Abstract

Imagination enables us not only to transcend reality but also to learn about it. In the context of reinforcement learning, an agent can rationally update its value estimates by simulating an internal model of the environment, provided that the model is accurate. In a series of sequential decision-making experiments, we investigated the impact of imaginative simulation on subsequent decisions. We found that imagination can cause people to pursue imagined paths, even when these paths are suboptimal. This bias is systematically related to participants' optimism about how much reward they expect to receive along imagined paths; providing feedback strongly attenuates the effect. The imagination effect can be captured by a reinforcement learning model that includes a bonus added onto imagined rewards. Using fMRI, we show that a network of regions associated with valuation is predictive of the imagination effect. These results suggest that imagination, although a powerful tool for learning, is also susceptible to motivational biases.

INTRODUCTION

Imagination is a fertile source of knowledge. Philosophers and scientists routinely use thought experiments to explore their mental models of the world and thereby make “discoveries” in the absence of new experience. Lucretius inferred the infinitude of space by picturing himself throwing spears at the boundary of the universe, and Einstein discovered relativity by picturing himself riding on a beam of light.

Imagination has also been put to practical use in computer science. Niyogi, Girosi, and Poggio (1998) described how an image classifier could be fed training examples synthesized by applying mental transformations to a set of objects. For example, suppose you were training a classifier to recognize faces. You might only have a single image for a given face, but in the real world, faces appear in many orientations and positions. If you have access to a 3-D model of the face, then you can mentally apply transformations that preserve identity (e.g., rotating the face). Each transformation yields a new image with the same label and more training data for the classifier.

A similar idea was applied to reinforcement learning by Sutton (1990): A model of the environment can be used to simulate training data (transitions and rewards) for a computationally cheap “model-free” learning algorithm that updates a set of cached value estimates (future reward expectations). In this architecture, the same learning algorithm operates on both real and simulated experiences. The key advantage is that a model-based action policy can be approximated without computationally expensive model-based algorithms like tree search or dynamic programming; the model-free cached values map directly to a policy without additional computation.

These examples illustrate how learning systems can be integrated with imaginative simulation to acquire knowledge in the absence of new experience. However, there is relatively little direct evidence that the brain uses imagination in this way.

Indirect evidence for the role of imaginative simulation in reinforcement learning comes from a series of retrospective revaluation experiments (Gershman, Markman, & Otto, 2014). In these experiments, human participants learned conflicting policies at different stages of a sequential decision task and were then tested for revaluation of the policy learned earlier in the task. A period of quiet rest before the test phase enhanced retrospective revaluation, consistent with the idea that model-free cached values can be updated via offline simulation. This finding cannot be explained by pure model-based or model-free accounts of learning or even by stochastic mixtures of the two (Daw, Gershman, Seymour, Dayan, & Dolan, 2011); it appears to require a particular kind of cooperative interaction between the systems.

In this article, we take a closer look at the role of imaginative simulation in reinforcement learning. We asked human participants to perform a sequential decision task with dynamic rewards, while intermittently having them imagine particular paths through the state space. Although participants do not gain any information from these imagination trials, it has a potent effect on their subsequent decision behavior, influencing them to pursue imagined paths that are in fact suboptimal. We show that this bias arises in part because participants are optimistic about the amount of reward they will receive in imagined states; the bias is reduced when participants are given feedback about the true reward. A simple reinforcement learning model with an “imagination bonus” can capture the bias. Using fMRI, we find that the bias is associated with activation in medial pFC and OFC, consistent with the role of those regions in reward expectation. Taken together, these findings suggest that imagination can drive reinforcement learning, although it can fall prey to miscalibrated reward expectations.

METHODS

Participants

Twenty healthy volunteers (10 women; mean age = 25.45 years, SD = 4.5 years) participated in the scanning portion of this study. These same 20 individuals also participated in a behavioral session to determine their eligibility for participation in the scanning portion. Participants gave informed consent before both sessions. The study was approved by the ethics committee of Harvard University. Participants earned $35 for the scanning session and $10 for the behavioral session, plus a performance-based bonus in both.

In addition, we recruited 230 human participants using the Amazon Mechanical Turk Web service. All participants were given informed consent and paid for their participation. This study was also approved by the ethics committee of Harvard University.

Design and Procedure: fMRI Experiment

The following describes the task that participants performed in the scanning experiment. There were two kinds of trials: “decision” trials and “imagination” trials (Figure 1). A block consisted of five decision trials followed by one imagination trial with the addition of a single decision trial at the end, because we were particularly interested in the decision trials immediately after an imagination trial. A run consisted of eight blocks. Participants performed five runs in the scanner. Most participants performed all five runs, but some participants had exceptions in the number of runs, with some participants completing fewer runs because of experimental glitches (two participants: two runs, one participant: three runs, two participant: four runs) and some initial participants completing more runs when we were first piloting the experiment (three participants: six runs, one participant: eight runs).

Figure 1. 

Experimental design. The design of the fMRI study is shown here, which is identical to the design of the behavioral studies except that the timing was faster in the behavioral studies (see Methods) and they used a continuous reward prediction rather than a binary judgment. (A) On decision trials, participants traversed the state space by making a sequence of two decisions, followed by a reward in the terminal state. State transition diagram is shown on the right; colored arrows correspond to available actions in each state. (B) Example reward sequence. Each line corresponds to a terminal state. (C) On imagination trials, participants performed the sequence of actions necessary to arrive at a particular terminal state (shown on the right) and then predicted whether the reward would be greater or less than 0 in that terminal state. In the behavioral studies, participants made a continuous numerical reward prediction.

Figure 1. 

Experimental design. The design of the fMRI study is shown here, which is identical to the design of the behavioral studies except that the timing was faster in the behavioral studies (see Methods) and they used a continuous reward prediction rather than a binary judgment. (A) On decision trials, participants traversed the state space by making a sequence of two decisions, followed by a reward in the terminal state. State transition diagram is shown on the right; colored arrows correspond to available actions in each state. (B) Example reward sequence. Each line corresponds to a terminal state. (C) On imagination trials, participants performed the sequence of actions necessary to arrive at a particular terminal state (shown on the right) and then predicted whether the reward would be greater or less than 0 in that terminal state. In the behavioral studies, participants made a continuous numerical reward prediction.

In decision trials, participants made two consecutive decisions of left or right and received feedback after each decision. These left or right decisions allowed the participant to navigate different states. Each trial began with the same start state. There were two intermediate states (one for left, one for right) and four terminal states (left or right from either of the second-level states). These states were represented by black and white pictures of objects or scenes. The transitions between states were deterministic. We showed participants the transition structure of these states before the start of the experiment.

The decision trials began with the participant seeing the first state and receiving a prompt for a forced-choice, two-alternative (left or right) decision. Participants had 1.5 sec to make this decision. If participants failed to make a decision, then they were shown a fixation cross during the remaining time allotted for the trial (8 sec from onset of the first picture to the end of final feedback). After the first decision, participants were given reward feedback and shown the picture associated with the intermediate state (one of two possible states depending on whether they chose left or right). The reward feedback after the first decision was always 0 and was shown for 1.5 sec. Participants were then prompted to make another forced-choice left/right decision. They had 1.5 sec to make this decision. Again, if they failed to make a decision, they were shown a fixation cross during the remaining time allotted for the trial. After they made their second decision, participants were given reward feedback and shown the picture associated with the terminal state they had selected. The feedback lingered for 1.5 sec before participants were shown a fixation cross for 2–4 sec of jitter, after which the next trial would begin.

The underlying rewards were predetermined for each trial, independent of the path chosen by the participant. The underlying reward structure defines the ground-truth optimal path. Rewards were randomly generated at the time of each new block. Rewards were symmetrically distributed, such that the highest and lowest rewards were on the same branch of the path structure (e.g., the highest and lowest could be associated with the two terminal states reachable from the left intermediate state) and the average expected reward was the same at both intermediate states. The highest reward was sampled from a uniform distribution between 15 and 25. The two intermediate rewards were sampled from a uniform distribution between 0 and 10. The lowest reward was sampled from a uniform distribution between −15 and −5. Rewards reset, on average, every 10 trials (chosen uniformly from 8 to 12). These rewards drifted according to a Gaussian random walk (SD = 0.5) until the next reset occurred. We chose this distribution, which was biased to yield positive rewards on average, so that participants would not get frustrated by experiencing a large number of losses. For some participants (n = 39), the mean rewards of the left and right branches of the tree were matched (i.e., the sum of the highest and lowest rewards was about equal to the sum of the two middle rewards). For the rest of the participants, the rewards were unmatched. These reward sequences were qualitatively similar, so we collapsed across the different sequence types.

In imagination trials, participants were shown the picture representing the start state and the picture representing one of the terminal states, with an arrow pointing from the start state to the terminal state. The terminal state was selected at random from one of the three states that did not offer the highest reward. Participants were asked to imagine the sequence of actions that would take them from the start state to the indicated terminal state and then to indicate the appropriate sequence of left or right decisions (e.g., press left and right or left and left). Participants had 4 sec to indicate the correct path, and 2–4 sec of jitter followed after indicating the imagined path. There was no fixation cross if the participants failed to make the decisions. Participants were then asked to predict whether the imagined path would yield a reward that was more or less than zero. They had 2.5 sec to respond and then were given 2–4 sec of jitter before the onset of the next decision trial.

We first recruited participants to participate in the behavioral portion of the experiment outside the scanner. In this behavioral session, a run consisted of eight blocks with the addition of a single decision trial at the end. Each participant performed four runs. Participants practiced the task for one run before beginning the actual experiment. After the participant had completed the behavioral session, we invited them to return for the scanning portion if their data showed an increased probability of selecting the imagined path on the decision trials immediately after the imagined trials (the basis for the effect in Experiment 1). We had 35 participants participate in this behavioral portion of the task, 15 of which were excluded from scanning because either they did not show the effect or they declined our invitation to return for the scanning session (8 of 35 participants did not show effect and were excluded from scanning accordingly; 7 of 35 participants declined invitation to return for scanning session). Although we selected participants for scanning on the basis of the imagination effect, we still found a significant effect on average when analyzing all 35 participants. More generally, the choice behavior reported in the Results section was quantitatively and qualitatively unchanged when including all 35 participants.

Individual trials were excluded from the behavioral and model analyses if participants failed to reach a terminal state (i.e., they did not make two decisions).

Design and Procedure: Behavioral Experiments

Experiment 1 featured the same experimental paradigm as the scanning experiment described above, except that participants made continuous (numerical) predictions in the imagination trial. Individual trials were excluded if participants made a prediction with an absolute value greater than or equal to 25. In addition, participants were required to indicate the correct imagined path before moving onto the next trial. For example, if the correct decision sequence was left and then right, they were prompted to repeat the decision sequence until they selected the correct one. The time constraints described in the scanning experiment were relaxed in these experiments. A block consisted of five decision trials and one imagination trial with the addition of a single decision trial at the end. Each participant performed 31 blocks.

Experiment 2 was the same as Experiment 1 described above, except that, after participants had made their predictions, they received veridical feedback about the reward associated with the imagined path.

Experiment 3 was the same as Experiment 1 described above, except that participants were asked neither to imagine the path nor to indicate the sequence of decisions to get there. They only made a prediction about the value of a given terminal state.

Computational Model Fitting and Comparison

We fit the four computational models described in the Results section to the choice data from the decision trials. Maximum likelihood estimates of each parameter were obtained for each participant individually using nonlinear optimization (MATLAB's fmincon function) with five random initializations to avoid local optima; the parameter estimates achieving the highest likelihood across the random initializations were used in subsequent analyses. We placed the following bounds on the parameters: inverse temperature [0,10], learning rate [0,1], eligibility trace [0,1], imagination bonus [0,20], and forgetting decay [1,3]. No transformations were applied to the parameters during model fitting.

Models were compared using random effects Bayesian model comparison (Rigoux, Stephan, Friston, & Daunizeau, 2014), which estimates the frequency of each model class in the population. The input to this procedure is the log model evidence for each participant, which we approximated using −0.5 × BIC, where BIC is the Bayesian Information Criterion. We used the exceedance probability (the posterior probability that a particular model is more frequent in the population than the other models under consideration) as a model comparison metric.

fMRI Data Acquisition

Neuroimaging data were collected using a 3-T Siemens Magnetom Prisma MRI scanner (Siemens Healthcare, Erlangen, Germany) with the vendor's 32-channel head coil. Anatomical images were collected with a T1-weighted multiecho MPRAGE sequence (176 sagittal slices; repetition time = 2530 msec; echo times = 1.64, 3.50, 5.36, and 7.22 msec; flip angle = 7°; 1-mm3 voxels; field of view = 256 mm). All BOLD data were collected via a T2*-weighted EPI pulse sequence that employed multiband RF pulses and Simultaneous Multi-Slice (SMS) acquisition (Xu et al., 2013; Feinberg et al., 2010; Moeller et al., 2010). For the six task runs, the EPI parameters were as follows: 69 interleaved axial–oblique slices (25° toward coronal from AC–PC alignment), repetition time = 2000 msec, echo time = 35 msec, flip angle = 80°, 2.2-mm3 voxels, field of view = 207 mm, and SMS = 3. The SMS-EPI acquisitions used the CMRR-MB pulse sequence from the University of Minnesota.

fMRI Data Preprocessing and Analysis

Data preprocessing and statistical analyses were performed using SPM12 (Wellcome Department of Imaging Neuroscience, London, UK). Functional (EPI) image volumes were realigned to correct for small movements occurring between scans. This process generated an aligned set of images and a mean image per participant. Each participant's T1-weighted structural MRI was then coregistered to the mean of the realigned images and segmented to separate out the gray matter, which was normalized to the gray matter in a template image based on the Montreal Neurological Institute reference brain. Using the parameters from this normalization process, the functional images were normalized to the Montreal Neurological Institute template (resampled voxel size = 2 mm isotropic) and smoothed with an 8-mm FWHM Gaussian kernel. A high-pass filter of 1/128 Hz was used to remove low-frequency noise, and a first-order autoregressive model was used to correct for temporal autocorrelations.

We defined two general linear models (GLMs) to analyze the fMRI data. Both GLMs included stimulus events (cues and outcomes) as impulse regressors convolved with the canonical hemodynamic response function (HRF). In GLM1, a boxcar regressor was defined over the entire imagination trial epoch and then convolved with the canonical HRF. Separate regression coefficients were estimated for imagination trials, which were followed by a choice of the imagined path, and trials, which were followed by a choice of the optimal path. In GLM2, the temporal difference prediction error from the imagination + forgetting model was entered as a parametric modulator of the outcome events on decision trials and orthogonalized with respect to the outcome event regressor and convolved with the canonical HRF.

Group-level results were analyzed using t contrasts with cluster-based FWE thresholding at the whole-brain level (p < .05) using a cluster-forming threshold of p < .001.

For the ventral striatum analysis, we used a bilateral anatomical mask taken from the automated anatomical labeling atlas (Tzourio-Mazoyer et al., 2002).

RESULTS

Behavioral Results

Human participants (N = 87) performed a reinforcement learning task in which they navigated through a sequence of states to maximize rewards (Figure 1A). Rewards were only delivered in the terminal states, and the reward magnitudes changed dynamically (Figure 1B), such that participants had to be continually updating their policy and exploring the decision tree. In addition to these “decision” trials, participants intermittently performed “imagination” trials in which they were asked to first enter the sequence of actions that would take them to a particular terminal state and then to make a prediction about how much reward they would obtain in that state (Figure 1C).

The key question we asked was how imagination trials affected behavior on subsequent decision trials. A participant's choice of path on a decision trial can be broken down into three categories: the objectively optimal path, the previously imagined path, and the two other possible paths, which are neither optimal nor imagined. Critically, we asked participants to imagine paths that were always suboptimal, setting up a conflict between optimal and imagined paths. We found that participants were more likely to choose the imagined path after an imagination trial compared with before an imagination trial (t(86) = 8.46, p < .0001; Figure 2A) and correspondingly less likely to choose the optimal path (t(86) = 11.5, p < .0001).

Figure 2. 

Imagination trials affect subsequent decisions. (A) Participants are more likely to take the imagined path after an imagination trial than before an imagination trial and correspondingly less likely to take the optimal path. They are also slightly more likely to take a path that is neither optimal nor imagined. (B) Providing reward feedback on imagination trials strongly attenuates the imagination effect. (C) Asking participants to make reward predictions without imagining the action sequence also strongly attenuates the imagination effect. (D) Comparison of effects across experiments. The y axis shows the difference in probability of choosing the imagined path after and before an imagination trial. Error bars denote SEM. Expt = experiment.

Figure 2. 

Imagination trials affect subsequent decisions. (A) Participants are more likely to take the imagined path after an imagination trial than before an imagination trial and correspondingly less likely to take the optimal path. They are also slightly more likely to take a path that is neither optimal nor imagined. (B) Providing reward feedback on imagination trials strongly attenuates the imagination effect. (C) Asking participants to make reward predictions without imagining the action sequence also strongly attenuates the imagination effect. (D) Comparison of effects across experiments. The y axis shows the difference in probability of choosing the imagined path after and before an imagination trial. Error bars denote SEM. Expt = experiment.

Participants were also more likely to choose an “other” path (t(86) = 5.28, p < .0001), suggesting the possibility that participants simply forgot the optimal path because of memory interference from the imagination trial, as opposed to being systematically biased toward the imagined path. However, the shift toward the imagined path was marginally stronger than the shift toward the other paths (t(86) = 1.88, p = .06). We will address the question of forgetting further using computational modeling in the next section.

We next explored several variations of our paradigm. In Experiment 2 (n = 46), participants received feedback about the true rewards after their predictions on imagination trials. This attenuated the imagination effect (change in probability of choosing the imagined path after an imagination trial) relative to Experiment 1 (t(131) = 4.05, p < .0001; Figure 2D), but the effect was still marginally significant (t(45) = 2.02, p = .05; Figure 2B). The imagination effect was significantly smaller than the change in probability of choosing one of the “other” paths (t(45) = 4.03, p < .001), and the magnitude of this “other” effect was comparable with Experiment 1, indicating that reward feedback selectively reduced the imagination effect without affecting the “other” effect.

In Experiment 3 (n = 97), participants made reward predictions (without feedback) but did not enter the path that would take them to the specified terminal state. We hypothesized that this experiment would reduce the demands on imaginative simulation. The imagination effect was again attenuated relative to Experiment 1 (t(182) = 3.81, p < .001; Figure 2D) but significantly greater than 0 (t(96) = 4.2, p < .0001; Figure 2C). There was no significant difference in the size of the imagination effect between Experiments 2 and 3 (p = .31).

One clue about the nature of the underlying mechanisms comes from inspection of the reward predictions themselves (Figure 3A): Participants are systematically miscalibrated across all three experiments (p < .0001), estimating the rewards to be greater than they actually are. In other words, reward predictions are optimistic, even when reward feedback is provided in Experiment 2 (although the miscalibration is significantly reduced relative to Experiment 1; t(131) = 2.35, p < .05). This miscalibration is predictive of behavior on subsequent decision trials in Experiment 1: The imagination effect is significantly greater after positively miscalibrated (optimistic) imagination trials compared with negatively miscalibrated (pessimistic) trials (t(77) = 3.91, p < .001; Figure 3B), although it is still significantly greater than 0 after negatively miscalibrated trials (t(77) = 5.00, p < .0001).

Figure 3. 

Miscalibration of reward predictions. (A) Participants are optimistic (positively miscalibrated) about expected reward in imagined states. This optimism is reduced, but not eliminated, by reward feedback. (B) Participants are more likely to switch to the imagined path when they are positively miscalibrated compared with when they are negatively miscalibrated. Error bars denote SEM. Neg = negative; Pos = positive.

Figure 3. 

Miscalibration of reward predictions. (A) Participants are optimistic (positively miscalibrated) about expected reward in imagined states. This optimism is reduced, but not eliminated, by reward feedback. (B) Participants are more likely to switch to the imagined path when they are positively miscalibrated compared with when they are negatively miscalibrated. Error bars denote SEM. Neg = negative; Pos = positive.

To summarize so far, the imagination effect depends on both reward feedback and imaginative simulation. An important (but not exclusive) contributing factor is the prevalence of miscalibrated reward predictions, such that imaginative simulation combined with optimistic reward predictions increase the probability of choosing the imagined path.

Computational Modeling

To disentangle the different possible mechanisms driving the imagination effect, we fit a family of reinforcement learning models to choice behavior. All of these models have in common the well-accepted idea that cached values are updated using temporal difference learning (Daw et al., 2011; Gläscher, Daw, Dayan, & O'Doherty, 2010; Seymour et al., 2004; Schultz, Dayan, & Montague, 1997). In addition, the models assume that the same learning algorithm applies to imagined paths and rewards. The critical differences between the models lie in how imagined rewards are distorted and whether cached values can be forgotten.

Cached values encode estimates of expected discounted future return in a lookup table. Specifically, we define the Q value of taking action a in state s as
Qsa=Et=0γtrts0=s,a0=a
where rt is the reward received at time t and γ is a discount factor that down-weights distal rewards. The temporal difference learning algorithm (specifically the SARSA algorithm; see Sutton & Barto, 1998) updates a cached value estimate Q^tsa according to the prediction error
δt=rt+γQ^tst+1at+1Q^tstat.
This same update can apply to both experienced and imagined state–action pairs, a key insight of Sutton's (1990) Dyna architecture. We refer to this as the “standard” model. Note that, although we focus on model-free learning algorithms in this article, our data can also be accounted for by model-based variants. We do not explore these here because they make essentially the same predictions.

We consider two modifications of the standard model. In the “forgetting” model, all the Q values are decayed toward 0 by a factor ω. This captures the idea that the imagination trial can lead to forgetting of the Q values, independent of any effect of imagination per se. In the “imagination bonus” model (“imagination” model for short), reward predictions are distorted by a fixed additive bias, ε. This captures the idea that imagination can be contaminated by optimistic or pessimistic beliefs about unknown rewards. Finally, we considered a hybrid of these two extended models (the “imagination + forgetting” model), which includes both parameters.

Parameters were estimated by fitting the model to the choice data from the decision trials (see Methods for details). We found that the imagination + forgetting model could qualitatively capture the pattern of experimental results (Figure 4A), and random effects Bayesian model comparison favored this model over the other variants (protected exceedance probability of .94; Figure 4B).

Figure 4. 

Computational modeling. (A) A reinforcement learning model that includes both an imagination bonus and a forgetting parameter can reproduce the pattern of choice behavior in Experiment 1 (compare with Figure 2A). (B) Bayesian model comparison favors the imagination + forgetting model over models with forgetting only, imagination only, or a standard model (neither imagination nor forgetting). The x axis represents the protected exceedance probability (Rigoux et al., 2014)—the probability that a particular model is more frequent in the population compared with all other models under consideration. (C) Models fit to decision trial data correlate with reward predictions on imagination trials in Experiment 1. The imagination + forgetting model has a significantly higher correlation compared with the next best model (forgetting only).

Figure 4. 

Computational modeling. (A) A reinforcement learning model that includes both an imagination bonus and a forgetting parameter can reproduce the pattern of choice behavior in Experiment 1 (compare with Figure 2A). (B) Bayesian model comparison favors the imagination + forgetting model over models with forgetting only, imagination only, or a standard model (neither imagination nor forgetting). The x axis represents the protected exceedance probability (Rigoux et al., 2014)—the probability that a particular model is more frequent in the population compared with all other models under consideration. (C) Models fit to decision trial data correlate with reward predictions on imagination trials in Experiment 1. The imagination + forgetting model has a significantly higher correlation compared with the next best model (forgetting only).

As an additional test of the models, we matched their reward predictions on the imagination trials to the empirical data (note that the models were not fit to these data). The average correlation between model and empirical reward predictions for the imagination + forgetting model was .57 ± .03 SEM (Figure 4C). After Fisher z transforming to approximate a normally distributed random variable, this correlation was significantly larger than the correlation for the forgetting model (t(86) = 3.18, p < .005). Thus, the reward prediction analysis recapitulates the results of the Bayesian model comparison, supporting the imagination + forgetting model as the best quantitative account of our behavioral data among the alternatives we considered.

Neuroimaging Results

A separate group of participants (n = 20) completed our task while their brains were scanned with fMRI. We first asked whether neural activity during imagination trials could predict whether imagined or optimal paths would be taken on the subsequent decision trial. The contrast between subsequently imagined versus subsequently optimal paths revealed a striking dissociation between several brain regions (Figure 5A). Medial pFC, OFC, and lateral temporal cortex showed greater activity during imagination trials that lead to choosing the imagined path on the next decision trial, compared with trials that lead to choosing the optimal path. The reverse contrast showed greater activity in regions of the parietal cortex as well as precuneus, fusiform gyrus, and calcarine sulcus.

Figure 5. 

Brain regions showing greater BOLD activity during imagination trials before choosing the imagined path, compared with trials before choosing the optimal path. (A) Medial pFC, OFC, and lateral temporal cortex were activated more strongly for imagined > optimal, whereas inferior parietal, occipital, precuneus, and fusiform regions were activated more strongly for optimal > imagined. Results are thresholded at p < .05, cluster FWE. (B) Bilateral activation in anatomical hippocampus ROI for optimal > imagined, p < .001, uncorrected. Note that this activation did not survive small-volume correction.

Figure 5. 

Brain regions showing greater BOLD activity during imagination trials before choosing the imagined path, compared with trials before choosing the optimal path. (A) Medial pFC, OFC, and lateral temporal cortex were activated more strongly for imagined > optimal, whereas inferior parietal, occipital, precuneus, and fusiform regions were activated more strongly for optimal > imagined. Results are thresholded at p < .05, cluster FWE. (B) Bilateral activation in anatomical hippocampus ROI for optimal > imagined, p < .001, uncorrected. Note that this activation did not survive small-volume correction.

Motivated by data indicating involvement of the hippocampus in imaginative simulation (Buckner, 2010), we tested the a priori hypothesis that the hippocampus would show greater activity for the imagined versus optimal contrast. The hippocampus showed weak bilateral activation for imagined > optimal (Figure 5B), although this effect did not survive small-volume correction within an anatomically defined ROI.

Reward prediction errors derived from temporal difference models reliably correlate with BOLD signal in the ventral striatum (Daw et al., 2011; Gläscher et al., 2010; Seymour et al., 2004). This is the case in our study as well (Figure 6A). Crucially, Bayesian model comparison applied to the ventral striatum strongly favored the imagination + forgetting model (exceedance probability of .99; Figure 6B). Thus, the neural and behavioral model comparisons provide converging evidence for a model in which imagination both decays and distorts cached values.

Figure 6. 

Neural model comparison in the ventral striatum. (A) Temporal difference prediction errors correlated with BOLD activity in the ventral striatum, p < .001, uncorrected. The prediction error regressor was derived from the imagination + forgetting model. (B) Bayesian model comparison within an anatomically defined ventral striatum ROI favored the imagination + forgetting model.

Figure 6. 

Neural model comparison in the ventral striatum. (A) Temporal difference prediction errors correlated with BOLD activity in the ventral striatum, p < .001, uncorrected. The prediction error regressor was derived from the imagination + forgetting model. (B) Bayesian model comparison within an anatomically defined ventral striatum ROI favored the imagination + forgetting model.

DISCUSSION

Whereas learning from experience has figured prominently in computational theories of reinforcement learning, learning from imagination remains poorly understood. Our experiments provide novel insights into the contribution of imagination, demonstrating that people will shift their policies toward imagined paths, even when these are objectively suboptimal. A key factor in this “imagination effect” is the miscalibration of reward predictions: People are consistently optimistic about how much reward they expect to receive in imagined states and are more likely to take imagined paths when they are more optimistic. This optimism can be captured in reinforcement learning models that learn from both experience and imagination (Gershman et al., 2014; Sutton, 1990). Our fMRI data provide converging evidence for such models, showing that classical value-coding regions, such as ventromedial cortex and OFC, are more active during imagination trials that lead to subsequently choosing the imagined path.

Two main conclusions can be drawn from our findings. First, they argue against a plausible alternative hypothesis that imagination is cognitively encapsulated from learning—a kind of “transcendent” use of the imagination (cf. Kind & Kung, 2016). This hypothesis would predict that the imagination trials should have no influence on subsequent decision-making, contrary to our findings. Instead, they support the “instructive” use of imagination, whereby an agent can learn new things about the world purely through acts of imagination. Philosophers have long debated the epistemic status of such acts, in particular, whether imagination can produce genuinely new knowledge (Sorensen, 1992), but regardless of the answer to this question, our findings demonstrate empirically that imagination can guide reinforcement learning.

The second conclusion is that imaginative simulation is susceptible to optimism bias (Sharot, 2011). This suggests that, although learning from the imagination is a powerful tool for going beyond limited experience, it is susceptible to, and may even amplify, certain cognitive biases.

One limitation of our study is that we cannot entirely rule out a demand effect where the participant assumes that the experimenter is implicitly recommending a destination in the imagination trials. However, this possibility does not explain why participants are sometimes negatively miscalibrated (i.e., pessimistic) and why this miscalibration predicts the imagination effect. Moreover, it does not explain why participants sometimes chose the nonimagined/nonoptimal path. Nonetheless, these observations do not exclude the possibility that demand effects are exerting an influence on behavior in our task; further control experiments will be necessary to decisively rule out demand effects.

Acquiring Knowledge through Imagination

Our findings dovetail with several other lines of research on the role of imagination in learning. Motor skills can improve after a rest period without additional training (Korman, Raz, Flash, & Karni, 2003; Walker, Brakefield, Morgan, Hobson, & Stickgold, 2002), and reactivating memories during sleep can enhance subsequent task performance (Oudiette & Paller, 2013). Explicit mental practice tasks have yielded similar results (Tartaglia, Bamert, Mast, & Herzog, 2009; Wohldmann, Healy, & Bourne, 2007; Driskell, Copper, & Moran, 1994).

Mast and Kosslyn (2002) provide a striking example of learning from imagination in the domain of visual perception. They presented participants with an ambiguous image whose alternative interpretation was only revealed after rotating it. Critically, participants could discover this alternative interpretation by mentally rotating the image, indicating that imagery is sufficient for discovering new information about the world.

Similar processes may underlie ubiquitous (yet still mysterious) animal learning phenomena such as spontaneous recovery and latent inhibition (Ludvig, Mirian, Kehoe, & Sutton, 2017). Another animal learning phenomenon that may lend itself to this analysis is “paradoxical enhancement of fear” (Rohrbaugh & Riccio, 1970): Animals conditioned to associate a tone and a shock will increase their fear after being presented with a single isolated tone, despite the fact that this presentation is operationally an extinction trial and would be expected to decrease fear. This finding might be accommodated by positing that the animal is learning from the reinforcing effects of an imagined shock.

Interactions between Model-based and Model-free Reinforcement Learning

The current standard theory of reinforcement learning in the brain depicts two systems (one model-based and one model-free) locked in competition for control of behavior (Kool, Cushman, & Gershman, 2016; Dolan & Dayan, 2013; Daw et al., 2011; Daw, Niv, & Dayan, 2005). Considerable evidence supports this theory, including the fact that the systems can be independently manipulated both neurally (Smittenaar, FitzGerald, Romei, Wright, & Dolan, 2013; Wunderlich, Smittenaar, & Dolan, 2012; Balleine & Dickinson, 1998) and behaviorally (Otto, Gershman, Markman, & Daw, 2013).

Despite its success, the competitive theory is incomplete; other lines of research indicate that several forms of cooperation between the systems also occur (see Kool, Cushman, & Gershman, in press, for a review). The model-free system may select goals for the model-based system to pursue (Cushman & Morris, 2015) or provide value estimates for approximate model-based planning (Keramati, Smittenaar, Dolan, & Dayan, 2016). Imaginative reinforcement learning is based on the idea that influence can flow in the opposite direction, with the model-based system supplying simulations for training the model-free system (Gershman et al., 2014; Pezzulo, Rigoli, & Chersi, 2013; Sutton, 1990).

Neural Substrates of Imaginative Reinforcement Learning

Several previous studies have examined the neural correlates of imagination during reward-based tasks. Bray, Shimojo, and O'Doherty (2010) asked participants to either experience or imagine rewards in the scanner, finding that medial OFC was active for both experienced and imagined rewards. This same region was sensitive to hypothetical rewards in a Pavlovian conditioning task, along with the midbrain, which parametrically tracked expectations about the amount of hypothetical reward (Miyapuram, Tobler, Gregorios-Pippas, & Schultz, 2012). Finally, Bulganin and Wittmann (2015) found that imagination of rewarding personal events activated the striatum, midbrain, and hippocampus as well as increased functional connectivity between these regions.

Johnson and Redish (2005) have suggested that place cells in the hippocampus may act as the neural substrate for a simulation engine. The key evidence for this hypothesis comes from studies showing that place cells replay sequences visited locations during rest and sleep (see Carr, Jadhav, & Frank, 2011, for a review). Many human brain imaging studies have also implicated the hippocampus in imaginative simulation (Buckner, 2010). Consistent with these prior results, we found weak evidence that hippocampal activity predicted whether imagined paths would be subsequently taken, with the caveat that this effect did not survive correction for multiple comparisons.

In addition to the hippocampus, our analyses revealed a collection of regions involved in imaginative effects on decision-making. Broadly speaking, relatively anterior regions (medial pFC, OFC, and lateral temporal cortex) predicted the choice of the imagined path, whereas relatively posterior regions (parietal and occipital cortex, precuneus, fusiform gyrus, and calcarine sulcus) predicted the choice of the optimal path. A perhaps overly simplistic functional division would be into anterior regions dedicated to evaluating the motivational consequences of decisions and posterior regions dedicated to simulating the perceptual consequences of decisions. Some of these same regions have been implicated in several different forms of prospection (Spreng, Mar, & Kim, 2009).

Prior studies have found that inferior parietal cortex and precuneus predict correct rejection of imagined information during memory retrieval (Kensinger & Schacter, 2006; Gonsalves et al., 2004). In some cases, false memories are associated with activity in ventromedial pFC (Kensinger & Schacter, 2006), consistent with our neuroimaging results. However, no prior studies have directly examined the neural processes involved in imagination during reinforcement learning.

Bug or Feature?

Is imagination useful or hurtful? Clearly, the ability to imagine certain scenarios without actually experiencing them can be useful, perhaps even indispensable in the real world. Most of us do not need to experience killing someone to know that it has undesirable consequences. Moreover, simulating such scenarios can exert a powerful effect on psychophysiological measures of aversion (Cushman, Gray, Gaffey, & Mendes, 2012), suggesting that acts of imagination approach the potency of real experience.

On the other hand, we have demonstrated that imagination falls prey to the well-known optimism bias (Sharot, 2011), and this in turn influences subsequent decisions. Our findings are also closely related to another bias: imagination inflation, the observation that simply imagining an event can increase one's judgment of its likelihood. For example, participants asked to imagine either Gerald Ford or Jimmy Carter winning the 1976 presidential race subsequently rated the imagined event as more likely (Carroll, 1978). In essence, our main finding is a reinforcement learning version of imagination inflation, whereby imagining an event increases one's judgment of its value.

Thus, overzealous use of the imagination could easily go awry. As philosophers have recognized (Kind & Kung, 2016; Sorensen, 1992), the instructive use of the imagination is critically dependent on its obedience to constraints imposed by the real world. If imagination can be untethered from these constraints, then we may find ourselves mistakenly using it to transcend reality rather than to learn about it.

Acknowledgments

This project was made possible through grant support from the National Institutes of Health (CRCNS R01-1207833). This work involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program, grant number S10OD020039. We acknowledge the University of Minnesota Center for Magnetic Resonance Research for use of the multiband-EPI pulse sequences. We are grateful to Bradley Doll for sharing his stimuli, to Florian Froehlich for helping to collect data, and to Adam Morris for comments on a previous draft of the article.

Reprint requests should be sent to Samuel J. Gershman, Department of Psychology, Harvard University, Room 295.05, 52 Oxford St., Cambridge, MA 02138, or via e-mail: gershman@fas.harvard.edu.

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