Map-like representations of emotion knowledge in hippocampal-prefrontal systems

Domain: neuroscience
Published: 2026-01-26
Editorial outcome: major_revision

Abstract
Emotional experiences involve more than bodily reactions and momentary feelings—they depend on knowledge about the world that spans contexts and time. Although it is well established that individuals conceptualize emotions using a low-dimensional space organized by valence and arousal, the neural mechanisms giving rise to this configuration remain unclear. Here, we examine whether hippocampal-prefrontal circuits—neural structures implicated in forming cognitive maps—also support the structural abstraction of emotional experiences. Using functional MRI data collected as participants viewed emotionally evocative film clips, we find that hippocampal activity represents emotion concepts in a structured hierarchy, whereas ventromedial prefrontal cortex more accurately tracks locations in a two-dimensional affective space. Computational modeling reveals that hippocampal-prefrontal responses to films can be predicted based on the statistical regularities of emotion transitions across multiple temporal scales. These findings demonstrate that hippocampal-prefrontal systems represent emotion concepts in a map-like way at multiple levels of abstraction, offering insight into how the brain organizes emotion knowledge.

Introduction
Our experience of emotions is more than just momentary reactions to the world and accompanying feelings. Humans rely on emotion concepts—knowledge that helps us categorize, communicate, and make sense of emotional events in our lives. Some emotional knowledge is grounded in the particulars of individual episodes, such as the adorable features of a specific childhood pet or the growl of an aggressive neighborhood dog. Other knowledge concerns properties of events that generalize across episodes, like an opportunity for a reward or the presence of a threat. Even though emotional events can differ widely in what we see, hear, think, or feel, we learn to abstract them into distinct categories of variable yet related instances.

Behavioral evidence demonstrates that humans compress information about complex emotional episodes into a low-dimensional affective space1–4. When individuals judge the meaning of words5,6, emotions conveyed by others7, predict how others are likely to feel in a given situation8, or self-report on their own experience9, their ratings primarily vary along dimensions of valence and arousal, evidence taken by some to suggest that these variables are among the most fundamental properties of the mind3. Based on these observations, it has been suggested that humans represent the structure of affect in a map-like way3,5 using dimensions of valence and arousal as the organizing axes. If represented in such a format, emotion concepts would be positioned at specific locations in a relational network, much like landmarks are placed on a Cartesian map. With experience, individuals would learn the structure of affective space, enabling them to predict transitions from one experience to the next (e.g., the tendency to shift from a state of anxiety to fear as a threat becomes more proximal10) and to make decisions based on the anticipated consequences of actions11.

Neuroscience research has begun to characterize how the brain represents knowledge about emotional events. Work in nonhuman animals12–16 and human neuroimaging studies17–21 has identified functionally dissociable systems involved in valence and arousal processing. Neuroimaging has revealed that different categories of emotional events are differentiated by patterns of activity that are distributed across subcortical and cortical brain networks22–24, with the content represented varying across regions. Responses to emotional situations range from more situation-dependent activity in sensory cortices25–28 and more general category representations in transmodal cortical areas29,30. Together, these studies show that the brain represents emotional events using multiple systems in parallel, and that information processing occurs in a hierarchical fashion.

Although it is well-established that multiple aspects of emotional events are reflected across brain systems, it remains unclear how emotionally relevant signals are transformed into low-dimensional affective spaces. One possibility is that memory systems necessary for organizing knowledge across domains are involved in mapping relationships between emotion events. Evidence suggests that the hippocampal formation—known for its involvement in spatial navigation31, memory32, and emotion33–35—represents knowledge in a map-like way36–40. To build a map of the environment, neural populations in the hippocampus bind together highly processed sensory inputs with their positions in relational space, forming conjunctive codes of concepts that jointly represent what is experienced and where it lies within a structured map41. Based on the coactivation of hippocampal inputs, neural populations in the entorhinal cortex42 and ventromedial prefrontal cortex (vmPFC) learn relations among concepts using a grid-like code36–40. This format abstracts away from the content of experiences to define a relational structure which can be used to guide decision making and navigation. If similar mechanisms are used to construct maps of emotion concepts43, then knowledge about distinct sets of emotional experiences should be encoded in patterns of hippocampal activity, whereas the spatial bases that define affective space (i.e., grid-like codes that span dimensions of valence and arousal) should be present in entorhinal cortex and vmPFC44.

In this work, we evaluate the proposal43 that hippocampal-prefrontal systems represent emotion concepts in a cognitive map. In this account, entorhinal and ventromedial prefrontal cortex learn a general relational structure that can be used to predict the valence and arousal of upcoming events. Through associative learning, the hippocampus binds this information with highly processed sensory inputs to link specific emotion concepts with locations in valence-arousal space. Thus, although both sets of regions can be considered to have map-like properties, the hippocampus uniquely represents emotion concepts embedded in a two-dimensional affective space. We test this account by analyzing fMRI signals acquired as human participants watched a series of cinematic videos (sampled from the Emo-FilM dataset45, Fig. 1). As information about prototypical emotional events is typically accessible to consciousness, we first probe whether self-report measures of emotional experience can be decoded from patterns of hippocampal-prefrontal activity, and whether representations of emotion categories are distinct from a two-dimensional representation of affective space. Finding evidence of map-like representations in hippocampal-prefrontal systems, we complement these analyses using a computational model of relational memory (the Tolman-Eichenbaum Machine46, TEM) that formulates how an agent might learn from event sequences to construct cognitive maps. We train artificial agents to learn the regularities of emotion-laden environments by binding sensory representations with structural knowledge about the world. After training, we show that patterns of BOLD time-series in hippocampal-prefrontal systems covary with internal representations in computational agents, and are associated with self-reported emotion. These experiments reveal that hippocampal-prefrontal systems represent emotion concepts in a map-like way, providing a neurocomputational explanation of how humans organize abstract emotion knowledge.

Results
The hippocampus represents multiple emotion concepts in a hierarchically organized structure

We first evaluated whether hippocampal activity represents multiple emotion concepts in a format that is not merely based on variation in affect. To this end, we trained multivariate decoders (Supplementary Fig. 2) that map patterns of hippocampal fMRI time series (9538 measurements in 1816 voxels, per participant on average) onto either multivariate emotion category ratings, or locations in a two-dimensional space derived from multidimensional scaling of emotion category ratings (hereafter referred to as affective space, see Supplementary Fig. 3). Models were trained using data acquired from multiple film stimuli and evaluated by testing them on independent stimuli (leave-one-film-out cross-validation, 13 films total for each of n = 29 participants) and comparing the readout of category and affective space decoders (see “Methods”). This analysis revealed that category ratings were more accurately decoded (z = 0.0525, 95% bootstrap CI [0.0454, 0.0592]) than locations in affective space (z = 0.0428, 95% bootstrap CI [0.0347, 0.0505]; Δz = 0.0097, 95% bootstrap CI [0.0025, 0.0169], d = 0.4946, p < .001; see Supplementary Figs. 4, 5 for contributions of individual voxels). Together, these results are consistent with the proposal that the hippocampus encodes multiple emotion concepts in a format that is not reducible to a two-dimensional representation of affect.

Because the hippocampus represents event sequences in a hierarchical manner47–49, we expected that hippocampal responses to concepts that tend to occur in temporal proximity (e.g., disgust, anger, and guilt or anxiety, fear, and surprise) would be more similar to one another48, reflecting the graded similarity structure typically observed in ratings of emotion terms5,50,51 as opposed to a simple structure in which emotion concepts are independent. We assessed whether this was the case by analyzing the outputs of hippocampal decoders using agglomerative clustering. For each participant, we computed the similarity of model predictions over time to produce a conceptual similarity matrix (Supplementary Fig. 6). We modeled the similarity structure in two ways: with a model that estimated temporal similarity by averaging across concepts of the same valence (referred to as the valence model), and with another model that estimated temporal similarity for all pairs of concepts (the full model; Fig. 2c). To estimate model generalizability, we performed a leave-one-participant-out procedure in which temporal correlations were averaged across all but one participant and compared to the similarity structure of the held-out participant. This generalization test revealed that the correspondence across participants in the fully specified model (Spearman’s r = .978, SD = .007, 95% bootstrap CI [.975, 0.980], p < .001) was greater than that of the valence only model (Spearman’s r = .842, SD = .013, 95% bootstrap CI [.838, .847], p < .001; Fig. 2d), and that a substantial portion of variance across participants remained after accounting for valence (Spearman’s r = .486, SD = .014, 95% bootstrap CI [.481, .491], p < .001). These results suggest that the hippocampus does not represent emotion concepts independently, but as a hierarchy that is stable across participants (Fig. 2b) and captures relationships between emotions primarily along a dimension of valence.

Central to accounts of memory, navigation, and emotion processing52, the hippocampal long axis is characterized by gradients in connectivity, gene expression53, neural timescale54, and behavioral specialization55,56. Particularly relevant for the representation of emotion knowledge are observations that hippocampal activity represents event sequences at multiple timescales, with more rapid changes coded in more posterior portions of the hippocampus54,56. Such variation leads to the prediction that fine-grained distinctions between emotional events should be captured by activity in posterior hippocampus, whereas more general distinctions that take place over longer timescales should be represented in more anterior portions of the hippocampus. To examine variation in emotion representation along the hippocampal long axis, we trained multivariate decoders to predict variation in fine-grained (e.g., fear, anger, pride, joy or surprise) and broad emotion concepts (e.g., good, or activated) using signals either in the posterior or anterior hippocampus (based on a segmentation at the plane y = −21 in MNI space). Model comparisons revealed that decoding performance depended on both hippocampal portion and representational scale (Δz = 0.0101, 95% bootstrap CI [0.0054, 0.0149], d = 0.7791, p = .003, Fig. 2a), with greater decoding performance in posterior hippocampus as emotion granularity increased (see Supplementary Fig. 8 for performance on all ratings).

Complementing observations that knowledge from multiple domains is hierarchically organized in the hippocampus, evidence shows that the amygdala encodes some forms of abstract relationships, albeit in a different format than the hippocampus57. To assess whether this was the case for emotion concepts in the present study, we trained a new set of decoders on amygdala responses and compared their performance to that of those trained on signals in anterior and posterior hippocampus. We found amygdala responses did not predict category ratings as accurately as signals in posterior hippocampus (Δz = −0.0117, 95% bootstrap CI [−0.0063, −0.0171], d = −0.7850, p = .001), particularly when compared to predictions of binarized valence-arousal ratings (Δz = −0.0125, 95% bootstrap CI [−0.0077, −0.0172], d = −0.9538, p = .002). By contrast, amygdala decoders performed comparably to anterior hippocampal decoders (category ratings: Δz = −0.0040, 95% bootstrap CI [−0.0095, 0.0015], d = −0.2660, p = .229; region × target variable interaction: Δz = −0.0023, 95% bootstrap CI [−0.0071, 0.0024], d = −0.1780, p = 0.481; Fig. 2a). These results are broadly consistent with recent findings showing that the hippocampus and amygdala differ in their representations of abstract social relationships57,58, yet suggest that posterior hippocampus may be particularly important for organizing more fine-grained emotion knowledge.

Ventromedial prefrontal cortex represents trajectories in a two-dimensional affective space

We found that hippocampal BOLD signals contained information about emotion concepts, and to a lesser degree, information about trajectories in affective space. These observations suggest the hippocampus could play an important role in mapping emotion concepts—conveying information to the entorhinal cortex or vmPFC to construct an abstract, two-dimensional space36–40. If this is the case, then decoders trained on signals from these neocortical regions should predict trajectories in affective space more accurately than emotion category ratings. Alternatively, we may have observed better hippocampal prediction of category ratings because they were driven by particularly salient events, or because they occurred at time scales that are more easily captured by fMRI. In such cases, decoding performance should be higher for category ratings than trajectories in affective space, regardless of the neural origin of BOLD signals being decoded. Therefore, to dissociate representations across regions, we compared decoders trained to predict emotion category ratings and affective space using signals acquired in the hippocampus, entorhinal cortex, vmPFC, and the amygdala.

Consistent with a map-like representation, we found that signals from vmPFC predicted locations in affective space (z = 0.0956, 95% bootstrap CI [0.0831, 0.1076]) more accurately than emotion category ratings (z = 0.0838, 95% bootstrap CI [0.0777, 0.0902]; Δz = 0.0118, 95% bootstrap CI [0.0047, 0.0191], d = 0.0118, p = .020), similar to observations from studies examining decision-making about abstract social relationships58. This difference substantiated a double dissociation, such that the hippocampus contained more information about emotion category ratings whereas vmPFC better captured information about trajectories in affective space (Δz = 0.0215, 95% bootstrap CI [0.0134, 0.0297], d = 0.9566, p = .001; Fig. 3c). By contrast, activity in the entorhinal cortex and amygdala predicted the two target variables with comparable accuracy (entorhinal cortex: Δz = −0.0004, 95% bootstrap CI [−0.0071, 0.0067], d = −0.0195, p = .918; amygdala: Δz = −0.0036, 95% bootstrap CI [−0.0102, 0.0033], d = −0.0195, p = .221). Together, these results show that multiple regions carry information about emotion concepts (see Supplementary Fig. 9 for region-wise effect sizes and Supplementary Fig. 10 for results from less constrained searchlight mapping), and that the vmPFC contained more information about affective trajectories.

Of structures involved in relational and affective processing, several are known to encode independent dimensions of valence and arousal59–63 in addition to integrated, map-like representations. We therefore tested whether direct coding of each affective dimension measured in self-report could be decoded from BOLD signals acquired during film viewing using the same approach used to decode trajectories in affective space. Decoding continuous self-report ratings of valence and arousal produced similar results to affective space decoding, with the exception that readouts in vmPFC were not better predictors of valence and arousal than emotion category ratings (Supplementary Table 1 and Supplementary Figs. 11–14). These data suggest that vmPFC contains more integrated representations of affect compared to simple coding of independent affective dimensions.

Structural abstraction of emotion knowledge in hippocampal-prefrontal systems

The results of multivariate decoding experiments indicated that hippocampal-prefrontal systems represent emotion concepts in ways that resemble a cognitive map. Hippocampal activity carried more information about emotion category ratings while preserving their similarity structure, reflecting a hierarchical organization. The vmPFC more accurately tracked trajectories in affective space, suggestive of a low-dimensional relational code. This dissociation of function aligns with theoretical accounts suggesting that hippocampal binding of information about “what” occurs during an event and “where” it occurs in space and time could explain the formation of cognitive maps64,65. To more explicitly probe the representational format of emotion concepts in hippocampal-prefrontal systems, we simulated concept formation using TEM46, a computational model of relational memory (Fig. 4).

In learning to map the environment, TEM factorizes sensory observations into two separate bases—one that represents the contents of experience and another that represents relationships between experiences—and binds them together into a conjunctive code. This conjunction is represented in layer p, which is used to make predictions about upcoming sensory observations and locations in layer g. Activity in layer p resembles hippocampal population activity that encodes “what” is located “where” in an environment, with localized firing fields for specific concepts (Fig. 4c). In contrast, activity in layer g resembles that of entorhinal cortex, with firing fields that span the entire space (e.g., grid cell-like activity; Fig. 4c), providing an abstract structural code that can be used for making inferences in any environment or in new problems that have a similar relational structure. In this way, TEM formalizes how an agent learns representations thought to be present in hippocampal-prefrontal systems. This model allowed us to test whether hippocampal signals more accurately predict the conjunction of concepts and their location in the affective space (activity in layer p), and whether vmPFC signals more accurately predict the relational structure of affective space (activity in layer g).

For TEM agents to experience transitions between emotional events that approximate human experiences, we created an artificial environment based on emotion category ratings provided by participants in the online sample. We discretized the affective space derived from multidimensional scaling (Fig. 3b) into a grid environment (Fig. 4a). During training, agents randomly explored this environment, learning the relational structure of emotion concepts through experience, mimicking one way the brain could learn to represent emotion concepts over time. This simulation enabled us to define representations of emotion concepts and relations between them and evaluate how accurately they can be decoded from patterns of hippocampal activity in the fMRI signal. To model human brain responses during film viewing, we generated TEM activation time series by averaging the activation of layers p and g in artificial agents as they navigated the affective space, weighting the responses in layers p and g using a linear combination of ratings on all emotions at each point. Similar to the multi-scale representations observed in place cells across the hippocampal long axis66,67, TEM includes artificial neurons at multiple levels of abstraction, allowing it to represent both fine-grained and coarse aspects of the environment. Because representations in layers p and g differed the most at small scales in our simulations (Fig. 4c and Supplementary Fig. 15), we predicted that hippocampal decoding of layer p would be more accurate than layer g for units with narrow firing fields (smaller scales), as they better capture transitions between specific emotions as opposed to large transitions in the affective space (larger scales). Consistent with this prediction, we found higher cross-validated decoding performance for layer p than g (Δz = 0.0017, 95% bootstrap CI [0.0001, 0.0034], d = 0.3773, p = 0.015; Fig. 5a). Further, a linear contrast across representational scales revealed that as scale decreased, decoding performance increased more for layer p than for layer g (Δz = 0.0352, 95% bootstrap CI [0.0254, 0.0448], d = 1.3232, p < .001; Fig. 5c), indicating that more accurate decoding of layer p activity was driven by information related to small-scale conjunctive codes.

Because firing field size varies along the hippocampal long axis66,67, we next tested whether information about smaller-scale layer p activity was more strongly represented in the posterior hippocampus. To this end, we trained separate decoding models to predict TEM activations at small (0 and 1) and large representational scales (2 through 4), and compared the accuracy of readouts from anterior versus posterior hippocampal fMRI activity. This analysis revealed differences in decoding accuracy that depended on both hippocampal portion and scale (Δz = 0.0052, 95% bootstrap CI [0.0033, 0.0071], d = 0.9909, p = .002; Fig. 5b, d), indicating that smaller-scale layer p activity was decoded more accurately in posterior than anterior hippocampus.

We observed that hippocampal decoders predicted variation in emotional experience as reflected in both human self-report and the activity of TEM agents. To determine whether decoders utilized common patterns of hippocampal response, we compared decoding performance before and after controlling for predictions of p (see “Methods” ‘Comparing the decoding performance of TEM activity’ for details). We found that accounting for hippocampal signals related to layer p activity impaired the readout of emotion category ratings (Δz = 0.0042, 95% bootstrap CI [0.0020, 0.0061]) more so than trajectories in affective space (Δz = 0.0014, 95% bootstrap CI [0.0000, 0.0029]) and valence-arousal ratings (Δz = 0.0002, 95% bootstrap CI [0.0010, 0.0014]; Δz = 0.0028, 95% bootstrap CI [0.0004, 0.0052], d = 0.4205, p = .003; Δz = 0.0039, 95% bootstrap CI [0.0021, 0.0057], d = 0.7890, p = .001). This result indicates that a conjunctive representation of emotion concepts can account for some, but not all, of the enhanced prediction of emotion category ratings compared to affective dimensions in the hippocampus.

The entorhinal cortex and vmPFC are thought to represent relations among objects, locations, and goal-states in a domain general way36,39,68,69. If these cortical areas play a similar role in representing relations among emotion concepts, decoding layer g activity from these regions should be more accurate than decoding layer p activity. We tested this prediction by comparing the decoding accuracy as a function of brain region (hippocampus, entorhinal cortex, and vmPFC), layer (p and g), and scale (small and large). Analysis of variance revealed that decoding performance depended on all three variables (a three-way interaction: F(8, 616) = 12.44, partial η² = 0.14, p < .001; Supplementary Fig. 16). Direct comparisons (Fig. 5c) indicated that there was better readout of layer g than layer p activity at large scales in vmPFC (Δz = 0.0712, 95% bootstrap CI [0.0614, 0.0809], d = 2.6747, p < .001), and to a lesser degree in the entorhinal cortex (Δz = 0.0099, 95% bootstrap CI [0.0001, 0.0199], d = 0.3680, p = .046). Decoding layer g was more accurate at larger scales in the vmPFC than in both hippocampus (Δz = 0.0542, 95% bootstrap CI [0.0447, 0.0640], d = 2.0455, p < .001; Fig. 5c) and entorhinal cortex (Δz = 0.0604, 95% bootstrap CI [0.0506, 0.0700], d = 2.2580, p < .001; Fig. 5c). Conversely, decoding layer p activity was better at small scales in the hippocampus compared to both entorhinal cortex (Δz = 0.0191, 95% bootstrap CI [0.0093, 0.0289], d = 0.7091, p < .001; Fig. 5c) and vmPFC (Δz = 0.0182, 95% bootstrap CI [0.0086, 0.0279], d = 0.6815, p = .003; Fig. 5c, left and right). Together, these results suggest that the vmPFC contains more information about large-scale structural abstractions (e.g., that one portion of a video was more pleasant than another), whereas the hippocampus represents conjunctive codes of multiple emotion concepts embedded in the affective space.

Methods
Emo-FilM dataset
Both the fMRI data (https://openneuro.org/datasets/ds004892/versions/1.0.0) and emotion ratings (https://openneuro.org/datasets/ds004872/versions/1.0.1) used in this work are from the Emo-FilM dataset45 publicly available on OpenNeuro. Both studies were approved by the Geneva Cantonal Commission for Ethics in Research and complied with the Code of Human Research Ethics (2014). No formal statistical procedure was used to predetermine sample size because this study analyzed pre-existing datasets.

fMRI data
Brain activity was measured using fMRI in 30 healthy participants who watched 14 short films (mean duration 11 min 26 s) in a pseudo-random order over four sessions (see Morgenroth et al.45 for detailed descriptions of the films). Film stimuli were presented using the Psychophysics Toolbox97 in MATLAB 2012. All participants were right-handed and met the inclusion criteria of normal or corrected-to-normal vision, high-level English comprehension, no history of psychiatric or neurological illness, and no neuropharmacological or recreational drugs. We excluded participant 07 due to missing data from 3 films on OpenNeuro, leaving 29 participants (18 females based on self-report, aged 18–34). All participants provided written informed consent. Sex/gender was not included as an experimental factor in the analyses because we did not have prior predictions about sex/gender-related differences in emotion knowledge. MRI data were acquired on a 3T Siemens Magnetom TIM Trio scanner with a 32-channel head coil (Siemens, Erlangen, Germany). Anatomical T1 images were acquired with a GRAPPA sequence for the purpose of co-registration (TR = 1.9 s, TE = 2.27 ms, flip angle = 9°, FOV = 256 mm, resolution = 1 mm3, in plane resolution of 256 × 256 × 192 sagittal slices). Functional images were acquired with the same multi-band frequency protocol (TR = 1.3 s, TE = 30 ms, flip angle = 64°, FOV = 210 mm2, resolution = 2.5 mm3, 54 interleaved slices).

fMRI preprocessing. Preprocessing of fMRI data, performed using FEAT (FMRI Expert Analysis Tool) Version 6.00 from FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl), included co-registration to structural, standard space, and each participant’s functional volume, motion correction, non-brain tissue removal, spatial smoothing (6 mm FWHM), grand-mean intensity normalization, high-pass temporal filtering (50 s cutoff), and regression of white matter, cerebrospinal fluid, and six motion parameters.

Emotion ratings. Emotion ratings98 were collected from 44 independent participants (23 females based on self-report, aged 20-39) recruited online, using the same inclusion criteria as the fMRI sample. Participants were from the Geneva area, matched with the fMRI sample, but with no overlap between the two samples. All participants provided written informed consent and were compensated at 20 CHF per hour. A total of 55 items were rated across six groups of items: Appraisal, Expression, Physiology, Motivation, Feeling, and Discrete Emotion. Participants completed the rating tasks at their own pace within a six-week period, using the annotation software CARMA99 on their own computers. To rate each item, participants continuously moved a mouse-controlled cursor along a bar (sampling rate = 1 Hz). Each participant rated six randomly assigned items in total. For each item, participants rated it for all films in a random order before moving to the next item, ensuring a sufficient delay between repetitions of the same film. Consensus ratings were derived by averaging from three or four raters per item and film (see Morgenroth et al.45 for a detailed description of consensus calculation and quality control procedures). No participants were excluded due to data quality issues.

In the current study, we used all 13 items (anger, anxiety, fear, surprise, guilt, disgust, sad, regard, satisfaction, warmheartedness, happiness, pride, love) in the Discrete Emotion category. For valence and arousal, we selected four items (valence: good, bad; arousal: calm (restless-calm), at ease (nervous-at ease) from the Feeling items, as they best align with our goal of capturing emotion concepts in terms of their location within the valence-arousal space. Other Feeling items including intense emotion, strong (weak-strong), and alert (tired-alert) were not used because they were less directly relevant to arousal or did not align with the goal of capturing conceptual knowledge of emotion rather than physiological states. For example, intense emotion reflects the magnitude of emotions, weak-strong may reflect a sense of capability and self-efficacy, and tired-alert is more indicative of wakefulness rather than the concept of arousal.

Defining regions of interest
Masks of the hippocampus and entorhinal cortex were obtained from the Julich-Brain Cytoarchitectonic Atlas100 Masks of anterior and posterior hippocampus were obtained by splitting the hippocampal mask at y = −21 in the MNI space101. For the vmPFC mask, we selected Brodmann areas 10, 11, 14, 24, 25, and 32, obtained from a multimodal parcellation of the human cortex102.

Simulating hippocampal learning with the Tolman-Eichenbaum Machine
TEM is an artificial neural network trained to navigate on a connected graph46. At each time step, it takes the current sensory observation (x) and an action (a) as inputs, and outputs a predicted sensory observation for the next timestep. To accomplish the objective of accurately predicting next sensory inputs even when taking paths that have not been experienced before, TEM factorizes its representation into two parts: (1) relations between locations on the graph and (2) the association between sensory observations and their location on the graph.

TEM factorizes sensory information using two layers that are inspired by the functional anatomy of the entorhinal cortex and hippocampus. The entorhinal layer of the network (g) generates distinct representations for each location and learns their relations through path integration. At each time step, g updates its representation based on the received action, using a recurrent architecture with weights updated via backpropagation during training. The hippocampal layer of the network (p) receives sensory input along with the estimated location from g. Memories of sensory-location associations are stored in Hebbian weights between units in p and can later be retrieved by attractor dynamics.

To efficiently represent environments, TEM is organized into multiple parallel streams, each capturing information at different temporal and spatial scales. Each stream processes input separately, applying a unique temporal smoothing filter, and transitions via path integration (see Whittington et al.46 for details). These streams remain distinct during learning but integrate during memory retrieval through attractor dynamics. Lower-index streams encode finer-grained details, while higher-index streams capture broader structure.

To enable TEM to learn a relational structure that generalizes across different environments, we trained the model in 16 distinct 11 × 11 grid environments, each with randomly generated 45-dimensional one-hot sensory inputs. At each time step, the agent could take one of five possible actions—moving up, down, left, right, or staying still—with equal probabilities. The results presented in the main text are based on the model trained for 32,000 iterations, at which point the loss and performance in predicting next sensory observations began to plateau (Supplementary Fig. 21). Additional analyses using models trained for 42,000 and 50,000 iterations showed consistent results (Supplementary Fig. 22 and Supplementary Tables 3–6), confirming the robustness of our findings.

Creating the TEM environment for emotion concepts
We first calculated the Pearson correlations of time series of ratings between each pair of emotion categories concatenated across all 14 films. Next, we computed the dissimilarity between pairs of emotion categories using the Pearson correlation (i.e., 1 – r) and performed metric multidimensional scaling103 (MDS) on the dissimilarity matrix. The resulting two-dimensional MDS space was discretized into 121 locations, forming an 11 × 11 grid, to match the environments used during training. Each emotion category was assigned to the location closest to its MDS coordinate. If two emotion categories occupied a single location, one was assigned to the nearest available, unoccupied location.

To ensure that the MDS solution accurately reflected the (dis)similarities between emotion categories as indicated by the ratings, we repeated the process 500 times using different random seeds for the MDS procedure. We selected the seed that resulted in the lowest Spearman correlation between the city-block distances between category pairs in the environment (which required the minimum number of time steps to transition from one category to another) and the Pearson correlations derived from the ratings.

As in the training environment, the agent in the emotion environment could take one of five actions—up, down, left, right, or staying in the same location—each with equal probability. To ensure that each emotion category had a unique sensory input, we randomly assigned a distinct 45-dimensional one-hot code to each emotion category. For locations without an associated emotion category, we assigned random 45-dimensional one-hot codes that did not overlap with those of the emotion categories.

Simulating emotional experience with TEM
With trained TEM model weights, the agent walked randomly in the environment created for emotion concepts for 12,100 steps, roughly sampling each of the 121 locations 100 times. For each emotion category, we calculated the average activations of all units in p and g across the second half of the steps to ensure that the agent had enough experience to develop a stable representation of the new environment it had never encountered during training. To generate a time series of responses to emotion concepts throughout the films, we computed the weighted average activation across all categories, as we assume that multiple emotion concepts can be coactivated during film viewing. Specifically, we weighted the activations of units in layers p and g based on emotion ratings at each time point, such that segments rated more highly on certain emotions resulted in stronger activations for those concepts, while minimizing contributions from unrelated emotions.

Computing trajectories of emotional experiences in a two-dimensional affective space
To derive a time series of locations in a low-dimensional affective space, we projected the time series of category ratings into the same MDS space that was used to construct the TEM environment. For each film, we multiplied the timepoint-by-category rating matrix with the category coordinates estimated from the MDS solution. The resulting time series of the two MDS coordinates were used as the outcome variables in subsequent decoding analysis.

Multivariate decoding of BOLD signals
We specified partial least squares (PLS) regression models (SIMPLS algorithm104) with 20 components separately for decoding patterns of BOLD signal to predict emotion category ratings, valence-arousal ratings, trajectories in affective space, and activation in different layers of TEM (Supplementary Fig. 2). Fitting separate models for different outcome variables simplifies the interpretation of model performance, because the different outcome blocks (e.g., category ratings and valence-arousal ratings) are correlated and could influence the estimation of betas for different outcome variables105,106. For all models, the predictor block consisted of an n timepoint × k voxel matrix of BOLD signal (n = 9,538 timepoints; k = 1,816 voxels, hippocampus; k = 711 voxels, entorhinal cortex; k = 5,597 voxels, vmPFC). Outcome blocks consisted of an n timepoint × k item matrix of ratings (k = 13 category ratings or k = 4 dimensional ratings), an n timepoint × k dimension matrix of affective space (k = 2 dimensions), or an n timepoint × k unit matrix of TEM activity (k = 400 units, p; k = 120 units, g). To align with BOLD sampling, time series of the outcomes (ratings and TEM activity) were resampled to match the sampling rate of BOLD data (i.e., 1/1.3 Hz) and convolved with a canonical double gamma response function to account for hemodynamic delay107. For each of the three regions of interest, we therefore developed five models (category, affective space, valence-arousal, p, and g). To test the granularity of emotion concepts and the scale of p represented along the hippocampal long axis, we additionally specified separate PLS models separately decoding signals from the anterior and posterior hippocampus (k = 745 anterior hippocampus voxels, k = 1139 posterior hippocampus voxels) to predict emotion category, binarized valence-arousal, small-scale p (k = 200 units), and large-scale p (k = 200 units).

We quantified decoding performance using leave-one-film-out cross-validation in which PLS regression models were trained on 13 films and tested on the left-out film. The predicted outcome time series in the test fold were correlated with the observed time series of the outcomes, and performance was calculated as the average correlation across the 14 validation sets (films) for each participant. To test whether self-report ratings predicted from BOLD signal were partially explained by the decoded TEM activity, we used the time series of decoded TEM activity to predict the ratings using PLS. We then performed a partial correlation analysis between the decoded ratings and the actual ratings, while controlling for the ratings predicted by the decoded TEM activity.

Characterizing the representational geometry of emotion concepts in the hippocampus
To assess the hierarchical organization of emotion concepts in hippocampal representations, we computed the similarity between predicted rating time series for each pair of emotion categories using the Pearson correlation coefficient. We then applied agglomerative hierarchical clustering using the average linkage method to the dissimilarity matrix (1 – Pearson correlation). This procedure was repeated for each participant, and the resulting dendrograms were used to visualize the hierarchical relationships among emotion concepts.

To examine whether a shared conceptual similarity structure was present across participants and whether it could be explained by valence, we implemented a leave-one-participant-out analysis. For each iteration, we averaged the pairwise category correlations across all participants except the one held out, creating a group-level model of similarity between emotion concepts. Two group-level models were defined: a full model consisting of the mean correlation for each pair of the 13 categories, and a valence model, in which category pairs were grouped by valence (positive–positive, negative–negative, and positive–negative) and averaged within each group. We assessed model fit by computing Spearman rank correlations between the held-out participant’s pairwise category correlation matrix and each group-level model. To test whether the full model explained variance in the participant’s similarity structure beyond what could be attributed to valence, we regressed out the ranked values of the valence model from the participant’s category correlations and then computed a Spearman correlation between the residuals and the full model. Inference on group-level correlation coefficients was performed using t tests against zero.

Comparing multivariate decoding performance across participants
We specified multiple linear mixed-effects models to compare decoding performance across models trained for different objectives (e.g., for categorical and dimensional self-report items) and using data from different regions (e.g., hippocampal and entorhinal regions of interest).

Comparing the decoding performance of self-report ratings. We specified a linear mixed-effects model to analyze decoding performance of self-reported ratings (i.e., Fisher z-transformed prediction-outcome correlation) as a function of rating item (including both category and valence-arousal), region, and their interaction as fixed effects. Random intercepts were included for participants nested within rating items and regions. To compare the decoding performance of category versus valence-arousal in the hippocampus, we contrasted the estimated marginal means of the hippocampus by taking the average of 13 category items and subtracting the average of the four valence-arousal items, while setting other regions to zero. To compare how the decoding performance of valence-arousal in the hippocampus differed from that in the entorhinal cortex and vmPFC, we contrasted the average estimated marginal means of the four valence-arousal items in the entorhinal cortex and vmPFC against that of the hippocampus ((entorhinal cortex + vmPFC)/2 – hippocampus) for valence-arousal items only, while setting category items to zero. To compare how the difference in rating type (emotion category vs. valence-arousal dimension) varied across brain regions, we tested the interaction between rating type and region (hippocampus-entorhinal cortex or hippocampus-vmPFC).

To compare decoding performance between emotion category and binarized valence-arousal items in the anterior and posterior hippocampus, we specified a linear mixed-effects model with rating item, hippocampal portion, and their interaction as fixed effects, and random intercepts for participants nested within rating items and hippocampal portions. We contrasted the estimated marginal means for emotion type and hippocampal portion. For the factor emotion, we subtracted the average of the four binarized valence-arousal items from the average of the 13 category items. For the factor hippocampal portion, we subtracted performance in the anterior hippocampus from that of the posterior hippocampus. We tested the interaction between these two factors to determine whether the difference in hippocampal portions (posterior - anterior) varied across emotion types/granularities (category-binarized valence-arousal).

Comparing the decoding performance of TEM activity. We specified a linear mixed-effects model to analyze decoding performance of TEM activity as a function of brain region, TEM layer, scale, and their interactions as fixed effects. Random intercepts were included for participants nested within regions, TEM layers and scales. In these models, we averaged decoding performance across units for each combination of brain region, TEM layer and scale with participants. This approach simplified the models and ensured a more appropriate estimation of degrees of freedom.

To compare the decoding performance of the p and g layers of TEM in the hippocampus, we contrasted the estimated marginal means of the hippocampus by taking the average of all the scales of p and subtracting the average of all the scales of g, while setting other regions to zero. To examine trends in decoding performance across different scales (0 through 4), we specified a linear contrast on the estimated marginal means, assigning weights from -2 to 2 to represent the change in performance from small to large scales. This contrast was performed separately for each combination of brain region and TEM layer. We then conducted pairwise comparisons to assess the differences in the estimated marginal means of the linear contrast on scale between each pair of regions separately for each TEM layer and between each pair of TEM layers separately for each region.

To compare the performance of models trained to predict the activity of small- and large-scale units in layer p of TEM from signals in the anterior and posterior hippocampus, we specified a linear mixed-effects model with representational scale, hippocampal portion, and their interaction as fixed effects, with random intercepts for participants nested within scale and hippocampal portion. We contrasted the estimated marginal means for the factors scale and hippocampal portion. For the factor scale, we subtracted the average of the three large scales (i.e., 2, 3, 4) from the average of the two small scales (i.e., 0, 1). For the factor hippocampal portion, we subtracted decoding performance in the anterior hippocampus from that of the posterior hippocampus. We tested the interaction between these two factors to determine whether the difference in decoding performance between hippocampal portions (posterior - anterior) varied across scales (small - large).

To compare the correlation between BOLD-predicted and observed self-report ratings after controlling for TEM p in the hippocampus, we specified a linear mixed-effects model with rating item, correlation type (correlation vs. partial correlation controlling for TEM p), and their interaction as fixed effects, with random intercepts for participants nested within rating items and correlation types. We contrasted the estimated marginal means for the factors correlation type and hippocampal portion. For the factor correlation type, we subtracted the average of the partial correlations from the average of the correlations. For the factor emotion, we subtracted the average of the four valence-arousal items from the average of the 13 category items. We tested the interaction between these two factors to determine whether the difference in correlation type (correlation - partial correlation) varied across emotion types (category-valence-arousal).

We assessed the statistical significance of all effects of interest using a nonparametric permutation test based on sign flipping108. Specifically, for each permutation, we randomly flipped the sign of all data within each participant, thereby preserving the within-participant correlation structure while assuming exchangeability of participants under the null hypothesis. This procedure maintains the dependency across repeated measures for each participant (e.g., brain regions, emotion ratings, TEM layers, TEM scales) and permits inference at the group level. For each randomized dataset, we refit the linear mixed-effects model and extracted estimates of interest. The randomization null distribution was constructed from 10,000 permutations, and two-sided p-values were computed as the proportion of permuted estimates that were more extreme than the observed estimate. We additionally performed parametric bootstrapping to estimate the standard error of parameter estimates and standardized effect sizes with 10,000 bootstrap samples109. For each sample, synthetic observations were generated based on the parameter estimates of the fitted model, and the mixed effects model was re-fit, producing a distribution of estimates use to derive 95% confidence intervals using the percentile method110. We also calculated a standardized effect size by dividing the observed contrast by the standard deviation of the bootstrap distribution, multiplied by the square root of the sample size.

All analyses were performed using CanlabCore Tools (https://github.com/canlab/CanlabCore) and SPM 12, as well as custom MATLAB(R2024a), Python (3.10.14) and R (4.4.1) code.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Ma and Kragel investigate whether emotional events are represented in a map-like way in the human hippocampus, entorhinal cortex, and prefrontal cortex. The authors analyze an fMRI dataset that measures participants watching a series of videos. They test whether self-report measures of emotional experience can be classified from hippocampal-prefrontal patterns and whether these representations of emotion categories are distinct from valence and arousal dimensions. The authors then use a computational model of hippocampal-entorhinal map-like knowledge generalization, the Tolman-Eichenbaum Machine(TEM) model, to show that hippocampal and entorhinal cortex responses map relational transitions in emotional status. I find this manuscript interesting, but I had trouble interpreting some of their results. Generally speaking, I have difficulty reconciling what their takeaway message is since most of their non-model findings show hippocampal-prefrontal representations of affective schematic information, while their model analyses show map-like computations.\n\nMajor comments\n1. I was a bit confused by the intro focusing on hippocampal cognitive maps of abstract knowledge and 2D conceptual spaces. I expected the authors to then test trajectories through a 2D affective space like the work they cite, but then their multivariate analysis mainly shows the opposite, where the seemingly 1D schematic information on emotional categories is best represented by the hippocampus, ERC, and vmPFC. Schematic categories aren’t necessarily map-like or structure-like, especially compared to conjunctive coding. This isn’t to say their results are particularly shocking, rather that they don’t appear to justify use of a cognitive map/structure learning model like the TEM. This is since their data doesn’t include abstract trajectories in their emotional space until their model analyses. Additionally, their interpretation of these findings is too vague in the abstract. The importance of the authors’ findings suffers if the authors can’t more robustly distinguish or rule out map-like vs. schematic representations in different medial temporal-prefrontal regions.\n2. Apologies if I missed it, but it was also hard to detect what was significantly classifiable compared to chance for the analyses in Figs. 1 and 2. As it’s presented now, it seems like everything is classifiable above chance in all three ROIs and both hippocampal subregions, but that some regions have higher classification accuracy compared to other regions and cognitive variables. It’d be a bit strange if this is the case and any clarity would be appreciated.\n3. I found the transition in the Results from Fig 1 to Fig 2 a bit hard to follow. I’d appreciate it if the authors unpacked their findings related to these figures a bit more in the Results. For example, the transition from the anterior/posterior hippocampal ROI effects and then comparing hippocampus, entorhinal cortex, and the vmPFC is very abrupt.\n4. Somewhat linked to my first comment, I wasn’t satisfied with the justification of using a computational model of cognitive map-like knowledge representations to model hippocampal-entorhinal and mPFC data that wasn’t shown to represent map-like affective knowledge. For example, it was unclear how the g parameter in the TEM relates to grid-like coding in the fMRI data? The authors could run a univariate or multivariate fMRI analysis of trajectories between emotional concepts and events in the 2D multidimensional scaling projection of the emotional concept data as a way to better justify their model-informed fMRI analyses.\n5. Given its important role in emotional memory and the authors’ previous work, I was surprised that the authors don’t present results from a bilateral amygdala ROI as a way to better distinguish the role of different regions in mapping different emotion concepts. The amygdala probably plays an important role in most cognitive processes that they’re interested in without having a strong association with cognitive maps. It would also further strengthen their anterior versus posterior hippocampal axis data.\n\nMinor comments\n1. It would be helpful to separate the Results and Discussion sections by giving them their own headings.\n2. In the Discussion, the authors say previous abstract cognitive map work relies on an “Impoverished sensory environment,” while the authors’ “use of naturalistic movie stimuli offers a more ecologically valid window into how hippocampal-prefrontal systems process dynamic, rich, emotional experiences”. However, I fail to see how interactive tasks like Tavares et al., 2015 Neuron or Park et al., 2021, Neuron, where participants learn social hierarchies with natural dimensions like popularity, power, or competence are any less interactive than the emotional concepts in this task. I recommend removing that sentence in the Discussion.", "reviewer_2": "The authors report results from an fMRI study in which participants (N = 29) viewed emotionally evocative film clips. The fMRI results show that activity in hippocampus and prefrontal cortex predict self-reported emotion across schematically distinct videos. Specifically, the results suggest that the hippocampus represents a hierarchy of emotion concepts (with greater granularity in the posterior hippocampus), and that the ventromedial prefrontal cortex represents large-scale structural abstractions. The methods seem sound and sophisticated, including interesting simulations using the Tolman-Eichenbaum machine applied to a 2D emotional space. The manuscript is well written and the results are exciting. Furthermore, the manuscript bridges two big topics: emotional processing and spatial navigation / cognitive maps, and it will thus be of interest to a large audience.\n\nMy specific comments are as follows:\n- Line 114: It would be helpful for the reader to first learn more about the task and behavior of participants before the fMRI results are presented. Currently, it's hard to follow the fMRI results without knowing what the task, stimuli, or participant behavior looked like. A new, separate figure on these aspects would be very helpful. This should also include how well the different emotions and the valence-arousal space was sampled.\n- Line 166: I'm confused about the statement that \"valence-arousal dimensions were more accurately decoded in vmPFC and entorhinal cortex compared to the hippocampus.\" In Fig. 2b, the blue dots for entorhinal cortex are at lower values than the blue dots for hippocampus, so why do the authors say that valence-arousal dimensions are more accurately decoded in entorhinal cortex?\n- Line 62: The statement \"suggesting that these variables are the most fundamental property of the mind\" seems too strong - I would suggest toning this down.\n- Line 150: Liu et al., Current Biology, 2023 (\"Multi-scale goal distance representations in human hippocampus during virtual spatial navigation\") is also relevant with regard to more rapid changes in the posterior human hippocampus.\n- The authors should state that fMRI cannot resolve a lot of neural processes that happen at rapid time scales and at the cellular level (due to its low temporal and spatial resolution), so there may be many neural processes of emotional processing that this study may have missed. This limitation (and also other limitations) should be stated clearly.\n- Were there differences between medial and lateral entorhinal cortex?\n- Line 351: Given the findings of the study, I was surprised that the authors used the term \"map-like representations of emotion concepts\" when summarizing them - wouldn't \"graph-like representations\" (or similar) be a better fit to the observations? Overall, the Conclusions paragraph from line 351 to 359 felt somewhat disconnected from the actual observations of the study."}, "author_response": "Response to Editors and Reviewers\nWe thank the reviewers for their thoughtful and constructive feedback on the manuscript. We\nhave made substantial revisions to address concerns raised during review. These revisions\ninclude new evidence of map-like representations of affective space in the hippocampus and\nvmPFC. We have also made several revisions to clarify 1) the logic of multivariate decoding\nand computational modeling, 2) the task and emotional content of stimuli used during fMRI,\nand 3) how the observed results are consistent with a map-like representation of emotion\nconcepts. We think these revisions have substantially improved the manuscript, as they more\nclearly convey how hippocampal-prefrontal systems represent emotion concepts in a map-\nlike way. Below we provide a point-by-point response to all comments raised during review.\nReviewer comments are provided in italics, and our responses are presented in normal font.\nQuotations from the revised manuscript are provided in indented blocks of text.\n----------------------------------------------------------------------------------------------------------------\nReviewer 1, Comment 1:\nMa and Kragel investigate whether emotional events are represented in a map-like way in the\nhuman hippocampus, entorhinal cortex, and prefrontal cortex. The authors analyze an fMRI\ndataset that measures participants watching a series of videos. They test whether self-report\nmeasures of emotional experience can be classified from hippocampal-prefrontal patterns\nand whether these representations of emotion categories are distinct from valence and\narousal dimensions. The authors then use a computational model of hippocampal-entorhinal\nmap-like knowledge generalization, the Tolman-Eichenbaum Machine (TEM) model, to show\nthat hippocampal and entorhinal cortex responses map relational transitions in emotional\nstatus. I find this manuscript interesting, but I had trouble interpreting some of their results.\nGenerally speaking, I have difficulty reconciling what their takeaway message is since most\nof their non-model findings show hippocampal-prefrontal representations of affective\nschematic information, while their model analyses show map-like computations.\nResponse to Reviewer 1, Comment 1:\nThank you for the frank evaluation of our manuscript and for highlighting the apparent\ninconsistencies in our experimental results. We have substantially revised the manuscript to\nimprove the clarity of our argument and present new analyses that more clearly show how\nhippocampal-prefrontal systems contain map-like representations of emotion concepts. We\nbelieve these revisions substantially clarify the takeaway message.\nMajor comments:\nReviewer 1, Comment 2.1:\nI was a bit confused by the intro focusing on hippocampal cognitive maps of abstract\nknowledge and 2D conceptual spaces. I expected the authors to then test trajectories through\na 2D affective space like the work they cite, but then their multivariate analysis mainly shows\nthe opposite, where the seemingly 1D schematic information on emotional categories is\nbest represented by the hippocampus, ERC, and vmPFC. Schematic categories aren’t necessarily\nmap-like or structure-like, especially compared to conjunctive coding.\nResponse to Reviewer 1, Comment 2.1:\nIn the referenced multivariate analyses, we decoded self-report variables as an initial test of\nwhether hippocampal-prefrontal systems contain information about emotion concepts and\nvariation in core affective dimensions. The emotion category decoders were not trained to\npredict one-hot codes in which each category is independent of others (i.e., multiple\nindependent one-dimensional schema), but rather a multivariate profile of 13 self-report\nvariables with internal structure. We observed that hippocampal decoders recovered a\nsimilarity structure that closely resembled that of self-report, as conceptually related emotions\nwere clustered together based on their valence and other factors (see Fig. 2b and 2c in the\nmain text). For instance, threat relevant emotions of fear and anxiety had similar predictions\nto one another, as did emotions most often associated with violations of social norms (i.e.,\nguilt, anger, and disgust). The observed similarity structure indicates that hippocampal\nrepresentations are map-like in that they contain hierarchically structured information related\nto valence (e.g., that an event is unpleasant and typical of fear). We have clarified this point\nin the results section of the manuscript on p. 6.\n“Because the hippocampus represents event sequences in a hierarchal manner (Deuker\net al., 2016; Hsieh et al., 2014; Schapiro et al., 2012), we expected that hippocampal\nresponses to concepts that tend to occur in temporal proximity (e.g., disgust, anger,\nand guilt or anxiety, fear, and surprise) would be more similar to one another (Deuker\net al., 2016), reflecting the graded similarity structure typically observed in ratings of\nemotion terms (Barrett & Fossum, 2001; Schimmack & Reisenzein, 1997; Shaver et\nal., 1987) as opposed to a simple structure in which emotion concepts are\nindependent.”\nAnd on p. 7.\n“These results suggest that the hippocampus does not represent emotion concepts\nindependently, but as a hierarchy that is stable across participants (Fig. 2b) and\ncaptures relationships between emotions primarily along a dimension of valence.”\nOur phrasing in the original manuscript focused on “individual emotion concepts” may have\nimplied that hippocampal representations are limited to schematic categories or a higher-\ndimensional representation that lacks internal structure (i.e., a simplex). This was not our\nintention. We have revised our language throughout the paper to more clearly convey the\nstructure observed in hippocampal responses.\nWe have revised the result subheading on p. 5:\n“The hippocampus represents multiple emotion concepts in a hierarchically organized\nstructure”.\nWe have revised text in the result section on lines 116-117:\n“We first evaluated whether hippocampal activity represents multiple emotion\nconcepts in a format that is not merely based on variation in a two-dimensional\naffective space.”\nAnd on lines 185-188:\n“These observations suggest the hippocampus could play an important role in\nmapping emotion concepts—conveying information to entorhinal cortex or vmPFC to\nconstruct an abstract, two-dimensional space (Constantinescu et al., 2016; Nitsch et\nal., 2024; Park et al., 2021; Viganò et al., 2023, 2021).”\nAnd also on lines 309-312:\n“Together, these results suggest that the vmPFC contains more information about\nlarge-scale structural abstractions (e.g., that one portion of a video was more pleasant\nthan another), whereas the hippocampus represents multiple emotion concepts\nembedded in the affective space.”\nReviewer 1, Comment 2.2:\nThis isn’t to say their results are particularly shocking, rather that they don’t appear to\njustify use of a cognitive map/structure learning model like the TEM. This is since their data\ndoesn’t include abstract trajectories in their emotional space until their model analyses.\nResponse to Reviewer 1, Comment 2.2:\nTo more directly test whether hippocampal-prefrontal systems contain information that would\noriginate from an affective map (as opposed to unidimensional encoding of valence and\narousal or emotion categories), we have conducted a new analysis decoding trajectories in a\ntwo-dimensional space derived from multidimensional scaling of category ratings. We\ncompared the performance of decoders trained to predict a higher-dimensional categorical\nspace versus the two-dimensional affective space.\nThis new analysis provides additional evidence for map-like representation across\nhippocampal-prefrontal systems, with different regions representing different components of\nthe map. We found that the hippocampus better predicted trajectories in a 13-dimensional\n“categorical space” as opposed to the abstract two-dimensional space derived from\nmultidimensional scaling of self-report ratings, although information about both sets of\nvariables was present in the hippocampus. The opposite pattern of results was observed in the\nvmPFC (Fig. R1). This dissociation suggests that although both regions contain information\nabout emotion concepts, the hippocampus more accurately represents multiple emotions\ncategories, whereas vmPFC more closely represents locations in the abstract two-dimensional\naffective space. These results are consistent with the idea that the hippocampus represents\nemotion concepts embedded in a two-dimensional map, whereas vmPFC represents a general\nrelational structure that could be used to organize emotions and other concepts.\nFigure R1. Decoding emotion category ratings and trajectories in affective space from\nBOLD response patterns in hippocampal-prefrontal systems and the amygdala. a\nRendering of parcellations of hippocampus (orange), entorhinal cortex (dark blue), and\nventromedial prefrontal cortex (vmPFC; light blue). b Trajectories of experience in an\nabstract, two-dimensional affective space from an example film (see Supplementary Fig. 3\nfor all films). Orange circles show the location of emotion categories embedded in the space.\nThe blue line shows the trajectory of one example film projected into the two-dimensional\nspace. Dots along the trajectory indicate successive timepoints, increasing in size to denote\nprogression through the film. For visualization, the trajectory coordinates were linearly\nrescaled to match the range of the category positions; decoding analyses were performed on\nunscaled trajectories. c Performance of decoding models trained to predict emotion category\nratings (orange circles) and locations in two-dimensional affective space (blue circles) from\nBOLD signal in hippocampus, entorhinal cortex, amygdala, and vmPFC. Each point\nrepresents the prediction-outcome correlation averaged across category rating items or two-\ndimensional locations from a single subject (n = 29 independent subjects). Points from the\nsame subjects are connected by gray lines, black lines indicate the mean, and gray dashed\nlines represent chance.\nResults of this new analysis are reported on lines 116-130 of the manuscript.\n“We first evaluated whether hippocampal activity represents multiple emotion\nconcepts in a format that is not merely based on variation in a two-dimensional\naffective space. To this end, we trained multivariate decoders (Supplementary Fig. 2)\nthat map patterns of hippocampal fMRI time series (9,538 measurements in 1,816\nvoxels, per subject on average) onto either i) multivariate emotion category ratings, or\nii) locations in two-dimensional affective space derived from multidimensional\nscaling of emotion category ratings (hereafter “affective space”, see Supplementary\nFig. 3). Models were trained using data acquired from multiple film stimuli and\nevaluated by testing them on independent stimuli (leave-one-film-out cross-\nvalidation, 13 films total for each of N = 29 subjects) and comparing the readout of\ncategory and affective space decoders (see Methods). This analysis revealed that\ncategory ratings were more accurately decoded (z = 0.0525, 95% bootstrap CI\n[0.0454, 0.0592]) than locations in the affective space (z = 0.0428, 95% bootstrap CI\n[0.0347, 0.0505]; Δz = 0.0097, 95% bootstrap CI [0.0025, 0.0169], d = 0.4946, p\n= .0010; see Supplementary Fig. 4-5 for contributions of individual voxels). Together,\nthese results are consistent with the proposal that the hippocampus encodes multiple\nemotion concepts in a format that is not reducible to a two-dimensional representation\nof affect.”\nAnd on lines 197-209.\n“Consistent with a map-like representation, we found that signals from vmPFC\npredicted locations in affective space (z = 0.0956, 95% bootstrap CI [0.0831, 0.1076])\nmore accurately than emotion category ratings (z = 0.0838, 95% bootstrap CI [0.0777,\n0.0902]; Δz = 0.0118, 95% bootstrap CI [0.0047, 0.0191], d = 0.0118, p = .0196).\nThis difference substantiated a double dissociation, such that the hippocampus\ncontained more information about emotion category ratings whereas vmPFC better\ncaptured information about trajectories in affective space (Δz = 0.0215, 95% bootstrap\nCI [0.0134, 0.0297], d = 0.9566, p = .0005; Fig. 3c). By contrast, activity in the\nentorhinal cortex and amygdala predicted the two target variables with comparable\naccuracy (entorhinal cortex: Δz = -0.0004, 95% bootstrap CI [-0.0071, 0.0067], d = -\n0.0195, p = .9181; amygdala: Δz = -0.0036, 95% bootstrap CI [-0.0102, 0.0033], d = -\n0.0195, p = .2213). Together, these results show that multiple regions carried\ninformation about emotion concepts (Supplementary Fig. 9), and that the vmPFC\ncontained more information about trajectories in an abstract affective space.”\nReviewer 1, Comment 2.3:\nThis isn’t to say their results are particularly shocking, rather that they don’t appear to\njustify use of a cognitive map/structure learning model like the TEM. This is since their data\ndoesn’t include abstract trajectories in their emotional space until their model analyses.\nAdditionally, their interpretation of these findings is too vague in the abstract. The\nimportance of the authors’ findings suffers if the authors can’t more robustly distinguish or\nrule out map-like vs. schematic representations in different medial temporal-prefrontal\nregions.\nResponse to Reviewer 1, Comment 2.3:\nWe have revised the abstract to more clearly state the dissociation between hippocampus and\nvmPFC.\n“Using functional MRI data collected as participants viewed emotionally evocative\nfilm clips, we found that hippocampal activity represented emotion concepts in a\nstructured hierarchy, whereas ventromedial prefrontal cortex more accurately tracked\nlocations in a two-dimensional affective space.”\nReviewer 1, Comment 3:\nApologies if I missed it, but it was also hard to detect what was significantly classifiable\ncompared to chance for the analyses in Figs. 1 and 2. As it’s presented now, it seems like\neverything is classifiable above chance in all three ROIs and both hippocampal subregions,\nbut that some regions have higher classification accuracy compared to other regions and\ncognitive variables. It’d be a bit strange if this is the case and any clarity would be\nappreciated.\nResponse to Reviewer 1, Comment 3:\nWe did not focus on the performance of individual decoders versus chance because the\nresults of this sort of analysis are difficult to interpret. This is the case because there is\nvariability in our estimate of generalization error (chance is not exactly equal to a correlation\nof zero) and because self-reports of emotion categories and valence/arousal dimensions are\ncorrelated with one another. It is not unexpected that emotion category and valence/arousal\nratings exhibited nonzero correlations in all regions of interest. The critical question is\ntherefore whether the information contained in each region more closely represents self-\nreports of emotion categories, valence–arousal dimensions, or some combination of the two.\nThis was the motivation for comparing decoding performance as a function of both brain\nregion (hippocampus, entorhinal cortex, and vmPFC) and self-report variables (categorical\nand dimensional). This allowed each region (or variable) of interest to serve as its own\ncontrol or “chance” level of performance. We now clearly describe this issue and the logic of\nour analysis on ps. 8-9 of the manuscript.\n“We found that hippocampal BOLD signals contained information about emotion\nconcepts, and to a lesser degree, information about trajectories in affective space.\nThese observations suggest the hippocampus could play an important role in mapping\nemotion concepts—conveying information to entorhinal cortex or vmPFC to construct\nan abstract, two-dimensional space (Constantinescu et al., 2016; Nitsch et al., 2024;\nPark et al., 2021; Viganò et al., 2023, 2021). If this is the case, then decoders trained\non signals from these neocortical regions should predict trajectories in affective space\nmore accurately than emotion category ratings. Alternatively, we may have observed\nbetter hippocampal prediction of category ratings because they were driven by\nparticularly salient events, or because they occurred at timescales that are more easily\ncaptured by fMRI. In such cases, decoding performance should be higher for category\nratings than trajectories in affective space, regardless of the neural origin of BOLD\nsignals being decoded. Therefore, to dissociate representations of emotional\nexperiences across regions, we compared decoders trained to predict emotion\ncategory ratings and affective space using signals acquired in hippocampus,\nentorhinal cortex, vmPFC, and the amygdala.”\nComparisons of decoding performance against chance are reported in Supplementary Tables\n1. The performance of decoders shown in Figs. 2 and 3 exceeded chance levels of zero\ncorrelations when assessed with a one-sample t-test. To more clearly show chance\nperformance in each region, we provide null distributions from a randomization using on a\nsign-flipping procedure (10,000 iterations; Fig. R2)\nFigure R2. Randomization null distributions of group-level decoding performance.\nGray bars show null distributions for group-level performance (n = 29 independent\nsubjects) of decoders trained to predict category ratings, affective space, and (binarized)\nvalence–arousal ratings from hippocampus, entorhinal cortex, ventromedial prefrontal\ncortex (vmPFC), amygdala, anterior hippocampus, and posterior hippocampus. The red\nvertical line indicates the observed decoding performance, and the blue shaded region\ndenotes the 2.5th-97.5th percentile range of the null distribution.\nReviewer 1, Comment 4:\nI found the transition in the Results from Fig 1 to Fig 2 a bit hard to follow. I’d appreciate it\nif the authors unpacked their findings related to these figures a bit more in the Results.\nFor example, the transition from the anterior/posterior hippocampal ROI effects and then\ncomparing hippocampus, entorhinal cortex, and the vmPFC is very abrupt.\nResponse to Reviewer 1, Comment 4:\nWe have added a new paragraph that smooths the transition between analyses focused on the\nhippocampus and comparisons across regions (see Response to Reviewer 1, Comment 3\nabove). The new text clarifies the rationale for dissociating representations in the\nhippocampus, entorhinal cortex, amygdala, and vmPFC.\nReviewer 1, Comment 5.1:\nSomewhat linked to my first comment, I wasn’t satisfied with the justification of using a\ncomputational model of cognitive map-like knowledge representations to model\nhippocampal-entorhinal and mPFC data that wasn’t shown to represent map-like affective\nknowledge. The authors could run a univariate or multivariate fMRI analysis of trajectories\nbetween emotional concepts and events in the 2D multidimensional scaling projection of the\nemotional concept data as a way to better justify their model-informed fMRI analyses.\nResponse to Reviewer 1, Comment 5.1:\nAlthough it may not have been clear, our intention was to first demonstrate that information\nabout emotion concepts and valence-arousal dimensions was present in hippocampal-\nprefrontal systems to justify the use of TEM. We took the hierarchical structure evident in\nhippocampal decoders to suggest that representations in these regions could be map-like\n(rather than just independent schema) and that they might resemble those learned by an agent\nexperiencing transitions between emotional events. We now make this strategy more explicit\nin the introduction of the manuscript on lines 103-106.\n“Given evidence of map-like representations in hippocampal-prefrontal systems, we\ncomplement these analyses using a computational model of relational memory (the\nTolman-Eichenbaum Machine (Whittington et al., 2020); TEM) that formulates how\nan agent might learn from event sequences to construct cognitive maps.”\nThat said, we could have provided stronger evidence of map-like representation based on\nself-report data before moving on to computational approaches. Following the reviewer’s\nsuggestion, we have conducted a new analysis in which we 1) use multidimensional scaling\nto transform category ratings into locations in a continuous, two-dimensional affective space,\nand 2) decode fMRI signal acquired as participants watched cinematic movies to predict\ntrajectories in this two-dimensional space. As predicted, we found that hippocampal activity\ncontained more information about emotion categories than either neocortical region. Partially\nconsistent with our prediction, we found that vmPFC contained more information about the\ntwo-dimensional affective space. Signals in entorhinal cortex did not robustly encode\nemotion concepts or locations in the two-dimensional affective space. These findings more\nstrongly justify the use of TEM to characterize map-like coding of emotion concepts. Results\nof this new analysis are included in Response to Reviewer 1, Comment 2.2 (Fig. R1) above.\nReviewer 1, Comment 5.2:\nFor example, it was unclear how the g parameter in the TEM relates to grid-like coding in\nthe fMRI data?\nResponse to Reviewer 1, Comment 5.2:\nWe now provide additional rationale explaining how activation in the population of g cells in\nTEM relates to BOLD signal in vmPFC on lines 237-244.\n“In contrast, activity in layer g resembles that of entorhinal cortex, with firing fields\nthat span the entire space (e.g., grid cell-like activity; Fig. 4c), providing an abstract\nstructural code that can be used for making inferences in any environment or in new\nproblems that have a similar relational structure. In this way, TEM formalizes how an\nagent learns representations thought to be present in hippocampal-prefrontal systems.\nThis model allowed us to test whether hippocampal signals more accurately predict\nthe conjunction of concepts and their location in the affective space (activity in the p\nlayer), and whether vmPFC signals more accurately predict the relational structure of\naffective space (activity in the g layer).”\nReviewer 1, Comment 6:\nGiven its important role in emotional memory and the authors’ previous work, I was\nsurprised that the authors don’t present results from a bilateral amygdala ROI as a way to\nbetter distinguish the role of different regions in mapping different emotion concepts. The\namygdala probably plays an important role in most cognitive processes that they’re\ninterested in without having a strong association with cognitive maps. It would also further\nstrengthen their anterior versus posterior hippocampal axis data.\nResponse to Reviewer 1, Comment 6:\nWe agree that including an analysis of amygdala activity helps contextualize our findings.\nPerforming the same decoding analyses used to investigate signals in the hippocampus,\nentorhinal cortex, and vmPFC, we found that amygdala activity predicted category ratings,\nlocations in affective space, and valence-arousal ratings to some degree (all > chance; Fig.\nR2). However, amygdala decoders exhibited low levels of performance and did not encode\nmore information about emotion categories than locations in a two-dimensional affective\nspace (Δz = -0.0036, 95% bootstrap CI [-0.0102, 0.0033], d = -0.0195, p = .2213). Direct\ncomparisons with hippocampal decoders showed that the amygdala was less accurate at\npredicting category ratings than the hippocampus, more so than for predicting valence-\narousal ratings (Δz = -0.0092, 95% bootstrap CI [-0.0037, -0.0149], d = -0.6004, p = .0146;\nFig. R3a).\nGiven prior evidence of strong anatomical and functional connectivity between the amygdala\nand anterior hippocampus (Freelin et al., 2025; Poppenk et al., 2013), and some evidence that\nthe amygdala encodes abstract knowledge (Kumaran et al., 2012), we additionally compared\namygdala decoding performance with that of anterior and posterior hippocampus separately\n(Fig. R3b). Amygdala responses did not predict category ratings as accurately as signals in\nthe posterior hippocampus (Δz = -0.0117, 95% bootstrap CI [-0.0063, -0.0171], d = -0.7850,\np = .0014), particularly relative to predicting binarized valence-arousal ratings (Δz = -0.0125,\n95% bootstrap CI [-0.0077, -0.0172], d = -0.9538, p = .0017). By contrast, amygdala and\nanterior hippocampus exhibited comparable levels of performance when decoding category\nratings (Δz = -0.0040, 95% bootstrap CI [-0.0015, -0.0095], d = -0.2660, p = .2286), with no\nevidence of differences between regions (amygdala vs. anterior hippocampus) in decoding of\ncategory vs. binarized valence-arousal variables (Δz = -0.0023, 95% bootstrap CI[-0.0024, -\n0.0071], d = -0.1780, p = 0.4813). This pattern is consistent with prior evidence implicating\namygdala (Kong & Zweifel, 2021), anterior hippocampus and their interactions in\nrepresenting broad affective context, which may in turn support the enhancement of memory\nfor emotionally salient events (Freelin et al., 2025; Poppenk et al., 2013).\nFigure R3. Decoding emotion category ratings, locations in affective space, and\nbinarized valence-arousal ratings from BOLD response patterns in hippocampal-\nprefrontal systems and the amygdala. a Performance of decoding models trained to\npredict emotion category ratings and locations in two-dimensional affective space from\nBOLD signal in hippocampus, vmPFC, and amygdala. b Performance of decoding models\ntrained to predict emotion category and binarized valence-arousal ratings from BOLD\nactivity in anterior hippocampus, posterior hippocampus, and amygdala. In both panels, each\npoint represents the prediction-outcome correlation averaged across category rating items,\ntwo-dimensional locations, or binarized valence-arousal items from a single subject (n = 29\nindependent subjects). Points from the same subjects are connected by gray lines, black lines\nindicate the mean, and gray dashed lines represent chance.\nThese new findings have been incorporated into the results section of the manuscript on lines\n167-182 and in revised figures 2 and 3.\nMinor comments\nReviewer 1, Comment 7:\nIt would be helpful to separate the Results and Discussion sections by giving them their own\nheadings.\nResponse to Reviewer 1, Comment 7:\nWe have added descriptive subheadings in the Results section and following the journal\nformatting guideline, we did not provide subheadings in the Discussion section, although we\nare open to this possibility if the journal is willing to accommodate.\nReviewer 1, Comment 8:\nIn the Discussion, the authors say previous abstract cognitive map work relies on an\n“Impoverished sensory environment,” while the authors’ “use of naturalistic movie stimuli\noffers a more ecologically valid window into how hippocampal-prefrontal systems process\ndynamic, rich, emotional experiences”. However, I fail to see how interactive tasks like\nTavares et al., 2015 Neuron or Park et al., 2021, Neuron, where participants learn social\nhierarchies with natural dimensions like popularity, power, or competence are any less\ninteractive than the emotional concepts in this task. I recommend removing that sentence in the Discussion.\nResponse to Reviewer 1, Comment 8:\nThis comment in the discussion section was intended to highlight that most work in this space\n(Park et al 2021 and Tavares et al 2015 included) use relatively simplified stimuli. Static\nimages of faces and narrative text are far from the sensory inputs that humans learn about\noutside of the scanning environment. We agree that other tasks are more naturalistic to the\nextent that they involve active navigation, but issues regarding the complexity of sensory\ninputs remain. We have revised the discussion to note this distinction in the revision on lines\n371-375.\n“Previous investigations have characterized brain responses to less complex stimuli\n(Constantinescu et al., 2016; Park et al., 2021; Viganò et al., 2021), such as static\nimages or text-based narratives. Although our experimental paradigm was not\ninteractive, our use of naturalistic movies offers a more ecologically valid window\ninto how hippocampal-prefrontal systems process dynamic, rich, emotional\nexperiences.”\n----------------------------------------------------------------------------------------------------------------\nReviewer 2, Comment 1:\nThe authors report results from an fMRI study in which participants (N = 29) viewed\nemotionally evocative film clips. The fMRI results show that activity in hippocampus and\nprefrontal cortex predict self-reported emotion across schematically distinct videos.\nSpecifically, the results suggest that the hippocampus represents a hierarchy of emotion\nconcepts (with greater granularity in the posterior hippocampus), and that the ventromedial\nprefrontal cortex represents large-scale structural abstractions. The methods seem sound and\nsophisticated, including interesting simulations using the Tolman-Eichenbaum machine\napplied to a 2D emotional space. The manuscript is well written and the results are exciting.\nFurthermore, the manuscript bridges two big topics: emotional processing and spatial\nnavigation / cognitive maps, and it will thus be of interest to a large audience.\nMy specific comments are as follows:\n- Line 114: It would be helpful for the reader to first learn more about the task and behavior\nof participants before the fMRI results are presented. Currently, it's hard to follow the fMRI\nresults without knowing what the task, stimuli, or participant behavior looked like. A new,\nseparate figure on these aspects would be very helpful. This should also include how well the\ndifferent emotions and the valence-arousal space was sampled.\nResponse to Reviewer 2, Comment 1:\nWe thank the reviewer for this helpful suggestion. We have added a new figure (Fig. R4; now\nFig. 1 in the main text) that illustrates the stimuli, fMRI and behavioral data, including the\ncoverage of emotion categories and the valence-arousal space in the self-report ratings.\nFigure R4. Experimental paradigm and behavior. a participants (n = 29) watched over 2.5 h\nof 14 brief cinematic videos while BOLD signal was measured with fMRI over the course of 4\nscanning sessions. The video stimuli were additionally rated by an independent group of 44\nsubjects. Here we examine continuous ratings of 13 emotion categories, 2 measures of valence,\nand 2 measures of arousal. b Hexagonal binned plot of self-report data. Each hexagon\nrepresents a region of a two-dimensional space defined by ratings of valence (x-axis) and\narousal (y-axis) across films. Bins are colored to indicate the category with the highest rating\nacross time points falling in that region. Blank areas indicate bins without observations in the\ncorresponding valence-arousal range. Hexagonal binned plots for each individual emotion\ncategory are shown in Supplementary Fig. 1.\nReviewer 2, Comment 2:\n- Line 166: I'm confused about the statement that \"valence-arousal dimensions were more\naccurately decoded in vmPFC and entorhinal cortex compared to the hippocampus.\" In Fig.\n2b, the blue dots for entorhinal cortex are at lower values than the blue dots for\nhippocampus, so why do the authors say that valence-arousal dimensions are more\naccurately decoded in entorhinal cortex?\nResponse to Reviewer 2, Comment 2:\nThe sentence in question originally referred to a contrast in which the entorhinal cortex and\nvmPFC were averaged together as a neocortical group and compared against the\nhippocampus ((entorhinal cortex + vmPFC)/2 - hippocampus). We included this contrast only\nfor the valence-arousal decoding analysis as an initial test of our hypothesis that neocortical\nregions implicated in structural abstraction (vmPFC and entorhinal cortex) would, as a group,\nmore accurately track valence-arousal dimensions compared to the hippocampus. The\noriginal phrasing was not clear and could be read as referring to the individual entorhinal\ncortex results. Indeed, the more accurate valence-arousal decoding in the neocortical group\nwas driven by the vmPFC (Δz = 0.0398, 95% bootstrap CI [0.0308, 0.0489], d = 1.5951, p\n< .0001), whereas absolute valence-arousal decoding performance in the entorhinal cortex\nwas lower than in the hippocampus (Δz = -0.0127, 95% bootstrap CI [-0.0218, -0.0033], d = -\n0.504, p = .0040). However, the key point was the relative difference between category and\nvalence-arousal; the category-dimension gap was larger in the hippocampus than in the\nentorhinal cortex or vmPFC. This interaction effect indicates that hippocampus is more\ncategory-focused, and neocortical areas preserved relatively more information about affective\ndimensions.\nIn the revised manuscript, we present a new analysis decoding locations in a two-dimensional\naffective space derived from multidimensional scaling of category ratings as a more direct\nmeasure of the relational space, as opposed to decoding self-reported valence and arousal.\nWe now report results separately for vmPFC and entorhinal cortex on lines XXXXX of the\nmanuscript and it was also included in Response to Reviewer 1, Comment 2.2 (Fig. R1)\nabove.\nReviewer 2, Comment 3:\n- Line 62: The statement \"suggesting that these variables are the most fundamental property\nof the mind\" seems too strong - I would suggest toning this down.\nResponse to Reviewer 2, Comment 3:\nWe agree—this may not be a generally accepted interpretation, nor is it a view that we\nstrongly hold. We have toned this language down to note that it is a perspective held by some\ntheorists.\nReviewer 2, Comment 4:\n- Line 150: Liu et al., Current Biology, 2023 (\"Multi-scale goal distance representations in\nhuman hippocampus during virtual spatial navigation\") is also relevant with regard to more\nrapid changes in the posterior human hippocampus.\nResponse to Reviewer 2, Comment 4:\nThis study is indeed relevant to the present work. We now include a reference to this work on\nlines 151-155.\n“Central to accounts of memory, navigation, and emotion processing (Fanselow &\nDong, 2010), the hippocampal long axis is characterized by gradients in connectivity,\ngene expression (Vogel et al., 2020), neural timescale (Liu et al., 2023), and\nbehavioral specialization (Collin et al., 2015; Komorowski et al., 2013). Particularly\nrelevant for the representation of emotion knowledge are observations that\nhippocampal activity represents event sequences at multiple timescales, with more\nrapid changes coded in more posterior portions of the hippocampus (Collin et al.,\n2015; Liu et al., 2023).”\nReviewer 2, Comment 5:\n- The authors should state that fMRI cannot resolve a lot of neural processes that happen at\nrapid time scales and at the cellular level (due to its low temporal and spatial resolution), so\nthere may be many neural processes of emotional processing that this study may have missed.\nThis limitation (and also other limitations) should be stated clearly.\nResponse to Reviewer 2, Comment 5:\nWe have included a discussion of limitations on lines 403-417.\n“Several limitations constrain the interpretation of our findings. Our focus on\ndimensions of valence and arousal necessarily oversimplifies emotional experience,\nwhich can be organized by far more than two dimensions (Cowen et al., 2019;\nFontaine et al., 2007). Emotion knowledge may be organized in low-dimensional\nembeddings of other variables, such as effort, certainty, and situational control.\nAdditionally, the spatiotemporal resolution of fMRI precludes measurement of neural\ndynamics and phase codes thought to support navigation (Burgess & O’Keefe, 2011;\nLisman & Jensen, 2013). To the extent that neural mechanisms are shared across\nspecies, future research using high-density neuronal recordings in rodents (Wen et al.,\n2024; Zutshi et al., 2025) and human intracranial electrophysiology (Qasim et al.,\n2021) will be critical for testing whether navigating affective spaces involves\ncomputations similar to those used for spatial navigation. Further, TEM is not the\nonly model of hippocampal computations. Alternative models grounded in\nreinforcement learning (Stachenfeld et al., 2017) may offer complementary insights,\nparticularly for studying emotion concepts, which inherently encode value and\nmotivational relevance. This framework aligns with evidence suggesting the role of\nthe anterior hippocampus and vmPFC in motivation and goal-directed behavior (Gazit\net al., 2020; Strange et al., 2014; Viard et al., 2011). Future research should examine\nwhether models of reinforcement learning can better predict hippocampal-prefrontal\nrepresentations of emotion concepts.”\nReviewer 2, Comment 6:\n- Were there differences between medial and lateral entorhinal cortex?\nResponse to Reviewer 2, Comment 6:\nWe did not previously analyze fMRI signal in lateral and medial entorhinal cortex separately,\nbecause we had expected entorhinal cortex as a whole to contain more information about\naffective space or valence-arousal ratings. Because we did not find signals related to these\nvariables, we did not probe further subdivisions to avoid over-interpreting noise or increasing\nthe risk of false positives. Had we found strong mappings between entorhinal cortex\nresponses and emotion concepts or affective space, we could have attempted to localize\nconcepts to lateral entorhinal cortex and the affective space to medial entorhinal cortex, as\npredicted by our account.\nBased on the reviewer’s suggestion, we conducted separate decoding analyses within the\nposteromedial and anterolateral entorhinal cortex using masks from Maass et al. (2015).\nUsing these masks, we did not observe differences between entorhinal subregions, nor\nbetween either subregion and the entorhinal cortex as a whole (Figs. R5 and R6).\nFigure R5. Performance of decoding models trained to predict emotion category and\nvalence-arousal rating in subregions of the entorhinal cortex. Performance of decoding\nmodels trained to predict emotion category ratings (orange circles) and valence-arousal ratings\n(blue circles) from BOLD signal in whole, anterolateral, and posteromedial entorhinal cortex.\nEach point represents the prediction-outcome correlation averaged across category rating\nitems or two-dimensional locations from a single subject (n = 29 independent subjects). Points\nfrom the same subjects are connected by gray lines, black lines indicate the mean, and gray\ndashed lines represent chance.\nFigure R6. Performance of decoding models trained to predict activity of p and g cells\nfrom BOLD activity in subregions of the entorhinal cortex. Each point represents the\nprediction-outcome correlation averaged across units at each scale from one subject (n = 29\nindependent subjects). Horizontal black lines indicate the group average, connected gray\nlines indicate performance for one subject, and the horizontal dashed line indicates chance.\nTEM = Tolman Eichenbaum Machine.\nOne might expect differences between posteromedial and anterolateral entorhinal cortex,\nsuggested as the human homologues of the rodent medial and lateral entorhinal cortex (Maass\net al., 2015; Navarro Schröder et al., 2015). However, several factors may have limited our\nability to detect such differences here. First, susceptibility artifacts are particularly severe in\nthe entorhinal cortex, which can lead to signal loss and distortion (Olman et al., 2009),\nthough our inspection of the variation of mean signal across subjects did not reveal unusual\ninstability or obvious signal dropout within this region (see Fig. R7). Second, segmenting\nposteromedial and anterolateral entorhinal cortex is challenging and typically requires manual\nsegmentation of high-resolution images at 7T (Maass et al., 2015; Navarro Schröder et al.,\n2015). These constraints, along with the generally lower decoding performance we observed\nin the entorhinal cortex, may have obscured functional distinctions between posteromedial\nand anterolateral entorhinal cortex in our analysis. Alternatively, it may be the case that the\nentorhinal cortex plays a different role in organizing knowledge about emotions and space.\nFigure R7. fMRI signal across subjects. For each subject, voxelwise signal intensity maps\nwere created by averaging preprocessed BOLD timeseries across all video viewing sessions.\nParametric maps show the group mean of signal intensity divided by the standard deviation\nacross subjects (i.e., Cohen’s d; n = 29 independent subjects). The colorbar indicates the\naverage signal intensity, with darker colors indicating values below average and warmer\ncolors indicating greater signal intensity. Note that preprocessing involved scaling the\ntimeseries relative to the grand mean and accounting for confounds. Note the lack of spatial\ndistortions or dropout in the anterior temporal lobe.\nReviewer 2, Comment 7:\n- Line 351: Given the findings of the study, I was surprised that the authors used the term\n\"map-like representations of emotion concepts\" when summarizing them - wouldn't \"graph-\nlike representations\" (or similar) be a better fit to the observations? Overall, the Conclusions\nparagraph from line 351 to 359 felt somewhat disconnected from the actual observations of\nthe study.\nResponse to Reviewer 2, Comment 7:\nWe agree that relations among emotion concepts can be described in a graph-like way, and\nindeed our simulation with TEM used both a discrete environment and action space.\nHowever, we used the term “map-like” following the idea that cognitive maps in\nhippocampal-prefrontal systems, for spatial and non-spatial problems, can be constructed\nfrom statistical regularities in the transitions between discrete states (Behrens et al., 2018). In\nshort, we use the term “map-like” to refer to both relational maps and continuous maps. Our\nfindings provide evidence for both types of representations. We now clarify this point in the\nDiscussion section of the manuscript on lines 380-390.\n“Our computational modeling results suggest that map-like representations in\nhippocampal-prefrontal systems could result from learning the statistical regularities\nof event transitions. By recasting continuous navigation as the problem of learning\ntransitions in a discrete state space, the same hippocampal-prefrontal representations\ncan be used to accomplish a variety of tasks, whether conceived as learning linkages\non a graph or directions in a two-dimensional Euclidean map (Behrens et al., 2018).\nOur fMRI results are consistent with both conceptions of a cognitive map, as we\nfound hippocampal-prefrontal activity predicted both locations in a two continuous\naffective space and activity in TEM—a model trained to perform structural\nabstraction through learning transitions between discrete states. Whether graph-like\nand map-like representations result from sequential stages of abstraction or\nqualitatively distinct processes remains an open question for future research.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I commend the authors for the substantial analyses and revisions they've made in response to my concerns. Prior to publication at Nature Communications, I have two minor suggestions that would make their findings more straightforward to interpret.\n-I appreciate the authors' response to my third comment about the performance of individual decoders versus chance. I still worry that readers will think their classifier analysis would find nonzero correlations everywhere in the brain, independent of whether the regions are involved in affective processing. For their supplemental results section, I encourage the authors to run a whole-brain searchlight analysis to show that at least some brain regions exhibit nonzero correlations for emotion category and valence/arousal.\n-I'd recommend revising the manuscript one more time to further distinguish map-like representations of hierarchically organized structure versus 2D representations of affective space. At first glance, it's unclear why one of the aforementioned representations would be more 'map-like' than the other since there are plenty of studies that study 2D relational representations in the hippocampus and elsewhere that are interpreted as cognitive maps.\nFor instance, in their response the authors state \"These results are consistent with the idea that the hippocampus represents emotion concepts embedded in a two-dimensional map, whereas vmPFC represents a general relational structure that could be used to organize emotions and other concepts\".\nThe \"general relational structure\" in vmPFC they mention would be thought of as cognitive map-like by many investigators.\nIt'd be helpful to unpack these theoretical differences a bit more in the manuscript.", "reviewer_2": "The authors have addressed all my comments and I am satisfied with their responses.\n(Remarks on code availability)\nI had a very quick look at the code and it looks OK. It's not just code for reproducing the figures but contains various other code related to data processing, which is commendable. The code also provides a README file with instructions."}, "author_response": "Response to Editors and Reviewers\nWe thank the reviewers for their thoughtful and constructive feedback on the manuscript. We\nhave 1) included the results of whole-brain searchlight analyses, and 2) clarified the distinct\nformats of map-like representations in the hippocampal-prefrontal systems. Below we\nprovide a point-by-point response to both comments raised during review. Reviewer\ncomments are provided in italics, and our responses are presented in normal font. Quotations\nfrom the revised manuscript are provided in indented blocks of text.\nReviewer 1, Comment 1:\nI commend the authors for the substantial analyses and revisions they've made in response to\nmy concerns. Prior to publication at Nature Communications, I have two minor suggestions\nthat would make their findings more straightforward to interpret.\n-I appreciate the authors' response to my third comment about the performance of individual\ndecoders versus chance. I still worry that readers will think their classifier analysis would\nfind nonzero correlations everywhere in the brain, independent of whether the regions are\ninvolved in affective processing. For their supplemental results section, I encourage the\nauthors to run a whole-brain searchlight analysis to show that at least some brain regions\nexhibit nonzero correlations for emotion category and valence/arousal.\nResponse to Reviewer 1, Comment 1:\nWe thank the reviewer for their positive evaluation of our revision and for the helpful\nsuggestions to further clarify our findings. We conducted a whole-brain searchlight analysis\nand visualized the variation of effect sizes of decoding performance (Fig. R1 and\nSupplementary Fig. 10). Cohen’s d ranged from 0 to 6.01 for the performance of decoders\ntrained to predict emotion category ratings, and from -0.19 to 2.98 for valence-arousal\ndecoding. As can be seen in the figure, most information about these variables was present in\nresponse patterns in medial prefrontal cortex, precuneus, parietal cortex, and temporal cortex.\nThese structures are frequently found to respond to emotion manipulations (Lindquist et al.,\n2012) and contain information about emotion categories (see Satpute & Lindquist, 2019 for\nreview of decoding studies). We now include the results of this searchlight analysis in the\nsupplemental materials to illustrate variability in the amount of emotion-related information\nin fMRI signal.\nWe stress that our key inferences stem from hypothesis-driven region-of-interest analyses\nthat directly compare decoding performance between regions, rather than testing each\ndecoder against chance. We prefer this approach because subthreshold activity can carry\nmeaningful information and regional differences can reflect quantitative rather than\nqualitative distinctions (Etzel et al., 2013; Henson, 2006). Moreover, because specific\nemotional states are characterized by information contained in spatially distributed yet\noverlapping response patterns, observations from searchlight analyses may miss out on\ninformation related to processes that unfold at larger spatial scales (Etzel et al., 2013; Kragel\net al., 2018). Based on these considerations, observing widespread and variable decoding\nperformance above chance across the brain is expected and complements our main results\nshowing how different regions represent emotion-related variables.\nFigure R1. Voxelwise effect sizes of decoding performance. Maps reflect effect sizes of\nthe performance of decoders trained to predict (a) emotion categories and (b) valence-arousal\nfrom searchlight spheres (radius = 4 voxels). The colorbar indicates Cohen’s d of Fisher-z\ntransformed prediction-outcome correlations across subjects (n = 29 independent subjects).\nReviewer 1, Comment 2:\n-I'd recommend revising the manuscript one more time to further distinguish map-like\nrepresentations of hierarchically organized structure versus 2D representations of affective\nspace. At first glance, it's unclear why one of the aforementioned representations would be\nmore 'map-like' than the other since there are plenty of studies that study 2D relational\nrepresentations in the hippocampus and elsewhere that are interpreted as cognitive maps.\nFor instance, in their response the authors state \"These results are consistent with the idea\nthat the hippocampus represents emotion concepts embedded in a two-dimensional map,\nwhereas vmPFC represents a general relational structure that could be used to organize\nemotions and other concepts\". The \"general relational structure\" in vmPFC they mention\nwould be thought of as cognitive map-like by many investigators. It'd be helpful to unpack\nthese theoretical differences a bit more in the manuscript.\nResponse to Reviewer 1, Comment 2:\nWe have revised the introduction to make it clear that both hippocampus and vmPFC exhibit\nmap-like representations but in different formats on lines 87-94:\n“To build a map of the environment, neural populations in the hippocampus bind\ntogether highly processed sensory inputs with their positions in relational space,\nforming conjunctive codes of concepts that jointly represent what is experienced and\nwhere it lies within a structured map (Manns & Eichenbaum, 2006). Based on the\ncoactivation of hippocampal inputs, neural populations in the entorhinal cortex\n(Hafting et al., 2005) and ventromedial prefrontal cortex (vmPFC) learn relations\namong concepts using a grid-like code (Constantinescu et al., 2016; Nitsch et al.,\n2024; Park et al., 2021; Viganò et al., 2023, 2021). This format abstracts away from\nthe content of experiences to define a relational structure which can be used to guide\ndecision making and navigate affective experiences.”\nAnd on lines 99-106:\n“Here we evaluate the proposal (Ma et al., 2025) that hippocampal-prefrontal systems\nrepresent emotion concepts in a cognitive map. In this account, entorhinal and\nventromedial prefrontal cortex learn a general relational structure that can be used to\npredict the valence and arousal of upcoming events. Through associative learning, the\nhippocampus binds this information with highly processed sensory inputs to link\nspecific emotion concepts with locations in valence-arousal space. Thus, although\nboth sets of regions can be considered to have map-like properties, the hippocampus\nshould uniquely represent emotion concepts embedded in a two-dimensional affective\nspace. We test this account by analyzing fMRI signals...”\nWe have also revised the discussion to clarify that our findings in vmPFC can be considered\nmap-like in certain respects on lines 363-367:\n“Considered alongside evidence that vmPFC and other nodes of the default network\nexhibit grid-like activity during tasks from multiple domains (Bao et al., 2019;\nConstantinescu et al., 2016; Park et al., 2021), these findings suggest that similar\nneural mechanisms may be involved for reward-based decision making (Grabenhorst\n& Rolls, 2011), social inferences (Lockwood et al., 2024), and reporting emotional\nexperience (Čeko et al., 2022)—all of which involve relational processing and could\nbe solved using map-based strategies (Kaplan et al., 2017)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Thank you for addressing my concerns."}, "author_response": ""}]

Decision letter