Complex decisions often require interpreting external sensory inputs in the context of outcome expectations and preferences. This kind of decision-making is pervasive in our daily lives, balancing what we observe with what we desire. Under controlled task conditions, both humans and non-human animals tend to achieve this balance in a roughly normative manner. Specifically, when the sensory evidence strongly supports a particular option, decision-makers tend to choose that option independent of alternative expectations and preferences. Conversely, when the sensory evidence is weak, decision-makers tend to make more and faster choices to options with preferred, expected outcomes (Maddox and Bohil, 1998; Voss et al., 2004; Diederich and Busemeyer, 2006; Liston and Stone, 2008; Whiteley and Sahani, 2008; Feng et al., 2009; Summerfield and Koechlin, 2010; Teichert and Ferrera, 2010; Gao et al., 2011; Leite, 2012; Mulder et al., 2012; Blank et al., 2013; Fan et al., 2018; Waiblinger et al., 2019). However, exactly how and where in the brain the computations needed for these flexible decision processes are implemented is not well understood.

The observed patterns of choices and reaction times (RTs) for these kinds of tasks are often consistent with an accumulate-to-bound (drift-diffusion) decision process (Ratcliff, 1978; Maddox and Bohil, 1998; Gold and Shadlen, 2002; Voss et al., 2004; Bogacz et al., 2006; Diederich and Busemeyer, 2006; Bogacz, 2007; Feng et al., 2009; Simen et al., 2009; Krajbich et al., 2010; Summerfield and Koechlin, 2010; Gao et al., 2011; Leite, 2012; Mulder et al., 2012; Blank et al., 2013; Fan et al., 2018). Within this framework, asymmetric reward-choice associations (reward contexts) induce biases in the evidence-accumulation process, the decision bounds for different choice options, or both. The relative contributions of these different forms of reward context-dependent bias likely reflect specific adaptive strategies and can vary by task design, subject, and testing days (Fan et al., 2018).

How does single-neuron activity relate to the computations required for incorporating reward and visual information to form decisions? Previously we trained monkeys to perform an asymmetric-reward direction-discrimination task (Figure 1A), in which the monkeys report their perception of the global motion direction of a noisy stimulus with eye movements under different reward contexts. We showed that activity of some caudate neurons was sensitive to both reward context and motion stimulus. In addition, caudate microstimulation affected the monkeys’ reward biases in a manner that reflected coordinated changes in drift rates and relative bound heights in a drift-diffusion decision framework (Doi et al., 2020).

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To provide additional insights into the neural implementation of these decision computations in multiple brain regions, we examined both the caudate nucleus and one of its major cortical input sources, the frontal eye field (FEF) of the lateral prefrontal cortex. Neurons in these two regions contribute to perceptual and reward-based decision making along with reward-modulated motor performance (for a very limited sample, see Thompson et al., 1996; Thompson et al., 1997; Kawagoe et al., 1998; Kim and Shadlen, 1999; Freedman et al., 2001; Schall, 2001; Coe et al., 2002; Kobayashi et al., 2002; Lauwereyns et al., 2002b; Lauwereyns et al., 2002a; Roesch and Olson, 2003; Heekeren et al., 2004; Samejima et al., 2005; Ding and Hikosaka, 2006; Nakamura and Hikosaka, 2006a; Nakamura and Hikosaka, 2006b; Boettiger et al., 2007; Lau and Glimcher, 2007; Lau and Glimcher, 2008; Pan et al., 2008; Ferrera et al., 2009; Basten et al., 2010; Ding and Gold, 2010; Cai et al., 2011; Ding and Gold, 2012c; Ding and Gold, 2012a; Heitz and Schall, 2012; Seo et al., 2012; Kim and Hikosaka, 2013; Teichert et al., 2014; Yanike and Ferrera, 2014b; Ding, 2015; Hanks et al., 2015; Santacruz et al., 2017; Amemori et al., 2018; Schall, 2019). Functional imaging and modeling studies also suggest that the two regions are involved in complex decisions that balance visual evidence and reward expectation to guide appropriate movements (Rao, 2010; Summerfield and Koechlin, 2010; Chen et al., 2015). However, their specific computational roles in these decisions, represented at the single-neuron level, remain largely speculative.

In this study, we focused on three questions: (1) How do FEF and caudate neurons encode key task factors including choice, motion strength, reward context, and reaction times (RT)? (2) Do any of these task-related modulations in either brain area reflect the monkeys’ behaviorally derived biases in drift rates? (3) Do any of these task-related modulations in either brain area reflect the behaviorally derived biases in relative bound heights?

We recorded from 149 FEF neurons from three monkeys (n = 85, 24 and 40 from monkeys A, C and F, respectively) and, in separate sessions, from 140 caudate neurons from the same monkeys (n = 18, 49, and 73 from monkeys A, C and F, respectively) performing the asymmetric-reward direction-discrimination task. As we reported previously (Fan et al., 2018), the monkeys’ choices and RTs tended to reflect the strength (coherence) and direction of the visual motion stimulus but with a bias toward the large-reward option (Figure 1B,C).

Diverse task-relevant sensory and reward encoding in both brain regions

Individual neurons in both the FEF and caudate showed a diversity of task-driven responses (several examples are illustrated in Figure 2; population summaries are shown in Figure 2—figure supplement 1 and Figure 3). The FEF neuron in Figure 2A responded to choice target presentation with phasic (transient) and tonic (sustained) activation, showed a dip in activity after motion onset, then had gradually increasing activity (more for trials resulting in a contralateral choice) during motion viewing until a saccade-related burst for the contraversive saccade and a return to baseline activity for the other saccade. The FEF neuron in Figure 2B was activated after target onset, with higher activation when the contralateral choice was paired with large reward (red curves > green curves). This modulation by reward context persisted during a gradual ramp in activity during motion viewing (more for trials with the contralateral choice and for blocks when the contralateral choice was paired with large reward; t-test for H₀: regression coefficient for reward context = 0, p<0.05 for all epochs 1–8). This neuron also showed a saccade-related burst for the contraversive saccade. The FEF neuron in Figure 2C showed phasic activation by choice targets and motion onset, with activity that decreased during motion viewing, more gradually for the contralateral choice and higher coherences (compare curves with different shades), until reaching a saccade-related suppression.

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The caudate neuron in Figure 2D did not respond to target onset but was activated after motion onset, with higher activity for the contralateral choice, at higher coherence, and in blocks when the contralateral choice was paired with large reward. These coherence and reward-context modulations persisted through saccade onset, with no convergence before saccade onset. The caudate neuron in Figure 2E also did not respond to target onset but was activated after motion onset for both choices, with a preference for ipsilateral choices that were paired with the small reward. After an initial large activation, this neuron gradually reduced firing toward saccade onset, largely maintaining reward-context and coherence modulation until after saccade onset.

These diverse trends were apparent across the populations of recorded FEF and caudate neurons. Most neurons in our sample had responses that were modulated, on average, over multiple time points during each trial, albeit with differences across neurons and brain areas. FEF neurons typically had elevated responses that began just after target onset and then persisted through motion viewing until the saccadic response (Figure 2—figure supplement 1A; for most neurons, spike rate increased just after target onset). Caudate neurons typically did not respond strongly to target onset but then had elevated responses during motion viewing and through the saccadic response (Figure 2—figure supplement 1B). Both regions included neurons with activity that increased and/or decreased relative to baseline at various time points during each trial.

To assess how these responses were modulated by choice, coherence, reward context, expected reward size for a given choice, and reaction time (RT), we first used linear regression applied to neural data in pre-defined task epochs in Figure 1A (Figure 3A–D). Because neurons in both brain areas represent a coherence- and time-dependent decision process (Kim and Shadlen, 1999; Ding and Gold, 2010; Ding and Gold, 2012c) that can conflate the effects of those two factors on neural responses, and because RT was modulated strongly by reward context in the current task, we conducted two sets of analyses: (1) using all of the factors listed above including coherence but not RT (Figure 3A,B), and (2) using all of the factors listed above including normalized RT (normalized separately for each reward context x choice combination) but not coherence (Figure 3C,D).

These epoch-based analyses showed several differences between the FEF and caudate populations, including: (1) a larger fraction of FEF neurons showed choice selectivity around and after saccade onset (Figure 3A and C, first column); (2) although selectivity for reward context emerged before motion onset in both populations and persisted through a trial, a larger fraction of caudate neurons showed such selectivity during motion viewing (second column); (3) a larger fraction of caudate neurons showed selectivity for reward size (third column); and (4) both populations showed significant coherence selectivity after motion onset, but a larger fraction of caudate neurons remained coherence-selective after a saccade was made (Figure 3A, fourth column). The caudate, but not FEF, population showed above-chance fractions of neurons with joint modulation by both reward and motion coherence (Figure 3B). Activity in both regions was related to the RT in similar fashions (Figure 3C and D). The RT-based regression also captured a larger variance of activity than the coherence-based regression in both populations (t-test on the explained variance, p<0.0001 and p=0.007 for FEF and caudate, respectively).

To examine these task-related modulations at a finer time resolution, we applied the RT-based linear regression to neural data in sliding windows (Figure 3E–I). These analyses produced results that were consistent with the epoch-based analyses and showed further between-region differences in the timing and direction of task-related activity modulations. Selectivity for choice tended to increase during motion viewing until around the saccade, with stronger selectivity for contralateral/upward choices in both regions but particularly in the FEF (Figure 3E). Selectivity for reward context was evident before motion onset and continued toward saccade onset, with a mixture of preferences for the two reward contexts (Figure 3F). For FEF neurons, a higher fraction preferred the contralateral-Large Reward context before and during early motion viewing and similar fractions preferred either contexts before saccade onset. Although this was also true for the caudate population, the extent of the laterality was weaker. Selectivity for reward size, independent of the actual choice made, was most evident for data aligned to the saccade, with similar fractions of neurons of the caudate population preferring large or small reward (Figure 3G). Very few FEF neurons showed reward-size selectivity. Selectivity for RT was evident in both regions, with a dominant preference for short RTs associated with contralateral choices (Figure 3H) and mixed preferences otherwise (Figure 3I). These general patterns were present in the three monkeys for both FEF and caudate data (Figure 3—figure supplements 1–3).

To further characterize the modulation patterns, we applied the demixed principal component analysis (dPCA) method for the two populations (Kobak et al., 2016). Although our sample size was relatively small and trials were inherently unbalanced for different reward-choice-coherence combinations for this method, the dPCA results corroborated several findings from the multiple linear regression analysis (Figure 3—figure supplements 4–7), including: (1) choice-related components tended to account for a larger portion of variance in FEF activity than caudate activity (panels B and C in each figure, purple); (2) reward context-related components tended to account for a larger portion of variance in caudate activity than FEF activity (orange), particularly for around saccade onset; (3) coherence-related components tended to account for a larger portion of variance in FEF activity around motion onset than for activity around saccade onset, while the opposite was true for caudate activity (cyan); and (4) coherence and reward context or size interactions accounted for substantial variance for both regions (dark and light green). Collectively, these results indicated that neurons in both FEF and caudate represent a variety of task-relevant signals that could, in principle, support reward-biased perceptual decisions, but with different prevalences and preferences.

Predictions of the biased decision variable in a drift-diffusion framework

As we showed previously, these monkeys’ patterns of choices and RTs on this task were consistent with a drift-diffusion model (DDM; Fan et al., 2018). According to this model, a decision is formed when accumulated motion evidence reaches one of two pre-defined (collapsing) decision bounds (Figure 4A). The monkeys’ reward-driven biases arose from coordinated, reward context-dependent adjustments of the rate of accumulation (drift rate, which scales with motion coherence) and relative bound heights (Figure 4E; for more details see Fan et al., 2018). A bias in the drift rate (ΔDrift, corresponding to the me parameter in the DDM) can be implemented as a constant offset to the momentary motion evidence (Figure 4B). A bias in the relative bound heights (ΔBound, corresponding to the z parameter in DDM) can be implemented as an offset in the starting value of the accumulation process (Figure 4C), an asymmetry in the absolute bound heights for the two choices (Figure 4D), or a combination of the two.

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These different ways of implementing reward-driven biases correspond to different predictions of how and when the accumulating decision variable is modulated by reward context during a trial. Specifically, in the presence of a bias in the drift rate, the slope of the decision variable would be modulated by reward context during evidence viewing. In the presence of a bias in the starting value, the baseline value of the decision variable would be modulated by reward context before evidence onset. In the example in Figure 4C, the baseline value would be higher when the reward bias favors the upper-bound choice. In the presence of a bias in the bound heights, the decision variable would be modulated by reward context at the time of decision commitment. In the example in Figure 4D, the ending value would be closer to the starting point of the evidence accumulation when the reward bias favors the upper-bound choice.

We examined whether FEF and caudate activity conform to these predictions. Given the asymmetric effects of these predicted biases on the two choices, we focused on neurons with reliable choice selectivity (Figure 5) and present results from other neurons as supplements when appropriate. The ‘choice-selective’ neurons were identified as showing significant and consistent choice modulation through motion viewing (epoch ^#5 in Figure 1) and before saccade onset (epoch ^#6), based on RT-based regression analysis results shown in Figure 3. The numbers of neurons meeting these criteria for the three monkeys are shown in Table 1. The average activity of choice-selective and other neurons is shown in Figure 5. Note that because different coherence levels were used for the three monkeys, we grouped the trials by quintiles of RT for these plots.

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Table 1

	FEF			Caudate
	Monkey A	Monkey C	Monkey F	Monkey A	Monkey C	Monkey F
Total	85	24	40	18	49	73
Consistently choice-selective	35	14	7	6	31	21
	41%	58%	18%	33%	63%	29%
Coherence-modulated slope of firing rate during motion viewing	44	12	26	10	33	41
	52%	50%	65%	56%	67%	56%
Reward context-modulated slope of firing rate during motion viewing	21	5	3	2	9	15
	25%	21%	8%	11%	18%	21%
Reward context-modulated activity before motion onset	38	12	19	15	34	39
	45%	50%	48%	83%	69%	53%
Reward context-modulated activity just before saccade onset	51	16	26	15	34	49
	60%	67%	65%	83%	69%	67%

Even using these common criteria for categorization, the average activity patterns of neurons in the same category differed for FEF and caudate. First, consider the choice-selective subpopulations. Whereas choice-selective FEF activity appeared to be roughly consistent with bound-crossing in the DDM (i.e., reaching a fixed level of activity at the end of the decision process and before saccade onset, regardless of the time it took to reach the decision), choice-selective caudate activity did not (Ding and Gold, 2010; Ding and Gold, 2012b). For trials in which the monkey made the preferred choice of the given neuron, the slope of FEF activity during motion viewing appeared to show more separations than the slope of caudate activity, between reward contexts and RT groups. The baseline FEF activity before motion onset appeared to differ more between reward contexts. The peri-saccade activity for the preferred choice appeared to show opposite selectivity for reward context in the two regions (the purple curves tended to be above and below the green curves for FEF and caudate, respectively).

Second, in the other subpopulations that did not exhibit consistent choice selectivity, the average caudate activity appeared to maintain RT separation through saccade generation and onward, whereas the average FEF activity appeared to converge around saccade onset (Figure 5B). These apparent differences suggest that activity in the two regions may relate differently to the predictions of the DDM, which we examine in more detail below.

FEF activity reflected behaviorally derived reward-driven drift-rate biases

We first examined whether FEF and caudate activity reflected evidence accumulation with a reward-driven bias in the drift rate. As illustrated in Figure 4B, such a signal is expected to show two features in neural activity. First, the rate of accumulation depends on motion coherence. For individual neurons, this dependence translates to motion-coherence modulation of the slope of firing rates during motion viewing. Figure 6A and B illustrate our procedure for estimating the slope of change in firing rates and its modulation by coherence, reward context, and their interaction. Second, the reward-context modulation of the slope of change reflects the behavioral reward bias in drift rate. In the model, the reward bias in drift rates is independent of coherence and the drift-rate scaling and dependent only on reward context. The corresponding modulation of the (slope of) activity of individual neurons is thus by reward context alone and not by the reward context-coherence interaction. Because neurons showed substantial variations in their firing-rate ranges, we used each neuron’s modulation by coherence to normalize its modulation by reward context. The second expectation thus translates to a correlation between this normalized quantity and the behaviorally estimated bias in drift rate across neurons/sessions.

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We found that many neurons in both regions showed motion-coherence modulation of the slope of firing rates (Figure 6, Table 1), consistent with an involvement of both regions in evidence accumulation (Ding and Gold, 2010; Ding and Gold, 2012b). For choice-selective FEF neurons, the slope of change tended to be greater for higher coherence for trials with the neurons’ preferred choices (i.e., positive coefficients; Figure 6C). Many FEF neurons also showed opposite modulation for trials with the null choices, but these effects were inconsistent, reflecting the lower reliability in estimating slope values from low firing rates. For choice-selective caudate neurons, the slope of change did not show a consistent relationship with coherence for either the preferred or null choices. The overall magnitude of the coefficients tended to be smaller for caudate neurons, reflecting the lower firing rates of caudate neurons.

FEF activity also aligned closely with the monkeys’ reward-driven bias in drift rates across sessions and monkeys. All three monkeys tended to use positive reward biases in drift rates; that is toward the large-reward choice (Figure 4E and Figure 6D). The ratios of regression coefficients for reward context and coherence also tended to be positive for choice-selective neurons in the FEF (Figure 6D, top row). Moreover, there was a significant correlation between the monkeys’ behavioral bias in drift rates and the ratio measured from neural data (Pearson’s correlation coefficient: 0.55 and 0.48, p=0.0084 and 0.0077, for activity aligned to motion and saccade onset, respectively). As expected given the smaller sample sizes, none of the per-monkey results was statistically significant (Figure 6—figure supplement 2). These results indicated a close relationship between FEF neurons and the neural implementation of reward biases in drift rates assessed across monkeys. In the caudate sample, the behavioral bias was mostly positive, but the neural ratio was more mixed, and the two measurements did not exhibit a significant positive correlation (Figure 6D, bottom row; note that there was a significant negative correlation for caudate activity aligned to motion onset, correlation coefficient: −0.38, p=0.032). These results appeared inconsistent with a direct involvement of caudate neurons in implementing the reward bias in drift rates (see Discussion for a potential sampling bias).

Although the DDM does not provide predictions for neurons without consistent choice selectivity, these neurons may participate in the other aspects of decision-making, such as decision evaluation, that also uses information about the reward biases. We performed the same analysis for these neurons. In the FEF, the reward-context modulation of the slope of the firing rates also covaried with the monkeys’ reward biases for activity aligned to motion onset, regardless of choices (Figure 6—figure supplement 1C; correlation coefficients: 0.55 and 0.38, p=0.003 and 0.018, for contralateral and ipsilateral choices, respectively). There was also a significant correlation for these not-choice-selective neurons in the caudate sample, for activity aligned to saccade onset in trials with ipsilateral choices (Figure 6—figure supplement 1D; correlation coefficient: 0.37, p=0.0095). Thus, FEF and caudate neurons might carry information about reward biases in drift rates for computations that are not directly related to decision formation.

In addition, a small number of neurons showed significant modulation of the slope of firing rates by the reward context-coherence interaction. In the DDM, such a modulation may relate to reward context-dependent changes in the scaling factor, k. However, the small sample size precluded the detection of any such relationship (data not shown).

Reward context-modulated baseline activity was inconsistent with reward biases in relative bound heights

As we showed above, reward-driven biases in the relative bound heights of the DDM, ‘bound bias’ in short, can, in principle, be implemented as an offset to the beginning of the accumulation process (Figure 4C), an offset to the end of the accumulation process (Figure 4D), or the combined effects of the two. Neural activity reflecting such biases is expected to show three features. First, the neural activity should be sensitive to reward context. Second, the sign of its reward-context modulation should be congruent with the reward bias. For example, if the monkey uses the bound bias to favor the large-reward choice, when its preferred choice is paired with the large reward, then the neuron should increase its baseline firing before motion onset (as an offset to the beginning of the accumulation) or decrease its firing before saccade onset (as an offset to the end of the accumulation). Third, in consideration of our lack of knowledge of whether a neuron provides an excitatory or inhibitory role in the decision network, we can relax our expectation for sign congruency . However, we may still expect that, on trials when the reward-context modulation of neural activity is strong, the monkey uses a larger bound bias. We tested these predictions on choice-selective neurons. Note that similar predictions cannot be specified for neurons without choice selectivity.

We found that many choice-selective neurons showed reward context-modulated activity before motion onset and/or before saccade onset in both regions (Figure 7A–D). We assessed the reward context modulation in running windows covering two time periods around motion onset and before saccade, respectively. Figure 7A–D shows heatmaps of regression coefficients for reward context using Equation 3 (same as Figure 3F, but only for choice-selective neurons with significant non-zero values in any time bins). A quick glance suggested that FEF neurons tended to show positive coefficients before motion onset (Figure 7A; warm colors: higher activity when the neuron’s preferred choice was paired with large reward). The coefficients were more mixed in signs for FEF activity before saccade onset (Figure 7C) and for caudate activity (Figure 7B and D).

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In contrast to the second expectation above, the signs of coefficients were not consistently congruent with the monkeys’ behavioral bound biases. As illustrated by the color bars at the right of each panel, the monkeys tended to use negative bound biases (favoring the small-reward choice). If the neural activity reflected such biases, the heatmap should be dominated by cool colors for activity before motion onset and by warm colors for activity before saccade onset. This appeared not to be the case. We quantified the fraction of congruent sessions (Figure 7E–H). The only time points with fractions that differed significantly from chance suggested incongruent neural modulation for FEF activity before motion onset (Chi-square test, p=0.05, uncorrected for multiple comparisons to reduce false negatives). We also performed running regression with coherence-based regressors (Equation 2) and observed a similar lack of congruent modulation (Figure 7—figure supplement 1).

In addition to the discrepancy in signs, the magnitude of the reward-context modulation in neural activity also did not co-vary with the magnitude of the reward bias in bound heights in either region. For each neuron with significant reward-context modulation in their activity before motion onset (epoch #3 in Figure 1), we split the trials into two groups with larger and smaller differences in activity between reward contexts, respectively (Figure 8A). If the activity reflects the bound bias, we expected the former group to show a larger bound bias. We fitted the DDM to these two groups of trials and found no consistent difference in their bound biases in either brain region (Figure 8B and C). These results suggested that the reward context-modulated baseline activity in choice-selective FEF and caudate neurons did not directly reflect the eventual bound bias that the monkeys used.

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Using a task with manipulations of visual stimuli and reward-choice associations, we found that a substantial fraction of neurons in both FEF and caudate were sensitive to both stimulus properties and the reward-choice association. Despite these coarse similarities, we also identified inter-regional differences in the prevalence and distribution of lateralized modulation by choice and reward context, reminiscent of previous results using tasks with either stimulus or reward-context manipulations (Ding and Hikosaka, 2006; Kobayashi et al., 2007; Ding and Gold, 2010; Ding and Gold, 2012c). For choice-selective FEF neurons, their average activity profile followed an accumulation-to-bound-like pattern (Thompson et al., 1996; Thompson et al., 1997; Kim and Shadlen, 1999; Purcell et al., 2010; Ding and Gold, 2012c). Their reward-context modulations were consistent with predictions of a reward-driven bias in drift rates, but not a reward-driven bias in relative bound heights. For choice-selective caudate neurons, their average activity profile was consistent with evidence accumulation, but not bound crossing (Ding and Gold, 2010). Their reward-context modulations did not show a consistent link with either form of reward-driven biases. These differences suggest that the two regions have distinct roles in implementing the computations required for this task.

The closer link between FEF activity with biases in drift rate versus bound heights may appear to be at odds with previous results implicating a more prominent role of the FEF and its rodent homolog in transforming the accumulated evidence into a categorical choice than in the evidence-accumulation process itself (Freedman et al., 2001; Ferrera et al., 2009; Hanks et al., 2015). However, these roles likely reflect the specific task and the subjects’ strategy for performing that task. For example, for tasks involving manipulations of category definitions, FEF activity showed strong correlates of decision rules (Freedman et al., 2001; Ferrera et al., 2009). For our task, the category definitions remained constant and monkeys tended to use consistent changes in drift rates to favor the large-reward choice and variable changes in relative bound heights that can favor the large- or small-reward choice, depending on the monkey and daily session (Fan et al., 2018). The propensity of FEF neurons to encode the changes in drift rates may thus reflect the relative importance of those particular biases to the decision process.

FEF shares many response properties with the lateral intraparietal area (LIP), particularly for decisions based on random-dot motion stimulus (e.g., Shadlen and Newsome, 1996; Kim and Shadlen, 1999; Roitman and Shadlen, 2002; Ding and Gold, 2012c; Meister et al., 2013). Interestingly, a previous study of monkey LIP activity for an asymmetric-reward motion discrimination task showed opposite relationships with behavioral reward biases than what we found for FEF (Rorie et al., 2010): LIP activity was consistent with an involvement in reward-biased bound heights but not drift rates. The contrasts between that study and ours suggest two possible interpretations. One possibility is that LIP and FEF perform complementary roles by implementing reward biases in relative bound heights and drift rate, respectively. Another possibility is that the two regions share similar roles, and the apparent differences from the two studies reflect differences in their task designs. Rorie and colleagues used a substantially different task design from ours, including experimenter- versus subject-controlled motion viewing and trial- versus block-wise manipulations of reward contexts. In principle, these differences could influence not only what strategy monkeys use, but also which brain regions are employed to implement the required computations through training. A direct comparison between LIP and FEF neurons in the same monkeys performing the same decision task would help disambiguate these possibilities.

The lack of a consistent link between caudate activity before motion onset and reward bias in relative bound heights was surprising. Previous studies using tasks with reward manipulations and salient visual stimuli showed shared time courses between caudate activity before target onset and reward biases in RT during reward-context transitions (Lauwereyns et al., 2002a) and with manipulations of the timing of target onset (Ding and Hikosaka, 2007), as well as trial-by-trial correlations between caudate activity and action values estimated from monkeys’ reward biases (Lau and Glimcher, 2008). We also showed previously that caudate microstimulation evoked changes in relative bound for a symmetric-reward motion discrimination task (i.e., without reward-driven biases; Ding and Gold, 2012a). These previous results naturally led to the hypothesis that the caudate activity preceding stimulus presentation helps to bias the starting value for evidence accumulation. Our negative results here imply that, for this task, caudate activity does not directly set the starting value. This finding is also consistent with our observation that, for the same asymmetric-reward motion discrimination task, caudate microstimulation did not cause consistent changes in bound biases (Doi et al., 2020). Taken together, we hypothesize that the caudate nucleus does not directly implement the bound bias, but rather coordinates the bound bias with the bias in drift rate. For simple tasks in which bound biases alone are sufficient, caudate activity appears to be directly correlated with bound bias. For complex tasks in which additional computations are involved, such a correlation could be substantially weakened.

For the lack of a relationship between caudate activity and the bias in drift rate, a caveat needs to be considered. To detect a correlation between neural activity and the reward bias in drift rates (Figure 6), it requires a sample that is large enough and, equally as important, with sufficient variations in behavioral biases. For practical reasons, our caudate samples were mostly from two monkeys (C and F) that tended to use smaller and less variable drift-rate biases across sessions than the other monkey (A). The more restricted ranges of drift-rate bias might have biased our results. Nevertheless, the negative correlation we observed in Figure 6D is consistent with our previous demonstration that caudate microstimulation tended to scale down reward biases in drift rates (Doi et al., 2020).

Besides decision formation, both FEF and the caudate nucleus have other hypothesized decision-related roles, including performance monitoring (Ding and Gold, 2010; Ding and Gold, 2012c; Ding and Gold, 2013; Teichert et al., 2014; Yanike and Ferrera, 2014a). As we showed, reward context-modulated neural activity was present in a substantial fraction of neurons that were not consistently selective for choice. These activity patterns were sensitive to reward biases in drift rates during motion viewing (Figure 6—figure supplement 1) or in the baseline firing before motion onset and saccade onset (Figure 7—figure supplement 2). In addition, some choice-selective neurons showed negative ratios of reward context and coherence coefficients (Figure 6D), which are not consistent with the decision variable predicted by the DDM but could reflect a choice confidence signal instead. It would be interesting to investigate further the exact functional roles of these activity patterns for solving decision-making tasks.

For our task, neither FEF nor caudate activity represented the full, latent decision variable as predicted in the DDM framework. For example, in addition to the disconnect between bound bias and reward context-modulated baseline activity, the example FEF neuron in Figure 6A showed a strong modulation by the coherence-reward context interaction, which was not predicted by the DDM. A striking observation for FEF was the relatively consistently opposite signs in the reward bias in bound heights and the reward-context modulation of pre-motion baseline activity in choice-selective neurons. This finding raises several possibilities, including: (1) the DDM framework does not accurately capture the monkeys’ decision-related computations; (2) the reward-context modulation of pre-motion baseline activity contributes to the reward bias in bound heights through an intermediary, sign-reversing mechanism; and/or (3) such activity does not contribute to the reward bias in bound heights. Relevant to the first possibility, we previously fitted the monkeys’ performance using two model variants (fixed-bound and collapsing-bound) and two fitting procedures (Hierarchical DDM using MCMC sampling and single-session DDM fits with multiple runs using maximum a posteriori) (Fan et al., 2018; Doi et al., 2020). These different ways of model fitting resulted in similar patterns of the signs of reward biases in bound heights and drift rates. These data argued against gross inaccuracy in DDM fits of reward bias in bound heights, but it remains to be tested whether a non-DDM framework could capture the monkey’s performance and predict modulations of decision variables more in line with those observed in FEF activity.

Many other brain regions undoubtedly contribute to decisions that combine sensory and reward information. These regions likely include the lateral intraparietal area (LIP), superior colliculus, and the premotor cortex in monkeys, each of which has been shown to represent the basic patterns of activity that are reminiscent of an accumulation-to-bound decision process (Roitman and Shadlen, 2002; Ratcliff et al., 2003; Thura and Cisek, 2016). Even more regions have shown reward-context modulated pre-stimulus activity and saccade-related activity in monkeys performing tasks with reward manipulations and salient visual stimuli, including those areas and many other nuclei in the basal ganglia (Platt and Glimcher, 1999; Coe et al., 2002; Lauwereyns et al., 2002a; Sato and Hikosaka, 2002; Ikeda and Hikosaka, 2003; Roesch and Olson, 2003; Isoda and Hikosaka, 2008). Reward manipulations also likely affect the sensory representation itself, leading to biased drift rates (Cicmil et al., 2015). More studies like ours that directly compare neural activity in different brain regions under the same task conditions are needed to better understand their overlapping and distinct roles in these kinds of decisions. A particularly intriguing target of such studies would be the superior colliculus, because of its convergent inputs from FEF, LIP, and the basal ganglia, as well as its well-documented roles in attentional control that are likely closely related to reward modulation (Krauzlis et al., 2018). The distribution of neural representations of biases in drift rates and relative bound heights would also help us understand the dissociated effects of Parkinson’s Disease on these two forms of bias (Perugini et al., 2016).

To summarize, FEF and caudate activity showed modulations by choice, reward context, and visual stimulus strength, in monkeys that combined reward context and visual input into categorical saccade choices. These two regions shared certain features in their activity, but also showed distinct patterns that implicated their different roles in complex decision making. It would be interesting to examine how the relative contributions of FEF and caudate neurons develop over training (Antzoulatos and Miller, 2011; Seo et al., 2012) and with induced changes in the subjects’ reward bias strategy.

Data used for this manuscript are included as supporting files.

1. 1. Amemori KI
   2. Amemori S
   3. Gibson DJ
   4. Graybiel AM

   (2018) Striatal microstimulation induces persistent and repetitive negative Decision-Making predicted by striatal Beta-Band oscillation

   Neuron 99:829–841.

   https://doi.org/10.1016/j.neuron.2018.07.022

   • PubMed
   • Google Scholar
2. 1. Antzoulatos EG
   2. Miller EK

   (2011) Differences between neural activity in prefrontal cortex and striatum during learning of novel abstract categories

   Neuron 71:243–249.

   https://doi.org/10.1016/j.neuron.2011.05.040

   • PubMed
   • Google Scholar
3. 1. Basten U
   2. Biele G
   3. Heekeren HR
   4. Fiebach CJ

   (2010) How the brain integrates costs and benefits during decision making

   PNAS 107:21767–21772.

   https://doi.org/10.1073/pnas.0908104107

   • PubMed
   • Google Scholar
4. 1. Blank H
   2. Biele G
   3. Heekeren HR
   4. Philiastides MG

   (2013) Temporal characteristics of the influence of punishment on perceptual decision making in the human brain

   Journal of Neuroscience 33:3939–3952.

   https://doi.org/10.1523/JNEUROSCI.4151-12.2013

   • PubMed
   • Google Scholar
5. 1. Boettiger CA
   2. Mitchell JM
   3. Tavares VC
   4. Robertson M
   5. Joslyn G
   6. D'Esposito M
   7. Fields HL

   (2007) Immediate reward Bias in humans: fronto-parietal networks and a role for the catechol-O-methyltransferase 158(Val/Val) genotype

   Journal of Neuroscience 27:14383–14391.

   https://doi.org/10.1523/JNEUROSCI.2551-07.2007

   • PubMed
   • Google Scholar
6. 1. Bogacz R
   2. Brown E
   3. Moehlis J
   4. Holmes P
   5. Cohen JD

   (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks

   Psychological Review 113:700–765.

   https://doi.org/10.1037/0033-295X.113.4.700

   • PubMed
   • Google Scholar
7. 1. Bogacz R

   (2007) Optimal decision-making theories: linking neurobiology with behaviour

   Trends in Cognitive Sciences 11:118–125.

   https://doi.org/10.1016/j.tics.2006.12.006

   • PubMed
   • Google Scholar
8. 1. Bruce CJ
   2. Goldberg ME

   (1985) Primate frontal eye fields. I. single neurons discharging before saccades

   Journal of Neurophysiology 53:603–635.

   https://doi.org/10.1152/jn.1985.53.3.603

   • PubMed
   • Google Scholar
9. 1. Cai X
   2. Kim S
   3. Lee D

   (2011) Heterogeneous coding of temporally discounted values in the dorsal and ventral striatum during intertemporal choice

   Neuron 69:170–182.

   https://doi.org/10.1016/j.neuron.2010.11.041

   • PubMed
   • Google Scholar
10. 1. Chen MY
    2. Jimura K
    3. White CN
    4. Maddox WT
    5. Poldrack RA

    (2015) Multiple brain networks contribute to the acquisition of Bias in perceptual decision-making

    Frontiers in Neuroscience 9:63.

    https://doi.org/10.3389/fnins.2015.00063

    • PubMed
    • Google Scholar
11. 1. Cicmil N
    2. Cumming BG
    3. Parker AJ
    4. Krug K

    (2015) Reward modulates the effect of visual cortical microstimulation on perceptual decisions

    eLife 4:e07832.

    https://doi.org/10.7554/eLife.07832

    • PubMed
    • Google Scholar
12. 1. Coe B
    2. Tomihara K
    3. Matsuzawa M
    4. Hikosaka O

    (2002) Visual and anticipatory Bias in three cortical eye fields of the monkey during an adaptive decision-making task

    The Journal of Neuroscience 22:5081–5090.

    https://doi.org/10.1523/JNEUROSCI.22-12-05081.2002

    • PubMed
    • Google Scholar
13. 1. Diederich A
    2. Busemeyer JR

    (2006) Modeling the effects of payoff on response Bias in a perceptual discrimination task: bound-change, drift-rate-change, or two-stage-processing hypothesis

    Perception & Psychophysics 68:194–207.

    https://doi.org/10.3758/BF03193669

    • PubMed
    • Google Scholar
14. 1. Ding L

    (2015) Distinct dynamics of ramping activity in the frontal cortex and caudate nucleus in monkeys

    Journal of Neurophysiology 114:1850–1861.

    https://doi.org/10.1152/jn.00395.2015

    • PubMed
    • Google Scholar
15. 1. Ding L
    2. Gold JI

    (2010) Caudate encodes multiple computations for perceptual decisions

    Journal of Neuroscience 30:15747–15759.

    https://doi.org/10.1523/JNEUROSCI.2894-10.2010

    • PubMed
    • Google Scholar
16. 1. Ding L
    2. Gold JI

    (2012a) Separate, causal roles of the caudate in Saccadic choice and execution in a perceptual decision task

    Neuron 75:865–874.

    https://doi.org/10.1016/j.neuron.2012.07.021

    • PubMed
    • Google Scholar
17. Book

    1. Ding L
    2. Gold JI

    (2012b)

    Neural Correlates of Perceptual Decisions That Incorporate Asymmetric Reward Information

    Society for Neuroscience.

    • Google Scholar
18. 1. Ding L
    2. Gold JI

    (2012c) Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field

    Cerebral Cortex 22:1052–1067.

    https://doi.org/10.1093/cercor/bhr178

    • PubMed
    • Google Scholar
19. 1. Ding L
    2. Gold JI

    (2013) The basal ganglia's contributions to perceptual decision making

    Neuron 79:640–649.

    https://doi.org/10.1016/j.neuron.2013.07.042

    • PubMed
    • Google Scholar
20. 1. Ding L
    2. Hikosaka O

    (2006) Comparison of reward modulation in the frontal eye field and caudate of the macaque

    Journal of Neuroscience 26:6695–6703.

    https://doi.org/10.1523/JNEUROSCI.0836-06.2006

    • PubMed
    • Google Scholar
21. 1. Ding L
    2. Hikosaka O

    (2007) Temporal development of asymmetric reward-induced Bias in macaques

    Journal of Neurophysiology 97:57–61.

    https://doi.org/10.1152/jn.00902.2006

    • PubMed
    • Google Scholar
22. 1. Doi T
    2. Fan Y
    3. Gold JI
    4. Ding L

    (2020) The caudate nucleus contributes causally to decisions that balance reward and uncertain visual information

    eLife 9:e56694.

    https://doi.org/10.7554/eLife.56694

    • PubMed
    • Google Scholar
23. 1. Elber-Dorozko L
    2. Loewenstein Y

    (2018) Striatal action-value neurons reconsidered

    eLife 7:e34248.

    https://doi.org/10.7554/eLife.34248

    • PubMed
    • Google Scholar
24. 1. Fan Y
    2. Gold JI
    3. Ding L

    (2018) Ongoing, rational calibration of reward-driven perceptual biases

    eLife 7:e36018.

    https://doi.org/10.7554/eLife.36018

    • PubMed
    • Google Scholar
25. 1. Feng S
    2. Holmes P
    3. Rorie A
    4. Newsome WT

    (2009) Can monkeys choose optimally when faced with noisy stimuli and unequal rewards?

    PLOS Computational Biology 5:e1000284.

    https://doi.org/10.1371/journal.pcbi.1000284

    • PubMed
    • Google Scholar
26. 1. Ferrera VP
    2. Yanike M
    3. Cassanello C

    (2009) Frontal eye field neurons signal changes in decision criteria

    Nature Neuroscience 12:1458–1462.

    https://doi.org/10.1038/nn.2434

    • PubMed
    • Google Scholar
27. 1. Freedman DJ
    2. Riesenhuber M
    3. Poggio T
    4. Miller EK

    (2001) Categorical representation of visual stimuli in the primate prefrontal cortex

    Science 291:312–316.

    https://doi.org/10.1126/science.291.5502.312

    • PubMed
    • Google Scholar
28. 1. Gao J
    2. Tortell R
    3. McClelland JL

    (2011) Dynamic integration of reward and stimulus information in perceptual decision-making

    PLOS ONE 6:e16749.

    https://doi.org/10.1371/journal.pone.0016749

    • PubMed
    • Google Scholar
29. 1. Gold JI
    2. Shadlen MN

    (2002) Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward

    Neuron 36:299–308.

    https://doi.org/10.1016/s0896-6273(02)00971-6

    • PubMed
    • Google Scholar
30. 1. Hanks TD
    2. Kopec CD
    3. Brunton BW
    4. Duan CA
    5. Erlich JC
    6. Brody CD

    (2015) Distinct relationships of parietal and prefrontal cortices to evidence accumulation

    Nature 520:220–223.

    https://doi.org/10.1038/nature14066

    • PubMed
    • Google Scholar
31. 1. Heekeren HR
    2. Marrett S
    3. Bandettini PA
    4. Ungerleider LG

    (2004) A general mechanism for perceptual decision-making in the human brain

    Nature 431:859–862.

    https://doi.org/10.1038/nature02966

    • PubMed
    • Google Scholar
32. 1. Heitz RP
    2. Schall JD

    (2012) Neural mechanisms of speed-accuracy tradeoff

    Neuron 76:616–628.

    https://doi.org/10.1016/j.neuron.2012.08.030

    • PubMed
    • Google Scholar
33. 1. Ikeda T
    2. Hikosaka O

    (2003) Reward-dependent gain and Bias of visual responses in primate superior colliculus

    Neuron 39:693–700.

    https://doi.org/10.1016/S0896-6273(03)00464-1

    • PubMed
    • Google Scholar
34. 1. Isoda M
    2. Hikosaka O

    (2008) A neural correlate of motivational conflict in the superior colliculus of the macaque

    Journal of Neurophysiology 100:1332–1342.

    https://doi.org/10.1152/jn.90275.2008

    • PubMed
    • Google Scholar
35. 1. Kawagoe R
    2. Takikawa Y
    3. Hikosaka O

    (1998) Expectation of reward modulates cognitive signals in the basal ganglia

    Nature Neuroscience 1:411–416.

    https://doi.org/10.1038/1625

    • PubMed
    • Google Scholar
36. 1. Kim HF
    2. Hikosaka O

    (2013) Distinct basal ganglia circuits controlling behaviors guided by flexible and stable values

    Neuron 79:1001–1010.

    https://doi.org/10.1016/j.neuron.2013.06.044

    • PubMed
    • Google Scholar
37. 1. Kim JN
    2. Shadlen MN

    (1999) Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque

    Nature Neuroscience 2:176–185.

    https://doi.org/10.1038/5739

    • PubMed
    • Google Scholar
38. 1. Kleiner M

    (2007)

    What’s new in Psychtoolbox-3?

    Perception 36:1–16.

    • Google Scholar
39. 1. Kobak D
    2. Brendel W
    3. Constantinidis C
    4. Feierstein CE
    5. Kepecs A
    6. Mainen ZF
    7. Qi XL
    8. Romo R
    9. Uchida N
    10. Machens CK

    (2016) Demixed principal component analysis of neural population data

    eLife 5:e10989.

    https://doi.org/10.7554/eLife.10989

    • PubMed
    • Google Scholar
40. 1. Kobayashi S
    2. Lauwereyns J
    3. Koizumi M
    4. Sakagami M
    5. Hikosaka O

    (2002) Influence of reward expectation on visuospatial processing in macaque lateral prefrontal cortex

    Journal of Neurophysiology 87:1488–1498.

    https://doi.org/10.1152/jn.00472.2001

    • Google Scholar
41. 1. Kobayashi S
    2. Kawagoe R
    3. Takikawa Y
    4. Koizumi M
    5. Sakagami M
    6. Hikosaka O

    (2007) Functional differences between macaque prefrontal cortex and caudate nucleus during eye movements with and without reward

    Experimental Brain Research 176:341–355.

    https://doi.org/10.1007/s00221-006-0622-4

    • PubMed
    • Google Scholar
42. 1. Krajbich I
    2. Armel C
    3. Rangel A

    (2010) Visual fixations and the computation and comparison of value in simple choice

    Nature Neuroscience 13:1292–1298.

    https://doi.org/10.1038/nn.2635

    • PubMed
    • Google Scholar
43. 1. Krauzlis RJ
    2. Bogadhi AR
    3. Herman JP
    4. Bollimunta A

    (2018) Selective attention without a neocortex

    Cortex 102:161–175.

    https://doi.org/10.1016/j.cortex.2017.08.026

    • PubMed
    • Google Scholar
44. 1. Lau B
    2. Glimcher PW

    (2007) Action and outcome encoding in the primate caudate nucleus

    Journal of Neuroscience 27:14502–14514.

    https://doi.org/10.1523/JNEUROSCI.3060-07.2007

    • PubMed
    • Google Scholar
45. 1. Lau B
    2. Glimcher PW

    (2008) Value representations in the primate striatum during Matching Behavior

    Neuron 58:451–463.

    https://doi.org/10.1016/j.neuron.2008.02.021

    • Google Scholar
46. 1. Lauwereyns J
    2. Watanabe K
    3. Coe B
    4. Hikosaka O

    (2002a) A neural correlate of response Bias in monkey caudate nucleus

    Nature 418:413–417.

    https://doi.org/10.1038/nature00892

    • PubMed
    • Google Scholar
47. 1. Lauwereyns J
    2. Takikawa Y
    3. Kawagoe R
    4. Kobayashi S
    5. Koizumi M
    6. Coe B
    7. Sakagami M
    8. Hikosaka O

    (2002b) Feature-based anticipation of cues that predict reward in monkey caudate nucleus

    Neuron 33:463–473.

    https://doi.org/10.1016/S0896-6273(02)00571-8

    • PubMed
    • Google Scholar
48. 1. Leite FP

    (2012) A comparison of two diffusion process models in accounting for payoff and stimulus frequency manipulations

    Attention, Perception, & Psychophysics 74:1366–1382.

    https://doi.org/10.3758/s13414-012-0321-0

    • PubMed
    • Google Scholar
49. 1. Liston DB
    2. Stone LS

    (2008) Effects of prior information and reward on oculomotor and perceptual choices

    Journal of Neuroscience 28:13866–13875.

    https://doi.org/10.1523/JNEUROSCI.3120-08.2008

    • PubMed
    • Google Scholar
50. 1. Maddox WT
    2. Bohil CJ

    (1998) Base-rate and payoff effects in multidimensional perceptual categorization

    Journal of Experimental Psychology: Learning, Memory, and Cognition 24:1459–1482.

    https://doi.org/10.1037/0278-7393.24.6.1459

    • Google Scholar
51. 1. Meister ML
    2. Hennig JA
    3. Huk AC

    (2013) Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making

    Journal of Neuroscience 33:2254–2267.

    https://doi.org/10.1523/JNEUROSCI.2984-12.2013

    • PubMed
    • Google Scholar
52. 1. Mulder MJ
    2. Wagenmakers EJ
    3. Ratcliff R
    4. Boekel W
    5. Forstmann BU

    (2012) Bias in the brain: a diffusion model analysis of prior probability and potential payoff

    Journal of Neuroscience 32:2335–2343.

    https://doi.org/10.1523/JNEUROSCI.4156-11.2012

    • PubMed
    • Google Scholar
53. 1. Nakamura K
    2. Hikosaka O

    (2006a) Facilitation of saccadic eye movements by postsaccadic electrical stimulation in the primate caudate

    Journal of Neuroscience 26:12885–12895.

    https://doi.org/10.1523/JNEUROSCI.3688-06.2006

    • PubMed
    • Google Scholar
54. 1. Nakamura K
    2. Hikosaka O

    (2006b) Role of dopamine in the primate caudate nucleus in reward modulation of saccades

    Journal of Neuroscience 26:5360–5369.

    https://doi.org/10.1523/JNEUROSCI.4853-05.2006

    • PubMed
    • Google Scholar
55. 1. Pan X
    2. Sawa K
    3. Tsuda I
    4. Tsukada M
    5. Sakagami M

    (2008) Reward prediction based on stimulus categorization in primate lateral prefrontal cortex

    Nature Neuroscience 11:703–712.

    https://doi.org/10.1038/nn.2128

    • PubMed
    • Google Scholar
56. 1. Pelli DG

    (1997) The VideoToolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision 10:437–442.

    https://doi.org/10.1163/156856897X00366

    • PubMed
    • Google Scholar
57. 1. Perugini A
    2. Ditterich J
    3. Basso MA

    (2016) Patients with Parkinson's Disease Show Impaired Use of Priors in Conditions of Sensory Uncertainty

    Current Biology 26:1902–1910.

    https://doi.org/10.1016/j.cub.2016.05.039

    • PubMed
    • Google Scholar
58. 1. Platt ML
    2. Glimcher PW

    (1999) Neural correlates of decision variables in parietal cortex

    Nature 400:233–238.

    https://doi.org/10.1038/22268

    • PubMed
    • Google Scholar
59. 1. Purcell BA
    2. Heitz RP
    3. Cohen JY
    4. Schall JD
    5. Logan GD
    6. Palmeri TJ

    (2010) Neurally constrained modeling of perceptual decision making

    Psychological Review 117:1113–1143.

    https://doi.org/10.1037/a0020311

    • PubMed
    • Google Scholar
60. 1. Rao RP

    (2010) Decision making under uncertainty: a neural model based on partially observable markov decision processes

    Frontiers in Computational Neuroscience 4:146.

    https://doi.org/10.3389/fncom.2010.00146

    • PubMed
    • Google Scholar
61. 1. Ratcliff R

    (1978) A theory of memory retrieval

    Psychological Review 85:59–108.

    https://doi.org/10.1037/0033-295X.85.2.59

    • Google Scholar
62. 1. Ratcliff R
    2. Cherian A
    3. Segraves M

    (2003) A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions

    Journal of Neurophysiology 90:1392–1407.

    https://doi.org/10.1152/jn.01049.2002

    • PubMed
    • Google Scholar
63. 1. Roesch MR
    2. Olson CR

    (2003) Impact of expected reward on neuronal activity in prefrontal cortex, frontal and supplementary eye fields and premotor cortex

    Journal of Neurophysiology 90:1766–1789.

    https://doi.org/10.1152/jn.00019.2003

    • Google Scholar
64. 1. Roitman JD
    2. Shadlen MN

    (2002) Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task

    The Journal of Neuroscience 22:9475–9489.

    https://doi.org/10.1523/JNEUROSCI.22-21-09475.2002

    • PubMed
    • Google Scholar
65. 1. Rorie AE
    2. Gao J
    3. McClelland JL
    4. Newsome WT

    (2010) Integration of sensory and reward information during perceptual decision-making in lateral intraparietal cortex (LIP) of the macaque monkey

    PLOS ONE 5:e9308.

    https://doi.org/10.1371/journal.pone.0009308

    • PubMed
    • Google Scholar
66. 1. Samejima K
    2. Ueda Y
    3. Doya K
    4. Kimura M

    (2005) Representation of action-specific reward values in the striatum

    Science 310:1337–1340.

    https://doi.org/10.1126/science.1115270

    • PubMed
    • Google Scholar
67. 1. Santacruz SR
    2. Rich EL
    3. Wallis JD
    4. Carmena JM

    (2017) Caudate microstimulation increases value of specific choices

    Current Biology 27:3375–3383.

    https://doi.org/10.1016/j.cub.2017.09.051

    • Google Scholar
68. 1. Sato M
    2. Hikosaka O

    (2002) Role of primate substantia nigra pars reticulata in reward-oriented saccadic eye movement

    The Journal of Neuroscience 22:2363–2373.

    https://doi.org/10.1523/JNEUROSCI.22-06-02363.2002

    • PubMed
    • Google Scholar
69. 1. Schall JD

    (2001) Neural basis of deciding, choosing and acting

    Nature Reviews Neuroscience 2:33–42.

    https://doi.org/10.1038/35049054

    • PubMed
    • Google Scholar
70. 1. Schall JD

    (2019) Accumulators, neurons, and response time

    Trends in Neurosciences 42:848–860.

    https://doi.org/10.1016/j.tins.2019.10.001

    • PubMed
    • Google Scholar
71. 1. Seo M
    2. Lee E
    3. Averbeck BB

    (2012) Action selection and action value in frontal-striatal circuits

    Neuron 74:947–960.

    https://doi.org/10.1016/j.neuron.2012.03.037

    • PubMed
    • Google Scholar
72. 1. Shadlen MN
    2. Newsome WT

    (1996) Motion perception: seeing and deciding

    PNAS 93:628–633.

    https://doi.org/10.1073/pnas.93.2.628

    • PubMed
    • Google Scholar
73. 1. Simen P
    2. Contreras D
    3. Buck C
    4. Hu P
    5. Holmes P
    6. Cohen JD

    (2009) Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions

    Journal of Experimental Psychology: Human Perception and Performance 35:1865–1897.

    https://doi.org/10.1037/a0016926

    • Google Scholar
74. 1. Summerfield C
    2. Koechlin E

    (2010) Economic value biases uncertain perceptual choices in the parietal and prefrontal cortices

    Frontiers in Human Neuroscience 4:208.

    https://doi.org/10.3389/fnhum.2010.00208

    • PubMed
    • Google Scholar
75. 1. Teichert T
    2. Yu D
    3. Ferrera VP

    (2014) Performance monitoring in monkey frontal eye field

    Journal of Neuroscience 34:1657–1671.

    https://doi.org/10.1523/JNEUROSCI.3694-13.2014

    • PubMed
    • Google Scholar
76. 1. Teichert T
    2. Ferrera VP

    (2010) Suboptimal integration of reward magnitude and prior reward likelihood in categorical decisions by monkeys

    Frontiers in Neuroscience 4:186.

    https://doi.org/10.3389/fnins.2010.00186

    • PubMed
    • Google Scholar
77. 1. Thompson KG
    2. Hanes DP
    3. Bichot NP
    4. Schall JD

    (1996) Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search

    Journal of Neurophysiology 76:4040–4055.

    https://doi.org/10.1152/jn.1996.76.6.4040

    • PubMed
    • Google Scholar
78. 1. Thompson KG
    2. Bichot NP
    3. Schall JD

    (1997) Dissociation of visual discrimination from saccade programming in macaque frontal eye field

    Journal of Neurophysiology 77:1046–1050.

    https://doi.org/10.1152/jn.1997.77.2.1046

    • PubMed
    • Google Scholar
79. 1. Thura D
    2. Cisek P

    (2016) Modulation of premotor and primary motor cortical activity during volitional adjustments of Speed-Accuracy Trade-Offs

    The Journal of Neuroscience 36:938–956.

    https://doi.org/10.1523/JNEUROSCI.2230-15.2016

    • PubMed
    • Google Scholar
80. 1. Voss A
    2. Rothermund K
    3. Voss J

    (2004) Interpreting the parameters of the diffusion model: An empirical validation

    Memory & Cognition 32:1206–1220.

    https://doi.org/10.3758/BF03196893

    • Google Scholar
81. 1. Waiblinger C
    2. Wu CM
    3. Bolus MF
    4. Borden PY
    5. Stanley GB

    (2019) Stimulus context and reward contingency induce behavioral adaptation in a rodent tactile detection task

    The Journal of Neuroscience 39:1088–1099.

    https://doi.org/10.1523/JNEUROSCI.2032-18.2018

    • PubMed
    • Google Scholar
82. 1. Whiteley L
    2. Sahani M

    (2008) Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes

    Journal of Vision 8:2–15.

    https://doi.org/10.1167/8.3.2

    • PubMed
    • Google Scholar
83. 1. Wiecki TV
    2. Sofer I
    3. Frank MJ

    (2013) HDDM: hierarchical bayesian estimation of the Drift-Diffusion model in Python

    Frontiers in Neuroinformatics 7:.

    https://doi.org/10.3389/fninf.2013.00014

    • PubMed
    • Google Scholar
84. 1. Yanike M
    2. Ferrera VP

    (2014a) Interpretive monitoring in the caudate nucleus

    eLife 3:e03727.

    https://doi.org/10.7554/eLife.03727

    • Google Scholar
85. 1. Yanike M
    2. Ferrera VP

    (2014b) Representation of outcome risk and action in the anterior caudate nucleus

    Journal of Neuroscience 34:3279–3290.

    https://doi.org/10.1523/JNEUROSCI.3818-13.2014

    • PubMed
    • Google Scholar
86. 1. Zylberberg A
    2. Fetsch CR
    3. Shadlen MN

    (2016) The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision

    eLife 5:e17688.

    https://doi.org/10.7554/eLife.17688

    • PubMed
    • Google Scholar

Author details

1. Yunshu Fan

   Department of Neuroscience and Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2597-5173

2. Joshua I Gold

   Department of Neuroscience and Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Funding acquisition, Visualization, Writing - review and editing

   Competing interests

   Senior Editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-6018-0483

3. Long Ding

   Department of Neuroscience and Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   lding@pennmedicine.upenn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1716-3848

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