This webpage is part of the historical archive of the Cognitive Science Society's website. For updated information, please consult the Society's homepage.


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.

Computational Complexity Analysis for Cognitive Scientists

Time: Full-day (9:00 - 17:30)
Organizers: Iris van Rooij, Johan Kwisthout, Mark Blokpoel, and Todd Wareham

A common property of computational- or rational-level models of cognition is that the cognitive capacities that they postulate are computationally intractable (e.g., NP-hard). Formally, this means that the computations that these models postulate require exponential time. Informally, this means that the postulated computations do not scale in any obvious way to explain how cognitive capacities can operate in the real world outside the lab. How can cognitive scientists overcome this explanatory obstacle? In this tutorial, participants will learn useful techniques from theoretical computer science for identifying model parameters that are responsible for intractability. These techniques can be used to generate hypotheses about how models can be constrained so as to make them computationally tractable with minimal loss of generality, thereby improving their scalability. The tutorial will include illustrations of concrete applications as well as discussions of relevant philosophical issues (e.g., pertaining to approximation, heuristics etc.).

Supplemental webpage

A General Purpose Architecture for Building Spiking Neuron Models of Biological Cognition

Time: Full-day (9:00 - 17:30)
Organizers: Chris Eliasmith and Terrence Stewart

This tutorial covers our method for converting high-level cognitive
algorithms into biologically realistic spiking neuron models. This
work resulted in Spaun, the first large-scale functional brain
simulation capable of performing cognitive tasks (recognizing digits,
memorizing lists, writing digits, pattern completion, mental addition,
etc.). We use Nengo, our open-source cross-platform
software to do this. The tutorial gives an introduction to the theory
behind this approach and hand-on experience with Nengo, allowing
people to specify their own theories and have the software generate
neural models that implement those theories.

Of particular emphasis this year is our new suite of tools that focus
on cognitive operations. We will be demonstrating how to use the
components we made for Spaun, including concept buffers, action
selection via the basal ganglia, distributed competition between
concepts, and binding. The goal is to develop this as a biologically
realistic general-purpose cognitive architecture.

Supplemental webpage

Dynamic Field Theory: Conceptual Foundations and Applications in the Cognitive and Developmental Sciences

Time: Full-day (9:00 - 17:30)
Organizers: John Spencer, Gregor Schoener, and Yulia Sandamirskaya

Dynamical Field Theory (DFT) offers a framework for thinking about representation-in-the-moment in neural systems and changes in thinking over learning and development. Dynamic Neural Fields are formalizations of how neural populations represent the continuous dimensions that characterize perceptual features, movements, and cognitive decisions. DFT has been used across a variety of contexts including studies of working memory, word learning, executive function, and autonomous robotics. One obstacle for researchers wishing to use DFT has been that the mathematical and technical skills required to make these concepts operational are not part of the standard repertoire of cognitive scientists. The goal of this tutorial is to provide the training and tools to overcome this obstacle. We will provide a systematic introduction to the central concepts of DFT and their grounding in both Dynamical Systems concepts and neurophysiology. We will provide all needed background and give participants hands-on experience using interactive simulators in MATLAB.

Supplemental webpage 1 Supplemental webpage 2

Using Quantum Probability Theory to Model Cognition

Time: Half-day (9:00 - 12:30)
Organizers: Emmanuel Pothos, Zheng Wang, and Jerome Busemeyer

This tutorial introduces why and how to build cognitive models using quantum probability (QP) theory. We will show that QP theory is inherently consistent with deeply rooted psychological conceptions and intuitions. It offers a fresh conceptual framework for explaining some puzzling empirical findings of cognition, and provides a rich new source of alternative formal tools, compared to classical probability (CP) theory, for cognitive modeling. Specifically, we will cover the basic mathematical principles of QP theory and compare them to those of CP theory. We will show how simple cognitive representations and models can be constructed and illustrate how some of these models can be implemented in Matlab. We will also discuss the range of cognitive phenomena which, though puzzling from a classical perspective, are afforded simple and powerful accounts within QP theory. The tutorial should be accessible to anyone with basic mathematical/ computing skills.

Supplemental webpage

How to Analyze Verbal Protocols

Time: Half-day (9:00 - 12:30)
Organizer: Thora Tenbrink

This tutorial will support researchers who consider using, or have already collected, verbal protocols (such as think-aloud or retrospective reports) as data. After discussing the extent to which language can be useful to identify cognitive processes and principles, we will examine each step of the process from data collection via transcription to analysis and triangulation. The main emphasis will be on the systematic analysis of linguistic choices, aiming to identify indicators for specific cognitive phenomena that are of interest for the research purpose at hand. Participants are encouraged to contribute actively to this tutorial by bringing ideas and samples from their own research, pertaining to each step of the research process. Email-based communication in advance of the tutorial will ensure a lively and highly interactive tutorial, supporting ongoing research in a practical way rather than theorizing about potential benefits. Feel free to get in touch!

Supplemental webpage

Virtual Humans: A New Toolkit for Cognitive Science Research

Time: Half-day (9:00 - 12:30)
Organizers: Jonathan Gratch, Arno Hartholt, Morteza Dehghani, and Stacy Marsella

Virtual humans (VHs) are digital anthropomorphic characters that exist within virtual worlds but are designed to perceive, understand and interact with real-world humans. Although typically conceived as practical tools to assist in a range of application (e.g., HCI, training and entertainment), the technology is gaining interest as a methodological tool for studying human cognition. VHs not only simulate the cognitive abilities of people, but also many of the embodied and social aspects of human behavior more traditionally studied in fields outside of cognitive science. By integrating multiple cognitive capabilities (e.g., language, gesture, emotion) and requiring these processes to support real-time interactions with people, VHs create a unique and challenging environment with-in which to develop and validate cognitive theories. In this tutorial, we will review recent advances in VH technologies, demonstrate examples of use of VHs in cognitive science research and provide hands on training using our Virtual Human Toolkit.

Supplemental webpage

Making Robust Classification Decisions: Constructing and Evaluating Fast and Frugal Trees (FFTs)

Time: Half-day (14:00 - 17:30)
Organizers: Hansjoerg Neth, Uwe Czienskowski, Lael Schooler, and Kevin Gluck

Fast and Frugal Trees (FFTs) are a quintessential family of simple heuristics that make binary classification decisions on the basis of limited and noisy data. As classifications made by FFTs tend to be effective, efficient, and robust, their predictions often perform remarkably well when compared to more complex methods. Practically useful FFTs have successfully been developed in a variety of applied domains, including medical, legal, and financial decision-making.

This half-day tutorial will familiarize participants with the simple heuristics framework, provide examples of FFTs, and elucidate the theoretical link between FFTs and signal detection theory (SDT). A range of lecture-style presentations, practical exercises, and interactive tools (involving pre-designed MS-Excel sheets and the purpose-built software tool FFT-builder) will enable participants to construct and evaluate FFTs for different data sets.

Types and states: Mixture and hidden Markov modelling for the cognitive sciences

Time: Half-day (14:00 - 17:30)
Organizers: Maarten Speekenbrink and Ingmar Visser

The objective of this tutorial is to provide participants with an accessible introduction to mixture models (MMs) and hidden Markov models (HMMs) and the necessary skills to apply them in their own research. These models are particularly useful to analyse aspects of cognition which are best understood in terms of discrete types and states (e.g., when people are expected to apply distinct strategies when performing a task). MMs and HMMs allow one to extract such types/states even when they are not known exactly beforehand. We will provide an intuitive introduction to the underlying theory of MMs and HMMs and will show how to apply the models in practice using freely available software. Throughout the tutorial, the techniques are illustrated with real data relevant to a cognitive science audience. Participants are encouraged to bring their laptop to follow the examples and apply the techniques to their own data.

Supplemental webpage

Using Complex Network Analysis in the Cognitive Sciences

Time: Half-day (14:00 - 17:30)
Organizers: Nicole Beckage, Michael Vitevitch, Alexander Mehler, and Eliana Colunga

This tutorial will provide an elementary introduction to network analysis as a tool within cognitive science, using examples from the domain of language. Participants will learn how to import, manipulate, and analyze network data using the R programming language. (Participants should bring laptops to the meeting as well as have R and a few select libraries [statnet, sna package and network package] installed. A zip file will be available with other necessary files for the completion of the tutorial material.) Participants who complete the tutorial will be able to perform basic network analyses, and use this powerful suite of analyses to examine relational data in their own domains of research.

Tutorial Chair

Dongkyu Choi (University of Kansas)