From n.a.taatgen at rug.nl Wed Dec 9 04:46:29 2015 From: n.a.taatgen at rug.nl (Niels Taatgen) Date: Wed, 9 Dec 2015 10:46:29 +0100 Subject: [ACT-R-users] Announcment: Groningen Spring School on Cognitive Modeling Message-ID: <51DF6E07-9966-4214-BE7F-6EAA1C31D452@rug.nl> Groningen Spring School on Cognitive Modeling ? ACT-R, Nengo, PRIMs, & Accumulator Models ? Date: April 4-8, 2016 Location: Groningen, the Netherlands Fee: ? 200 (late fee ?50 after January 31) Registration: www.ai.rug.nl/springschool The Groningen Spring School on Cognitive Modeling will cover four different modeling paradigms: ACT-R, Nengo, PRIMs, and Accumulator models. It thereby offers a unique opportunity to learn the relative strengths and weaknesses of these approaches. Each day will consist of four theory lectures, one on each paradigm. Each modeling paradigm also includes hands-on assignments. Although students are free to chose the number of lectures they attend, we recommend students to sign up for lectures on two of the modeling paradigms, and complete the tutorial units for one of the paradigms. At the end of each day there will be a plenary research talk, to show how these different approaches to modeling are applied. Admission is limited, so register soon! ACT-R Teachers: Jelmer Borst, Hedderik van Rijn, Niels Taatgen (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, and the timing extension to ACT-R. Nengo Teacher: Terry Stewart (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: 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. Accumulator Models Teacher: Marieke van Vugt (University of Groningen) Decisions can be described in terms of a process of evidence accumulation, modeled with a drift diffusion mechanism. The advantage of redescribing the behavioral data with an accumulator model is that those can be decomposed into more easily-interpretable cognitive mechanisms such as speed-accuracy trade-off or quality of attention. In this course, you will learn about the basic mechanisms of drift diffusion models and apply it to your own dataset (if you bring one). You will also see some applications of accumulator models in the context of neuroscience and individual differences. ================================================= Niels Taatgen - Professor University of Groningen, Artificial Intelligence web: http://www.ai.rug.nl/~niels email: n.a.taatgen at rug.nl Telephone: +31 50 3636435 Office: Bernoulliborg 322 ================================================= -------------- next part -------------- An HTML attachment was scrubbed... URL: From db30 at andrew.cmu.edu Mon Dec 21 16:55:44 2015 From: db30 at andrew.cmu.edu (db30 at andrew.cmu.edu) Date: Mon, 21 Dec 2015 16:55:44 -0500 Subject: [ACT-R-users] Updated ACT-R software release Message-ID: <218B12A0756B689D03E8F770@actr6b.cmu.edu> A new version of the ACT-R 7 software is now available from the ACT-R website: . The new release is primarily to address some issues with running the software under the newest versions of SBCL and CCL. There are no significant changes to the architecture with this release, but there are two new options which may be useful in some situations. The first is the addition of motor module commands for using the numeric keypad on the default keyboard device. The command hand-to-keypad (and a manual request with the same name) will move the model's hand to the numeric keypad with the fingers over the center row, and the press-key request accepts the keys: clear, keypad-=, keypad-/, keypad-*, keypad-7, keypad-8, keypad-9, keypad-minus, keypad-4, keypad-5, keypad-6, keypad-plus, keypad-1, keypad-2, keypad-3, keypad-0, keypad-period, and enter to hit the corresponding keys when the hand is on the keypad. The other is a speculative change to the utility learning credit assignment mechanism which is being tested as a way to deal with situations where rewards may be delayed relative to the actions which should be credited. Setting :ul to complete (instead of t) will enable utility learning with the new mechanism. When :ul is set to complete then only productions which have "completed" their actions will receive a reward when one is provided. If a production's actions have not completed then it will remain in the history to receive a reward in the future. What it means for a production's actions to be completed is that all of the requests which it makes have been reported as complete by the modules which received the requests. Signaling completion of a request is up to the module that receives the request, and the current modules have been updated to signal completion of requests at the same time that they signal a state change back to being "free" (except in the case of overlapping manual requests which mark a request as completed when it has finished its last step even if the overall module is still busy with another request). If you have any questions or problems with the new version please let me know. Dan