Predictive Maps in Rats and Humans for Spatial Navigation

Abstract

Much of our understanding of navigation has come from the study of rats, humans and simulated artificial agents. To date little attempt has been made to integrate these approaches into a common framework to understand mechanisms that may be shared across mammals and the extent to which different instantiations of agents best capture mammalian navigation behaviour. Here, we report a comparison of rats, humans and reinforcement learning (RL) agents in a novel open-field navigation task (Tartarus Maze) requiring dynamic adaptation (shortcuts and detours) to changing obstructions in the path to the goal. We find humans and rats are remarkably similar in patterns of choice in the task. The patterns in their choices, dwell maps and changes over time reveal that both species show the greatest similarity to RL agents utilising a predictive map: the successor representation. Humans also display trajectory features similar to a model-based RL agent. Our findings have implications for models seeking to explain mammalian navigation in dynamic environments and highlight the utility of modelling the behaviour of different species in the same frame-work in comparison to RL agents to uncover the potential mechanisms used for behaviour.

Hippocampal place cells encode global location but not changes in environmental connectivity in a 4-room navigation task

“…These findings and their implications for current models of place cells are discussed below. Cothi et al 2020). Extending these results, we found that rats were able to learn and remember the status of doors being locked or unlocked, in the absence of directly perceivable changes other than the direct feedback when pushing on a door.…”

Hippocampal place cells encode global location but not connectivity in a complex space

“…Rats and humans can flexibly adapt to changes in environmental connectivity. 13 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 Here, rats adapted to the locked or unlocked status of doors, even door sides, in the absence of directly perceivable changes. Importantly, their bias toward open doors persisted into the last session suggesting learning of connectivity.…”

Striatal and hippocampal contributions to flexible navigation in rats and humans

“…Beyond the classic divisions of model-free and model-based literature in decision-making tasks, there are other families of RL algorithms that provide alternative accounts, including hierarchical RL, linear RL, and successor representation ( Botvinick et al, 2009 ; Dayan, 1993 ; Gershman, 2018 ; Piray and Daw, 2019 ; Russek et al, 2017 ; Stachenfeld et al, 2017 ; Tessereau et al, 2020 ). In particular, successor representation can account for flexible behaviour of rats and humans in complex mazes ( De Cothi et al, 2020 ) and humans in reward devaluation protocols ( Momennejad et al, 2017 ). Interestingly, components of the successor representation during simulations show similarities to properties of place cells and grid cells, including the influence of goal locations on place field over-representation observed in specific paradigms and influence of environmental geometry on grid field integrity ( Duvelle et al, 2019 ; Ekstrom et al, 2020 ; Krupic et al, 2015 ; Stachenfeld et al, 2017 ).…”