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Sixth Groningen Spring School on Cognitive Modeling
– ACT-R, Nengo, PRIMs –

Date: 27-31 March 2023
Location: Groningen, the Netherlands
Fee: € 305 (late fee after February 26 will be € 355)
More information and registration: www.cognitive-modeling.com/springschool
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Dear colleagues and students,

We are excited to announce the sixth Spring School on Cognitive Modeling in 
Groningen, from 27-31 March 2023!

This time, the Spring School will cover three different modeling paradigms: 
ACT-R, Nengo, and PRIMs. Each of these topics consists of a series of lectures, 
as well as a number of hands-on exercises (tutorials).

Past years have shown that students get most out of the spring school if they 
really emerge themselves into one modeling paradigm. We therefore recommend you 
choose one topic for which you will attend both the lectures as well as the 
tutorials. In addition, you can select a second paradigm, for which you attend 
the lectures only.

To give students a broader picture, there will also be three guest lectures 
throughout the week. These lectures each give an introduction to yet another 
modeling paradigm: accumulator models (Leendert van Maanen), error-driven 
learning models (Jacolien van Rij), and dynamical systems (Herbert Jäger). 
Everyone is encouraged to attend those lectures.

To round of the program, there will be a poster session, where students present 
themselves and their research, as well as a city tour, and our (in)famous 
spring school dinner.

Registration is now open.

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

We are looking forward to welcoming you (again) in Groningen,

The Spring School team
[email protected]
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ACT-R
Teachers: Jelmer Borst, Stephen Jones, & Katja Mehlhorn (University of 
Groningen)
Website: 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.

Nengo
Teacher: Terry Stewart and Andreas Stöckel (University of Waterloo)
Website: 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.

PRIMs
Teacher: Niels Taatgen (University of Groningen)
Website: https://www.ai.rug.nl/~niels/prims/index.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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—
Thomas Tiotto,
PhD Candidate, CogniGron - Groningen Cognitive Systems and Materials

University of Groningen
Nijenborgh 9, 9747 AG Groningen,
The Netherlands

Email: [email protected]
Profile: https://www.rug.nl/staff/t.f.tiotto/
Office: 5161 0318
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