1 / 16

Walking Data Analysis

Walking Data Analysis. Background. Jovancevic et al 2006 Where do you look while navigating? Same experimental environment you used. But… Any pedestrian could collide at 10% probability Leader & No-Leader trial types 18 laps of experience per trial type

donat
Download Presentation

Walking Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Walking Data Analysis

  2. Background • Jovancevic et al 2006 • Where do you look while navigating? • Same experimental environment you used. • But… • Any pedestrian could collide at 10% probability • Leader & No-Leader trial types • 18 laps of experience per trial type • Another condition where colliders sped up

  3. Results • Looks at pedestrians are about ~30% of fixations • Fixation patterns are sensitive to low probability events • But collisions aren’t always detected (~60%) • Bottom up capture is weak • Top down scheduling seems more plausible • Open Question (addressed by your lab): • How do people learn about the probabilities? • Probability matching? • At what rate?

  4. Other results • Speeding up had little effect • Stimulus properties don’t correlate well with fixations (eg. color, angle of rotation, distance away). • Fixating a collider makes the subject slow down

  5. Experimental Manipulation • Two trial types • Controls: No collisions (baseline rate of fixation) • Experimental (with collisions) • Probabilities of collision: • 3 pedestrians: • Walking in subjects direction • 80%, 50%, and 20% collision rate • 2 pedestrians: • Walking in subjects direction • No (intentional) collisions

  6. Results: Qualitative • What were the goals? • Stay on the sidewalk (near middle) • Avoid Pedestrians • Where do the eyes generally go? • Sidewalk • Pedestrians • Surroundings • Wall

  7. Quantitative • Do the rates of fixation match the rates of collision? • 1) Determine how many times each pedestrian appeared & collided • Calculate the pedestrian collision rate per subject • 2) For each appearance determine if the pedestrian was fixated.

  8. Quantitative cont. • 3) Calculate overall fixation probability (all ~12 laps) per pedestrian • 4) Calculate fixation probability per pedestrian for early, middle, and late trials • Calculate per subject and do means across (if you have enough data) • Compare control vs. experimental trials • Do subjects fixate peds more frequently in the exp. Condition? • Is the learning rate (early, middle, late) flat for control trials? • Experimental • What is the overall rate of fixation probability • What is the rate of fixation for early, middle, late trials

  9. Movie & Excel Example • Start : 00:25 • Collision 0:30, 45

  10. Collision Rate

  11. Fixation Rate

  12. Discussion • Do subjects fixate colliders more often? (compare experimental vs. control) • Do your results show that subjects adjust fixation rates to the rates of collision? • If so how quickly? • If not what happened instead? • How does this fit with the Jovancevic paper? • If you want to analyze more… • Fixation Latencies, Duration distributions (like Figure 6)

More Related