Biology subjects

Griesbauer, E.-M.

Publications and source records attributed to Griesbauer, E.-M..

5 recordsLinked to original sources

bioRxiv2024

London taxi drivers leverage regional boundaries to optimise route choices and improve their navigation skill across three decades

The world is defined by boundaries. They segment our experience of time and space, and in virtual environments have been shown to impact navigational choices. Here, we test the impact of boundaries on route choices in a real-world environment (London, UK) with a group of expert navigators: licensed London taxi drivers who are required to memorise the layout of over 26,000 streets to obtain their licence. After presenting photographs of a start location and a goal location, taxi drivers were asked to either accept or reject a third target street as forming part of the direct route or not. Performance increased across the adult life-span period in this group (age: 34 to 67). Taxi drivers were faster and more accurate when the target location formed part of a street network boundary (e.g. streets on the edge of the London neighbourhood Soho). Our results are consistent with taxi drivers exploiting the graph structure of the street network to plan routes, as well as consistent with the formation of hierarchical state representations to reduce the dimensionality of the planning problem. Taken together, we show that navigational skill can improve over decades of exposure and that experts exploit regional boundaries for optimal choices, providing a scaffolding over which to form action plans.

neuroscience↗

bioRxiv2024

Expert Navigators Deploy Rational Hierarchical Priorization Over Predictive Maps For Large-Scale Real-World Planning

Efficient planning is a distinctive hallmark of intelligence in humans, who routinely make rapid inferences over complex world contexts. However, studies investigating how humans accomplish this tend to focus on naive participants engaged in simplistic tasks with small state-spaces, which do not reflect the intricacy, ecological validity, and human specialisation in real-world planning. In this study, we examine the street-by-street route planning of London taxi drivers navigating across more than 26,000 streets in London (UK). We explore how planning unfolded dynamically over different phases of journey construction and identify theoretic principles by which these expert human planners rationally precache decisions at prioritised environment states in an early phase of the planning process. In particular, we find that measures of path complexity predict human mental sampling prioritisation dynamics independent of alternative measures derived from the real spatial context being navigated. Our data provide real-world evidence for complexity-driven remote state access within internal models and precaching during human expert route planning in very large structured spaces. Significance statementHumans can plan efficiently in incredibly complex situations. Existing work has looked at naive participants in simple tasks, which might not be representative of how experts plan in the real world. Here, we study the real-world planning process of London taxi drivers - famous for their expert knowledge of London. By analyzing their response times as a proxy for thinking times, we reveal that at an early stage in their thought process, they store decisions at key street junctions to keep them in mind for later planning. Using computational modeling, we show that taxi drivers prioritize inference at street junctions according to normative metrics measuring how critical a particular decision is for reducing the complexity of planning across the entire city.

neuroscience↗

bioRxiv2024

London taxi drivers exploit neighbourhood boundaries for hierarchical route planning

Humans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large due to the time to compute all possible sequences. Hierarchical route planning has been proposed as an alternative, offering a computationally efficient mechanism in which the representation of the environment is segregated into clusters. Current evidence for hierarchical planning comes from experimentally created environments which have clearly defined boundaries and far fewer states than the real-world. To test for real-world hierarchical planning we exploited the capacity of London licensed taxi drivers to use their memory to construct a street by street plan across London, UK (>26,000 streets). The time to recall each successive street name was treated as the response time, with a rapid average of 1.8 seconds between each street. In support of hierarchical planning we find that the clustered structure of Londons regions impacts the response times, with minimal impact of the distance across the street network (as would be predicted by tree-search). We also find that changing direction during the plan (e.g. turning left or right) is associated with delayed response times. Thus, our results provide real-world evidence for how humans structure planning over a very large number of states, and give a measure of human expertise in planning.

neuroscience↗

bioRxiv2021

Learning The Knowledge: How London Taxi Drivers Build Their Cognitive Map of London

Licenced London taxi drivers have been found to show changes in the grey matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the Knowledge of London, the names and layout of over 26,000 streets and thousands of points of interest in London. Here we examined the process of how this knowledge is acquired and we detail key steps that include: systematic study of maps, travel on selected overlapping routes, the mental visualisation of places and the optimal use of subgoals. We provide the first map of the street network covered by the routes used to learn, allowing insight into where gaps in the network exist. The methods could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence (AI) systems to efficiently learn new environments.

neuroscience↗

bioRxiv2020

Predictive Maps in Rats and Humans for Spatial Navigation

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework, integrating across humans, rats and simulated reinforcement learning (RL) agents to interrogate the dynamics of behaviour during spatial navigation. We developed a novel open-field navigation task (Tartarus Maze) requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions in the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilising a successor representation, which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments, and highlight the utility of modelling the behaviour of different species to uncover the shared mechanisms that support behaviour.

animal behavior and cognition↗