In agreement with the existing literature about reward and perceptual decisions, available reward size affected perceptual choices. Trials from psychophysical (non-microstimulated) experimental blocks were separated according to available reward size and pooled across blocks and animals. There was a significant interaction between the slope of the fitted psychometric function and available reward size, indicating better performance in large reward trials (χ² likelihood-ratio test of nested models Equations 2a,b, ‘Materials and methods’: p = 0.001; Figure 3). Since the reward schedule was dependent on performance history, fluctuations in animals' performance over the course of a block could have contributed to a correlation between large reward trials and high performance. However, the control analyses for performance fluctuations (see below) show that such fluctuations do not explain the main results of the study.

Figure 3

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It is not well understood at which stages of cortical processing for perceptual decision-making neural signals for reward are integrated (Figure 1). However, the neural mechanisms of perceptual decision-making per se have been modelled successfully by a bounded evidence-accumulation process in which representations of sensory information are noisily integrated over time towards decision bounds that represent the possible perceptual outcomes (Gold and Shadlen, 2007; Ratcliff and McKoon, 2008) (Figure 4A). Considering the possible effects of reward on different parameters in the bounded accumulation model can help us to interpret how expected reward influences perceptual decisions.

Figure 4

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In the bounded accumulation model, sensory evidence about the cylinder stimulus is represented as the difference in activity of two pools of sensory neurons, where the conjoint motion and disparity tuning of each pool represents one of the two possible interpretations of cylinder rotation direction (Mazurek et al., 2003; Palmer et al., 2005; Gold and Shadlen, 2007; Krug et al., 2013) (Figure 4B). Sensory evidence is integrated over time to influence the drift-diffusion of the decision variable (DV) (Huk and Shadlen, 2005). The DV starts at a particular distance from the two decision bounds and, during stimulus viewing, drifts stochastically towards one or other decision bound at a rate proportional to the sensory evidence favouring the prospective choices. Upon stimulus offset, the animal makes a saccade towards the target that corresponds to its perceptual choice, which depends upon either the first bound that was crossed or, if no bound was crossed, the bound nearer to the DV final position (Kiani et al., 2008; Fetsch et al., 2014) (Figure 4B). The distance between the DV starting point and the decision bounds predicts the reaction time distribution in a reaction time task; in the present fixed viewing duration task, it offers predictions of decision times that are shorter than the stimulus duration and, critically for our data, it predicts performance accuracy (Kiani et al., 2008; Fetsch et al., 2014).

According to the bounded accumulation model, an increase in either the stimulus sensitivity (represented by parameter k) or the distance between the DV starting point and the bounds (represented by parameter B, Equation 3a, ‘Materials and methods’; Figure 4B) can improve performance (Figure 4C; compare to Figure 3). This is because increasing the perceptual sensitivity to the stimulus allows smaller stimulus disparity signals to have a greater effect on the perceptual decision; on the other hand, increasing the diffusion distance to the decision bounds decreases the relative impact of spurious noise on the final decision (Bogacz et al., 2006; Ratcliff and McKoon, 2008). The contributions of these parameters cannot be differentiated based upon these perceptual choice data alone because they both improve performance. However, these parameters make different predictions about what should happen to visual cortical microstimulation when performance improves. Electrical microstimulation biases perceptual decisions towards the PREF direction of the stimulated MU site (Figure 4D; Krug et al., 2013). If performance in large reward trials improves as a result of an increase in B, this will not affect the size of the microstimulation shift, because B acts at an integration stage that is positioned after the location at which visually evoked and electrically evoked signals have been combined (Figure 4E; see also Figure 1). If performance improves as a result of an increase in k, this will decrease the relative influence of microstimulation on perceptual choices (illustrated by a smaller horizontal shift between microstimulated and non-microstimulated psychometric functions) because k acts specifically on visually evoked sensory representations before they are combined with electrical microstimulation signals (Figure 4F). Therefore, the insertion of the electrical microstimulation signal at the level of representation of sensory evidence (area V5/MT) allows us to separate the effects of parameters k and B according to this model.

We fit the bounded accumulation model of decision-making (Equation 3a, ‘Materials and methods’) to behavioural choice data combined across significant PREF microstimulation sites (sites with a significant microstimulation effect in their PREF cylinder rotation direction), separately for each animal (Figure 5). Prior to pooling, cylinder disparity values were normalised by the maximum disparity from the range used at each site. The size of the microstimulation effect - the horizontal shift between microstimulated and non-microstimulated psychometric functions—was decreased in large reward trials (dashed lines) compared to small reward trials (smooth lines), implicating a change in stimulus sensitivity (parameter k) by reward (see Figure 4F). Using a χ² likelihood-ratio test, we compared the goodness-of-fit of the full bounded-accumulation model in which all parameters were allowed to vary by reward (Equation 3a, ‘Materials and methods’) to a nested model in which the parameter in question (either k or B) was frozen, that is, not allowed to vary by reward (Equations 3b,c, respectively).

Figure 5

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For both animals, the overall best-fitting model was the full model in which both parameters B and k were affected by reward condition (Equation 3a). For both animals, parameter k was significantly increased in large compared to small reward trials (χ² likelihood-ratio test of nested models Equations 3 a,b: Fle: p < 0.001; Ica: p < 0.001; Table 1). This result supports the interpretation that the improvement in performance seen in large reward trials is in part due to an increase in parameter k, that is, increased sensitivity of sensory representations to the visual stimulus when reward is large. The estimated distance between the starting point of DV integration and the decision bounds, represented by parameter B, was also significantly affected by reward, decreasing in large compared to small reward trials (χ² likelihood-ratio test of nested models Equations 3a,c: Fle: p = 0.007; Ica: p < 0.001; Table 1). Altogether these results indicate that expected reward affects perceptual decision-making both at the sensory representation stage (parameter k) and the integration stage (parameter B). Statistical results are unaffected when data were pooled across all microstimulation sites.

An independent site-by-site analysis confirmed that the effect of microstimulation was significantly decreased in large reward trials compared to small reward trials (Figure 6). For each significant PREF microstimulation site, psychometric functions were fit with a pair of cumulative Gaussian functions (Equation 1a, ‘Materials and methods’), whose means were allowed to differ by microstimulation condition but whose standard deviation was constrained to be the same (Krug et al., 2013). We illustrate these psychometric functions for example site fle302. Here, MU neurons were selective for a CCW cylinder rotation direction (Figure 6—figure supplement 1A). The effect of electrical microstimulation was equivalent to adding binocular disparity of −0.023° in magnitude to the stimulus dots, biasing choices toward the PREF cylinder direction CCW (Figure 6—figure supplement 1B). We then separated trials according to whether available reward was large or small (Figure 6—figure supplement 1C,D). Microstimulation always biased the animal's response toward CCW rotation, the PREF rotation direction at this site. However, when reward size was large, the effect of electrical microstimulation was equivalent to addition of −0.019° of binocular disparity (Figure 6—figure supplement 1C), representing a reduction of about 30% compared to when reward size was small, where the microstimulation current was equivalent to addition of −0.027° (Figure 6—figure supplement 1D).

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Over all significant PREF microstimulation sites over both animals, we considered both the unit-less normalised microstimulation-induced shift (divided by the cylinder threshold at the site; Figure 6A), and the raw shift measured in degrees of visual angle (Figure 6B). The normalised microstimulation shift was significantly smaller for large expected reward trials (median = 0.67) compared to small expected reward trials (median = 0.88; Wilcoxon sign-rank test of normalised shift values, two-sided: n = 28, p < 0.001) and also when considered separately for each animal Fle (median large reward trials = 0.94; small reward trials = 1.37; n = 13, p = 0.005) and Ica (median large reward trials = 0.54; small reward trials = 0.68; n = 15, p = 0.022). Similarly, the raw microstimulation shift was significantly smaller for large expected reward trials (median = 0.0064°) compared to small expected reward trials (median = 0.0093°; Wilcoxon sign-rank tests of raw shift values: n = 28, p < 0.001) and also when considered separately for each animal Fle (median large reward trials = 0.0099°; small reward trials = 0.024°; n = 13, p = 0.013) and Ica (median large reward trials = 0.0025°; small reward trials = 0.0071°; n = 15, p = 0.005). Statistical results were unaffected when data were pooled across all stimulation sites.

We tested the possibility that the animals detect some subtle difference between trials with and without electrical microstimulation, and accordingly adjust their decision criterion to bias their choices against the microstimulation effect - and that they might do this more in large reward trials to reduce their error rate. This seems unlikely, as in principle the animals could then have applied a different criterion to all microstimulation trials and thereby eliminated the effect of microstimulation altogether, resulting in far fewer errors overall and thereby improving their acquisition of rewards. Nevertheless, to examine the possibility of such a strategy, we performed a set of psychophysical control experiments with monkey Ica that reproduced the effect of electrical microstimulation using a visual stimulus perturbation. This variation omitted the electrical microstimulation itself, but retained the cognitive cues that might be delivered to the animal during electrical microstimulation.

Using the same experimental parameters as for electrical microstimulation sessions, we inserted an additional binocular disparity signal, ‘∆dx’, to the cylinder stimulus pseudo-randomly on 50% of trials (see Salzman et al. 1992; Fetsch et al. 2014 for a similar manipulation of visual motion signals in a motion direction discrimination task). In our experiment, the trials with the ∆dx signal were overtly flagged by a change in the appearance of the cylinder stimulus from all black dots to all white dots (Figure 7). This change in cylinder appearance is highly salient to human observers and we assume it is also salient for the monkey. Importantly, the animal was only rewarded for perceptual choices that would have been correct had the additional disparity signal not been present. This creates a form of ‘microstimulation’ experiment in which the presence of the extra disparity cue ∆dx is clearly signalled by a change in stimulus colour from black to white. The animal was first trained over 10 days and 45,000 trials on this task. Unlike the electrical microstimulation experiments, which contained a mixture of microstimulation biases across different V5/MT sites, favouring sometimes CW and sometimes CCW choices, the added disparity signal ∆dx always gave a consistent shift towards CCW perceptual choices. This increased the chance that the animal would engage in a strategy that exploited all available information to reduce its error rate and gain more reward.

Figure 7

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Despite the fact that trials with ∆dx were clearly signalled, the psychometric function for ∆dx trials was horizontally shifted towards the response direction predicted by the additional disparity signal, successfully emulating the horizontal shift of the psychometric function due to V5/MT electrical microstimulation (Figure 7). As observed in the electrical microstimulation experiments, there was a significant improvement in performance in trials where a larger reward was available for a correct choice (χ² likelihood-ratio test of nested models Equations 2a,b: Ica-∆dx: p < 0.001). However, there was no significant interaction between reward condition and ∆dx-induced shift in the ∆dx psychometric functions (χ² likelihood-ratio test of nested models Equations 2a,c: Ica-∆dx: p > 0.05; Figure 7), in contrast to the results when the same test was applied to the electrical microstimulation datasets (χ² likelihood-ratio test of nested models Equations 2a,c: Fle: p < 0.001; Ica: p < 0.001). Therefore, there is no evidence to support the view that microstimulation induces a different percept that the animal discounts more effectively when reward is higher, since the animal does not make an adjustment in large reward trials when the presence of additional disparity is clearly signalled.

We explored further trends in the data to evaluate the potential relationship between the reward modulation of visual cortical microstimulation, stimulus eccentricity, discrimination threshold, and raw overall microstimulation effect at each site. Visual area V5/MT contains a retinotopic map of contralateral visual space (Albright and Desimone, 1987) and the receptive field (RF) location of each microstimulated MU varies in eccentricity, that is, distance from the fovea (Desimone and Ungerleider, 1986). The cylinder stimulus was matched to the MU RF location at each microstimulation site. Both animals' cylinder discrimination thresholds significantly correlated with stimulus eccentricity, worsening as eccentricity increased (Figure 8A). The raw overall microstimulation effect also significantly correlated with the cylinder discrimination threshold at each site (Figure 8B). The reward modulation of the raw microstimulation shift effect was significantly correlated with stimulus eccentricity and with the raw overall microstimulation effect size for both animals considered together, and for one animal considered alone (Ica), such that the biggest raw effects of reward on microstimulation occurred for sites with largest eccentricity (where thresholds were high) and the biggest raw overall microstimulation effect (Figure 8C,D). However, these correlations disappeared for reward modulation of the microstimulation shift effect when normalised by cylinder discrimination threshold at each site (Figure 8E,F). This shows that the reward effect is constant when microstimulation is expressed as a fraction of the discrimination threshold.

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In these experiments, available reward size for a correct choice depended upon animals' performance in the preceding trials, increasing gradually when animals performed correctly over consecutive trials (Figure 2). Sequences of correct trials are more likely when the animal is more engaged in the task, perhaps due to attentional or motivational states. In an alternative interpretation, slower fluctuations in task engagement, rather than expected reward size, are the cause of the observed effects. Variations in task engagement would be expected to occur at least an order of magnitude slower (over the space of around 30 trials) than differential valuations of upcoming reward (which occur over the space of two or three trials; Figure 2). We therefore address this concern with a time-series analysis of task engagement.

For the experimental session at each microstimulation site for each animal, a smoothed performance average was calculated using a sliding time window of size 30 trials (Figure 8—figure supplement 1A). The extent of performance fluctuation over the experimental session was estimated as the area between the smooth performance curve and average (normalised by total number of trials). If fluctuations in task engagement underlie the observed association between reward and microstimulation shift, there should be a correlation between the extent of performance fluctuation and size of reward effect. However, there was no correlation for either animal considered separately or together (p > 0.05 in all cases; Figure 8—figure supplement 1B). Furthermore, the effect of expected reward on microstimulation shift remained when trials were separated according to whether they occur in ‘good’ or ‘bad’ epochs of task engagement (χ² likelihood-ratio test of nested models Equations 2a,c, data pooled across sites and animals: good epochs: p < 0.001; bad epochs: p < 0.001; Figure 8—figure supplement 1C,D). Therefore, the modulation of cortical microstimulation by available reward size does not seem to be associated with or dependent upon slower fluctuations in task engagement.

In further controls, we found that the effect of reward could not be explained by the waning of microstimulation effect over time during an experimental session, combined with a propensity to have a greater proportion of large reward trials at the end of a microstimulation session (Figure 8—figure supplement 2). Also, there were no significant differences in proportion of microstimulated trials or mean absolute disparity between reward conditions (Supplementary file 1).

Our results could not be explained by an adaptive change in the animals' strategy based upon recognizing the presence of microstimulation. In a control experiment, in which an additional visual disparity (∆dx) signal that mimicked the microstimulation effect was clearly cued by a change in stimulus colour, the effect of ∆dx on choices was not affected by expected reward size, even though overall task performance was better in large reward trials as seen in the electrical microstimulation sessions. This indicates that the animal did not apply a strategy to compensate for the ∆dx signal specifically on large reward trials, even when trials that contained additional visual disparity were clearly identifiable. This experiment controls for the situation in which microstimulation produces a different percept that the animal is able to discount more effectively when available reward is higher. However, this psychophysical experiment cannot simulate possible noise or other aberrance in inputs associated with electrically evoked sensory neuronal activity, which, for example, might be weighed less compared to more ‘normal’, visually evoked inputs; weights that could change based on expected reward. We discuss this issue further in the next section.

In the present study, reward depended on animals' performance in the preceding trials, increasing gradually when animals performed correctly over consecutive trials, which is more likely to happen when the animal is more engaged in the visual task. Therefore, instead of expected reward size, fluctuations in task engagement (for example due to attentional or motivational states) could result in epochs of good and bad performance, which are inevitably associated with more large reward or small reward trials respectively. These fluctuations could therefore potentially underlie the observed association between large reward trials and better performance, and the observed effects on the microstimulation shift. However, a time-series analysis of performance over the course of the microstimulation experimental sessions showed that there was no correlation between the performance fluctuation and the differential effect of microstimulation by reward. Furthermore, when trials in each microstimulation session were separated according to whether they occurred in a ‘good epoch’ (a window of better-than-average performance) or ‘bad epoch’ (poorer-than-average performance), the modulation of microstimulation shift by reward size remained within both types of epoch. These analyses, which monitor closely engagement with the task over time, provide evidence against the hypothesis that the differential microstimulation effect can be accounted for purely by fluctuations in task engagement. Future experiments can directly rule out this possibility by varying reward size independently of performance, for example by using a block design or trial-by-trial cueing of the available reward size.

In our interpretation of the results, we assume that electrically evoked signals in visual area V5/MT are thereafter integrated into the perceptual decision-making process in a manner identical to visually evoked evidence (Figure 5). This interpretation is supported by the behavioural shift induced by electrical microstimulation in perceptual decisions. However, the circuit mechanisms of the effect of electrical microstimulation are incompletely understood, and microstimulation has been reported to have unanticipated inhibitory effects on downstream cortical targets (Logothetis et al., 2010; Sultan et al., 2011). Such observations have generally been made in anaesthetised animals, without the concurrent presentation of a visual stimulus expected to activate the same brain areas as microstimulation, and at stimulation currents orders of magnitude higher than used in this study (250–1000 μA, compared to our 20 μA). Therefore, it is difficult to interpret these effects in the context of our experimental protocols.

A remaining possibility is that odd or aberrant inputs from electrically evoked sensory neurons might be weighed less by decision-making areas compared with more ‘normal’ that is, visually evoked inputs, and that these weights could change based upon expected reward. Our ∆dx experiment cannot control for this case because we added the additional stimulus disparity signal via visual stimulation that by this argument cannot mimic the artificially induced microstimulation signal. However, we argue that there is currently little evidence to suggest that the brain applies differential weights to electrically and visually evoked sensory signals in this way. It is important to note that microstimulation affects behaviour in both large and small reward trials, suggesting that it is not an aberrant signal but a clear signal that affects perceptual choices in all cases. When combined with relevant visual stimulation, electrical microstimulation in many visual cortical areas significantly and reliably affects behaviour in a variety of visual perceptual tasks (Cicmil and Krug, 2015). If the microstimulation signal could affect final motor output via a neural path independent from visually evoked signals, it would be expected that microstimulation would be detectable and/or processed by the brain in some qualitatively different way. But a recent study that modelled in detail the relationship between microstimulation, perceptual choices and decision confidence found that their data did not support the view that microstimulation induces a change in neural activity that is processed qualitatively differently from visually evoked activity (Fetsch et al., 2014), and our ∆dx control shows that the animal cannot use an obvious difference in the stimulus percept to discard some of the visual information provided.

Electrophysiological recordings from stimulus-relevant neurons during visual perceptual decision-making are necessary to directly show whether expected reward modulates the neuronal firing rates and tuning functions at a specific visual cortical site in the manner predicted by an increase in stimulus sensitivity under large reward. Yet, the unique contribution of a microstimulation intervention to this question is to show that reward modulations at a level prior to the integration stage have a causal effect on the perceptual decision, rather than representing a top-down echo of decision-formation that might not be causally relevant to the perceptual choice itself.

The form of the bounded accumulation model considered here is a simplified case of the race model, in which evidence for each choice is integrated separately by two decision variables (Usher and McClelland, 2001). We used the assumption that the accumulator inputs are perfectly negatively correlated, which corresponds to the action of one accumulator only (Figure 5A,B) (Palmer et al., 2005; Ratcliff and McKoon, 2008). To avoid unnecessary assumptions and over-fitting, we chose the simplest model framework that could explain our findings. The microstimulation signal was modelled as additional sensory evidence towards the cylinder rotation direction preferences of the microstimulated neurons. The null choice bias, whereby animals apply an overall bias towards the non-preferred direction of the stimulated neurons in order to match overall reward proportions for different choices (Salzman et al., 1992), was modelled as an offset to the overall psychometric function (Equation 3a, ‘Materials and methods’). The parameters of the bounded accumulation model are commonly used to explain both choice and reaction time. However, bounded integration underlies perceptual decisions even when stimulus viewing duration is dictated by the environment (Kiani et al., 2008), and the model can be fit usefully in these circumstances (Fetsch et al., 2014). Although we employed a fixed viewing duration task, differential effects of reward on parameter k, coding for stimulus scaling sensitivity, and parameter B, coding for distance-to-bound, could be differentiated in our model due to the insertion of the microstimulation signal in visual area V5/MT (Figure 4).

The lack of reaction-time measurements in the present experiment leaves open the possibility that changes to parameter B may also arise from an overall increase in the gain of accumulation of sensory evidence. The consensus from electrophysiological studies (Rorie et al., 2010), human neuroimaging (Summerfield and Koechlin, 2010; Mulder et al., 2012), and psychophysical studies (Diederich and Busemeyer, 2006; Diederich, 2008) is that reward effects on the DV accumulation process occur through changes to the distance-to-bound; we therefore follow this interpretation. A microstimulation-reaction time task, or detailed measurement of neuronal firing rates in sensorimotor structures such as LIP that represent the integration stage, would be necessary to resolve this issue. Regardless, the specific interpretation of parameter B's effects does not affect the main conclusions of this study.

We have argued that the most parsimonious explanation for our observations is that larger available reward increases the sensitivity of visually evoked sensory representations about the visual stimulus. This bears strong similarity to the effect on neuronal responses when attention is allocated to a specific spatial location or to a PREF stimulus feature (Treue and Maunsell, 1996, 1999; McAdams and Maunsell, 2000; Corbetta and Shulman, 2002; Saenz et al., 2002). Indeed, manipulations of reward and attention are often difficult to separate (Maunsell, 2004). However, these effects cannot trivially explain our findings.

With regard to feature attention, our trial-by-trial reward schedule was balanced so that both perceptual interpretations of the cylinder were equally rewarded, if correctly identified. Feature attention was therefore not directed in favour of one perceptual interpretation of cylinder rotation direction. Our experimental task always directed spatial attention to the location of the visual stimulus, because the animal responded to the visual stimulus in every trial to obtain a reward, whether small or large. Thus, results from spatial attention studies, which measure differences in neuronal activity when attention is directed into and away from their RF (Treue and Maunsell, 1996, 1999; McAdams and Maunsell, 2000), cannot directly be applied to our results. The definition of spatial attention could be extended to encompass gradations of attention applied to a particular spatial location according to expected reward size, which may be manipulated by experiments such as ours. In that case, the present study would be the first to provide data demonstrating such an effect during perceptual decision-making, and to integrate such modulations of sensory representations coherently with reward manipulation and decision model theory (Smith and Ratcliff, 2009).

In summary, we found that the effect of V5/MT electrical microstimulation on perceptual decisions was less effective when available reward was larger. In the context of a bounded evidence-accumulation model, this suggests that reward modulates sensory representations during perceptual decision-making, in addition to altering the integration of sensory evidence into the DV in sensorimotor structures (Gold and Shadlen, 2007). Further electrophysiological research should record in area LIP to show directly how the electrically introduced signal in V5/MT is integrated into the perceptual decision under different reward states, and should record in visual area V5/MT to show how reward modulates the signals of sensory evidence in visual cortex during perceptual decisions.

Microstimulation experiments were conducted in one cortical hemisphere in two adult male rhesus macaque monkeys (Macaca mulatta). Prior to the experiments, each monkey was surgically implanted under general anaesthesia with a head-holding device and a recording chamber placed over a craniotomy above the occipital lobe. For monkey Fle, scleral magnetic search coils were implanted in both eyes under general anaesthesia, to monitor eye position and vergence. Monkey Ica had one scleral magnetic search coil only. Monkeys were trained to fixate on a binocularly presented marker and to perform the visual task that required the animals to discriminate the motion and binocular disparity information in a rotating SFM cylinder stimulus (Figure 2).

Experiments were conducted at two locations: monkey Fle was tested at Oxford University, UK; monkey Ica was tested at the National Institutes of Health, Bethesda, USA (henceforth referred to as Oxford and NIH, respectively). Except where specifically described, the protocols were identical in the two locations.

At Oxford, all procedures complied with United Kingdom Home Office regulations on animal experimentation. At NIH, all procedures complied with US Public Health Service policy on the humane care and use of laboratory animals, and all protocols were approved by the National Eye Institute Animal Care and Use Committee.

The visual stimulus for the discrimination task was a rotating SFM cylinder made up of two transparent surfaces of random dots moving in opposite directions (random dot kinematogram) (Treue et al., 1991) (Video 1). The dots had a sinusoidal velocity profile as would be expected in an orthographic projection of a three-dimensional rotating cylinder. When the two dot surfaces are presented at the same binocular disparity, the cylinder rotation direction is bistable and over time will undergo spontaneous fluctuations in the perceived direction of rotation (Wallach and O'Connell, 1953; Ullman, 1979). Adding opposite horizontal disparity separates the front and back walls of the cylinder in depth, thereby disambiguating the direction of rotation. The disparity of each dot was scaled sinusoidally to match the velocity profile, with the maximal disparity difference between the two dot surfaces at the central axis of the cylinder. Equal but opposite disparities were added to the front and back surfaces of the cylinder, so that its principal axis remained in the plane of fixation. Below, we use the term ‘disparity’ to refer to this maximal difference in disparity between dots moving in opposite directions. We used the following convention: when the front wall of dots moved to the left with respect to cylinder orientation axis, this corresponded to a CW rotating cylinder as viewed from above; when the front wall moved to the right, this corresponded to a CCW rotating cylinder. To allow pooling across microstimulation sites, quantitative behavioural analysis was carried out with reference to PREF choices. For the pooled data, positive disparities refer to the stimulus rotation direction for which the site was selective.

Cylinder stimuli consisted of 50% black dots and 50% white dots presented at maximum contrast (99% of full contrast) on a mid-grey background at a dot density of 25%. Half the dots (randomly selected) moved in one direction and the remaining dots moved in the opposite direction. When a dot reached the edge of the cylinder, it reversed direction.

In Oxford, stimuli were displayed binocularly using a Wheatstone stereoscope configuration with two monitors (Eizo FlexScan 78, Eizo, Bracknell, UK). A pair of mirrors positioned in front of the monkey reflected the images from each of the two monitor screens separately into the left and right eyes. Frame rate was 72 Hz. Monitor screens were positioned 84 cm from the monkey, and covered approximately 21° × 17° of the visual field. Mean luminance was 42 cd/m² and dot diameter was 0.2°. The dot refresh rate was 2%, that is, on each video frame, 2% of the dots were relocated to a randomly chosen location on the cylinder surface, so that dots had a mean lifetime of 615 ms in each location.

At NIH, stimuli were displayed binocularly using two DLP projectors (Projection Design evo2sx+, projectiondesign®/Barco, Inc., Xenia, OH) with polarizing filters. The image was projected onto a polarisation-preserving screen (Filmscreen 150, Stewart Filmscreen, Amelia, OH). Frame rate was 60 Hz. The screen was 112 cm away from the monkey, and covered approximately 41° × 32°. Mean luminance of the display viewed through polarized filters was 17.5 cd/m² and dot diameter was 0.18°. Dot refresh rate was 1%.

The psychophysical task was a two-alternative forced choice discrimination of the direction of rotation of the SFM cylinder. Stimulus disparities were matched to the psychophysical threshold of the animal in order to obtain a full psychometric function. To initiate a trial, the animal had to acquire the fixation point. The visual stimulus was then presented for 2000 ms (Figure 2). At stimulus offset, the fixation point also disappeared and two choice targets corresponding to the two possible directions of cylinder rotation appeared. In the Oxford, targets were located to the left and right side of the fixation point. In the NIH set-up, targets were also located opposite one another on either side of the fixation point but on an axis normal to the cylinder orientation. The animals indicated their perceptual decision by making a saccadic eye-movement to one of the two targets. A saccade to the correct target resulted in a fluid reward. If the choice was incorrect, the animal received no reward and there was a short time-out before the start of the next trial. For ambiguous cylinders (zero-disparity trials), 50% of the trials were rewarded at random. If the animal broke fixation during stimulus presentation, no reward was given.

Animals worked on the task to gain fluid rewards to meet their daily requirements. Reward size available for a correct choice on each trial depended upon the number of immediately preceding consecutive correct responses that the animal had made, increasing in two steps up to a maximum (Figure 1). Choices in zero-disparity trials were rewarded 50% of the time at random, and for the purposes of calculating the reward sequence they were discounted. For monkey Ica, reward size was 0.08 ml on the first and second consecutive correct choices after an error, 0.12 ml for the third consecutive correct choice, and 0.2 ml on the fourth and all subsequent consecutive correct trials. For monkey Fle, reward size was 1/3 of maximum for the first correct choice, 2/3 of maximum for the second, and reached maximum size (usually 0.18 ml) for the third and all subsequent consecutive correct choices. Thus reward size increased more quickly for Fle than Ica as a function of consecutive correct responses. For both animals, we categorized trials into two conditions: maximal (large) reward size and sub-maximal (small) reward size, where for both animals the average size of the two sub-maximal reward sizes was half that of the maximal reward size. Animals were familiar with their respective reward schedule because it was used throughout all training and recording sessions with the discrimination task; it was not introduced only for the microstimulation experiments.

Recording from MU neuronal clusters was carried out to characterize and select the cortical microstimulation sites. Single tungsten microelectrodes coated with polyimide tubing were used (0.1–0.3 MΩ impedance at 1 kHz; MicroProbe, Inc., Gaithersburg, MD). A hydraulic microdrive mounted on the recording cylinder advanced the electrode through a guide tube. Electrical signals from the electrode were filtered, amplified and displayed through visual and audio monitors, and stored to computer disk. Online classification of activity and pre-processing steps were done with the DataWave Discovery system (DataWave Technologies, Loveland, CO) at Oxford and Spike 2 (Cambridge Electronic Design Ltd., Cambridge, UK) data acquisition program at NIH.

Area V5/MT was approached posteriorly through the recording chamber in incremental steps of about 100 µm. Area V5/MT was identified by established physiological criteria: (1) the approach through the grey/white matter pattern comprising the striate cortex, lunate sulcus and the posterior bank of the superior temporal sulcus (STS) (Zeki, 1974); (2) by neurons' characteristic direction selectivity and binocular disparity selectivity and its clustering in V5/MT (Van Essen et al., 1981; Maunsell and Van Essen, 1983; Albright and Desimone, 1987; Bradley et al., 1995, 1998; DeAngelis et al., 1998; DeAngelis and Newsome, 1999); (3) the known relationship between the size and eccentricity of V5/MT receptive fields (Desimone and Ungerleider, 1986); and (4) from penetration to penetration, the relationship between RF positions and known topography of V5/MT (Albright and Desimone, 1987).

Monkeys maintained fixation on a central fixation point while direction selectivity was established for an MU cluster at a particular electrode location, using kinetic dots moving coherently in different directions. The direction evoking the greatest response on average was assigned to be the PREF motion direction for the MU cluster. The RF boundaries were then mapped using a patch of dots moving in the PREF motion direction. After quantitative confirmation of direction tuning within these boundaries, the binocular disparity preference of the MU cluster was measured by presenting a plane of moving dots in the PREF direction at different binocular disparities around the fixation plane. Only MU sites with discernible motion and disparity tuning were further explored. The directions of motion of the two transparent planes of dots that made up the cylinder were aligned with the PREF and the opposite (NULL) directions of the MU cluster and the MU cylinder preference was calculated online over a range of disparities.

When the direction- and cylinder disparity-preference of MU cluster activity were found to remain constant over at least 300 µm of cortex, the electrode was retracted to the middle of that stretch. At the proposed stimulation site, the MU RF size, direction selectivity and cylinder preference were quantitatively re-measured. The cylinder stimulus position, size, orientation, and dot speed were carefully matched to the preferences of the MU at the stimulation site. The monkey first performed 50 to 140 trials of the psychophysical task without microstimulation, prior to the microstimulation experiment, in order to allow the selection of a range of stimulus disparities near threshold at that site.

The total number of cortical sites at which microstimulation was applied was 20 in monkey Fle and 28 in monkey Ica. This number of biological replicates (different stimulation sites) was chosen to be as close as possible to the number of microstimulation sites used in previous studies of this cortical area (V5/MT; in particular, DeAngelis et al. 1998). Stimulation sites were selected freshly on each day of recording and the choice of sites for stimulation was independently determined on each occasion of performing the experiment, by reference to the selection criteria presented above.

Electrical microstimulation was applied during the 2000 ms of stimulus presentation on half of the trials, pseudo-randomly interleaved with non-microstimulated trials. Stimulation consisted of 20 µA biphasic pulses of 200 µs cathodal stimulation followed by 200 µs anodal stimulation, delivered at 200 Hz. Only cortical microstimulation sites with at least 10 microstimulation trials and 10 non-stimulation trials at each of at least 5 different stimulus disparities were included in subsequent analyses. All included sites showed significant tuning to disparity in the cylinder stimulus (one-way ANOVA, p < 0.05).

For each microstimulation site, we plotted the proportion of choices made by the monkey towards the PREF rotation direction, at each stimulus disparity, separately for microstimulated and non-microstimulated trials. Behavioural data were fitted for each site with a two-mean cumulative Gaussian distribution:

where C is the cylinder disparity (positive in PREF direction); µ_i is the mean of the distribution; σ is the s.d.; erf is the error function; β = 1 for microstimulated trials and β = 0 for non-microstimulated trials. P_PREF corresponds to the probability of making a decision in the PREF direction of the microstimulated site. To test whether microstimulation significantly shifted the psychometric function, the two-mean model was compared to a model in which mean was not allowed to vary by microstimulation (Krug et al., 2013):

A χ² likelihood-ratio test was used to ascertain whether the full model fitted the data significantly better than the nested model (p < 0.05; 1 degree of freedom as models differed by 1 parameter). Maximum likelihood estimate model fitting was performed with MATLAB's fminsearch function and was repeated multiple times from a wide range of starting parameter values, in order that results would not be driven by local minima.

The microstimulation effect is quantified by taking the estimated value of parameter µ₁; this is equivalent to a horizontal shift in the psychometric curve. The size of the electrical microstimulation effect can thus be expressed in terms of the size of binocular disparity that would need to be added to the stimulus to shift perceptual behaviour to the same extent (Krug et al., 2013).

A cumulative Gaussian function was used to evaluate the interaction between reward, task performance and microstimulation for psychometric data pooled across multiple microstimulation sites. Prior to pooling, we normalized stimulus disparity values at each site by dividing them by the maximum disparity value of the site. This ensured that the maximum normalized disparity was 1 and minimum was −1, with the other disparity values lying in between. In the full model both the mean µ and standard deviation (s.d.) σ are allowed to vary by microstimulation condition and reward condition,

where C is the cylinder disparity (positive in PREF direction); µ_i represents the mean; σ_i represents s.d., α = 1 in trials were available reward is large, otherwise α = 0; β = 1 in trials where additional stimulation is introduced, otherwise β = 0; erf is the error function. P_PREF corresponds to the probability of making a decision in the PREF direction of the additional stimulation introduced. To test whether reward affected task performance, this full model was compared with a nested model in which s.d. σ (a measure of performance threshold) was not allowed to vary by reward condition:

A χ² likelihood-ratio test was used to ascertain whether the full model fitted the data significantly better than the nested model (p < 0.05; 1 degree of freedom as models differed by 1 parameter). Similarly, the effect of reward on microstimulation was evaluated with a χ² likelihood-ratio test to compare the full model with a nested model in which the change in mean by microstimulation, represented by βµ_i, was not allowed to vary by reward condition:

Model fitting was performed as described in the previous section. We report uncorrected p values; however, all statistically significant results survive Bonferroni correction for multiple comparisons across the two animals (where relevant).

A logistic regression model representing the effect of cylinder stimulus disparity, microstimulation and trial reward condition on the psychometric functions was derived from the simple, one-dimensional drift-diffusion model of perceptual decision-making (Palmer et al., 2005; Gold and Shadlen, 2007) (see also Figure 1). The full bounded evidence-accumulation model explains perceptual choices in the PREF direction (P_PREF) according to the following equation:

where C is the (normalized) cylinder disparity (positive in PREF direction); α = 1 for large reward trials and α = 0 for small reward trials; β = 1 for microstimulated trials and β = 0 for non-microstimulated trials. The relation between stimulus strength C and the aspect of DV drift that is driven by visually evoked sensory evidence is represented by parameter k_i (see also Figure 4A,B). The distance to the decision bounds from the starting point of the drift-diffusion process and the overall gain of the drift rate are represented by parameter B_i. Behavioural null bias in non-stimulated trials (Salzman et al., 1990, 1992) is represented by parameter x_i. To test whether reward affected parameter k_i, this full model was compared with a nested model in which k_i was not allowed to vary by reward condition:

A χ² likelihood-ratio test was used to ascertain whether the full model fitted the data significantly better than the nested model (p < 0.05; 1 degree of freedom as models differed by 1 parameter). Similarly, the effect of reward on parameter B_i was evaluated with a χ² likelihood-ratio test to compare the full model with a nested model in which B_i was not allowed to vary by reward:

Model fitting was performed as described in previous sections. We report uncorrected p values; however, all statistically significant results survive Bonferroni correction for multiple comparisons across the two animals (where relevant). Quantile–quantile comparisons between the fit of this decision model and the cumulative Gaussian model described in the previous section indicate a reasonable correspondence between model fits (Figure 8—figure supplement 3).

At each microstimulation site, trials were divided according to whether the available reward size for a correct choice in the trial was large or small. The size of the horizontal shift of the psychometric function induced by electrical microstimulation was re-calculated as described in the previous section, separately for large and small reward trials. To normalise the effect of microstimulation across sites with different associated threshold due to stimulus eccentricity (see Figure 8), the horizontal shift of the psychometric function in each reward condition was divided by the psychometric threshold for that reward condition that is, the standard deviation (s.d.) of the fitted cumulative Gaussian (Equation 1a; Uka and DeAngelis 2006). A Wilcoxon sign-rank test across sites (p < 0.05, two-sided) was used to ascertain whether there was an overall significant difference in the size of the microstimulation shift between the large and small reward conditions. A non-parametric test was used because although the normalized shifts were normally distributed (Lilliefors test, p > 0.05), the underlying distribution of raw shift values was not normally distributed (Lilliefors test, p = 0.001). We report uncorrected p values; however, all statistically significant results survive Bonferroni correction for multiple comparisons across the two animals (where relevant).

We performed a control experiment on one monkey, Ica, in which we used a visual stimulus to mimic the psychophysical effect of microstimulation. We matched stimulus and task parameters to the microstimulation experiment but no electrical microstimulation took place. Instead, we added a signal of +0.005° disparity, termed ‘∆dx’, to the stimulus in half of the trials, pseudo-randomly selected (see Salzman et al. 1992; Fetsch et al. 2014 for a similar manipulation in a visual motion task). This extra signal was not included in the determination of the correct response, that is, we rewarded the monkey's perceptual choices according to the stimulus disparity without the addition of ∆dx.

The ∆dx trials were overtly cued by a change in the colour of the cylinder stimulus, from 100% white dots to 100% black dots (Figure 7A,B). This allowed us to explore the behavioural strategy adopted by the animal when trials with and without an additional, non-rewarded visual disparity signal are clearly cued. We kept reward schedules the same. Therefore, for maximal reward accumulation, the monkey's optimal strategy would to bias its perceptual reports away from the fake signal cylinder disparity on the trials that were overtly cued as ∆dx.

Over 5 days, 15 experimental sessions of psychophysical data was collected, with all experimental parameters remaining constant. There were approximately 1000 to 2000 trials in each experimental session. By contrast, there were on average 500 trials in each experimental block at the electrical microstimulation sites. The ∆dx control experiment comprised 12,149 trials in total, whilst the Fle and Ica electrical microstimulation experiments comprised 9,411 and 10,592 trials in total, respectively, over all microstimulation sites. Since our objective was to ascertain whether the animal learns to treat the ∆dx trials differently from non-∆dx trials, we kept the direction of the ∆dx signal the same throughout the control experiment. If the animal fails to adjust its criterion on a reward-size basis in this situation when it is clearly apparent when the additional visual disparity signal is added, it seems very unlikely that they could do so when the signal introduced by microstimulation varied between experiments (as it did for the real microstimulation).

Performance accuracy (proportion of correct choices) was calculated over a sliding time window 30 trials wide. To estimate the amount of performance fluctuation over the microstimulation session at a given site, the area between the smoothed performance curve and the horizontal line indicating the mean overall performance over that session was calculated using trapezoidal numerical integration implemented by MATLAB's trapz function (see Figure 8—figure supplement 1A). Since different sites had microstimulation sessions of different lengths, this area was normalised across sites by division by the total number of trials in the microstimulation session at each site. ‘Good’ and ‘bad’ performance epochs were identified as stretches of smoothed performance that were above or below mean site performance, respectively, for at least the length of the smoothing time window (i.e. at least 30 trials). To evaluate whether the reward effect on microstimulation remained within good and bad epochs considered individually, trials from good and bad epochs were pooled across sites and animals and fit with cumulative Gaussians to test whether there was significant interaction between reward and microstimulation shift (χ²-test of nested models Equations 2a,c, p < 0.05, as described in previous sections).

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

NC held an Usher Cunningham Studentship (DPAG and Exeter College, Oxford). KK is a Royal Society University Research Fellow. This work was supported by the Wellcome Trust and National Institutes of Health, Bethesda, MD.

Animal experimentation: Animal experimentation was conducted at two locations: University of Oxford, UK, and National Institutes of Health (NIH), Bethesda, MD, United States. At Oxford, all procedures were approved by the United Kingdom Home Office, and strictly complied with the restrictions and provisions contained in the Animals (Scientific Procedures) Act of 1986. At NIH, all procedures strictly complied with US Public Health Service policy on the humane care and use of animals, and the protocol was approved by the National Eye Institute (NEI) Animal Care and Use Committee (protocol #NEI-567). Every effort was made to minimise potential sources of pain, suffering, distress or lasting harm to the animals involved in the study.

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