Cartoon me on the ferris wheel, waving goodbye and having a good time :)
Overall, we hope that our paper sheds some light on how we combine strategies for flexible navigation, and how different representations in the brain may support different strategies. + see paper for some additional results about how humans select landmarks! (6/6)
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Plots showing that units are differentially correlated with the ikelihood of using ‘direction’ or ‘state’ actions, and lesioning these units differentially affects agents’ use of directions vs. states. Plot showing that ‘vector’ units are more like to have spatial activation patterns and ‘transition’ units are more likely to be modulated by landmarks.
We also identify ‘modules’ that appear causally important for implementing each strategy. These modules represent the environment differently: for eg, ‘vector’ units are more likely to have stable spatial representations, while ‘transition’ units are more likely to carry landmark information (5/6)
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Activation patterns of example units as networks navigated the grid environment. Some units are responsive to location of landmarks, stable regions of space across environments, or a conjunction of both.
We examined the representations learnt by networks for hints about how these strategies might be implemented in the brain. We find units that represent the environment in different ways - resembling diverse spatial representations observed in mammalian navigation systems. (4/6)
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Plots showing both humans and models perform best when they can freely arbitrate between strategies, and prefer to use ‘states’ at landmarks and other learnt locations.
We find that humans did best when they could freely arbitrate between strategies - preferring vector-based strategies overall but transition-based strategies near learnt landmarks. Interestingly, deep meta-learning models developed strikingly similar behavioural profiles. (3/6)
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Cartoon image of me thinking about how to get to the ferris wheel. I consider the direction of the ferris wheel from my current location (‘vectors’) and the buildings I need to pass to get there (‘transitions’)
Schematic diagram of the task where participants learn a few landmarks (locations of objects on a grid) and then navigate the grid in two ways - choosing a direction (tapping on vector-based strategies) or choosing an object representing a neighbouring state (tapping on transition-based strategies)
We hypothesised that flexible navigation requires a mix of strategies, involving either a spatial sense of direction (‘vectors’) or associative knowledge between landmarks (‘transitions’). We designed a task to help dissociate between strategies while humans navigated unfamiliar grid worlds. (2/6)
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Cartoon image of me looking at a map, with a stadium behind me and a hotel and ferris wheel across the river in the background. I am thinking about going to the ferris wheel
My first PhD paper - with @lhuntneuro.bsky.social and @summerfieldlab.bsky.social - is now out in @plosbiology.org! We ask: how do humans (and deep neural networks) navigate flexibly even in unfamiliar environments, such as a new city? Link: plos.io/45uSwNm 🧵 (1/6)
07.08.2025 20:36 — 👍 31 🔁 11 💬 1 📌 0
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PhD student @ National University of Singapore | Ex @EdinburghUni @ZhejiangUni | Computational Neuroscience
https://jiachuan-wang.github.io
Professor at UC Davis. Interested in neurobiological mechanisms of learning and memory.
Assistant Professor at UCLA | Alum @MIT @Princeton @UC Berkeley | AI+Cognitive Science+Climate Policy | https://ucla-cocopol.github.io/
DPhil(PhD) student @ox.ac.uk, @oxexppsy.bsky.social studying #CognitiveNeuroscience | B.S. from @pku1898.bsky.social.
Website: https://www.psy.ox.ac.uk/people/deng-pan
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Professor for Cognitive Modeling at the Institute for Cognitive Science in Osnabrück, Germany. Previously at Oxford & ETH Zurich.
Experimental Psychology PhD student @ University of Oxford 🌞
BSc Mathematics and Psychology, MSc Psychological Research
researching visual attention and temporal predictions with @dynacog-lab.bsky.social @abclab.bsky.social @brognition.bsky.social
She/her | email: gt342@cam.ac.uk | PhD student http://orben.group | computational & cognitive approaches to study technology use | Views my own | Happy to chat about Masters or PhD applications/experiences, send me an email!
Interested in complex systems and in simple systems who believe they are complex systems. Leader of the Oxford Self-Modelling Group (Dept. of Experimental Psychology, University of Oxford);
Prof. of Computational Cognitive Science at TU Darmstadt & PI of the Human and Machine Cognition lab | hmc-lab.com
cognitive neuroscience, U Nottingham
Postdoc at Helmholtz Munich (Schulz lab) and MPI for Biological Cybernetics (Dayan lab) || Ph.D. from EPFL (Gerstner lab) || Working on computational models of learning and decision-making in the brain; https://sites.google.com/view/modirsha
PhD candidate w/ Helen Barron and Jill O’Reilly at NDCN, University of Oxford. Interested in cognitive maps, memory and psychosis.
Assistant professor at Hunter College-CUNY, studying the cognitive science of decision-making.
https://www.evanrussek.com/
computational cog sci • problem solving and social cognition • asst prof at NYU • https://codec-lab.github.io/
Neuroscience PhD Candidate @sinaibrain.bsky.social | she/her
How do we learn to achieve our goals? 🧠Computational Cognitive Neuroscience PhD student UC Berkeley 🌿
Neuroscientist interested in representations of space & memory. Using tools from experimental & theoretical neuroscience as well as machine learning. @UCL
https://barry-lab.com
Postdoc with Helen Barron at the University of Oxford. Former PhD student with
@summerfieldlab.bsky.social . Interested in cognitive maps, learning and memory.