# Learning optimal decisions with confidence

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Edited by Paul W. Glimcher, New York University, New York, NY, and accepted by Editorial Board Member Thomas D. Albright October 18, 2019 (received for review April 19, 2019)

## Significance

Popular models for the trade-off between speed and accuracy of everyday decisions usually assume fixed, low-dimensional sensory inputs. In contrast, in the brain, these inputs are distributed across larger populations of neurons, and their interpretation needs to be learned from feedback. We ask how such learning could occur and demonstrate that efficient learning is significantly modulated by decision confidence. This modulation predicts a particular dependency pattern between consecutive choices and provides insight into how a priori biases for particular choices modulate the mechanisms leading to efficient decisions in these models.

## Abstract

Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron–antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice’s difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.

Decisions are a ubiquitous component of everyday behavior. To be efficient, they require handling the uncertainty arising from the noisy and ambiguous information that the environment provides (1). This is reflected in the trade-off between speed and accuracy of decisions. Fast choices rely on little information and may therefore sacrifice accuracy. In contrast, slow choices provide more opportunity to accumulate evidence and thus may be more likely to be correct, but are more costly in terms of attention or effort and lost time and opportunity. Therefore, efficient decisions require not only a mechanism to accumulate evidence but also one to trigger a choice once enough evidence has been collected. Drift-diffusion models (or diffusion decision models) (DDMs) are a widely used model family (2) that provides both mechanisms. Not only do DDMs yield surprisingly good fits to human and animal behavior (3⇓–5), but they are also known to achieve a Bayes-optimal decision strategy under a wide range of circumstances (4, 6⇓⇓⇓–10).

DDMs assume a particle that drifts and diffuses until it reaches one of two boundaries, each triggering a different choice (Fig. 1*A*). The particle’s drift reflects the net surplus of evidence toward one of two choices. This is exemplified by the random-dot motion task, in which the motion direction and coherence set the drift sign and magnitude. The particle’s stochastic diffusion reflects the uncertainty in the momentary evidence and is responsible for the variability in decision times and choices widely observed in human and animal decisions (3, 5). A standard assumption underlying DDMs is that the noisy momentary evidence that is accumulated over time is one-dimensional—an abstraction of the momentary decision-related evidence of some stimulus. In reality, however, evidence would usually be distributed across a larger number of inputs, such as a neural population in the brain, rather than individual neurons (or neuron/antineuron pairs; Fig. 1*A*). Furthermore, the brain would not know a priori how this distributed encoding provides information about the correctness of either choice. As a consequence, it needs to learn how to interpret neural population activity from the success and failure of previous choices. How such an interpretation can be efficiently learned over time, both normatively and mechanistically, is the focus of this work.

The multiple existing computational models for how humans and animals might learn to improve their decisions from feedback (e.g., refs. 11⇓⇓–14) do not address the question we are asking, as they all assume that all evidence for each choice is provided at once, without considering the temporal aspect of evidence accumulation. This is akin to fixed-duration experiments, in which the evidence accumulation time is determined by the environment rather than the decision maker. We, instead, address a more general and natural case in which decision times are under the decision maker’s control. In this setting, commonly studied using “reaction time” paradigms, the temporal accumulation of evidence needs to be treated explicitly, and—as we will show—the time it took to accumulate this evidence impacts how the decision strategy is updated after feedback. Some models for both choice and reaction times have addressed the presence of high-dimensional inputs (e.g., refs. 15⇓–17). However, they usually assumed as many choices as inputs, were mechanistic rather than normative, and did not consider how interpreting the input could be learned. We furthermore extend on previous work by considering the effect of a priori biases toward believing that one option is more correct than the other, and how such biases can be learned. This yields a theoretical understanding of how choice biases impact optimal decision making in diffusion models. Furthermore, it clarifies of how different implementations of this bias result in different diffusion model implementations, like the one proposed by Hanks et al. (18).

## Results

### Bayes-Optimal Decision Making with Diffusion Models.

A standard way (8, 10, 19) to interpret diffusion models as mechanistic implementations of Bayes-optimal decision making is to assume that, in each trial, an unobservable latent state μ (called “drift rate” in diffusion models) is drawn from a prior distribution,

Having after some time *Materials and Methods*). Then, the posterior belief about μ being positive (e.g., leftward motion) results in the following (8):*A*). The accumulated evidence follows a diffusion process, *A*). By Eq. 1, the posterior belief about *B*).

Note that

### Using High-Dimensional Diffusion Model Inputs.

To extend diffusion models to multidimensional momentary evidence, we assume it to be given the *k*-dimensional vector *B*). As the activity of neurons in a population that encodes limited information about the latent state μ is likely correlated across neurons (23, 24), we chose the momentary evidence statistics to also feature such correlations (*Materials and Methods*). In general, we choose these statistics such that *C*), reflecting the uncertainty about μ, and that late choices are likely due to a low μ, which is associated with a hard trial, and thus low decision confidence. This counterintuitive drop in confidence with time has been previously described for diffusion models with one-dimensional inputs (8, 25) and is a consequence of a trial difficulty that varies across trials. Specifically, it arises from a mixture of easy trials associated with large *SI Appendix*). The confidence remains constant over time only when the difficulty is fixed across trials (i.e.,

### Using Feedback to Find the Posterior Weights.

So far, we have assumed the decision maker knows the linear input weights w to make Bayes-optimal choices. If they were not known, how could they be learned? Traditionally, learning has been considered an optimization problem, in which the decision maker tunes some decision-making parameters (here, the input weights w) to maximize their performance. Here, we will instead consider it as an inference problem in which the decision maker aims to identify the decision-making parameters that are most compatible with the provided observations. These two views are not necessarily incompatible. For example, minimizing the mean squared error of a linear model (an optimization problem) yields the same solution as sequential Bayesian linear regression (an inference problem) (26). In fact, as we show in *SI Appendix*, our learning problem can also be formulated as an optimization problem. Nonetheless, we here take the learning-by-inference route, as it provides a statistical interpretation of the involved quantities, which provides additional insights. Specifically, we focus on learning the weights while keeping the diffusion model boundaries fixed. The decision maker’s reward rate (i.e., average number of correct choices per unit time), which we use as our performance measure, depends on both weights and the chosen decision boundaries. However, to isolate the problem of weight learning, we fix the boundaries such that a particular set of optimal weights

To see how learning can be treated as inference, consider the following scenario. Before having observed any evidence, the decision maker has some belief,

The likelihood *SI Appendix*).

As in Eq. 4, the likelihood parameters, w, are linear within a cumulative Gaussian function, such problems are known as “probit regression” and do not have a closed-form expression for the posterior. We could proceed by sampling from the posterior by Markov chain Monte Carlo methods, but that would not provide much insight into the different factors that modulate learning the posterior weights. Instead, we proceed by deriving a closed-form approximation to this posterior to provide such insight, as well as a potential mechanistic implementation.

### Confidence Controls the Learning Rate.

To find an approximation to the posterior in Eq. 4, let us assume the prior to be given by the Gaussian distribution, *Materials and Methods*) resulted in the choice confidence to be given by the following:*C*).

Next, we found a closed-form approximation to the posterior (Eq. 4). For repeated learning across consecutive decisions, the posterior over the weights after the previous decision becomes the prior for the new decision. Unfortunately, a direct application of this principle would lead to a posterior that changes its functional form after each update, making it intractable. We instead used assumed density filtering (ADF) (26, 29) that posits a fixed functional form *Materials and Methods*). Choosing *SI Appendix*). In Eq. 6, the factor *D*, *Top*; see *Materials and Methods* for mathematical expression). For incorrect choices, for which the decision confidence is *SI Appendix*, Fig. S1, and *Materials and Methods*).

Decision confidence is not the only factor that impacts the learning rate in Eq. 6. For instance, *D*, *Bottom*). This plot revealed a slight down-weighting of the learning rate for low-confidence choices when compared to

### Performance Comparison to Optimal Inference and to Simpler Heuristics.

The intuitions provided by near-optimal ADF learning are only informative if its approximations do not cause a significant performance drop. We quantified this drop by comparing ADF performance to that of the Bayes-optimal rule, as found by Gibbs sampling (*Materials and Methods*). Gibbs sampling is biologically implausible as it requires a complete memory of inputs and feedbacks for past decisions and is intractable for longer decision sequences, but nonetheless provides an optimal baseline to compare against. We furthermore tested the performance of two additional approximations. One was an ADF variant that assumes a diagonal covariance matrix *Materials and Methods*).

Furthermore, we tested whether simpler learning heuristics can match ADF performance. We focused on three rules of increasing complexity. The delta rule, which can be considered a variant of temporal-difference learning, or reinforcement learning (32), updates its weight estimate after the nth decision by the following:*D*, *Right*). Our simulations revealed that the delta rule excessively and suboptimally decrease in the weight size

We evaluated the performance of these learning rules by simulating weight learning across 1,000 consecutive decisions (called “trials”; see *Materials and Methods* for details) in a task in which use of the optimal weight vector maximizes the reward rate. This reward rate was the average reward for correct choices minus some small cost for accumulating evidence over the average time across consecutive trials and is a measure we would expect rational decision makers to optimize. For each learning rule, we found its reward rate relative to random behavior and optimal choices.

Fig. 2*A* shows this relative reward rate for all learning rules and different numbers of inputs. As can be seen, the performance of ADF and the other probabilistic learning rules is indistinguishable from Bayes-optimal weight learning for all tested numbers of inputs. Surprisingly, the ADF variant that ignores off-diagonal covariance entries even outperformed Bayes-optimal learning for a large number of inputs (Fig. 2*A*, yellow line for 50 inputs). The reason that a simpler learning rule could outperform the rule deemed optimal by Bayesian decision theory is that this simpler rule has less parameters and a simpler underlying model that was nonetheless good enough to learn the required weights. Learning fewer parameters with the same data resulted in initially better parameter estimates, and better associated performance. Conceptually, this is similar to a linear model outperforming a quadratic model when fitting a quadratic function if little data are available, and if the function is sufficiently close to linear (as illustrated in *SI Appendix*, Fig. S2). Once more data are available, the quadratic model will outperform the linear one. Similarly, the Bayes-optimal learning rule will outperform the simpler one once more feedback has been observed. In our simulation, however, this does not occur within the 1,000 simulated trials.

All other learning heuristics performed significantly worse. For low-dimensional input, the delta rule initially improved its reward rate but worsens it again at a later stage across all learning rates. The normalized delta rule avoided such performance drops for low-dimensional input, but both delta rule variants were unable to cope with high-dimensional inputs. Only stochastic gradient ascent on the log-likelihood provided a stable learning heuristic for high-dimensional inputs, but with the downside of having to choose a learning rate. Small learning rates lead to slow learning, and an associated slower drop in angular error. Overall, the probabilistic learning rules significantly outperformed all tested heuristic learning rules and matched (and in one case even exceeded) the weight learning performance of the Bayes-optimal estimator.

### Tracking Nonstationary Input Weights.

So far, we have tested how well our weight learning rule is able to learn the true, underlying weights from binary feedback about the correctness of the decision maker’s choices. For this, we assumed that the true weights remained constant across decisions. What would happen if these weights change slowly over time? Such a scenario could occur if, for example, the world around us changes slowly, or if the neural representation of this world changes slowly through neural plasticity or similar. In this case, the true weights would become a moving target that we would never be able to learn perfectly. Instead, we would after some initial transient expect to reach steady-state performance that remains roughly constant across consecutive decisions. We compared this steady-state performance of Bayes-optimal learning (now implemented by a particle filter) to that of the probabilistic and heuristic learning rules introduced in the previous section. The probabilistic rules were updated to take into account such a trial-by-trial weight change, as modeled by a first-order autoregressive process (*Materials and Methods*). The heuristic rules remained unmodified, as their use of a constant learning rate already encapsulates the assumption that the true weights change across decisions.

Fig. 2*B* illustrates the performance of the different learning rules. First, it shows that, for low-dimensional inputs the various probabilistic models yield comparable performances, but for high-dimensional inputs the approximate probabilistic learning rules outperform Bayes-optimal learning. In case of the latter, these approximations were not actually harmful, but instead beneficial, for the same reason discussed further above. In particular, the more neurally realistic ADF variant that only tracked the diagonal of the covariance matrix again outperformed all other probabilistic models. Second, only the heuristic learning rule that performed gradient ascent on the log-likelihood achieved steady-state performance comparable to the approximate probabilistic rules, and then only for high input dimensionality and a specific choice of learning rate. This should come as no surprise, as its use of the likelihood function introduces more task structure information than the other heuristics use. The delta rule did not converge and therefore never achieved steady-state performance. Overall, the ADF variant that focused only on the diagonal covariance matrix achieved the best overall performance.

### Learning Both Weights and a Latent State Prior Bias.

Our learning rule can be generalized to learn prior biases in addition to the input weights. The prior we have used so far for the latent variable,

This additional term has two consequences. First, appending the elements m and *Materials and Methods*). Second, the term requires us to rethink the association between decision boundaries and choices. As Fig. 3*C* illustrates, such a prior causes a time-invariant shift in the association between the accumulated evidence, *C*, blue/red decision areas). Hence, we have lost the mechanistically convenient unique association between decision boundaries and choices. We recover this association by a boundary counter shift, such that these boundaries come to lie at the same decision confidence levels for opposite choices, making them asymmetric around *C*, shift by *SI Appendix*]. Therefore, a prior bias is implemented by a bias-dependent simple shift of the accumulation starting point, leading to a mechanistically straightforward implementation of Bayes-optimal decision making with biased priors.

A consequence of the shifted accumulation starting point is that, for some fixed decision time t, the decision confidence at both boundaries is the same (Fig. 3 *C*, *Right*). This seems at odds with the intuition that a biased prior ought to bias the decision confidence in favor of the more likely option. However, this mechanism does end up assigning higher average confidence to the more likely option because of reactions times. As the starting point is now further away from the less likely correct boundary, it will on average take longer to reach this boundary, which lowers the decision confidence since confidence decreases with elapsed time. Therefore, even though the decision confidence at both boundaries is the same for the given decision time, it will on average across decision times be lower for the a priori nonpreferred boundary, faithfully implementing this prior (see *SI Appendix* for a mathematical demonstration).

Our finding that a simple shift in the accumulation starting point is the Bayes-optimal strategy appears at odds with previous work that suggested that the optimal shift of the accumulator variable *C*), an alternative implementation is to multiply *D*), again resulting in *D*). Furthermore, undoing this shift to regain a unique association between boundaries and choices not only requires a shifted accumulation starting point, but additionally a time-dependent additive signal [*D*; *SI Appendix*], as was proposed in ref. 18. Which of the two approaches is more adequate depends on how well it matches the prior implicit in the task design. Our approach has the advantage of a simpler mechanistic implementation, as well as yielding a simple extension to the previously derived learning rule. How learning prior biases in the framework of ref. 18 could be achieved remains unclear (but see ref. 33).

### Sequential Choice Dependencies due to Continuous Weight Tracking.

In everyday situations, no two decisions are made under the exact same circumstances. Nonetheless, we need to be able to learn from the outcome of past choices to improve future ones. A common assumption is that past choices become increasingly less informative about future choices over time. One way to express this formally is to assume that the world changes slowly over time—and that our aim is to track these changes. By “slow,” we mean that we can consider it constant over a single trial but that it is unstable over the course of an hour-long session. We implemented this tracking of the moving world, as in Fig. 2*B*, by slowly allowing the weights mapping evidence to decisions to change. With such continuously changing weights, weight learning never ends. Rather, the input weights are continuously adjusted to make correct choices more likely in the close future. After correct choices, this means that weights will be adjusted to repeat the same choice upon observing a similar input in the future. After incorrect choices, the aim is to adjust the weights to perform the opposite choice, instead. Our model predicts that, after an easy correct choice, in which confidence can be expected to be high, the weight adjustments are lower than after hard correct choices (Fig. 1 *D*, *Top*, green line). As a consequence, we would expect the model to be more likely to repeat the same choices after correct and hard, than after correct and easy trials.

To test this prediction, we relied on the same simulation to generate Fig. 2*B* to measure how likely the model repeated the same choice after correct decisions. Fig. 4*A* illustrates that this repetition bias manifests itself in a shift of the psychometric curve that makes it more likely to repeat the previous choice. Furthermore, and as predicted, this shift is modulated by the difficulty of the previous choice and is stronger if the previous choice was easy (i.e., associated with a large *B*). Therefore, if the decision maker expects to operate in a volatile, slowly changing world, our model predicts a repetition bias to repeat the same choices after correct decisions, and that this bias is stronger if the previous choice was easy.

### Unreliable Feedback Reduces Learning.

What would occur if choice feedback is less-than-perfectly reliable? For example, the feedback itself might not be completely trustworthy, or hard to interpret. We simulated this situation by assuming that the feedback is inverted with probability β. Here, *C*) as follows. First, it reduces the overall magnitude of the correction, with weaker learning for higher feedback noise. Second, it results in no learning for highly confident choices that we are told are incorrect. In this case, one’s decision confidence overrules the unreliable feedback. This stands in stark contrast to the optimal learning rule for perfectly reliable feedback, in which case the strongest change to the current strategy ought to occur.

## Discussion

Diffusion models are applicable to model decisions that require some accumulation of evidence over time, which is almost always the case in natural decisions. We extended previous work on the normative foundations of these models to more realistic situations in which the sensory evidence is encoded by a population of neurons, as opposed to just two neurons, as has been typically assumed in previous studies. We have focused on normative and mechanistic models for learning the weights from the sensory neurons to the decision integrator without additionally adjusting the decision boundaries, as weight learning is a problem that needs to be solved even if the decision boundaries are optimized at the same time.

From the Bayesian perspective, weight learning corresponds to finding the weight posterior given the provided feedback, and resulted in an approximate learning rule whose learning rate was strongly modulated by decision confidence. It suppressed learning after high-confidence correct decisions, supported learning for uncertain decisions irrespective of their correctness, and promoted strong change of the combination weights after wrong decisions that were made with high confidence (Fig. 1*D*). Evidence for such confidence-based learning has already been identified in human experiments (34), but not in a task that required the temporal accumulation of evidence in individual trials. Indeed, as we have previously suggested (22), such a modulation by decision confidence should arise in all scenarios of Bayesian learning in *N*-AFC tasks in which the decision maker only receives feedback about the correctness of their choices, rather than being told which choice would have been correct. In the 2-AFC task we have considered, being told that one’s choice was incorrect automatically reveals that the other choice was correct, making the two cases coincide. Moving from one-dimensional to higher-dimensional inputs requires performing the accumulation of evidence for each input dimension separately [Fig. 1*B*; Eqs. 6 and 12 require

Continual weight learning predicts sequential choice dependencies that make the repetition of a previous, correct choice more likely, in particular if this choice was difficult (Fig. 4). Thus, based on assuming a volatile environment that promotes a continual adjustment of the decision-making strategy, we provide a rational explanation for sequential choice dependencies that are frequently observed in both humans and animals (e.g., refs. 37 and 38). In rodents making decisions in response to olfactory cues, we have furthermore confirmed that these sequential dependencies are modulated by choice difficulty, and that the exact pattern of this modulation depends on the stimulus statistics, as predicted by our theory (39) (but consistency with ref. 40 is unclear).

Last, we have clarified how prior biases ought to impact Bayes-optimal decision making in diffusion models. Extending the work of Hanks et al. (18), we have demonstrated that the exact mechanisms to handle these biases depend on the specifics of how these biases are introduced through the task design. Specifically, we have suggested a variant that simplifies these mechanisms and the learning of this bias. This variant predicts that the evidence accumulation offset, that has previously been suggested to be time-dependent, to become independent of time, and it would be interesting to see whether the lateral intraparietal cortex activity of monkeys performing the random-dot motion task, as recorded by Hanks et al. (but see ref. 41), would change accordingly.

## Materials and Methods

We here provide an outline of the framework and its results. Detailed derivations are provided in *SI Appendix*.

### Bayesian Decision Making with One and Multidimensional Diffusion Models.

We assume the latent state to be drawn from

In the above, all proportionalities are with respect to μ, and we have defined

We extend diffusion models to multidimensional inputs with momentary evidence

### Probabilistic and Heuristic Learning Rules.

We find the approximate posterior

with learning rate modulators *C*) changes the likelihood to assume reversed feedback with probability β, and follow the same procedure as above to derive the posterior moments (*SI Appendix*). The ADF variant that only tracks the diagonal covariance elements assumes *SI Appendix* for details). All heuristic learning rules are described in the main text.

We modeled nonstationary input weights by

Bayes-optimal weight inference was for stationary weights performed by Gibbs sampling for probit models, and for nonstationary weights by particle filtering (*SI Appendix*).

### Simulation Details.

We used parameters *SI Appendix*). The diffusion model bounds

To compare the weight learning performance of ADF to alternative models (Fig. 2*A*), we simulated 1,000 learning trials 5,000 times, and reported the reward rate per trial averaged across these 5,000 repetitions. To assess steady-state performance (Fig. 2*B*), we performed the same procedure with nonstationary weights and reported reward rate averaged over the last 100 trials, and over 5,000 repetitions. The same 100 trials were used to compute the sequential choice dependencies in Fig. 4 *A* and *B*. To simulate decision making with diffusion models and uncertain weights, we used the current mean estimate <w> of the input weights to linearly combine the momentary evidence. The probabilistic learning rules were all independent of the specific choice of this estimate. The learning rate in Fig. 1*D* shows the prefactor to

## Acknowledgments

This work was supported by a James S. McDonnell Foundation Scholar Award (220020462) (J.D.) and grants from the National Institute of Mental Health (R01MH115554) (J.D.), the Swiss National Science Foundation (www.snf.ch) (31003A_143707 and 31003A_165831) (A.P.), the Champalimaud Foundation (Z.F.M.), the European Research Council (Advanced Investigator Grants 250334 and 671251) (Z.F.M.), the Human Frontier Science Program (Grant RGP0027/2010) (Z.F.M. and A.P.), the Simons Foundation (Grant 325057) (Z.F.M. and A.P.), and Fundação para a Ciência e a Tecnologia (A.G.M.).

## Footnotes

- ↵
^{1}To whom correspondence may be addressed. Email: jan_drugowitsch{at}hms.harvard.edu.

Author contributions: J.D., A.G.M., Z.F.M., and A.P. designed research; J.D. and A.P. performed research; J.D. analyzed data; and J.D., A.G.M., Z.F.M., and A.P. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission. P.W.G. is a guest editor invited by the Editorial Board.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1906787116/-/DCSupplemental.

Published under the PNAS license.

## References

- ↵
- K. Doya,
- S. Ishii,
- A. Pouget,
- R. P. N. Rao

- ↵
- ↵
- ↵
- ↵
- ↵
- P. I. Frazier,
- A. J. Yu

- ↵
- S. Tajima,
- J. Drugowitsch,
- A. Pouget

- ↵
- J. Drugowitsch,
- R. Moreno-Bote,
- A. K. Churchland,
- M. N. Shadlen,
- A. Pouget

- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- T. D. Hanks,
- M. E. Mazurek,
- R. Kiani,
- E. Hopp,
- M. N. Shadlen

- ↵
- ↵
- ↵
- J. O. Berger

- ↵
- ↵
- ↵
- R. Moreno-Bote et al

- ↵
- R. Kiani,
- M. N. Shadlen

- ↵
- C. Bishop

- ↵
- ↵
- T. M. Cover,
- J. A. Thomas

- ↵
- K. P. Murphy

- ↵
- T. Graepel,
- J. Quiñonero-Candela,
- T. Borchert,
- R. Herbrich

- ↵
- W. Chu,
- M. Zinkevich,
- L. Li,
- A. Thomas,
- B. Tseng

- ↵
- R. S. Sutton,
- A. G. Barto

- ↵
- A. Zylberberg,
- D. M. Wolpert,
- M. N. Shadlen

- ↵
- F. Meyniel,
- S. Dehaene

- ↵
- ↵
- ↵
- L. Busse et al

- ↵
- ↵
- A. G. Mendonça et al

- ↵
- ↵
- V. Rao,
- G. C. DeAngelis,
- L. H. Snyder

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