Active physics learning data 2016 - 2016, UCL, NYU and MIT

Principal Investigator(s): View help for Principal Investigator(s) Neil Bramley, NYU; Tobias Gerstenberg, MIT; Todd Gureckis, NYU; Joshua Tenenbaum, MIT

Version: View help for Version V1

Name	File Type	Size	Last Modified
experiment1.rdata	application/gzip	47 MB	07/05/2017 12:57:PM
experiment2.rdata	application/gzip	113.1 MB	07/03/2017 02:43:PM

Project Citation:

Bramley, Neil, Gerstenberg, Tobias, Gureckis, Todd, and Tenenbaum, Joshua. Active physics learning data 2016 - 2016, UCL, NYU and MIT. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-07-05. https://doi.org/10.3886/E100799V1

Project Description

Summary: In these experiments we bring together research on active learning and intuitive physics to explore how people actively about physical properties in "microworlds" with continuous spatiotemporal dynamics. In two experiments, participants interacted with objects in simulated two-dimensional microworlds governed by a real-time physics engine, with the goal of identifying latent physical properties of the objects in the scenes, such as their masses, and forces of attraction or repulsion. We find an advantage for active learners over passive and yoked controls, and show that active learners generate evidence specific to whatever physical property it is their goal to identify. Consequently, yoked learners do better when asked to identify the same property. Our active participants spontaneously performed various "natural experiments" which revealed the objects' properties with varying success. In our research papers we highlight, and begin to and formalize these experiments, and finally outline further steps to categorize and explore active learning in the wild.

Scope of Project

Subject Terms: Mechanical Turk; Behavioral research

Geographic Coverage: United States of America

Time Period(s): 1/18/2016 – 5/19/2017

Universe: Adult noninstitutionalized population of the United States living in households.

Data Type(s): aggregate data; experimental data; survey data

Methodology

Response Rate: N/A

Sampling: random

Data Source: Amazon Mechanical Turk online platform (https://www.mturk.com/mturk/welcome) using PsiTurk (http://psiturk.readthedocs.io/en/latest/)

Collection Mode(s): web-based survey

Related Publications

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Published Versions

V1 [2017-07-05]

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