[Comp-neuro] Error-driven learning at the Groningen Spring School on Cognitive Modeling

Jelmer Borst j.p.borst at rug.nl
Tue Jan 22 11:36:12 CET 2019

Update: New tutorial on Error-driven learning

Fourth Groningen Spring School on Cognitive Modeling 
– ACT-R, Nengo, PRIMs, & Error-driven learning–
Date: April 8-12, 2019 
Location: Groningen, the Netherlands
Fee: € 250 (late fee after February 15 will be € 300)
More information and registration: www.cognitive-modeling.com/springschool <http://www.cognitive-modeling.com/springschool>
As briefly announced earlier, this year we will be offering a new tutorial on error-driven learning during the spring school. Error-driven learning (also called discrimination learning) allows to simulate the time course of learning. It can be applied for all domains in cognitive science, but is especially useful for modeling language processing and language learning. More information about this simple and elegant approach can now be found on our website <http://www.cognitive-modeling.com/springschool/home/error-driven-learning/>.

As in previous years, the Spring School will also cover the ACT-R, Nengo, and PRIMs paradigms. A preliminary version of the program can now be found on our website.

The early registration deadline ends on February 15, so make sure to sign up before then.

Please let us know if you have any questions or check out our website for more information. 

Best regards,
the spring school team

Please feel free to forward the information to anyone who might be interested in the Spring School.

Error-driven learning
Teachers: Jacolien van Rij and Dorothée Hoppe (University of Groningen)

Error-driven learning (also called discrimination learning) allows to simulate the time course of learning. It is based on the Rescorla-Wagner model (Rescorla & Wagner, 1972) for animal cognition, which assumes that learning is driven by expectation error, instead of behaviorist association (Rescorla, 1988). The equations formulated by Rescorla and Wagner have been used to investigate different aspects of cognition, including language acquisition (e.g., Hsu, Chater, and Vitányi, 2011; St. Clair, Monaghan, and Ramscar, 2009), second language learning (Ellis, 2006), and reading of  complex words (Baayen et al, 2011). Although error-driven learning can be applied for all domains in cognitive science, in this course we will focus on how it could be used for modeling language processing and language learning.

Teachers: Jelmer Borst & Katja Mehlhorn (University of Groningen)
Website: http://act-r.psy.cmu.edu <http://act-r.psy.cmu.edu/>.
ACT-R is a high-level cognitive theory and simulation system for developing cognitive models for tasks that vary from simple reaction time experiments to driving a car, learning algebra, and air traffic control. ACT-R can be used to develop process models of a task at a symbolic level. Participants will follow a compressed five-day version of the traditional summer school curriculum. We will also cover the connection between ACT-R and fMRI.

Teacher: Terry Stewart (University of Waterloo)
Website: http://www.nengo.ca <http://www.nengo.ca/>
Nengo is a toolkit for converting high-level cognitive theories into low-level spiking neuron implementations. In this way, aspects of model performance such as response accuracy and reaction times emerge as a consequence of neural parameters such as the neurotransmitter time constants. It has been used to model adaptive motor control, visual attention, serial list memory, reinforcement learning, Tower of Hanoi, and fluid intelligence. Participants will learn to construct these kinds of models, starting with generic tasks like representing values and positions, and ending with full production-like systems. There will also be special emphasis on extracting various forms of data out of a model, such that it can be compared to experimental data.

Teacher: Niels Taatgen (University of Groningen)
Website: http://www.ai.rug.nl/~niels/actransfer.html <http://www.ai.rug.nl/~niels/actransfer.html>

How do people handle and prioritize multiple tasks? How can we learn something in the context of one task, and partially benefit from it in another task? The goal of PRIMs is to cross the artificial boundary that most cognitive architectures have imposed on themselves by studying single tasks. It has mechanisms to model transfer of cognitive skills, and the competition between multiple goals. In the tutorial we will look at how PRIMs can model phenomena of cognitive transfer and cognitive training, and how multiple goals compete for priority in models of distraction. 

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