Biology subjects

Garvert, M. M.

Publications and source records attributed to Garvert, M. M..

11 recordsLinked to original sources

bioRxiv2026

Global structure of new relational knowledge networks is represented in retrosplenial complex, and node-distance in hippocampus

Much of our knowledge is structured in networks, but how the brain represents such networks remains unclear. In most nested knowledge structures a subset of the nodes will be highly connected to other nodes in the network. High levels of connection can exist either locally (high degree centrality) or at a global level (high closeness/betweenness centrality). Here, we explored how the human brain represents network structure using functional magnetic resonance imaging (fMRI) and a novel learning paradigm. During training, participants learned the transition structure for travel between a set of fictious partially connected alien planets. Despite encountering only individual connections, participants choices indicated they could infer the broader network structure. Next day, they viewed each stimulus during a cover task while undergoing fMRI. Representational-similarity analysis showed that shortest-path distances within the learned graph were encoded in the posterior hippocampus and right retrosplenial complex, whereas global connectivity (closeness/ betweenness centrality) was represented in the left retrosplenial complex. These findings extend the view that brain networks associated with spatial navigation also process abstract relational knowledge using principles akin to mapping physical space. The results are also consistent with proposals that the hippocampus represents distance information and that the retrosplenial cortex acts as hub for integrating recently acquired knowledge.

neuroscience↗

bioRxiv2024

Abstract rule generalization for composing novel meaning recruits the frontoparietal control network

The ability to generalize previously learned knowledge to novel situations is crucial for adaptive behavior, representing a form of cognitive flexibility that is particularly relevant in language. Humans excel at combining linguistic building blocks to infer the meanings of novel compositional words, such as "un-reject-able-ish". How do we accomplish this? While recent research on compositional generalization in relational memory, action planning and vision strongly implicates a medial prefrontal-hippocampal network (Baram et al., 2021; Barron et al., 2020; Schwartenbeck et al., 2023), it remains unclear whether the same network supports compositional inference in language. To this end, we trained participants on an artificial language in which the meanings of compositional words could be derived from known stems and unknown affixes, using abstract affixation rules (e.g., "good-kla" which means "bad", where "-kla" reverses the meaning of the stem word "good"). According to these rules, word meaning depended on the sequential relation between the stem and the affix (i.e., pre- vs. post-stem). During fMRI, participants performed a semantic priming task, with novel compositional words as either sequential order congruent (e.g., "white-kla") or incongruent primes (e.g., "kla-white"), and synonyms of the composed meaning as targets ("black"). Our results show that the compositional process engages a broad temporoparietal network, including the hippocampus, while the composed meanings are represented in left-lateralized language areas. Notably, newly composed meanings were decodable already at the time of the prime. Finally, we found that the composition process recruits abstract structure rule representations in a lateral frontoparietal network, rather than the predicted medial frontotemporal network, perhaps because abstract rules in language are more readily formatted as production rules rather than as relational map structures.

neuroscience↗

bioRxiv2024

An abstract relational map emerges in the human medial prefrontal cortex with consolidation

Understanding the structure of a problem, such as the relationships between stimuli, supports fast learning and flexible reasoning. Rodent work has suggested that abstraction of structure away from sensory details occurs over the course of multiple days, in cortex. However, direct evidence of such explicit relational representations in humans is scarce, and it is unclear whether they emerge on similar timescales. Here, we combine a graph-learning paradigm with functional magnetic resonance imaging (fMRI) to look for such a relational map in the human brain. We first trained participants on two associative graphs with the same structure. We then scanned participants twice while they used this knowledge, with several days between scanning sessions. Using fMRI repetition suppression, we found an abstract relational representation in the medial prefrontal cortex (mPFC), that emerged across the two scanning sessions. This finding was also replicated using representational similarity analysis (RSA). These results shed new light on how neural representations organising relational knowledge change with time.

neuroscience↗

bioRxiv2024

Neural mechanisms of credit assignment for delayed outcomes during contingent learning

Adaptive behavior in complex environments critically relies on the ability to appropriately link specific choices or actions to their outcomes. However, the neural mechanisms that support the ability to credit only those past choices believed to have caused the observed outcomes remain unclear. Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. We find that the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) code for the causal choice identity when credit needs to be assigned for choices that are separated from outcomes by a long delay, even when this delayed transition is punctuated by interim decisions. Further, we show when interim decisions must be made, learning is additionally supported by lateral frontopolar cortex (lFPC). Our results indicate that lFPC holds previous causal choices in a "pending" state until a relevant outcome is observed, and the fidelity of these representations predicts the fidelity of subsequent causal choice representations in lOFC and HC during credit assignment. Together, these results highlight the importance of the timely reinstatement of specific causes in lOFC and HC in learning choice-outcome relationships when delays and choices intervene, a critical component of real-world learning and decision making.

neuroscience↗

bioRxiv2024

Integrating episodic and spatial context signals in the hippocampus

Episodic and spatial memory are the two key components of the mnemonic system in humans. Episodic memory enables us to remember events from the past whereas spatial memory enables us to form a map-like representation of the environment. Interestingly, these seemingly different functions rely on the same brain structure: the hippocampus. Yet, how the hippocampus supports both at the same time remains unclear. Here, we tested the hypotheses that the hippocampus supports these two systems either via a common coding mechanism or via a parallel processing mechanism. To this end, we combined functional magnetic resonance imaging (fMRI) with an episodic life-simulation task and a spatial virtual reality task to manipulate episodic and spatial context associations of objects. We then investigated fMRI adaptation effects between these objects as a result of shared contexts. We found that the fMRI signal in the anterior hippocampus scaled with the combined prediction of shared episodic and spatial contexts, in line with the idea of a common coding mechanism. We found no evidence for a parallel processing mechanism, as there were no differences between episodic and spatial effects. The common coding effect for episodic and spatial memory dovetails with the broader notion of domain-general hippocampal cognitive maps.

neuroscience↗

bioRxiv2023

Grid-like entorhinal representation of an abstract value space during prospective decision making

Everyday decisions require us to predict how valuable different choice options will be in the future. Prior studies have identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. Here, we investigated whether the entorhinal cortex integrates relational information about changing values by representing an abstract value space. To this end, we combined fMRI with a prospective decision making task that required participants to track and predict changing values of two choice options in a sequence. Such a sequence formed a trajectory through an underlying two-dimensional value space. Our results show that participants successfully integrated and extrapolated changes along the two value dimensions. Participants choice behavior was explained by a prospective reinforcement learning model and the degree to which they updated values over time correlated with self-reported navigational abilities and preferences. Crucially, while participants traversed the abstract value space, the entorhinal cortex exhibited a grid-like representation, with the phase of the hexadirectional fMRI signal (i.e., the orientation of the estimated grid) being aligned to the most informative axis through the value space. A network of brain regions, including the ventromedial prefrontal cortex (vmPFC), tracked the prospective value difference between options and the occipital-temporal cortex represented the more valuable option. These findings suggest that the entorhinal grid system might support the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional signals of the value difference between options and their identities for choices. Thus, these findings provide novel insight for our understanding of cognitive maps as a mechanism to guide prospective decision making in humans.

neuroscience↗

bioRxiv2022

Parallel cognitive maps for short-term statistical and long-term semantic relationships in the hippocampal formation

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information, such as the transition probabilities between objects. However, objects can often be characterized in terms of different types of relations simultaneously, e.g. semantic similarities learned over the course of a lifetime as well as transitions experienced over a brief timeframe in an experimental setting. Here we ask how the hippocampal formation handles the embedding of stimuli in multiple relational structures that differ vastly in terms of their mode and timescale of acquisition: Does it integrate the different stimulus dimensions into one conjunctive map, or is each dimension represented in a parallel map? To this end, we reanalyzed functional magnetic resonance imaging (fMRI) data from Garvert et al. (2017) that had previously revealed an entorhinal map which coded for newly learnt statistical regularities. We used a triplet odd-one-out task to construct a semantic distance matrix for presented items and applied fMRI adaptation analysis to show that the degree of similarity of representations in bilateral hippocampus decreases as a function of semantic distance between presented objects. Importantly, while both maps localize to the hippocampal formation, this semantic map is anatomically distinct from the originally described entorhinal map. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps reflecting the embedding of objects in diverse relational structures.

neuroscience↗

bioRxiv2021

Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization

The brain forms cognitive maps of relational knowledge, an organizing principle thought to underlie our ability to generalize and make inferences. However, how can a relevant map be selected in situations where a stimulus is embedded in multiple relational structures? Here, we find that both spatial and temporal cognitive maps influence generalization in a choice task, where spatial location determines reward magnitude. Mirroring behavior, the hippocampus not only builds a map of spatial relationships but also encodes temporal distances. As the task progresses, participants choices become more influenced by spatial relationships, reflected in a strengthening of the spatial and a weakening of the temporal map. This change is driven by orbitofrontal cortex, which represents the evidence that an observed outcome is generated from the spatial rather than the temporal map and updates hippocampal representations accordingly. Taken together, this demonstrates how hippocampal cognitive maps are used and updated flexibly for inference.

neuroscience↗

bioRxiv2021

Parallel representation of context and multiple context-dependent values in ventro-medial prefrontal cortex

Value representations in ventromedial prefrontal-cortex (vmPFC) are known to guide the choice between options. But the value of an option can be different in different task contexts. Goal-directed behavior therefore requires to know the current context and associated values of options, and to flexibly switch between value representations in a task-dependent manner. We tested whether task-relevant and -irrelevant values influence behavior and asked whether both values are represented together with context signals in vmPFC. Thirty-five participants alternated between tasks in which stimulus color or motion predicted rewards. As expected, neural activity in vmPFC and choices were largely driven by task-relevant values. Yet, behavioral and neural analyses indicate that participants also retrieved the values of irrelevant features, and computed which option would have been best in the alternative context. Investigating the probability distributions over values and contexts encoded in multivariate fMRI signals, we find that vmPFC maintains representations of the current context, i.e. task state, the value associated with it, and the hypothetical value of the alternative task state. Crucially, we show that evidence for irrelevant value signals in vmPFC relates to behavior on multiple levels, competes with expected value signals, and interacts with task state representations. Our results thus suggest that different value representations are represented in parallel and imply a link between neural representations of task states, their associated values and their influence on behavior. This sheds new light on vmPFCs role in decision making, bridging between a hypothesized role in mapping observations onto the task states of a mental map, and computing value expectations for alternative states.

neuroscience↗

bioRxiv2020

Similarities and differences in spatial and non-spatial cognitive maps

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions. Author summaryThere is a resurgence of interest in "cognitive maps" based on recent evidence that the hippocampal-entorhinal system encodes both spatial and non-spatial relational information, with far-reaching implications for human behavior. Yet little is known about the commonalities and differences in the computational principles underlying human learning and decision making in spatial and non-spatial domains. We use a within-subject design to examine how humans search for either spatially or conceptually correlated rewards. Using a Bayesian learning model, we find evidence for the same computational mechanisms of generalization across domains. While participants were sensitive to expected rewards and uncertainty in both tasks, how they leveraged this knowledge to guide exploration was different: participants displayed less uncertainty-directed and more random exploration in the conceptual domain. Moreover, experience with the spatial task improved conceptual performance, but not vice versa. These results provide important insights about the degree of overlap between spatial and conceptual cognition.

animal behavior and cognition↗

bioRxiv2018

Connecting conceptual and spatial search via a model of generalization

The idea of a \"cognitive map\" was originally developed to explain planning and generalization in spatial domains through a representation of inferred relationships between experiences. Recently, new research has suggested similar principles may also govern the representation of more abstract, conceptual knowledge in the brain. We test whether the search for rewards in conceptual spaces follows similar computational principles as in spatial environments. Using a within-subject design, participants searched for both spatially and conceptually correlated rewards in multi-armed bandit tasks. We use a Gaussian Process model combining generalization with an optimistic sampling strategy to capture human search decisions and judgments in both domains, and to simulate human-level performance when specified with participant parameter estimates. In line with the notion of a domain-general generalization mechanism, parameter estimates correlate across spatial and conceptual search, yet some differences also emerged, with participants generalizing less and exploiting more in the conceptual domain.

animal behavior and cognition↗