{"pk":28488,"title":"At the Zebra Crossing: Modelling Complex Decision Processes with Variable-Drift\nDiffusion Models","subtitle":null,"abstract":"Drift diffusion (or evidence accumulation) models have found\nwidespread use in the modelling of simple decision tasks.\nExtensions of these models, in which the model’s\ninstantaneous drift rate is not fixed but instead allowed to\nvary over time as a function of a stream of perceptual inputs,\nhave allowed these models to account for more complex\nsensorimotor decision tasks. However, many real-world tasks\nseemingly rely on a myriad of even more complex underlying\nprocesses. One interesting example is the task of deciding\nwhether to cross a road with an approaching vehicle. This\naction decision seemingly depends on sensory information\nboth about own affordances (whether one can make it across\nbefore the vehicle) and action intention of others (whether the\nvehicle is yielding to oneself). Here, we compared three\nextensions of a standard drift diffusion model, with regards to\ntheir ability to capture timing of pedestrian crossing decisions\nin a virtual reality environment. We find that a single\nvariable-drift diffusion model (S-VDDM) in which the\nvarying drift rate is determined by visual quantities describing\nvehicle approach and deceleration, saturated at an upper and\nlower bound, can explain multimodal distributions of crossing\ntimes well across a broad range vehicle approach scenarios.\nMore complex models, which attempt to partition the final\ncrossing decision into constituent perceptual decisions,\nimprove the fit to the human data but further work is needed\nbefore firm conclusions can be drawn from this finding.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"complex decision making; road crossing;\nvariable-drift diffusion models"}],"section":"Papers with Oral Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/95p445vm","frozenauthors":[{"first_name":"Oscar","middle_name":"","last_name":"Giles","name_suffix":"","institution":"University of Leeds","department":""},{"first_name":"Gustav","middle_name":"","last_name":"Markkula","name_suffix":"","institution":"University of Leeds","department":""},{"first_name":"Jami","middle_name":"","last_name":"Pekkanen","name_suffix":"","institution":"University of Leeds","department":""},{"first_name":"Naoki","middle_name":"","last_name":"Yokota","name_suffix":"","institution":"Keio University","department":""},{"first_name":"Naoto","middle_name":"","last_name":"Matsunaga","name_suffix":"","institution":"Keio University","department":""},{"first_name":"Natasha","middle_name":"","last_name":"Merat","name_suffix":"","institution":"University of Leeds","department":""},{"first_name":"Tatsuru","middle_name":"","last_name":"Daimon","name_suffix":"","institution":"Keio University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28488/galley/18359/download/"}]}