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. Author manuscript; available in PMC: 2021 Dec 20.
Published in final edited form as: Cell Rep. 2021 Nov 30;37(9):110065. doi: 10.1016/j.celrep.2021.110065

The hippocampus and orbitofrontal cortex jointly represent task structure during memory-guided decision making

Eda Mızrak 1,5,6,*, Nichole R Bouffard 3, Laura A Libby 1, Erie D Boorman 1,2,4, Charan Ranganath 1,2,*
PMCID: PMC8686644  NIHMSID: NIHMS1760954  PMID: 34852232

SUMMARY

The hippocampus, well known for its role in episodic memory, might also be an important brain region for extracting structure from our experiences in order to guide future decisions. Recent evidence in rodents suggests that the hippocampus supports decision making by representing task structure in cooperation with the orbitofrontal cortex (OFC). Here, we examine how the human hippocampus and OFC represent task structure during an associative learning task that required learning of both context-determined and context-invariant probabilistic associations. We find that after learning, hippocampal and lateral OFC representations differentiated between context-determined and context-invariant task structures. The degree of this differentiation within the hippocampus and lateral OFC is highly correlated. These results advance our understanding of the hippocampus and suggest that the hippocampus and OFC support goal-directed behavior by representing information that guides the selection of appropriate decision strategies.

In brief

Mizrak et al. use fMRI to demonstrate that hippocampus and orbitofrontal cortex generalize across decisions that share the same task sub-structure compared with different task sub-structures. Results show that the hippocampus, in coordination with OFC, supports decision making by extracting structure from past experiences.

Graphical Abstract

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INTRODUCTION

Our decisions often rely on our past experiences. For instance, in your local grocery store, you might skip the produce section because previously the produce did not live up to your expectations. Humans can efficiently extract regularities across experiences to make adaptive decisions (i.e., not buying the produce), and several possible mechanisms have been proposed to explain this process.

One simple possibility is that people draw from specific experiences (i.e., the banana I bought last week at the supermarket was not good). Neuroscience research has reliably shown that the hippocampus is critical for episodic memory formation and retrieval (Moscovitch et al., 2016; Ranganath, 2010). It has been suggested that hippocampus bridges past experiences with future decisions (Biderman et al., 2020). According to some models, the hippocampus binds and represents item and context information from events and episodes, such as banana and local grocery store. The binding of items in context, or creating strong associations between events and their spatial or non-spatial contexts, has been widely studied for animal and human hippocampus (Davachi, 2006; Manns and Eichenbaum, 2009; McKenzie et al., 2014; Ranganath, 2010). A related view is that the hippocampus might form chains of overlapping associations (Horner et al., 2015; Schlichting and Preston, 2015) or statistical regularities (Schapiro et al., 2016, 2017) that integrate across multiple events.

Another non-exclusive possibility is that we might form more general representations that capture the abstracted structure of an environment or task, so that we can make future decisions in a flexible manner. For instance, when shopping, you might have a structured representation or “cognitive map” (Tolman, 1948) of your neighborhood about which store provides the best produce and which stores to skip. Later, you will use this cognitive map to guide grocery shopping decisions. One theory proposes that the hippocampus might encode the cognitive maps that might be generally deployed during decision making (O’Keefe and Nadel, 1978). Although the concept of cognitive maps has been historically applied to maps of physical space, recent work has emphasized a more general role for the hippocampus, in capturing the structure of a task, or a “task space” that transcends spatial contexts (Behrens et al., 2018; Bellmund et al., 2018; Schiller et al., 2015). A task space consists of information about the interrelationships among all the factors that are relevant to a decision for attaining the desired goal. For instance, the hippocampus could form structured representations of task-relevant information that has been learned and generalized across past experiences: task structure representation.

In addition to the hippocampus, orbitofrontal cortex (OFC) is another region that supports decision making (i.e., not buying a banana at the local grocery store) by drawing on outcomes of previous experiences (for reviews see Rushworth et al., 2011; Wallis, 2007). According to this view, OFC represents the predicted and experienced value of behavioral outcomes and is responsible for signaling this value to other regions to help guide current or future decisions (Murray and Rudebeck, 2018; Padoa-Schioppa and Conen, 2017; Rushworth et al., 2011; Wallis, 2019). For example, the next time you are at the grocery store, the OFC can provide value prediction for bananas that can be used to predict the outcome and to guide a shopping decision. A recent theory, however, suggests that OFC might have a more general function beyond representing value, and additionally the OFC might represent the structure of the task or environment (Wilson et al., 2014). For instance, studies have provided evidence that OFC represents the current task state in a cognitive map of the task spaces (Park et al., 2020; Schuck et al., 2016; Wikenheiser and Schoenbaum, 2016; Zhou et al., 2021). In this view, task structure representation is a more general role for OFC that also includes value representation that can be derived from the task structure.

Recent studies have also unpacked the relationship between hippocampus and OFC in representing task structure. For instance, it has been suggested that the hippocampus might encode concrete and detailed information about the environment. This information can be spatiotemporal context or incidental associations between events, which is then received by the OFC as an input and tailored for the cognitive needs, extracting the most relevant information for decision making. Additionally, Wikenheiser et al. (2017) showed that when hippocampal inputs to OFC were suppressed, OFC encoding of the latent context, a critical part of the reversal task structure, was impaired. This finding suggested that OFC representation of the task structure is shaped by the inputs received from the hippocampus. In sum, it has been proposed that both the hippocampus and OFC encode the cognitive map of a task space: the task structure (reviewed in Wikenheiser and Schoenbaum, 2016).

In the present study we examined the role of hippocampus and OFC in representing task-related information. We used representational similarity analysis (RSA) of functional magnetic resonance imaging (fMRI) data to investigate what type of information the hippocampus and OFC represented in a memory-guided decision-making task. Participants learned about desirability of foods in different stores, for which a latent task structure was available. Specifically, participants learned about customer preferences for eight food items in four different grocery stores. After learning the customer preferences in each store, participants were scanned while they decided whether a food was desired or not by customers on the basis of previous learning for each food in each store context.

The task was designed such that the desirability of some of the foods was deterministic, either “liked” or “disliked” depending on the specific store context (Figure 1). These foods are referred to as “context-determined” (CD) foods, because information about the context was needed in order to make accurate decisions about the customer’s preference (i.e., probability of being “liked” = 1.0 or 0 depending on the store). The desirability of other foods was probabilistic, and this probability did not change across stores (i.e., probability of being “liked” = 0.75 or 0.25 at every store). These foods are referred to as context-invariant (CI) because the context was irrelevant to decisions about the preferences. This difference between the foods introduced two distinct sub-structures of the task. In the case of our study, these two sub-structures determined whether food outcomes depended on the store context or not. Participants were not explicitly informed about these two sub-structures; they needed to learn this abstract information from the distribution of like and dislike probabilities across stores and foods. In other words, the task structure was hidden or latent because it needed to be inferred from the food outcome distributions. If participants were able to learn these two sub-structures, they could generalize over them and lump these foods together; representing them more similarly to each other. In addition to the latent task structure, some foods had similar outcomes across stores, or some stores provided similar outcomes across foods (Figure 1B). The study design enabled us to observe how participants learn a simple latent task structure and how task-related information is represented and retrieved in the hippocampus and OFC when participants are making decisions.

Figure 1. Experimental paradigm and behavioral results.

Figure 1.

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(A) Learning phase. Illustration of a trial from the learning phase. On day 1, participants learned the customer preferences for each food in each store. For the first 8 blocks, subjects are in one store for the entire block and loop through all eight foods four times. In blocks 9–16, subjects loop through all four stores within a single block, looping through all eight foods once in each of the four stores. For each trial, one food item is shown on the store context, and the participant is asked to predict if customers like this food item in this store context or not. After making their predictions, participants were given feedback and were told whether they were correct, along with a picture of a customer’s reaction to that food. At the end, participants would have seen each food item 16 times in each store.

(B) Task structure. During the learning phase, participants made predictions about food preferences, and then they received feedback about the actual outcome: customer preference for that food in that store. The matrix illustrates the distribution of outcomes for eight different foods (columns) in each of the four stores (rows). Each cell corresponds to the probability of a like outcome for the given food within the given store; 1.0 means 100% liked, and 0 means 0% liked or 100% disliked. Context-determined foods are defined as those with outcomes that are determined by the store context (left side of the matrix), and context-invariant foods are those with probabilities that are the same across stores (right side of the matrix). The task was designed so that there two task sub-structures: context determined and context invariant. Additionally, there were pairs of similar contexts for which the same foods were liked and disliked in both stores.

(C) Learning performance. Performance across all 16 blocks of learning. Foods were separated into four categories on the basis of their like outcome probabilities: 1, 0, 0.75, and 0.25. A repetition consisted of four loops of learning of each food in each store. Therefore, the first repetition was the first time a food category was learned at each store. Learning performance was calculated for each food category by averaging across trials of the foods at each store. Performance corresponded to the proportion of responses of the most likely outcome for a food in a store; like responses for foods with like probabilities of 0.75 and 1 and dislike responses foods with like probabilities of 0.25 and 0. Performance increased with repetitions, and food preferences for each food category were learned with above 60% performance at the end of the learning blocks. Error bars denote within-subject 95% confidence intervals.

One possibility is that the hippocampus represents each individual event with a unique neural pattern (item + context). Another possibility is that shared item (i.e., food) and context (i.e., store) information is represented in a structured way that can be observed as a neural pattern generalization across these shared dimensions. The OFC might behave similarly to the hippocampus and represent foods on the basis of their outcomes in a structured way and could generalize across foods with similar outcomes (Zhou et al., 2019). Accordingly, there were different types of information that could be extracted from the overarching task structure; the sub-structures (CD versus CI), item, and context-specific information. We investigated the representational structure of human hippocampus and OFC, and their relationship, while people made context-dependent and CI decisions.

RESULTS

Learning phase: Participants learned the desirability of foods in stores

Participants first learned about the implicit relationships between the foods and store contexts before they used this knowledge to make decisions about the foods (Figure 1A). Participants were not explicitly instructed about the task structure (Figure 1B) that determined the probabilities of customer preferences for each food. They learned desirability of foods by trial and error on the basis of the feedback they received. Participants completed 12 blocks of training with feedback on decision outcomes on day 1. After this training phase, we assessed participants’ performance. Only participants who performed above a criterion performance of 60% optimal predictions for both CD and CI foods were admitted to the second part of the study in the scanner.

We hypothesized that learning performance for CD versus CI foods would be different for two reasons: (1) CD foods required learning of the importance of context for the outcome, and generalization across contexts would be harmful for these foods, whereas for CI foods, context was not a decision-relevant variable, and (2) CD foods were deterministic and CI foods were probabilistic. These differences across the two structures could result in two types of decision-making: (1) deterministic, context-dependent, and (2) stochastic, CI. If the participants differentiated across these two task structures, they could perform differently for CD and CI foods.

To assess learning differences between the two sub-structures, we scored each decision during the learning phase as a function of whether the participant made the optimal decision for that trial type. On CI trials, the optimal decision was defined as the most likely outcome for that food item. If a food was liked on 75% of trials, “like” predictions were scored as optimal, whereas if it was liked on only 25% of the trials, “dislike” predictions were deemed optimal. In other words, even without any knowledge of the task structure, participants could make accurate predictions solely from learning the relative frequencies of “like” and “dislike” predictions for that food item. For example, in the example depicted in Figure 1B, it would be optimal to choose “dislike” for the banana and “like” for the donut, regardless of the current store context.

On CD trials, the optimal prediction was based entirely on the store context. For example, with the information depicted in Figure 1B, it would be optimal to decide that the apple is liked in stores A and B and disliked in stores C and D. Across all trials, the “like” probabilities for all CD foods summed to 50%. Accordingly, outcomes on CD trials could be accurately predicted only if the participant learned to adopt context-dependent decisions.

We calculated proportion of optimal responses for foods with different outcome probabilities (CD foods, 0 and 1; CI foods, 0.25 and 0.75) and for each repetition averaging across stores. Figure 1C shows learning performance over the course of training. We only analyzed the data from blocks 13–16 on day 2 after learning had occurred and performance stabilized with a repeated-measures ANOVA including sub-structure (CD versus CI), repetition (13–16), and valence (like versus dislike). As can be seen in Figure 1C, the proportion of optimal predictions was significantly higher on CD trials than on CI trials (main effect of sub-structure: F[1, 21] = 31.18, p < 0.001).

Decision phase: Participants used their knowledge of the task structure when making decisions about foods

After completing the learning phase, participants were asked to decide whether a food was liked or not in a store context and were scanned during the decision phase. As in the training phase, each trial commenced with presentation of a food image overlaid on a store context, and the participant was instructed to predict the customer preference (either like or dislike) for the presented food in the given store (Figure 2A). Critically, during this phase, no feedback was given, so participants needed to rely on what was learned during the training phase to optimally predict customer preferences for each food.

Figure 2. Hippocampus and lateral OFC both represent task structure.

Figure 2.

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(A) Decision phase. The trial procedure was like learning blocks 9–16, with the only difference being that no feedback was given after decisions. The same eight foods were shown in each mini-block in different contexts but in a randomized order. Each run consisted of eight mini-blocks.

(B) Decision performance. Foods were categorized as context dependent (CD) or context invariant (CI), and the proportion of optimal decisions was calculated for CD and CI foods separately and averaged across stores for each run. Decision performance showed that participants learned the task structure well enough to make optimal decisions for both CD and CI foods. CD foods were slightly better than CI foods (p = 0.049).

(C) Task structure. We calculated pattern similarity (PS) for the two same sub-structure trial pairs (CD-CD or CI-CI) and different sub-structure trial pairs (CD-CI).

(D) Task structure representation in hippocampus. Behavioral performance indicated that participants learned the difference between CD and CI task structures. Pattern similarity for same task structure trial pairs was higher than different task structure.

(E) Task structure representation in OFC. Lateral OFC pattern similarity was higher for same task structure compared with different task structure.

(F) Learning and task structure in hippocampus. We calculated a neural index for task structure representation in hippocampus, PS same-different task structure, and an index for learning differentiation, performance CD-CI for each participant. The learning index (i.e., difference in learning performance between CD and CI trials) was positively correlated with the neural index (i.e., differences in neural representation of same versus different sub-structure).

(G) Learning and task structure in lateral OFC. There was a positive correlation between neural index of task structure representation in lateral OFC and learning index.

(H) Hippocampus and lateral OFC represent task structure similarly. There was a strong correlation between the hippocampus neural index and lateral OFC neural index.

Error bars denote within-subject 95% confidence intervals.

We found that participants responded optimally on 90% (SD = 9%) of CD trials and 82% (SD = 14%) of CI trials. A direct comparison between the sub-structures showed that the proportion of optimal decisions (as defined in the learning phase section) was higher for CD than CI trials (F[1, 21] = 4.48, p = 0.049). The near-ceiling accuracy levels for CD trials indicates that participants successfully learned to make decisions on the basis of the context, rather than overall frequency of “like” responses (Figure 2B).

Task structure representation in hippocampus and OFC: Differentiation of task structures

Although the difference between CD and CI foods was not explicitly stated, the behavioral data described above indicate that participants successfully learned and differentiated between the two types of sub-structures during the learning and decision phases.

If the hippocampus represents the learned task structure and generalizes across the decisions that shared that structure, we would predict higher pattern similarity (PS) between pairs of CD trials (e.g., an optimal decision about the apple in store A and an optimal decision about the carrot in store C) and between pairs of CI foods (e.g., an optimal decision about the banana in store A and an optimal decision about the donut in store D), compared with the similarity between CD and CI trials (e.g., an optimal decision about the apple in store A and an optimal decision about the donut in store D).

To test whether the task structure was represented in a manner that generalized across the two sub-structures that were possibly differentiated during learning, we computed voxel-wise hippocampal PS (collapsed across hemispheres) for same sub-structure trial pairs (CD-CD or CI-CI) and different sub-structure trial pairs (CD-CI). Analyses of these data revealed that hippocampal PS was significantly higher for same than different trial pairs (significant effect of task structure; F[1, 21] = 4.74, p = 0.04), consistent with the hypothesis that the hippocampus represents information about the task structure (Figures 2C and 2D).

Next, we examined the task structure representation in the OFC. On the basis of anatomical connectivity-based parcellations (Neubert et al., 2015), OFC can be divided into three subregions; medial, lateral, and central. Several studies have provided evidence for dissociable functions of subdivisions of the OFC (Noonan et al., 2010; Rudebeck and Murray, 2011). Therefore, we separated OFC into its subdivisions and examined task structure representation with a two-way repeated-measures ANOVA including task sub-structure (same, different) and OFC subdivision (lateral, medial, and central) as factors. There was an interaction between task structure and OFC subdivision (F[1, 21] = 4.20, p = 0.037). Further analyses showed that PS values were higher for same task structure trial pairs than for different task structure trial pairs in lateral (same > different, t[41.3] = 3.65, p < 0.001) OFC, whereas no significant effects were observed in medial (t[41.3] = 1.77, p = 0.084) or central (t[41.3] = 0.87, p = 0.39) OFC (Figure 2E).

Learning performance predicts hippocampal representations

In addition to examining task structure representations in hippocampus and OFC during the decision phase, we also investigated the relationship between behavioral performance during the learning phase on day 1/day 2 and neural representations in the hippocampus and OFC during the decision phase on day 2. On day 1 and the beginning of day 2, participants implicitly learned the outcomes between each food item and each store context through trial and error. The underlying task structure (i.e., CD and CI foods) was not explicitly instructed; therefore participants demonstrated different learning patterns. Some individuals differentiated between CD and CI foods and showed better performance for CD foods than CI foods, while others had similar performance for both CD and CI foods. If participants learned the task structure in a way that differentiated between the two task structures (instead of just memorizing each individual event), then this differentiation might be evident in the neural representations during the decision phase. In other words, a greater difference in performance on CD and CI foods during learning would be predictive of a greater difference in neural representations of CD and CI foods during the decision phase.

To understand how individual differences in learning performance are related to neural representation of the task structure during the decision phase, we computed the average difference in performance between CD and CI foods from the learning phase on days 1 and 2 for everyone (i.e., a learning index). We then computed the difference between neural representations of CD and CI foods in the hippocampus and OFC during the decision phase (i.e., a neural index). Specifically, for each participant, we calculated average of the “same sub-structure” representational values (i.e., CD/CD and CI/CI) and subtracted it from the average of “different sub-structure” representational values (i.e., CD/CI). We correlated the learning index with the neural index to examine the relationship between learning performance and neural representations of the task structure (Figures 2F and 2G).

The results showed that the learning index was significantly correlated with the neural index in hippocampus (R = 0.51, p = 0.016), whereas the correlation in OFC was relatively high but not statistically significant (R = 0.41, p = 0.055). The correlations showed that larger learning differences between CD and CI trials predicted stronger neural differentiation of the two sub-structures, suggesting that the learning of the task structure predicted the extent to which the structure was evident subsequently during decisions in hippocampal voxel patterns (Figures 2E and 2F).

Related task structure representations in hippocampus and OFC

We next investigated the relationship between the hippocampus and OFC in this task. If hippocampus and OFC both represent the task structure, then we reasoned that task structure representation indices should be similar for the two regions across subjects. For each participant, we calculated average of the “same sub-structure” representational values (i.e., CD/CD and CI/CI) and subtracted it from the average of “different sub-structure” representational values (i.e., CD/CI) for both hippocampus and OFC. When we correlated these values from each participant with each other, we found a striking correlation between task structure representation in the hippocampus and lateral OFC: those individuals with stronger representations of task structure in hippocampus also had stronger representations of task structure in OFC (R = 0.74, p < 0.0001). This finding suggests that there was strong concurrence between hippocampal and OFC representations of task structure (Figure 2G).

Item and context representations in hippocampus and OFC

To further investigate what type of information hippocampus and OFC extract and represent from the task, we next examined whether hippocampal and OFC activity patterns also carry information about the specific contexts in which the decisions were made and/or the items the decisions were made about. There are at least two ways in which items and/or contexts can be represented. One possibility is that hippocampus generalizes across any trials that share similar properties. That is, overlap of the food-preference mappings between stores or between foods could result in similar neural patterns. For example, foods that have the same outcomes across all stores (i.e., apple and muffin) might have more similar neural patterns compared with two foods that have different outcomes across all stores (i.e., apple and carrot). There is also overlap between stores that share the same food-preference mappings across all foods, which could result in similar neural patterns between stores (e.g., stores A and B would have a similar neural pattern, and stores C and D would have a similar neural pattern). Another possibility, based on findings from many recent fMRI studies (see Ritvo et al., 2019, for a review), is that overlap between item or context features could instead result in dissimilar patterns in the hippocampus, leading to greater dissimilarity between foods or stores that share similar properties.

Generalization can occur at the item level (i.e., across similar foods) or at the context level (i.e., similar stores) on the basis of their outcomes (i.e., like versus dislike probabilities). Given that there was evidence of sub-structure representation in both the hippocampus and OFC (i.e., CD and CI foods were differentiated), we investigated item and context representations for CD and CI foods separately.

For CD foods, we performed repeated-measures ANOVAs on PS values with factors for food type (similar, different) and store type (similar, different) separately for hippocampus and OFC, and we performed the same analyses for CI foods. These analyses revealed no significant effects involving food type or store type for either hippocampus (F < 2.69 and p > 0.11 for all) or OFC (F < 2.39 and p > 0.13 for all; see Figure S1 and Tables S2A, S2B, S3A, and S3B in the Supplemental information for the full results).

DISCUSSION

The goal of the present study was to investigate the role of the hippocampus and OFC in a memory-guided decision-making task. In our experiment, participants needed to learn and integrate information about items (i.e., foods), contexts (i.e., stores), and task structures (CD versus CI probabilistic) that could help in making accurate future decisions. Even though participants were never explicitly informed about the distinction between CD and CI trials, activity patterns in the hippocampus and lateral OFC carried information about this task structure. Moreover, task structure representation in the hippocampus was behaviorally relevant, in that it predicted the extent to which participants showed behavioral differences between CD and CI trials during learning. Finally, we found a striking correlation between task structure representations in hippocampus and lateral OFC. Collectively, these findings are consistent with the hypothesis that hippocampus and OFC collectively represent task structure during decision making.

Representation of task structure in hippocampus

If memories of past experiences are used to guide a decision, then the critical question is, how is decision-relevant information represented in memory? In our paradigm, there are many kinds of information that could conceivably be retrieved, some of which would be relevant for predictions and some of which would be irrelevant. The simple “episodic memory retrieval” interpretation would be that, when making each prediction, subjects would simply recall past experiences with each item, as they would in a traditional episodic memory task (Bornstein and Norman, 2017). In other words, participants could have represented outcomes for each food in each store individually, as 32 unique events. It is conceivable that participants could have used this kind of simple memory retrieval strategy and memorized each of the 32 food-store-outcome associations separately. This account would suggest that participants would form highly differentiated representations, such that there would be no generalization across food items or contexts: every item-context pairing should be associated with a unique pattern. This prediction is based on standard models of hippocampal memory functions (Davachi, 2006; Eichenbaum et al., 2007; Ranganath, 2010) but does not fully account for the present results. Instead, we see generalization across foods that were CD or CI.

Another possibility is that the hippocampus generalizes across decisions made in particular stores (contexts), which would be expected from fMRI studies of episodic memory paradigms (e.g., Dimsdale-Zucker et al., 2018; Komorowski et al., 2013; Libby et al., 2019; Ritchey et al., 2015; Schlichting and Preston, 2015; Shohamy and Wagner, 2008; Tompary and Davachi, 2017; Zeithamova and Preston, 2010). Alternatively, the hippocampus might have generalized across items on the basis of similar outcomes (e.g., (Zeithamova et al., 2018)). We were surprised to find that neither of these predictions adequately accounted for the present results. We did not find evidence that the hippocampus generalized more across foods that had the same outcomes (e.g., were liked in the same stores) than foods that had different outcomes (e.g., were liked in different stores). Instead, we found that the hippocampus represented decision-relevant information from past experiences at the level of task structure. Of course, it remains possible that such information is represented in the hippocampus, as we cannot make any inferences or conclusions from null results. Nonetheless, we did find that the hippocampus represented the task-relevant information that was the task structure.

Hippocampal representations during the decision phase paralleled the structure of the task: on every trial, the participant was asked to make a prediction about a particular food in a particular store. CD and CI are abstract properties that are inferred from knowledge of the task structure. In other words, one would generalize across CD foods and differentiate them from CI foods only if one understood this aspect of the task structure. Consistent with this idea, task structure representation was strongest for participants who showed more behavioral differentiation between CD and CI trials during initial learning (Figure 2F).

Our results add in an important way to results from other studies investigating representation of decision-relevant information in the hippocampus and medial temporal lobe cortex during decision making (Aronov et al., 2017; Bellmund et al., 2018; Park et al., 2020). For instance, Eichenbaum and colleagues used single-unit recording studies in rats to investigate hippocampal representation of deterministic, item-context-outcome associations (McKenzie et al., 2014, 2016). Their results indicated that the hippocampus represented task information in a hierarchical manner. Other work has shown that the hippocampus and/or entorhinal cortex represent information about abstract state spaces during performance of decision-making tasks that have a two-dimensional stimulus structure (Aronov et al., 2017; Garvert et al., 2017; Theves et al., 2019). In all these studies, the tasks were designed such that one can adopt a single strategy or model to solve the task. In our experiment, the task was structured such that optimal performance could be achieved by differentiating between CD and CI trials, and surprisingly, this factor dominated hippocampal representations during later decision making. Thus, our results suggest that in addition to examining representations of stimuli or spatial contexts, it might be important to manipulate task structure to fully understand hippocampal contributions to prediction and decision making.

Representation of task structure in OFC

In addition to the hippocampus, researchers have recently proposed that the OFC might also form cognitive maps that are used for decision making. Consistent with this idea, several findings suggest that the OFC represents the space of information that is used to make decisions (Farovik et al., 2015; Schuck et al., 2016). Farovik et al. (2015), for instance, examined how decision-relevant task dimensions were represented in OFC of rats as they performed a context-guided object discrimination task. Results showed that OFC neurons sharply differentiated between events associated with reward outcomes and those that were not. Within each category of rewarded and nonrewarded outcomes, hippocampal neurons generalized across encounters with the same object in the same context.

We did not find evidence to support the idea that the OFC formed generalized representations of foods on the basis of their probability of preference (item similarity); instead, we found that OFC carried information about whether participants were making CD or CI decisions. There are several important differences between the study of Farovik et al. (2015) and the present study. Farovik et al. (2015) provided explicit rewards for correct performance, whereas we examined activity during trials in which participants judged customer preferences, in which participants were not presented with any feedback about the accuracy of their judgments. Another key difference is that in Farovik et al. (2015), the correct decision was dependent entirely on the context, whereas, in our study, context was relevant only for CD trials and not for CI trials. Putting these factors together, it is likely that our task-oriented participants to focus more on task structure rather than item specific preferences per se.

Our results align well with a model recently proposed by Zhou et al. (2021), who argued that representation of value in OFC is relevant to its more general role in representations of task structure. One prominent study that provided evidence for the role of OFC in task representation was by Schuck et al. (2016), who designed an fMRI study in which participants were asked to judge whether a face or a house was young or old on each trial. Images of a face and a house were spatially superimposed and were presented on the same trial. Participants were asked to judge only the face or the house on each trial, and whether to judge a face or a house was determined by a change of age on the previous trial. This required participants to infer the trial type, a hidden state that needed to be learned from previous trials, and created 16 unique trial types. The investigators showed that OFC represented these task states and that it is an important region for task structure representation. That study did not have explicit values assigned to faces or images, so OFC involvement in the task was not directly tied to a value representation. Other studies have also provided evidence that OFC represents hidden states in a task (Park et al., 2020; Vaidya et al., 2021; Wimmer and Büchel, 2019). Sometimes these hidden states could be predictive of a reward as well (e.g., Zhou et al., 2019). In these cases, it was shown that OFC represented both value and task states but could be identified separately in OFC.

Again, this does not diminish a role of OFC in value representation. It is also possible that in tasks that have a simple structure with explicitly rewarded outcomes, OFC will predominantly represent value information (Enel et al., 2020), whereas in situations in which one might adopt multiple models to guide decision making, task structure information might be more prominent.

Related task structure representations in hippocampus and OFC

In the past, research on decision making has focused on OFC, whereas research on the hippocampus has focused on memory. More recently, it has become apparent that hippocampus and OFC interact with each other during learning and decision making (Knudsen and Wallis, 2020; Wikenheiser et al., 2017), and it has been suggested that these regions collectively represent task structure (Wikenheiser and Schoenbaum, 2016). Medial portions of OFC receive direct inputs from the hippocampus, and available evidence suggests that the hippocampus might be a source of context information for the OFC (Eichenbaum, 2017; Navawongse and Eichenbaum, 2013). Wikenheiser and Schoenbaum (2016) suggested that it is possible hippocampus and OFC encode variables in parallel but interact with each other during making of cognitive maps, a unified representation of an environment to guide future behavior. Furthermore, one recent study showed that representation of task structure in OFC was dependent on hippocampal output (Wikenheiser et al., 2017). It has been suggested that hippocampus and OFC play complementary roles in representing task structure in which they cooperate in organizing information that guides behavior (Zhou et al., 2019).

On the basis of the results described above, we investigated the relationships between representation of task-relevant information in the hippocampus and OFC. Our findings showed that lateral OFC, which receives direct projections from the hippocampal formation (Barbas and Blatt, 1995), generalized across decisions that involved a similar task structure (i.e., CD-CD and CI-CI > CD-CI), whereas this effect was not apparent in central and medial OFC. Notably, this lateral subdivision on OFC is thought to be most similar in terms of both its anatomical connectivity and function to the rodent OFC ((Murray et al., 2007); Rushworth et al., 2011). We found a striking correlation between task structure representation indices of hippocampus and lateral OFC. Individuals who showed stronger representations of task structure in hippocampus showed stronger representations of decision type in OFC. This finding suggests that there was strong concurrence between hippocampal and OFC representations of task structure. Thus, our results align with accumulating evidence showing parallel development of task structure representations in the hippocampus and OFC.

In our task, hippocampus and OFC represented the task structure and no other task-related information. However, this does not necessarily imply that the two regions capture the same information from the task. If we had introduced explicit rewards or if we tracked representational similarity during the learning phase, we might have observed differences between the two regions. Additionally, “task structure” involved the extent to which food outcomes were both context dependent and deterministic or context independent and probabilistic. Future studies can separately manipulate context dependence and the type of contingency (probabilistic versus deterministic) to address the extent to which these factors might be represented by different brain regions, including hippocampus and OFC.

General conclusion

In summary, the results suggest that after learning, hippocampus and OFC carry information about the task structure, presumably the most relevant to decisions. Additionally, we demonstrate a relationship between hippocampus and OFC in representing the task-relevant abstracted structure and the extent to which people’s behavior used this information. Our findings add to a growing body of literature showing that goal-relevant information is extensively represented in the hippocampus, and the task structure is jointly represented in hippocampus and OFC.

STAR★METHODS

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Eda Mizrak (edamizrak@gmail.com).

Materials availability

This study did not generate new unique reagents.

Data and code availability

  • The data used for the main analyses of the paper (i.e., behavioral data and single trial models from the fMRI data) has been deposited on the Open Science Framework and is publicly available as of the date of publication.

  • All original analysis code has been deposited on the Open Science Framework and is publicly available as of the date of publication. The DOI is listed in the key resources table.

  • Any additional information required to reanalyze the data reported in this paper is available from the Lead Contact upon request.

KEY RESOURCES TABLE.
REAGENT or RESOURCE SOURCE IDENTIFIER
Deposited data
Dataset OSF DOI 10.17605/OSF.IO/P5HE9
Software and algorithms
FSL 6.0.0 FMRIB; Smith, 2002; Jenkinson et al., 2012 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki
MATLAB2015b MathWorks https://www.mathworks.com/
Freesurfer 6.0.0 Desikan et al., 2006; Fischl et al, 2004 https://surfer.nmr.mgh.harvard.edu
RSA toolbox Nili et al., 2014 https://git.fmrib.ox.ac.uk/hnili/rsa
HCP-MMP1.0 atlas Glasser et al., 2016 https://osf.io/azup8/
R/Rstudio Rstudio https://www.r-project.org/, https://www.rstudio.com
Afex package for R Henrik Singmann https://cran.r-project.org/web/packages/afex/index.html
Data analysis scripts This paper DOI 10.17605/OSF.IO/P5HE9
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EXPERIMENTAL MODEL AND SUBJECT DETAILS

Thirty-five healthy young adults (female = 20; mean age = 22. 18 years, SD = 3.85 years) without any neurological or psychiatric disorders were recruited from the University of California, Davis Psychology Department subject pool. All thirty-five participants completed the behavioral learning session, however, eight participants performed below the set criteria for learning (60% performance) and did not participate in the second session. One participant did not complete the second session due to a technical issue and another participant who achieved criterion level performance was unable to take part in the second session. The remaining 25 participants completed the second MRI scanning session which included a learning, decision, and a choice phase. Participants with head motion greater than 3 mm from origin (N = 3) were excluded from the analysis, leaving 22 participants (female = 12) who completed both sessions and were included in the analyses reported here. Participants were compensated with either a check or an amazon gift card. All procedures were approved by the University of California, Davis Institutional Review Board.

METHOD DETAILS

Stimuli and materials

Stimuli consisted of 8 food categories items and 4 grocery store scenes. Grocery store images were the same across all participants. Participants were presented with one food item from each of the 8 categories (apple, muffin, carrot, bread, donut, onion, banana, cookie), which was randomly selected from a pool of 16 different exemplars of that food category. All participants, therefore, saw an exemplar from each of the 8 food categories, however the exact food images were different (e.g., every participant saw a banana but not every participant saw the same banana picture). Additionally, food items were randomly assigned to either “like” or “dislike” preferences in each of the four stores. This food-preference mapping was consistent within each participant but randomized across participants (e.g., for one participant banana was liked in store A but for another participant banana could be disliked in store A).

Procedure

The experiment consisted of two sessions that took place across two consecutive days. During session 1, participants completed the learning task outside of the scanner, where they learned which foods were preferred in which stores through repeated trials of cycles of decisions and feedback. Participants were instructed to act as a market researcher and predict whether or not customers like or dislike foods within different stores. They were told that not all foods are liked or disliked all the time, and that they should pay attention to the store just in case it matters. Participants saw the same 8 foods in each of the four stores. They were told that at first they would be making guesses, but that overtime they should learn to predict customer preferences in the different stores. After the instructions, all participants completed the pre-familiarization phase, where they were shown the images of every store and food.

The first eight blocks of the learning phase trained participants only in one store such that the block trials only showed one store and provided each food’s desirability in that store. The following four blocks trained participants in different stores. In mixed blocks (blocks 9 to 12), each store was trained, the order of stores was randomized. Participants completed two blocks of learning on the second day which was similar to the last four blocks of day 1 learning. Learning performance was based on the last four blocks of day 1 and blocks of learning on day 2.

For the first 8 learning blocks (1–8) on the store context (i.e., background of mini-block) was constant (same store for all four mini-blocks). In blocks 9–12, the store context changed with each mini-block. The order of the mini-block store contexts was randomized, such that stores 1–4 were presented in a random order in blocks 9–12. During the mini-block, participants completed 8 trials. During each trial, a store scene was presented on the screen followed and a picture of a food item appeared, overlaid on top of the of the store along with response options for “like” or “dislike.” Participants had 2 s to select a response using the j and k keys. After 2 s, the food item disappeared and the word “CORRECT” or “INCORRECT” was displayed along with a picture of a customer’s reaction to that food (i.e., a disgusted or pleased face). This feedback was presented on the screen for 2 s, followed by an inter-trial interval (ITI). The ITI during the learning phase on Day 1 (outside of the scanner) was fixed at 2 s. The ITI on Day 2 (inside the scanner) was jittered with a range of 2–8 s (mean = 4 s). The participants made like/dislike judgments on all 8 foods within a mini-block, then the store context changed, and a new mini-block began. The time between each mini-block was jittered the same as the time between trials within the mini-block (jittered, mean = 4 s) and a fixation cross was presented on the screen. Upon the start of each mini-block, and the appearance of a new store context, just the image of the store was presented on the screen for 6 s, followed by the eight food trials. The presentation order of the foods within a mini-block was also randomized.

Participants returned the next day to complete session 2 and were scanned while they completed two additional runs of the learning phase (to ensure that the associations were well learned), two runs of the decision task, and four runs of a choice task. The fMRI analyses presented in this paper focused on the decision phase which included participants deciding whether customers preferred presented foods in a given store or not based on previous learning. The decision phase on Day 2 (inside of the scanner) followed the same task structure as blocks of the Day 2 learning phase, with the exception that the 2 s feedback period was removed from the trial. After making their response, the food item disappeared from the screen, leaving only the store context on the screen until the start of the next trial with a new food image (jittered ITI, mean 4 s).

During the decision phase, participants completed trials where a grocery store appeared on the screen followed by a food item, which was overlaid on top of the store. Using what they had learned the previous day, participants had 2 s to predict what the customer reactions were to that food item in that store by selecting either “like” or “dislike” using the j and k keys. Participants were not given any feedback about their answer (they did receive feedback in the learning trials). There was an inter-trial interval time between the offset of the decision of one trial to the onset of the next trial which was jittered and lasted between 2–8 s (mean = 4 s). Participants completed two runs of the decision phase, where each run consisted of 8 mini-blocks. Each mini-block had 8 trials (so every run had 64 trials). During the mini-blocks, the background image of the store remained on the screen for the entire mini-block and changed to a new store after each mini-block (e.g., participants visited all four stores twice in every full run, each store was a mini-block). Participants made like/dislike judgments on all 8 foods in each mini-block. The time between each mini-block was jittered (mean = 4 s). Each mini-block started with the presentation of the store for 6 s, followed by the eight foods. The order of the foods was randomized within each mini-block. The mini-blocks following each other always presented a different store.

“Like” probabilities.

The food item and store context pairs varied in their “like” probability so that there were context-determined (CD) and context-invariant (CI) foods. Foods with “like” probability of 1 or 0 depending on the store they were presented in are considered CD, while the foods whose “like” probability are 0.75 or 0.25 across all stores are CI. There are also two stores that share the same “like” preference across all 8 foods, so these are considered as “similar contexts.” The food preference probability matrix is shown in Figure 2. The mean like preference of all foods presented within a store was 0.5 such that no store had more favorable preferences (probability of a row in the matrix) than another store. CI foods had the same “like” preference probability across all stores and the average probability of them was either 0.75 or 0.25 (mean probability of a CI food column). CD foods on the other hand were 100% liked in half of the stores and 0% liked in the other half of the stores. In order to predict the like preference of the CD foods, participants had to understand the relationship between the store and food preference. The overall average like probability of CD foods was 0.5 (mean probability of a CD food column). This probability did not provide any information about the likeability of that food across all the stores.

Image acquisition and preprocessing

Scanning was performed on a Siemens Skyra 3T scanner system with a 32-channel head coil at the UC Davis Facility for Integrative Neuroscience. High-resolution T1-weighted structural images were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) pulse sequence (1 mm3 voxels; matrix size = 256 × 256; 208 slices). An additional T1-weighted image with only sagittal oriented slices was used for aligning the field of view box in the subsequent functional scans (i.e., the box was adjusted for each participant to make sure that the temporal lobes were not cut off and as much of the brain as possible was in the box). Functional images were acquired over eight runs using a whole-brain multiband gradient echo planar imaging (EPI) sequence (TR = 1220 ms; TE = 24 ms; flip angle = 67°; FOV = 192 mm; multi-band factor = 2; 38 interleaved slices; voxel size = 3 mm3; image matrix = 64 × 64).

Processing of the fMRI data was carried out using FEAT (FMRI Expert Analysis Tool) Version 6.00, part of FSL (FMRIB’s Software Library, https://www.fmrib.ox.ac.uk/fsl). The two functional scans from the decision phase underwent the following preprocessing steps: (1) Skull stripping of the anatomical images was carried out using FSL’s BET function (Smith, 2002). (2) Motion correction was carried out using FSL’s MCFLIRT function (Jenkinson et al., 2002), whereby volumes in the functional scans were aligned to the center volume of each scan with rigid body registration. (3) Grand-mean intensity normalization of the entire 4D dataset by a single multiplicative factor. (4) A high pass filter cut off was set at 100 and a high pass temporal filter was applied to remove low frequency noise from the signal (Gaussian-weighted least-squares straight line fitting, with sigma = 50.0 s). (5) Functional scans were coregistered to each subject’s native space anatomical scan using the boundary-based registration (BBR) (Jenkinson et al., 2002; (Jenkinson and Smith, 2001)) cost function with FLIRT.

QUANTIFICATION AND STATISTICAL ANALYSIS

ROI definition and masks

Bilateral ROI masks for the hippocampus were manually segmented on the high-resolution anatomical using the guidelines supplied by Frankó et al. (2014).

Masks for the OFC were labeled with FreeSurfer (Desikan et al., 2006; Fischl et al., 2004). T1-weighted anatomical scans were preprocessed using FreeSurfer which included intensity normalization, removal of non-brain tissue, transformation to Talaraich space, and segmentation of gray matter, white matter, and CSF. Surfaces were calculated for the white matter-gray matter, and gray matter-pial interface. Surface-based registration to the HCP-MMP1.0 atlas (Glasser et al., 2016) was performed and subject-specific cortical regions were calculated according to atlas boundaries. Surface-based cortical regions were converted to volumetric regions of interest and transformed into functional native space.

All ROIs were co-registered to the example functional image of the participant in FSL by using the FLIRT function and applxfm option applying the same parameters that were used to co-register their anatomical image with the functional images and pattern similarity analyses were done with the co-registered ROI masks.

fMRI data analysis: Pattern similarity analysis

Multivoxel pattern similarity analyses was performed on the fMRI data from the decision task runs. The analyses were conducted on unsmoothed functional images in the native space and the EPI timeseries underwent motion correction and high-pass filtering (0.01 Hz) in FMRIB’s Software Library (FSL).

For each trial, a single beta image was estimated by single trial models, based on the approach called Least-squares Single (LSS), for event-related blood oxygenation level-dependent (BOLD) signal change, controlling for signal change due to all other trials and motion artifact, using ordinary least-squares regression, resulting in 128 single-trial beta images (Mumford et al., 2012, 2014). Parameter estimates for each trial were computed using a general linear model, with the first regressor as a stick function placed at the onset of each trial and a second regressor containing all the other trials.

Single-trial beta images from run 2 were coregistered with single-trial beta images from run 1 using FSL’s FLIRT linear registration software (6 degrees of freedom). Coregistered single-trial beta images with atypically high mean absolute z-score (based on the distribution of beta estimates for each gray matter voxel across all trials) were excluded from further analysis. Based on a mean absolute z threshold of 1.5, between 0 and 10 trials were excluded per subject with a median of 4. Beta images went through a second visual inspection to make sure all the deviant trials were excluded. This noise trial exclusion procedure is adopted from the previous pattern similarity studies in the lab (Libby et al., 2019).

The representational similarity analyses were then conducted using the RSA toolbox by Nili et al.(2014). For each region of interest (HP and OFC) and subregions of OFC (medial, lateral, central OFC), all trial patterns were correlated with each other using Pearson’s r resulting in a 128 * 128 pattern similarity matrix for each ROI. From these matrices, we extracted each pairwise correlation and made a data frame with the following variables: 1) Run, 2) Food, 3) Store, 4) Store Similarity (similar, different), 5) Food Similarity (similar, different), 6) Task structure (both CD, both CI, mix), 7) Task structure Similarity (same, different), 8) Response (both like, both dislike, mix), 9) Response Similarity (same, different), 10) Performance (both optimal, both suboptimal, mix), 11) Beta Quality (good versus bad). The data frame for PS analyses was first filtered to exclude pairwise combinations that consisted of a trial with bad quality beta.

Our PS analyses included trial pairs that consisted of trials from the different runs (between-run analysis) excluding trial pairs from the same run. This was done to prevent any temporal autocorrelations trials within the same run could have. The noise from two different runs is unlikely to be correlated, therefore, this method prevents possible inflations in PS values (Dimsdale-Zucker and Ranganath, 2018). Additionally, the presented analyses included only the pairwise combinations in which the participant gave the “optimal” response to both trials. The exclusion was done with the filter function from dplyr package in R. The variables for filtering were: 1) Run = = “different,” 2) Performance = = “bothoptimal.” The analyses scripts and data frames are available on OSF. After the exclusion process, the remaining pairwise combinations were then passed to repeated-measures of ANOVAs conducted for each ROI. Analyses of variance were performed using the Afex package in R (https://github.com/singmann/afex), including Greenhouse-Geisser corrections for any non-sphericity.

In order to make sure, differences in response times to optimal decisions for different sub-structures was not the source of pattern similarity, we examined the response times to CD and CI trials. We conducted a one-way ANOVA with task structure as a factor. Our analysis showed that participants responded to both sub-structures in similar times in circa 1 s (Mean CD = 0.97, Mean CI = 0.94, F(1, 21) = 1.31, p = 0.265).

We conducted a control analysis to determine whether pattern similarity in hippocampus could be driven by the similarity of motor responses between trials, independent of other decision variables. Therefore, the analysis was based on the data from between runs but with no further exclusion based on the optimality of the decisions. Evoked patterns of activity across voxels in hippocampus ROIs were correlated for each pair of trials given either a “Like” response or “Dislike” response. For each participant, a like response was always given with the same key. If motor response was a source of pattern similarity, Like-Like trial pairs (i.e., within trials with the same motor response) would be more similar to each other than Like-Dislike trial pairs. (i.e., between trials with different motor responses). Mean similarity estimates were entered into a response similarity (same response versus different response) repeated-measures ANOVA, controlling for sub-structures (CD versus CI) and subregions in the case of OFC. Results indicated no significant main effect of response similarity and no significant interactions. See Tables S1A and S1B for OFC and Hippocampus results.

Supplementary Material

1
2

Highlights.

  • Learning of associations based on different task sub-structures

  • Memories of task structures guide future decisions

  • Greater representational similarity based on the same structure in hippocampus and OFC

  • Representation of task structure is highly correlated between hippocampus and OFC

ACKNOWLEDGMENTS

This project was supported by National Institute on Aging (NIA) grant 1R03AG063224-01 to C.R. and Office of Naval Research (ONR) grant N00014-15-1-0033 to C.R.

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2021.110065.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1760954-supplement-1.pdf (729.6KB, pdf)
2
NIHMS1760954-supplement-2.pdf (2.9MB, pdf)

Data Availability Statement

  • The data used for the main analyses of the paper (i.e., behavioral data and single trial models from the fMRI data) has been deposited on the Open Science Framework and is publicly available as of the date of publication.

  • All original analysis code has been deposited on the Open Science Framework and is publicly available as of the date of publication. The DOI is listed in the key resources table.

  • Any additional information required to reanalyze the data reported in this paper is available from the Lead Contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE SOURCE IDENTIFIER
Deposited data
Dataset OSF DOI 10.17605/OSF.IO/P5HE9
Software and algorithms
FSL 6.0.0 FMRIB; Smith, 2002; Jenkinson et al., 2012 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki
MATLAB2015b MathWorks https://www.mathworks.com/
Freesurfer 6.0.0 Desikan et al., 2006; Fischl et al, 2004 https://surfer.nmr.mgh.harvard.edu
RSA toolbox Nili et al., 2014 https://git.fmrib.ox.ac.uk/hnili/rsa
HCP-MMP1.0 atlas Glasser et al., 2016 https://osf.io/azup8/
R/Rstudio Rstudio https://www.r-project.org/, https://www.rstudio.com
Afex package for R Henrik Singmann https://cran.r-project.org/web/packages/afex/index.html
Data analysis scripts This paper DOI 10.17605/OSF.IO/P5HE9
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RESOURCES