S and to the right (monkey’s left) of the midline for monkey R. No recordings were performed in SEF. In both monkeys, some of the recordings were in area F2, lateral to SMA-proper (Luppino et al. 1991). In monkey S, the majority of the recordings were directly medial to the genu of the arcuate sulcus, whereas in monkey R, the recordings were medial and somewhat posterior. The SEF is medial to the arcuate sulcus and somewhat anterior, although there is some variability in the precise location of SEF as described in previous studies. See Sommer and Tehovnik (1999) for review.

Microstimulation

Electrical stimulation experiments show the progression of body movement responses typical of the SMA (Mitz and Wise 1987). Because eye movements were not observed to be elicited in either of the monkeys by stimulation of 50 µA, which is the upper limit of the low-threshold criterion for eliciting eye movements in the SEF (Russo and Bruce 1993), or even currents as high as 200 µA, the recordings were not in the oculomotor area SEF.

Population characteristics

The average spike activity recorded during memory saccades from all sites for each monkey is summarized in Fig. 2, C and D. The average firing rate aligned to the target acquire event (end of saccade) is shown. The activity from monkey R (Fig. 2D) is exclusively postsaccadic. In monkey S, saccadic and memory period activity was also observed (Fig. 2C); this could be due to the more anterior placement of the chamber. In monkey S, the dominant peak of activity still occurs after a delay following the target acquire event.

Although there were many neurons with modulated activity during different epochs of the task (see Table 2), very few were spatially tuned. A two-way ANOVA between baseline firing rates and 1) the firing rates from different task intervals and 2) the spatial locations of the targets was used to confirm this observation. A very small number of neurons passed the significance test (P < 10^–3) for dependence of firing rate on task interval and target location (cue: 1; memory: 7; saccade: 6).

Shift in burst onset times

The postsaccadic burst in both trial types (Fig. 3, A and B—memory; C and D—object) for one of these neurons is illustrated with raster plots of spike traces aligned to the target acquire event (A and C) and the reward delivery (B and D). Bursts of activity identified with the burst detection algorithm (METHODS) are shown as horizontal blue lines beneath the spike trains. The bursts in the object task (C) begin at a time that could be related to saccade generation. However, the bursts of activity in the memory task (A) come substantially later, revealing that these bursts do not participate in the generation of a saccade or at least not in the context of the memory saccade task. For this neuron, the postsaccadic firing terminates with reward delivery. Other neurons (see Fig. 5 for example) were also observed to terminate just before or soon after reward delivery.

View larger version (35K): [in this window] [in a new window]   FIG. 3. Shift in burst onset times. Peri-event time histograms in memory (A and B) and object (C and D) saccades tasks. Spikes are represented in red, aligned to the target acquire event (A and C) and the reward delivery event (B and D). The smoothed average firing rate for normal trials is plotted as a blue curve, for no-visual feedback trials in green. Horizontal blue lines indicate periods of burst activity for the spike trains above. Green stars forming a bar on the right edge of the panels indicate trials in which visual feedback was withheld. Cyan markers indicate the reappearance of the target.

View larger version (53K): [in this window] [in a new window]   FIG. 5. Example response to control trials. A: withheld visual feedback control trials. All trials shown are from the memory-saccade task. Green stars forming a bar on the edge of the panel indicate trials in which visual feedback was withheld. Smoothed average firing rates for normal trials are drawn in blue and can be compared with the average firing rates during the withheld feedback trials drawn in green. B: withheld reward block of trials. All trials shown are from the memory-saccade task. Black stars forming a bar slightly inset from the right edge of the panel indicate the successfully completed trials in which the reward was not delivered. Green stars forming a bar on the right edge of the panel indicate trials in which visual feedback was withheld. Only the spike trains from successfully completed trials are shown. Trials are arranged chronologically from top to bottom. Smoothed average firing rates for normal trials are drawn in blue and can be compared with the average firing rates during the withheld reward trials shown in black, and the withheld feedback trials drawn in green. Cyan markers indicate the reappearance of the target.

View larger version (16K): [in this window] [in a new window]   FIG. 4. Histograms of burst onset times. A: distribution of time to burst for each trial of the memory () and object () saccade tasks for the cell shown in Fig. 3. Mean time to burst is: memory task: 537 ms, object task 105 ms. Memory-task data clusters to the right of the object task data. B: differences in time to burst onset after the target acquire event in memory versus object tasks. Only cells with bursts in 30% of the trials in both tasks are shown (n = 30). Mean difference: 202 ms. The population of average shift times is significantly different from 0 (t-test, P < 10–5).

The onset time of the burst corresponded to the appearance of the visual feedback, which was immediate in the object-based task but delayed in the memory-guided saccade task. In both cases, the visual feedback could serve as a predictor of a reward. The hypothesis that bursting activity reflects an expectation of reward was then tested in a series of control experiments outlined in the following text.

Bursting does not accompany nonrewarded target acquisitions

It could be argued that the neurons simply signal the acquisition of any target, regardless of the expectation of reward. We tested this possibility by comparing the activity of the neurons after the initial acquisition of the fixation point with the activity after the reappearance of the target in the memory-guided saccade task. We used ANOVA on two intervals: the first interval was between the fixation acquire event and the appearance of the cue (250 ms), and the second interval was between the target onset and the reward delivery (250 ms). This analysis reveals that of the 50 neurons with a significant postsaccadic modulation, none were significantly active during the initial acquisition of the fixation point.

Bursting is not a visual response

Control trials of the memory-guided saccade task in which the visual feedback was withheld were run to test whether or not the bursting activity is related to the visual feedback signal. As shown in Fig. 3 and again in Fig. 5, removal of the visual feedback (indicated in the figures with the green bar composed of green stars) does not eliminate the onset of the bursting activity, though it may reduce the intensity, or vary the onset time. Figure 5A shows an example in which the postsaccadic bursting activity was slightly extended by this control, although otherwise unchanged.

The bursting signal is therefore not indicating the reappearance of the target, although the visual reinforcement serves to sharpen and intensify the neural discharge. This control was run on 34 neurons, and 12 of them showed no significant difference in the mean firing rate from the time the target appeared (or should have appeared) until the end of the trial (ANOVA, P < 10^–5) in control versus normal trials. Of the remaining neurons, many exhibited a temporal shift in their active periods, or a decrease in firing, but only one showed an extinction of the bursting activity. This control shows that visual feedback could be dissociated from the reward delivery, and the neural response remained.

Bursting properties in the absence of reward

To see if, all else being equal, the absence of reward would have an effect on the neural activity, we occasionally withheld the reward for a block of trials during the memory-guided saccade task, even for correctly performed trials. In the no reward blocks, the monkeys generally continued to correctly perform the task for 30 trials before stopping, and this comprehension of changing task conditions was reflected in the recorded neural activity. After a few trials, bursting activity would stop altogether. This control was run while recording 11 neurons, and all of these showed a significant difference in the mean firing rate from the target acquire event until the end of the trial (ANOVA, P < 10^–5) in control versus normal trials. The vast majority (10) ceased firing during the prereward interval in the no-reward blocks, and the remaining neuron (of the 11 that were modulated) increased its firing rate after the reward period. An example neuron is shown in Fig. 5B. The firing activity is gradually extinguished in the no reward block (black bar). In contrast, the activity during the no-visual feedback trials (green bar) has a less precise onset time but does not extinguish. While the no-visual feedback trials show the effect of removing a predictor of reward, the no-reward blocks reveal the dynamic effects of the monkeys coming to understand that they should no longer expect a reward.

Unexpected reward trials

By removing the reward, the possibility that the bursting activity encoded an orofacial (e.g., licking) motor response was not eliminated. Every time the monkey expected a reward, he would presumably also plan a licking movement to acquire it. The monkey would often stop licking the juice tube in the blocks of trials in which the reward was turned off, and this would correspond to the termination of the bursting activity. Of course, if the monkey no longer expected to be rewarded for the eye movements, he also had no reason to lick the juice tube.

To dissociate licking movements from the expectation of reward, control trials were run in which an unexpected reward was delivered. A bonus reward would be delivered with a 5% probability at the end of the fixation interval, just before the cue presentation. To quantify a response, an ANOVA (P < 10^–5) compared firing rates in the 200-ms interval during the bonus reward delivery, with the corresponding 200-ms period at the end of the fixation interval in normal trials. This first interval is the actual interval that the valve regulating the flow of reward was open. While running this control, 25 neurons with reward related activity were recorded, and 23 of these demonstrated no correlated activity in the unexpected juice-delivery period. This control shows that the majority of the recorded neurons are not responsive to rewards when they are not expected, ruling out the possibility that the neural activity is attributable to motor commands required to obtain the reward, such as licking and swallowing.

To address the possibility that the neural activity reflected monkey’s postural responses or attempted postural responses or preparation for either, we recorded muscle activity in three muscles active during postural adjustments. We recorded EMG (see METHODS) from left and right Latissimus dorsi and right Semitendinosus of monkey S during the performance of both tasks. We observed that these muscles were active during trunk movements and leg movements. While there was activity recorded from these muscle groups during the execution of the task, we found that it was not temporally locked to reward expectation. These negative EMG results rule out the possibility that the monkey is consistently making postural adjustments in anticipation of the reward delivery.

	DISCUSSION

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Bursting activity related to reward expectation was found in a cortical area that is not directly responsible for the generation of the behavior (saccade) that achieves the reward. This report separates itself from other reports by investigating the reward expectation activity in the motor area SMA, and showing a shift of activity in time course based on a secondary reinforcer, the visual feedback that usually predicts the upcoming reward. In the following text, we outline and justify our findings, compare our results with the results of other studies, and suggest a functional role for the representation of reward expectancy in SMA during eye movement tasks.

Onset of reward-expectancy signal corresponds to a secondary reinforcer

The activity generally started with the secondary reinforcer and stopped with the delivery of the reward. The secondary reinforcer in this context was visual feedback that occurred before reward delivery. In the memory task, the target reappears after 250 ms of fixation on the remembered target location. This visual feedback helped ensure accuracy in the initial learning of the task but also became a predictor of the upcoming reward. In the object task, the saccade target is visible, and so the monkey can be sure that he made a saccade to the target because he can see it. The onset time of the reward expectancy signal corresponds to the onset of the visual feedback in the tasks, either 250 ms after the correct saccade in the memory task or immediately during the correct saccade in the object task.

As shown in control experiments, although the secondary reinforcer helped to synchronize the timing of the bursting activity, it was not necessary for the neurons to burst. This and other controls discussed in the following text confirm that the bursting discharge carries a reward expectancy signal.

Control experiments establish the reward expectancy interpretation

In a series of control experiments, the argument was built that this activity reflects an expectation of reward. First, the bursting activity was dissociated from visual feedback with the demonstration that visual feedback is not required for the neurons to discharge, although it regularizes the timing of the onset. Second, when the reward was removed for a block of trials, the reward expectancy activity gradually disappeared, showing that this activity represents a dynamic variable corresponding to the comprehension of a changed task condition. Finally, the possibility that the neural trace signified a licking plan or a detection of reward was ruled out because there was generally no response to unexpected reward delivery.

Reward-related activity in the SEFs

Reward-related neural signals have already been described in the SEF (Amador et al. 2000; Roesch and Olson 2003; Stuphorn et al. 2000). We found a reward expectancy signal in the SMA that appears very similar to types of activity found by Amador and colleagues and Stuphorn and colleagues.

Reward expectancy and reward prediction

Reward prediction (RP) neurons have been described in SEF along with a set of complementary reward detecting (RD) neurons (Amador et al. 2000). The neural activity we are describing as reflecting reward expectation is similar to the RP neural activity. We did not find evidence of RD activity.

The firing rates of RP neurons increase before the occurrence of a reward, then abruptly cease firing at reward delivery, just as we found in the bursting activity of many of the neurons in this study. Our results, combined with the results of Amador and colleagues, are therefore evidence that reward expectation can be found in both SMA and SEF and likely throughout the DMFC. Our study adds to the findings of RP neurons by recording neural responses during the unexpected delivery of reward and submitting the monkey to short blocks of no-reward trials.

We choose to use the term reward expectancy because "expectancy" captures the way the neural activity continues until reward delivery. Furthermore, this designation separates itself from reward prediction nomenclature found in the dopamine neuron literature. To predict is to foretell on the basis of experience, whereas to expect is to await or look forward to the coming or occurrence. The reward prediction signal found in midbrain dopamine neurons and the reward expectancy signal in the DMFC likely play different roles in learning and goal-oriented behavior (see following text).

Reward expectancy and reinforcement

Reinforcement signals have been found in SEF using a countermanding saccade task (Stuphorn et al. 2000). The reinforcement neurons were shown to increase activation while awaiting reward. The term reward expectancy describes the function of this activity. Again, the results of this study are evidence that the reward expectation signal found in the SMA is also present in the SEF.

Reward expectancy versus enhanced motivation

The postsaccadic burst cannot be a correlate of motivation (Roesch and Olson 2003,2004) simply because it comes after the behavior it would presumably motivate. Our use of the burst detection algorithm (Hanes et al. 1995; Thompson et al. 1996) establishes that this bursting activity comes in the postsaccadic interval.

In the Roesch and Olson study, the preferred direction of a neuron was first identified, and then a memory-guided saccade task was run to and away from the preferred direction of the cell. Because we rarely found neurons to be spatially tuned, we may have been recording from different types of neurons in the SMA.

Interestingly, the authors noted that in areas in which reward effects were common, such as the SMAr, neurons "fired more strongly than reward-insensitive neurons during the period extending from the completion of the saccade to delivery of the ingested reward." The authors did not think their paradigm capable of distinguishing between various interpretations of the significance of this effect, such as preparation and execution of liking movements, or increased intensity of reward anticipation. In the present study, our control experiments show that the postsaccadic activity in SMA reflects reward expectation.

Reward expectancy versus attention

The expected value of a reward has been shown to modulate activity during the performance of a task (Ikeda and Hikosaka 2003; Platt and Glimcher 1999; Shidara and Richmond 2002; Watanabe 1996). It has been argued that so far sufficient controls for these studies have not been performed to determine whether the cognitive state being manipulated is expected value or attention because these two states likely occur together and can be easily confounded. Likewise, studies examining attention may have recorded the effects of expected value (Maunsell 2004).

In the current study, it is unlikely that the reward expectancy signal is actually an attention signal. The signal occurs after the task and is not spatially tuned and thus cannot reflect attention to the saccade location. It also cannot reflect attention to the reward because there was no activity when the reward was presented unexpectedly—novelty is a powerful attractor of attention.

Reward signals and reinforcement learning algorithms

A reinforcement learning algorithm (Sutton and Barto 1988) has been proposed to account for different reward-related signals that have been found in the brain, such as the error of reward prediction in midbrain dopamine neurons (Schultz et al. 1997). The prediction error signal is widely recognized as evidence for the implementation of a reinforcement learning algorithm in the brain. For example, the prediction error can serve to update action value estimates, so that the animal can have accurate estimates of the reward that can be expected for an action. In this formalism, the expected value, V, is updated after every trial according to the experienced reward by the equation

Where R^t is the amount of reward obtained at time t, V^t is the amount of reward expected at time t, is the learning rate, and V^t+1 is the updated estimate of expected value at time t+1. In this formulation, the time steps are individual trials, and the signal that corresponds to the error of reward prediction found in dopamine neurons (Schultz et al. 1997) is the term in the parentheses, R^t –V^t. The action value signal, V, that we are describing would not be used instead of a prediction error signal, R – V. Rather, both signals are supposed components of a larger reinforcement learning mechanism.

The dynamics of the reward expectancy signal in SMA corresponds to the dynamics of the expected value of the action, V. Specifically, this algorithm captures the way the postsaccadic firing activity in the SMA gradually dissipates in no-reward blocks. When the reward, R, is zero for a series of trials, the preceding equation will diminish the expected value of the action, V, until it reaches the new value of the reward, 0. The learning rate parameter, , determines how quickly the estimate of the expected value approaches the new value. This equation also describes how the neural activity will return to normal firing when the reward is again delivered as usual.

Functional significance of reward expectancy in DMFC—a signal to guide learning

As in neural network models of reinforcement learning (Mazzoni et al. 1991; Suri and Schultz 1999), the reward signal found in DMFC could be used to train other parts of cortex to perform visuospatial tasks requiring arbitrary sensorimotor transformations. The reward expectancy signal found in the SEF (Amador et al. 2000; Stuphorn et al. 2000) is in position to shape future oculomotor behavior through its connections with the frontal eye fields (FEF) (Schall et al. 1993) and the superior colliculus (SC) (Fries 1984).

A reward-expectancy signal is better than the detection of the reward itself for training purposes for two reasons. First, the reward-expectancy signal implies that there is an internal model with an expected sensory outcome for a behavior, in this case, the secondary reinforcer. This model can be matched with a reward signal and refined as often as rewards are delivered or unexpectedly withheld. Second, the reward expectancy signal comes at a time that is more proximal to the behaviors which earned the reward, and thus may be able to reinforce the high level motor signals in DMFC related to those behaviors.

Usefulness of reward expectancy in SMA during an eye-movement task

A reward-expectancy signal present in DMFC could maintain and enhance the high-level representations of behaviors that earn a reward. But why would this activity be present in the SMA, which is concerned with movements of the body and limbs, during an eye-movement task? It is possible that the reward-expectancy signal is maintained throughout DMFC so that it can enhance any volitional motor acts that precede reward. For instance, in hand-eye coordination tasks, the reward signal can reinforce activity in the limb area of SMA and SEF together. Other areas of SMA that do not have a convergence of activation of the motor map activation and reward signal would not be reinforced and would not produce learning (Sutton and Barto 1988). Thus the expectation signal may be more widely broadcast than the motor activations of a particular behavior. This broader signal may serve to learn new coordinations of different body parts for particular tasks.

	GRANTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS REFERENCES

 
This work was supported by the National Institutes of Health and a James G. Boswell Professorship.

	FOOTNOTES

 
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Address for reprint requests and other correspondence: M. Campos, Computation and Neural Systems, California Institute of Technology, MC 216-76, Pasadena, CA 91125 (E-mail: mcampos{at}caltech.edu)

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