Beyond the Lab: Using Big Data to Discover Principles of Cognition
July 9-12, 2017 | Madison, Wisconsin, USA
Workshop Tickets - SOLD OUT
Organizers
Description
With more than 100 years of collective practice, experimental psychologists have become highly sophisticated in their application of well-controlled laboratory experiments to reveal principles of human cognition and behavior. This approach has yielded rigorous experimental designs with extensive controls and it should be valued and encouraged. But the very expertise with which psychologists wield their tools for achieving laboratory control may now be limiting our field to the ways in which we can discover principles of cognition by going beyond the lab.
This workshop will focus on two “beyond the lab” approaches that have seen explosive growth in the last five years (for example, in just 2015 about 7000 scholarly articles made use of the Amazon Mechanical Turk crowdsourcing service). The first focus is on extending traditional laboratory techniques beyond the lab. These include the use of crowdsourcing services to conduct experiments of the type that are impractical or impossible to conduct in the lab, “gamifying” traditional data-collection such that participants actively want to participate in our studies, and organizing contests as a way of efficiently exploring the solution space to projects that are beyond the capabilities of a single research team. The second focus is on using “naturally occurring datasets” wherein creative interrogation of a diverse range of large, real-world data sets can reveal principles of human judgment, perception, categorization, decision making, language use, inference, problem solving, and mental representation. Both of these approaches fit into the broader “big-data” initiatives that are transforming the social sciences.
Posters
Although the invited speakers have already been determined (see below), we are pleased to be able to offer some additional workshop participant slots for interested individuals pursuing relevant research. If you would like to participate and present a poster on a topic related to the workshop, please send us (lupyan@wisc.edu) an email with a 300-800 word description of your project no later than June 11, 2017 and 1-3 sentences describing your reason for wanting to attend the workshop. There will be a nominal registration fee of about $50. Relevant topics include but are not limited to:
- Harvesting data from online databases
- Network analysis
- Linguistic corpora
- Educational data analytics
- Crowdsourcing experimentation, coding, and analysis
- Gamification of data collection and analysis
- Statistical methods for dealing with naturally occurring data sets
- Deriving causal relations from data
- Integrating experiments and naturally occurring data sets
- Psychological investigations of patterns in specific databases for understanding: language, search, reasoning, problem solving, decision making, vision, performance, and group behavior
- Creating online environments for collecting behavioral data
- Environmental statistics and cognition
Invited Speakers
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How Can Artificial
Vision Models
Teach Us About Human
Scene Understanding?
Elissa Aminoff
Fordham
University, USA
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Megastudies: From Visual
Word Recognition to
Cable News Biases
David Balota
Washington
University
in St Louis, USA
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Luke J. Chang
Dartmouth College, USA |
Data from Online
Video Games:
Solving Real-World Problems and Evaluating Design Decisions
Seth Cooper
Northeastern
University, USA
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Decision Contamination
in the Wild: Sequential Dependencies
in Online
Review Ratings
Rick Dale
University of
California, Merced, USA
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Mapping the
Lexicon Using
Large-Scale Empirical
Semantic Networks
Simon De Deyne
University of
Leuven, Belgium
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Using Large-Scale
Datasets to Advance
Developmental Theory
Mike Frank
Stanford University, USA |
Bias and Learning
in Major League Baseball Umpires’
Perceptual
Judgments
Robert Goldstone
Indiana University
Bloomington, USA |
Examining Individual
Differences in Cognition in
Three Different Online Populations
Shawn Green
University of Wisconsin- Madison, USA
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Putting the ACL in
Computational
Social Science
Lillian Lee
Cornell University, USA |
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Using Distributional
Semantics to Understand
How We Know
What We Know
Gary Lupyan
University of Wisconsin-
Madison, USA |
Language and
Mood in
Social Media
Winter Mason
Facebook |
Widening the
Scope of Big Data: The Importance of Multi-Dimensional Analyses for the Understanding of Human Language and Behavior
Danielle
McNamara
Arizona State University, USA |
Data on the Mind:
A Community Resource for Naturally Occurring Data in Cognitive Science
Alexandra Paxton
University of California, Berkeley, USA |
Learning Curves
From Game Data
Tom Stafford
University of Sheffield, United Kingdom
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Studying Language
Development Using Daylong
Home Audio Recordings
Anne S.
Warlaumont
University of California,
Merced, USA
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What Can Children
Learn from
Six Million Words?
Jon A. Willits
University of California,
Riverside, USA
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