ICCM 2016 will host four tutorials on the day before the actual conference (Wednesday, August 3). We believe that these high-quality tutorials and workshop will be of interest to many of the conference attendees. You can register for one of these during the general ICCM registration, they’re included in the conference fee.
Please see the Call for Papers if you would like to propose a tutorial.
Stream: A Toolkit for Developing High-Precision Experiments
Brad Wyble – Assistant Professor of Psychology, Penn State (http://wyblelab.com)
Gregory Wade – PhD Student, University of Deleware
9:00 – 12:30 (with 30 min. break)
Creating behavioral experiments with carefully rendered stimuli and highly precise timing requires a specialized style of programming that interacts directly with computer hardware, including keyboard, and sound and video drivers. To facilitate the development of such experiments, we have created the Stream Toolkit, a freely available toolkit that builds on Matlab and Psychtoolbox to allow rapid development of high-precision experimental procedures that involve multiple modalities (e.g. visual, auditory and parallel-port output; keyboard, eye tracking, & mouse input).
The toolkit allows users to assemble an experimental procedure by preloading stimuli and then using events to control their timing with high precision. During the experiment itself, all stimuli and responses are recorded with timestamps at the ms level and stored in a data structure that ensures no information will be lost. For novice programmers, Stream provides a framework that is familiar to behavioral scientists and is a good way to extend programming skills. For advanced programmers, Stream accelerates the development of complex experiments (e.g. involving eye tracking, EEG, and realtime, user-responsive stimuli).
Required background: Some familiarity with programming in MATLAB or a similar language. Bring your own laptop if it has MatLab on it.
Stream can be downloaded via Bitbucket.
ACT-R Phi – ACT-R and a Physiological Model
Chris Dancy – Assistant Professor, Bucknell (www.bucknell.edu/x105
W Andrew Pruett – Instructor, University of Mississippi Medical Center
9:00 – 12:30 (with 30 min. break)
The mind is embodied, physically and chemically, receiving and passing information to the body in myriad physiological feedback loops. The mind-body interface induces connections between different cognitive functions, and is an integral part of cognition. Understanding how physiological and cognitive mechanisms interact to result in behavior will need to involve exploring the representative and systematic ways we can connect systems on the physiological and cognitive levels.
As physiological sensors continue to become cheaper, more pervasive, and more accurate, computational cognitive modelers will have a unique opportunity to predict and explain human behavior using process models with representations on both the physiological and cognitive levels. This shift will result in models that more realistically operate over longer periods of time, allowing modelers access to more mechanistic models and predictions of behaviors given moderators like sleep deprivation, caffeine, or stress.
In the first hour, we will discuss physiological and cognitive processes, and interactions between systems at these levels, that are useful for modeling and simulating behavior on both the physiological and cognitive levels. We will use two representative systems (HumMod and ACT-R) as well as an integrated version of the two systems (ACT-R/Phi) to ground the discussed connections and interactions to a computational system. Tutees will then have two hours to build a hybrid computational physio-cognitive model, run the model in a simulated experiment, and interpret the predicted physiological and cognitive output against existing behavioral data.
Dancy, C. L., Ritter, F. E., & Gunzelmann, G. (2015). Two ways to model the effects of sleep fatigue on cognition. In Proceedings of the 13th International Conference on Cognitive Modeling 2015, 258-263.
Dancy, C. L., Ritter, F. E., Berry, K., & Klein, L. C. (2015). Using a cognitive architecture with a physiological substrate to represent effects of psychological stress on cognition. Computational and Mathematical Organization Theory, 21(1), 90-114.
Hester, R. L., Brown, A. J., Husband, L., Iliescu, R., Pruett, D., Summers, R., & Coleman, T. G. . (2011). HumMod: A modeling environment for the simulation of integrative human physiology. Frontiers in Physiology, 2(12).
Distributed Adaptive Control: A Theory of the Mind, Brain, Body Nexus
Paul Verschure – Universitat Pompeu Fabra, Barcelona (http://specs.upf.edu/people/paul-fmj-verschure)
14:00 – 17:30 (with 30 min. break)
This tutorial introduces the Distributed Adaptive Control (DAC), a theory of the design principles underlying the Mind, Brain, Body Nexus (MBBN) that has been developed over the last 20 years. DAC assumes that the brain maintains stability between an embodied agent, its internal state and its environment through action. It postulates that in order to act, or know how, the brain has to answer 5 fundamental questions: why, what, where, when and who? Thus the function of the brain is to continuously solve the so-called H5W problem with H standing for the How an agent acts in the world. The DAC theory is expressed as a neural-based architecture implemented in robots and organized in two complementary structures: layers and columns. The organizational layers are called: reactive, adaptive and contextual, and its columnar organization defines the processing of states of the world, the self and the generation of action. DAC has been shown to be both rational in a Bayesian sense while epistemically autonomous. In addition, it has been mapped to the main brain structures in an effort to validate its specific predictions.
After an overview of the key elements of DAC, the mapping of its key assumptions towards the invertebrate and mammalian brain is described. The general overview of DAC’s explanation of MBBN is combined with examples of application scenarios in which DAC has been validated, including mobile and humanoid robots, neuro-rehabilitation and the large-scale interactive space Ada. In this tutorial we will provide the elements necessary to implement an autonomous control system based on the DAC architecture and we will explore how the different layers of DAC contribute to solve a foraging task.
Required background: Some knowledge of modeling and Python programming would be helpful but is not essential.
Verschure, P.F.M.J., T. Voegtlin, and R.J. Douglas, Environmentally mediated synergy between perception and behaviour in mobile robots. Nature, 2003. 425: p. 620-4.
Verschure, P.F.M.J., Distributed Adaptive Control: A theory of the Mind, Brain, Body Nexus. Biologically Inspired Cognitive Architecture – BICA, 2012. 1(1): p. 55-72.
Verschure, P.F.M.J., C.M.A. Pennartz, and G. Pezzulo, The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Philosophical Transactions of the Royal Society B: Biological Sciences, 2014. 369(1655).
Maffei, G., et al., An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X. Neural Networks, 2015. 72: p. 88-108.
DAC tutorials and supporting material can be downloaded from csnetwork.eu
Tools for Cognitive Modeling: Developing tasks for universal access by models and human participants, exploring a massive parameter space to find the best fit of model to data, and analyzing the persuasiveness of the best-found fit.
Vladislav “Dan” Veksler – ARO
14:00 – 17:30 (with 30 min. break)
The aim of this tutorial is to walk participants through much of the cognitive modeling research cycle, from experiment/simulation development, to parameter exploration for finding the best fit of model predictions to empirical results, to determining the persuasiveness of the found fit (vis-a-vis Roberts & Pashler, “How persuasive is a good fit?”). This tutorial will provide hands-on experience with (1) STAP — a technology that enables reuse of task software for human participants in lab, online, and on mobile devices, and computational participants regardless of computational framework and programming language; (2) mindmodeling.org — a free online parallel computing resource for exploring large parameter spaces; and (3) Model Flexibility Analysis — a method for estimating model complexity/flexibility.