The tutorial program allows participants to gain new insights, knowledge, and skills from a broad range of topics in the field of cognitive science. Please see below for details.
Quantum Models of Cognition and Decision
This tutorial is an exposition of a rapidly growing new alternative approach to building computational models of cognition and decision based on quantum theory. It will provide an exposition of the basic assumptions of classic versus quantum information processing theories. These basic assumptions will be examined, side by side, in a parallel and elementary manner. The logic and mathematical foundation of classic and quantum theory will be laid out in a simple and elementary manner that uncovers the mysteries of both theories. Our main point will be to show that quantum theory provides a unified and powerful explanation for a wide variety of paradoxes found in human cognition and decision ranging across findings from attitudes, inference, causal reasoning, decision making, conceptual combinations, memory recognition, and associative memory.
Using Bayes to Interpret Non-Significant Results
Time: Full-day (9:30 - 17:00)
Organizer: Zoltan Dienes
The purpose of the workshop is to present simple tools for dealing with non-significant results, an area which cognitive scientists have consistently found problematic. In particular, people will be taught how to apply Bayes Factors and likelihood intervals to draw meaningful inferences from non-significant data, using free easy-to-use on-line software: Software which allows one to determine whether there is strong evidence for the null and against one-s theory, or if the data are just insensitive, a distinction p-values cannot make. These tools have greater flexibility than power calculations and allow null results to be interpreted over a wider range of situations. Such tools should allow the publication of null results to become easier.
Nengo and the Neural Engineering Framework: From Spikes to Cognition
This tutorial introduces the Neural Engineering Framework (NEF; Eliasmith and Anderson, 2003) and the associated open-source toolkit Nengo, which offer a general method for implementing high-level cognitive theories using biologically realistic spiking neurons. It combines the theoretical bases of the Neural Engineering Framework with hands-on examples of practically applying these concepts using Nengo. Participants are expected to bring a laptop to follow along with these tutorials (Windows, OS X, and Linux are all supported, and software is provided). The tutorial covers using the NEF to represent scalars and vectors, perform linear and nonlinear transformations on these values, and store information over time. We examine closely how cognitive theories can be expressed in terms of vectors and transformations, and explore recent results in building whole-brain models using the NEF.
Probability, Programs, and the Mind: Building Structured Bayesian Models of Cognition
This tutorial aims to introduce students to key ideas of, and new tools for constructing, structured probabilistic models. We will use the probabilistic programming language Church (Goodman, Mansinghka, Roy, Bonawitz, & Tenenbaum, 2008) to introduce key ideas and examples of probabilistic modeling. A Church program represents a probabilistic model, and hence inferences that can be drawn from this model, without committing to a process level implementation of inference. We will assume only basic familiarity with probability and with programming (i.e. minimal mathematical or statistical background). The tutorial will thus be appropriate for a general Cognitive Science audience, as well for practitioners of Bayesian modeling who want to learn about probabilistic programming.
Using Machine Learning for Exploratory Data Analysis
Machine learning has a PR problem. The field has developed many techniques that cluster, classify, or reduce the dimensionality of data, and most techniques could be profitably applied to scientific data sets. Researchers that are not machine learning experts face a daunting question, however-"which techniques should I use to analyze my data? We have developed a software platform called Divvy that gives researchers access to a wide variety of unsupervised machine learning algorithms. Divvy specifically emphasizes speed and visualization in order to allow users to more easily interact with the algorithms. This tutorial will give pragmatic guidance concerning the use of unsupervised machine learning techniques (clustering and dimensionality reduction) for exploratory data analysis. We will split instruction between concise conceptual introductions to unsupervised machine learning concepts and hands on analysis of real and synthetic datasets using Divvy.
Practical Advice on How to Run Human Behavioral Studies
This tutorial will provide participants with an overview of how to run studies with human participants, that is, not how to design or analyze them, but the practicalities of how to setup, debug, and run studies. It will help people running experiments to run them more effectively safely, and comfortably. Our purpose is to provide hands-on knowledge about experimental procedure. In particular, the tutorial will cover the role of identifying the research problem and reading in the general area; preparation for running a study, including piloting and IRB proposals; preparing to run a formal study, including advertising and recruiting subjects; running study sessions; and wrapping up a study.
And Now for Something Completely Different: Python in Cognitive Science
The objective of this tutorial is to introduce and motivate the use of the Python programming language in cognitive science research. Within the last 10 years, the development of scientific and numerical libraries in Python has grown to the point where Python can now be used as a scientific and numerical computing environment comparable to products like Matlab and Mathematica. As of yet, however, it appears that knowledge of the potential applications of Python to research in cognitive science is still rather limited. The aim of this tutorial, therefore, is to describe these areas of application and to advocate the advantages and appeals of using Python as the principal programming language in cognitive science research.
Glenn Gunzelmann (U.S. Air Force Research Laboratory)