Humans can navigate complex graph structures acquired during latent learning

Rmus, Milena; Ritz, Harrison; Hunter, Lindsay E; Bornstein, Aaron M.; Shenhav, Amitai

doi:10.1016/j.cognition.2022.105103

Cited by 26 publications

(15 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we found preliminary evidence (small sample correlation) that participants who show efficient graph learning tend to have a higher self-reported estimated tendency to navigate via maps and thinking in terms of maps to navigate, based on the NSQ. This dovetails with recent evidence that people who make better inferences about the structure of graph networks show more model-based planning on a multi-step planning task 70 . Future research with large-scale cohorts online 71 would be a useful way to explore the robustness of such correlations and what other moderating factors may influence these relationships.…”

Section: Discussionsupporting

confidence: 82%

Sleep targets highly connected global and local nodes to aid consolidation of learned graph networks

Feld

Bernard

Rawson

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Much of our long-term knowledge is organised in complex networks. Sleep is thought to be critical for abstracting knowledge and enhancing important item memory for long-term retention. Thus, sleep should aid the development of memory for networks and the abstraction of their structure for efficient storage. However, this remains unknown because past sleep studies have focused on discrete items. Here we explored the impact of sleep (night-sleep/day-wake within-subject paradigm with 25 male participants) on memory for graph-networks where some items were important due to dense local connections (degree centrality) or, independently, important due to greater global connections (closeness/betweenness centrality). A network of 27 planets (nodes) sparsely interconnected by 36 teleporters (edges) was learned via discrete associations without explicit indication of any network structure. Despite equivalent exposure to all connections in the network, we found that memory for the links between items with high local connectivity or high global connectivity were better retained after sleep. These results highlight that sleep has the capacity for strengthening both global and local structure from the world and abstracting over multiple experiences to efficiently form internal networks of knowledge.

show abstract

Section: Discussionsupporting

confidence: 82%

Sleep targets highly connected global and local nodes to aid consolidation of learned graph networks

Feld

Bernard

Rawson

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Collectively, these findings suggest that humans can learn properties of graph structure such as modular organization, and can leverage that structure to create more effective predictions. Converging neuroimaging evidence suggests that neural representations of stimuli encode properties of the interaction networks, including cluster identity [24] and graph distance between items [25], [38]. To date, however, it remains unclear how such graph-induced representation structure differs when the same elements are arranged in a distinct organizational pattern.…”

Section: Discussionmentioning

confidence: 99%

Network structure influences the strength of learned neural representations

Kahn

Szymula

Bassett

et al. 2023

Preprint

View full text Add to dashboard Cite

Human experience is built upon sequences of discrete events. From those sequences, humans build impressively accurate models of their world. This process has been referred to as graph learning, a form of structure learning in which the mental model encodes the graph of event-to-event transition probabilities, typically in medial temporal cortex. Recent evidence suggests that some network structures are easier to learn than others, but the neural properties of this effect remain unknown. Here we use fMRI to show that the network structure of a temporal sequence of stimuli influences the fidelity with which those stimuli are represented in the brain. Healthy adult human participants learned a set of stimulus-motor associations following one of two graph structures. The design of our experiment allowed us to separate regional sensitivity to the structural, stimulus, and motor response components of the task. As expected, whereas the motor response could be decoded from neural representations in postcentral gyrus, the shape of the stimulus could be decoded from lateral occipital cortex. The structure of the graph impacted the nature of neural representations: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity in a held-out run and displayed higher intrinsic dimensionality. Our results demonstrate that even over relatively short timescales, graph structure determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded. More broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning over different timescales.

show abstract

“…It has been suggested that predictive representations such as the successor representation can be leveraged to identify such abstractions (17,23). Predictive learning is ubiquitous in humans (50–53) and individual differences in predictive learning have previously been linked to individual differences in goal- directed decision making (54). Specific evidence exists that humans can learn predictive representations that are sensitive to higher-order aspects of tasks (29,30,40,41).…”

Section: Discussionmentioning

confidence: 99%

The successor representation subserves hierarchical abstraction for goal-directed behavior

Wientjes

Holroyd

2023

Preprint

View full text Add to dashboard Cite

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of hierarchical statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

show abstract

Humans can navigate complex graph structures acquired during latent learning

Cited by 26 publications

References 68 publications

Sleep targets highly connected global and local nodes to aid consolidation of learned graph networks

Sleep targets highly connected global and local nodes to aid consolidation of learned graph networks

Network structure influences the strength of learned neural representations

The successor representation subserves hierarchical abstraction for goal-directed behavior

Contact Info

Product

Resources

About