The 26th Annual ACT-R Workshop will take place on July 19, 2019 during the MathPsych/ICCM conference in Montreal, Canada.
If you are considering participating and would be interested in giving a presentation or organizing a panel session (similar organization to last summer in Madison), please contact Christian Lebiere as soon as possible with a title/topic and a short paragraph abstract.
The 25th Annual ACT-R Workshop takes place on July 21, 2018 at the University of Wisconsin in Madison during the 2018 MathPsych/ICCM conference.
A one-day ACT-R Workshop took place at University College London on July 26, 2017.
The purpose of the workshop is to discuss issues of interest to the ACT-R community, including but not limited to new architectural modifications and developments, modeling and methodology discussions, scaling and efficiency issues, and ideas for applications and expansion.
Niels Taatgen Maybe 640k Is Not Good Enough Anymore: How To Take ACT-R to the Next Level?
Attendees of the 2016 ACT-R Post-Graduate Summer School from left to right (and front to back when one directly behind the other): Marc Halbrügge, Niels Taatgen, Robert Thomson, Christopher Stevens, Dan Bothell, Jelmer Borst, Wayne Gray, David Peebles, Michael Martin, David Reitter, Christian Lebiere, John Anderson, Andrea Stocco, Burcu Arslan, Michael Collins, Othalia Larue, Trudy Buwalda, Robert West, Qiong Zhang, Cvetomir Dimov, Bella Veksler, Troy Kelley, Glenn Gunzelmann, Matthew Kelly, Kevin Gluck, Ion Juvina, Farnaz Tehranchi, Vladislav Veksler, Frank Ritter, Cleotilde Gonzalez, Lael Schooler, Bill Kennedy, Harmen de Weerd.
The 2016 ACT-R Post Graduate Summer School took place from August 7 to 9, 2016 at the Cork Factory Hotel in Lancaster, Pennsylvania. Abstracts from the talks are provided below, and the slides from many are linked from the title.
Jelmer Borst (Groningen) – ACT-R and Neuroscience Revisited: What Did We Learn from EEG and MEG?
Since the early 2000s, we have been using fMRI as a means of informing ACT-R models. As a direct result, the imaginal buffer was introduced to the architecture. After the mapping between ACT-R modules and brain regions became more established, fMRI could also be used as a means of testing and constraining ACT-R models, in some cases requiring a significant redevelopment of models. The drawback of fMRI is its low temporal resolution. To approach the level of temporal detail of ACT-R operations, we have recently turned to EEG and MEG, which provide data at a millisecond-resolution. These experiments have provided evidence for additional processes in the standard fan-experiment: a familiarity process and a more involved decision process. In this talk I will discuss whether these results should lead to changes to the architecture or only to updates of existing models.
Although a cognitive process is typically a long sequence of cognitive events (e.g., the sequence of productions in ACT-R), cognitive scientists must infer mental steps based largely on the end points of the process – the stimulus and response. This presentation will examine the relationship between Evoked Response Potentials (ERPs) and the cognitive events they signify, using empirical data to segment between the start and end of a cognitive process. Further, I will examine how to implement these ideas into ACT-R by describing three models. With the successful inclusion of brain localization in ACT-R, this presentation will lay out the case for why it is important to incorporate temporal properties of brain events as well. A program of research is proposed to help improve production time estimation in ACT-R.
In the canonical association between modules and brain regions, ACT-R’s procedural module has been mapped to the basal ganglia. This mapping has found a number of experimental verifications with fMRI, and two neural network models have been put forward that provide a robust biological justification for it. There are two potential problems with this. First, the procedural module makes specific and detailed predictions about the directionality of production rules, which have not been tested against fMRI data. The second problem is that the basal ganglia can only perform a subset of the functions of the procedural module. In this presentation, I will try to address these problems by showing a different way of analyzing fMRI data that provides information about the directionality of variable transfer as well as the demand functions of a module. I will then compare the predictions of ACT-R’s procedural module with the results of this analysis, and use the parts where the model predictions do not match to suggest either modified mappings or perhaps simple modifications to the procedural module.
I will describe priorities for the future of the architecture as “Pace, Persistence, and Scale.” I will comment on the extent to which there is any progress on these fronts, describe some enduring challenges, and discuss how these considerations relate to issues of robotic autonomy.
The cognitive mechanisms integrated in ACT-R continue to provide robust demonstrations of computational representations of human behavior. In many ways, this is good news: the essence of human behavior across many environments is alike. However, most of our efforts have focused on demonstrating how the existent mechanisms can account for traditional phenomena rather than on developing new mechanisms to address new classes of phenomena. I will reflect on some open problems in the hope of motivating a discussion of how to represent them computationally.
I will describe updates to the current software covering both architectural and functional changes. The most notable architectural change is a new option for the credit assignment of rewards provided in the utility learning mechanism. The change is to only apply rewards to the productions that have “completed” all of their actions instead of to all those which have been selected. The most significant change to the functionality is a redesign of the history tools available in the ACT-R Environment to provide a more uniform interface and a way to save that information for later inspection.
The focus of this talk will be on a fatigue module that provides the capacity for fluctuations in performance as a function of sleep loss, circadian rhythms, and time on task. The module is unusual relative to other ACT-R modules for several reasons: it has no buffers and is not directly responsible for the information processing capacity of the architecture, it influences parameters in other modules through direct module-to-module connections that are not moderated by central cognition, and is fundamentally about the limitations of human cognition. These features, though unusual, are consistent with the existing literature on fatigue, and add an essential dimension often lacking in computational theories of human cognition but necessary to achieve the vision of unified theories of cognition.
In a series of related projects, we study how trust mediates learning and how trust itself is learned during strategic interaction. We describe an ACT-R model of trust dynamics that accounts for learning within and between the games Prisoner’s Dilemma and Chicken, and a series of validation studies aimed at expanding the range of conditions and tasks to which the model can be applied, including team-based learning. Our work on improving the original model poses fundamental questions about the architecture such as how models can represent other models and learn to interact with them. Complex interactions between trust and learning can be best studied in a cognitive architecture such as ACT-R, which already includes a variety of learning mechanisms. However, we found that new equations and mechanisms need to be developed to deal with the intricacies of trust development, calibration, and repair, including a trust update equation that provides a unified account for a number of effects from the trust literature.
ACT-R does not provide an implemented theory of social or emotional cognition, i.e., beyond rational cognition. Humans are particularly good at social cognition, specifically, identifying animate behavior and reading the minds and simulation of other agents. These capabilities have been localized in the brain, but that functionality is not yet provided within ACT-R. While the architecture includes a very effective model of memory including similarity and priming effects, these capabilities do not include the effect of emotional valence on memory, necessary to explain the experimental findings. This talk will describe these additional capabilities within ACT-R and their application to human phenomena.
We propose a new architectural mechanism to be used for modeling affective states in ACT-R. Some of the existing approaches to modeling affect are inspired by appraisal theories and tend to focus on explicit representations that are hardwired rather than learned. Other approaches attempt to touch upon the implicit aspects of affective processes by investigating the physiological correlates of affect. We propose a solution inspired by the core affect theory, which places implicit visceral reactions to sensory stimuli along two continuous dimensions of affect: arousal (intensity) and valence. We translate the two dimensions in ACT-R as activation and valuation, respectively. The proposed approach unifies the emotion and cognition theories by naturally integrating affect into the existing ACTR mechanisms without requiring additional modules or a major reorganization of the architecture.
We extended the architecture with a spatial module and modeled learning and the influence of familiar objects in a mental rotation task. The aim is to predict behavior of people in applied task that depend on spatial competence. What kind of technical support would help them with task, when are errors most probable, in what situation is that task too difficult and the user needs further information.
As part of the development of the Symbolic and Sub-symbolic Robotics Intelligence System (SS-RICS) we have implemented a memory store to allow a robot to retain knowledge from previous experiences. As part of the development of the event memory store, justification for an off-line, unconscious memory process was tested. Three strategies for the recognition of previous events were compared. We found that the best strategy used a post-processing process for all memories using pruning, abstraction, and cueing. Pruning removed memories, abstraction used categories to reduce metric information and the cueing process provided pointers for the recognition of episodes. Additionally, we found that post-processing memories for retrieval as a parallel process was the most efficient strategy. This presentation will review the lessons learned from this work and discuss the implications for cognitive architectures.
We present Holographic Declarative Memory (HDM), a module that replaces the slot-value strings of ACT-R’s declarative memory with holographic vectors. HDM reproduces the functionality of DM using vectors as symbols, which confers advantages when dealing with large databases. We have demonstrated the suitability of HDM as a substitute for DM on variants of the fan effect task. In HDM, association strengths emerge from the geometries of the vector space. HDM is also scalable: HDM is based on previous holographic models that have been used to infer the semantics of concepts from large untagged corpuses. Finally, HDM have been used in neurally plausible cognitive architectures.
We describe a Feature-Selection model that combines the instance-based learning paradigm (to categorize objects defined by configurations of features) with production utility learning (to learn a subset of features relevant to the categorization). The model is intended to eventually serve as a general model that helps anchor perceptual labels in autonomous systems. Although feature selection and categorization are often addressed separately in machine learning projects, they can be integrated in a cognitive architecture such as ACT-R as a combination of mechanisms including blending, flexible chunk types and and reinforcement learning. We will discuss architectural issues including controlling the feedback loop between declarative and procedural memory, adjusting the level of noise with experience, learning across module boundaries, and accuracy-efficiency tradeoffs.
In this talk I will present high-level behavior representation languages in general and one in particular (called Herbal) that helps model performance on a spreadsheet task. The task takes novices about 25 minutes to perform and experts about 18. We created a tool to author models from a hierarchical task analysis. The tool creates 12 models, a completely novice model that has to proceduralize everything, and then 11 levels of initial expertise, ranging from basically a novice to someone that can do the task using just procedures. The models predict the novice to expert transition, and were created particularly quickly. The models fit the data surprisingly well, which suggests that we can now model novices and their learning using a GOMS-like model, and that behavioral modeling languages should be used more.
The true potential of cognitive architectures is to generalize over multiple tasks and domains of cognition. However, research in cognitive architectures mainly consists of building models of particular experimental tasks, and therefore has many of the limitations of standard cognitive psychology research that were lamented in Newell’s 20 questions paper. In order to achieve a truly integrated cognitive theory, a number of additional pieces of theory are necessary. The first is knowledge transfer: how can procedural and declarative knowledge be used for more than one task? With PRIMs, I have implemented a set of mechanisms for procedural transfer between tasks. However, this is only a first step towards an architecture that can contain knowledge of many different tasks, and that is capable of learning additional tasks by itself. For this we need mechanisms that are able to specify and prioritize goals, and mechanisms that can learn new tasks from examples, instruction or exploration.
One of the benefits of employing computational process models in Cognitive Science is that such models can perform the same tasks as human participants. Unfortunately, task interfaces do not often lend themselves to be easily parsed by computational models and agents. Even task software written specifically for the purposes of behavioral simulations is often limited in that it is tailored for a specific modeling framework, making it difficult to do cross-framework model comparison. Simple Task-Actor Protocol (STAP) is a basis for task-development methodology that makes psychological experiment software easier to develop, to connect to computational models, and to set up for data logging and playback. STAP-compliant task development enables model-code reuse across tasks, and task reuse across models.
I describe two methods for ACT-R model parameter optimisation that allow the search of a multidimensional parameter space using populations of models. The first, differential evolution, is a simple, general purpose algorithm that employs an iterative process of mutation, recombination and selection on a population of candidate solutions to converge on a global optimum. The second method employs HTCondor, an open source, cross-platform software system designed to enable high throughput computing on networked computers. HTCondor facilitates parameter search by allowing modellers to run large numbers of instances of the same model in parallel. I will describe the basic principles of differential evolution and HTCondor and draw upon my experience with both to demonstrate how they can be used to accelerate the development of ACT-R models.
Several theories of decision making contend that the use of decision strategies can be determined by the mental effort required. But how to measure the effort—or cognitive costs—associated with a strategy? Previous analyses have mainly focused on the number of attributes used and the type of information aggregation. We propose an approach based on the ACT-R cognitive architecture, called the Resource Demand Decomposition Analysis, that quantifies the time costs of strategies for using specific underlying cognitive resources and takes into account interactions between processing operations as well as the possibility of parallel processing. Using this approach, we quantify, decompose, and compare the time costs of two prominent decision strategies, take-the-best (TTB) and tallying (TALLY). Our results show that claims about the “simplicity” of decision strategies need to consider not only the amount of information processed but also the cognitive system in which the strategy is embedded.
A good fit of model predictions to empirical data is often used as an argument for model validity. However, if the model is flexible enough to fit a large proportion of potential empirical outcomes, a good fit becomes less meaningful. Model Flexibility Analysis (MFA) is a method for estimating the proportion of potential empirical outcomes that the model can fit. MFA aids model evaluation by providing a metric for gauging the persuasiveness of a given fit. MFA is more informative than merely discounting the fit by the number of free parameters in the model, as the number of free parameters does not necessarily correlate with the flexibility of the model. We contrast MFA with other flexibility assessment techniques, provide examples of how MFA can help to inform modeling results, and discuss a variety of issues relating to the use, disuse, and misuse of MFA in model validation.
Scaling up cognitive models for application to real world sociotechnical systems presents several challenges. The first is that building these models from scratch is prohibitively time consuming. The second is that there are different ways to build models of the same task; the more complex the task, the more different ways there are to model it. The idea of a macro cognitive architecture is based on the claim that people tend to use their micro cognitive architecture (e.g., ACT-R) in consistent ways to solve high-level problems that occur across tasks, including problem solving, navigation, competition, negotiation, and planning. SGOMS is a macro cognitive architecture for modeling how people use expert knowledge in multi-agent, dynamic environments with interruptions and re-planning. It is implemented in ACT-R and provides a template for quickly adding task-specific knowledge to create a complex model. Thus it solves two problems: how to jump-start a complex model and how to deal with specific high-level macro cognitive issues. This talk discusses some of the issues and lessons learned in creating and testing SGOMS and how we might develop a suite of ACT-R-based macro cognitive architectures.
The 22nd Annual ACT-R Workshop occurred on July 17-19, 2015 at Carnegie Mellon University. The progam/proceedings are located below.
Robert West  	Is the amygdala a production system?
The 19th Annual ACT-R Workshop occurred on July 27, July 28, and July 29, 2012 at Carnegie Mellon University. The progam/proceedings are located below.
This is a copy of the program materials. Other information is available on the official 2011 PGSS site.
The PGSS is organized in sessions consisting of extended presentations and open discussions around a specific theme. Each day features three 90-minute sessions from 9am to 3pm, plus a free-ranging evening commentary of the topics of the day led by a discussant. The time between the end of the afternoon session at 3pm and the evening session is set aside for recreation, dinner, and informal discussions and collaboration. The final program, with links to the talk slides, appears below.
All sessions will be in Adamson Wing (room 136A), which is on the first floor of Baker Hall.
Greg Trafton, Raj Ratwani & Len Breslow, A Color Perceptual Process Theory: Letting ACT-R see Colors.
Jong Kim, Frank Ritter & Richard Koubek, Learning and Forgetting in ACT-R.
6:30-10:00 Party at the Pittsburgh Centre for the Arts, 6300 Fifth Avenue, Pittsburgh.
Daniel Hasumi-Dickison and Niels Taatgen, Individual differences in the Abstract Decision Making Task.
models for tasks that vary from simple reaction time to air traffic control.
until August 1. See http://simon.lrdc.pitt.edu/~iccm/ for details.
The Eleventh Annual ACT-R Summer School will be held at Carnegie Mellon University in Pittsburgh in July 2004.
The summer school will take place from Friday July 23 to Wednesday July 28.
are expected to complete during the day and evening. Computing facilities will be provided.
with early notification upon request. Admission to the summer school is free.
on the ACT-R web site (http://act-r.psy.cmu.edu/).
Applications are due APRIL 20. Acceptance will be notified by MAY 4.
Applicants MUST include a curriculum vitae and a short statement of purpose.
such as a completed model, should also be included in the application.
send money. See http://www.housing.cmu.edu for further housing information.
I will stay from ……………. to …………….
2. … I want a double room and I will room with …………….
ROOM PAYMENT IS DUE UPON CHECK-IN. DO NOT SEND MONEY.
walking distance of CMU. Contact the Wyndham directly at +1 (877) 662 6242.
proceedings Richard Young Should ACT-R include rule refraction?
proceedings James McClelland Developing a domain-general framework for cognition: What is the best approach?
From model to application: developing a believable opponent in the game of Set!
All the lectures take place in Adamson Wing, Baker Hall 136A.
The Saturday lunch demo session takes place in Baker Hall 332P.
The Saturday evening dinner party takes place at 217 S. Dallas.
Niels Taatgen        Why do children learn to say "broke"?
Anna Cox             device learning ... but does it fit with ACT-R?
of the ACT theory.  The early registration deadline is JULY 1.
language and platform, standardization and use of the web.
your talk (if applicable) and any suggestion for a session topic.
early registration deadline is June 15.
registered, feel free to send your suggestions by email.
ACT-R is a simulation system for developing cognitive models for tasks that vary from simple reaction time to air traffic control. Each year a workshop and summer school is held to train researchers in the use of the system and to enable current users of the system to exchange results.
Train researchers in the use of ACT-R for cognitive modeling.
Theory lectures, homework assignments and research projects.
Participants are expected to stay on for tutorial and workshop.
Novices will be required to attend a two-day pre-session.
Train researchers in the use of new features of ACT-R 3.0.
Includes new development environment, visual/action interface and integrated experiment software.
Open to a limited number of workshop participants.
Preference given to graduates of previous summer school and those who can bring their own portable computer.
Presentations of new research and development in ACT-R.
The new ACT-R 3.0 system will be introduced and each participant will receive a copy of the system.