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2017 Leading Edge Workshop
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Beyond the Lab: Using Big Data to Discover Principles of Cognition

July 9-12, 2017   |   Madison, Wisconsin, USA
Workshop Tickets - SOLD OUT


Call for Papers
The proposal deadline was November 30, 2017.

Special Issue
Behavior Research Methods

Beyond the Lab:
Using Big Data to
Discover Principles
of Cognition

Gary Lupyan
University of Wisconsin-

Email   |   Website

Robert Goldstone
Indiana University

Email   |   Website



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.

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 ( 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


How Can Artificial
Vision Models
Teach Us About Human
Scene Understanding?

Elissa Aminoff

University, USA

Megastudies: From Visual
Word Recognition to
Cable News Biases

David Balota

in St Louis, USA

Luke J. Chang
Dartmouth College, USA
Data from Online
Video Games:
Solving Real-World Problems and Evaluating Design Decisions

Seth Cooper

University, USA

Decision Contamination
in the Wild: Sequential Dependencies
in Online
Review Ratings

Rick Dale

University of
California, Merced, USA


Mapping the
Lexicon Using
Large-Scale Empirical
Semantic Networks 

Simon De Deyne
University of
Leuven, Belgium

Using Large-Scale
Datasets to Advance
Developmental Theory

Mike Frank

Stanford University, USA
Bias and Learning
in Major League Baseball Umpires’

Robert Goldstone

Indiana University
Bloomington, USA
Examining Individual
Differences in Cognition in
Three Different Online Populations

Shawn Green

University of Wisconsin- Madison, USA
Putting the ACL in
Social Science

Lillian Lee
Cornell University, USA
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
Widening the
Scope of Big Data: The Importance of Multi-Dimensional Analyses for the Understanding of Human Language and Behavior


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
Studying Language
Development Using Daylong
Home Audio Recordings

 Anne S.

University of California,
Merced, USA

What Can Children
Learn from
Six Million Words?

Jon A. Willits
University of California,
Riverside, USA


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