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)

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)

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)

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)

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)

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)