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Tamas Soton 2008

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Tamas Soton 2008

  1. 1. Optimizations in Spatial Cognition: Strategies and Trade-offs
  2. 2. Overview <ul><li>Optimality in Spatial Cognition </li></ul><ul><ul><li>Spatial Abilities, Trade-off, Exploratory Strategies </li></ul></ul><ul><li>Empirical Studies </li></ul><ul><ul><li>Experiment I.: Physical Environment </li></ul></ul><ul><ul><li>Experiment II.: Agent-based Simulation </li></ul></ul><ul><li>Overall Summary </li></ul>
  3. 3. Optimality in Spatial Cognition <ul><li>Spatial Cognition involves skills that enables us to : </li></ul><ul><ul><li>interact effectively and efficiently with our environment </li></ul></ul>(Theoretical) Optimum ‘ do the best possible’ Behavioural Optimum ‘ do the best you can’ increase spatial knowledge (memory) reduce travel distance (energy) Optimization find the target with minimal effort exploratory strategies ?
  4. 4. Overview <ul><li>Optimality in Spatial Cognition </li></ul><ul><ul><li>Spatial Abilities, Trade-off, Exploratory Strategies </li></ul></ul><ul><li>Empirical Studies </li></ul><ul><ul><li>Experiment I.: Physical Environment </li></ul></ul><ul><ul><li>Experiment II.: Agent-based Simulation </li></ul></ul><ul><li>Overall Summary </li></ul>
  5. 5. Research question & hypotheses 1. <ul><li>RQ: What are the cognitive and behavioural factors that influence spatial exploration? </li></ul><ul><li>Hy1: Humans explore novel physical environments differently, according to how they optimize their spatial cognition: </li></ul><ul><ul><li>increase spatial knowledge (memory)  extensive exploration </li></ul></ul><ul><ul><li>reduce distance travelled (energy)  limited exploration </li></ul></ul><ul><li>Hy 2: Humans are optimizing their explorations in terms of a trade-off b/w: </li></ul><ul><ul><li>spatial memory </li></ul></ul><ul><ul><li>distance travelled </li></ul></ul>
  6. 6. Experiment I. – Physical Environment <ul><li>Experimental Design </li></ul><ul><li>41 participants; 2 omitted </li></ul><ul><li>24 female; 17 male </li></ul><ul><li>3.5m x 3.5m squared space </li></ul><ul><li>black curtain on the walls </li></ul><ul><li>5 objects in boxes to explore </li></ul><ul><li>3 phases: </li></ul><ul><ul><li>Free exploration* </li></ul></ul><ul><ul><li>Learning </li></ul></ul><ul><ul><li>Navigation test </li></ul></ul><ul><li>2 measures of navigation: </li></ul><ul><ul><li>extendedness of routes (memory) </li></ul></ul><ul><ul><li>distance travelled (energy) </li></ul></ul>Makany, T., Redhead, E., & Dror, I. E. (2007). Spatial exploration patterns determine navigation efficiency: Trade-off between memory demands and distance travelled. QJEP, 60, 1594-1602 .
  7. 7. Experiment I. – Physical Environment <ul><li>Results </li></ul><ul><ul><li>2 initial exploratory strategies were found: </li></ul></ul>extensive exploration (higher memory demands) shorter overall distance travelled (lower energy cost) limited exploration (lower memory demands) longer overall distance travelled (higher energy cost) Makany, T., Redhead, E., & Dror, I. E. (2007). Spatial exploration patterns determine navigation efficiency: Trade-off between memory demands and distance travelled. QJEP, 60, 1594-1602 . (n=28) (n=11) Memory Distance
  8. 8. Experiment I. – Physical Environment <ul><li>Summary </li></ul><ul><ul><li>we found 2 distinct patterns of exploration: axial & circular </li></ul></ul><ul><ul><li>these patterns seem to reflect on different spatial optimization strategies: </li></ul></ul><ul><ul><ul><li>spatial knowledge (memory) optimization </li></ul></ul></ul><ul><ul><ul><li>travelled distance (energy) optimization </li></ul></ul></ul><ul><ul><li>interaction of navigation performances suggests a trade-off between memory & distance strategies </li></ul></ul><ul><li>Further Steps </li></ul><ul><ul><li>empirically test whether the manipulation of these exploratory strategies result in optimization trade-off </li></ul></ul><ul><ul><li>build a computational model to simulate human spatial exploratory behaviour </li></ul></ul>Makany, T., Redhead, E., & Dror, I. E. (2007). Spatial exploration patterns determine navigation efficiency: Trade-off between memory demands and distance travelled. QJEP, 60, 1594-1602 .
  9. 9. Overview <ul><li>Optimality in Spatial Cognition </li></ul><ul><ul><li>Spatial Abilities, Trade-off, Exploratory Strategies </li></ul></ul><ul><li>Empirical Studies </li></ul><ul><ul><li>Experiment I.: Physical Environment </li></ul></ul><ul><ul><li>Experiment II.: Agent-based Simulation </li></ul></ul><ul><li>Overall Summary </li></ul>
  10. 10. Research question & hypothesis 2. <ul><li>RQ: What are the cognitive and behavioural factors that influence spatial exploration? </li></ul><ul><li>Hy3: Human exploratory behaviour can be simulated by using simple optimization strategies </li></ul><ul><ul><li>follow/avoid known routes (memory-strategy)  extensive exploration </li></ul></ul><ul><ul><li>minimize/maximize overall distance travelled (energy-strategy)  limited exploration </li></ul></ul><ul><li>Hy 4: Our model will reproduce the same trade-off as in humans b/w: </li></ul><ul><ul><li>spatial memory </li></ul></ul><ul><ul><li>distance travelled </li></ul></ul>
  11. 11. Experiment 2. – AB Simulation <ul><li>Agent-based model in NetLogo </li></ul><ul><li>Single artifical agent per run </li></ul><ul><li>2D 6x6 grid square lattice space </li></ul><ul><li>5 objects to explore </li></ul><ul><ul><li>Task: visit all objects based on an object cost function : </li></ul></ul><ul><ul><ul><li>e i,j = distance to object j from position i </li></ul></ul></ul><ul><ul><ul><li>m = steps already taken on the way </li></ul></ul></ul><ul><ul><ul><li>α= parametre weight (complementary) </li></ul></ul></ul><ul><ul><ul><li>121 test runs; full parametrization </li></ul></ul></ul><ul><li>2 measures of navigation: </li></ul><ul><ul><li>extendedness of routes (memory) </li></ul></ul><ul><ul><li>distance travelled (energy) </li></ul></ul>Makany, T., & Makowsky, M. (submitted). Human spatial exploratory strategies in an agent-based model: Trade-off between distance and memory demands. Spatial Cognition Conf., Freiburg (Germany).
  12. 12. Experiment 2. – AB Simulation <ul><li>Results </li></ul><ul><ul><li>2 exploratory strategies were found: </li></ul></ul>extensive exploration (higher memory demands) limited exploration (lower memory demands) longer overall distance travelled (higher energy cost) shorter overall distance travelled (lower energy cost) Circular (n=84) Axial (n=35) Makany, T., & Makowsky, M. (submitted). Human spatial exploratory strategies in an agent-based model: Trade-off between distance and memory demands. Spatial Cognition Conf., Freiburg (Germany). Memory Distance
  13. 13. Experiment 2. – AB Simulation <ul><li>Summary </li></ul><ul><ul><li>we found 2 patterns of exploration, similar to Exp. 1.: axial & circular </li></ul></ul><ul><ul><li>simulation results confirmed the strategy optimization trade-off </li></ul></ul><ul><li>Further Research </li></ul><ul><ul><li>test the model with other environmental parameters (e.g., virtual) </li></ul></ul><ul><ul><li> </li></ul></ul>Makany, T., & Makowsky, M. (submitted). Human spatial exploratory strategies in an agent-based model: Trade-off between distance and memory demands. Spatial Cognition Conf., Freiburg (Germany). Memory Distance Memory Distance
  14. 14. Overview <ul><li>Optimality in Spatial Cognition </li></ul><ul><ul><li>Spatial Abilities, Trade-off, Exploratory Strategies </li></ul></ul><ul><li>Empirical Studies </li></ul><ul><ul><li>Experiment I.: Physical Environment </li></ul></ul><ul><ul><li>Experiment II.: Agent-based Simulation </li></ul></ul><ul><li>Overall Summary </li></ul>
  15. 15. Overall Summary RQ: What are the cognitive and behavioural factors that influence spatial exploration? - Hy1: Humans explore novel physical environments differently, according to how they optimize their spatial cognition - Hy 2: Humans are optimizing their explorations in terms of a trade-off - Hy3: Human exploratory behaviour can be simulated by using simple optimization strategies - Hy 4: Our model reproduced the same trade-off as in humans ✔ ✔ ✔ ✔ <ul><li>Spatial Cognition involves skills that enables us to : </li></ul><ul><ul><li>interact effectively and efficiently with our environment </li></ul></ul>increase spatial knowledge (memory) reduce travel distance (energy) Optimization find the target with minimal effort exploratory strategies ?
  16. 16. Overall Summary <ul><li>Impact of Research </li></ul><ul><li>Theoretical : </li></ul><ul><ul><li>how the human cognitive system optimizes information when exploring novel spaces </li></ul></ul><ul><ul><li>individual differences between good/poor explorers </li></ul></ul><ul><ul><li>cognitive/behavioural efficiency </li></ul></ul><ul><li>Applied : </li></ul><ul><ul><li>predict spatial performances based on initial exploratory behaviours </li></ul></ul><ul><ul><li>aid poor explorers from a very early phase of their spatial learning </li></ul></ul><ul><ul><li>spatial design applications in multiple environments </li></ul></ul>
  17. 17. Optimizations in Spatial Cognition: Strategies and Trade-offs Thank you! Acknowledgements Dr. Edward Redhead – University of Southampton Dr. Itiel Dror – University of Southampton Dr. Anne McBride – Univerisity of Southampton *T.M. was supported by the School of Psychology PhD Scholarship
  18. 18. Cluster Analysis Dendrogram I.
  19. 19. Cluster Analysis Dendrogram 2.

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