Cognitive Architectures I+II

Transcription

1 Cognitive Architectures I+II Felix Putze & Lecture Cognitive Modeling SS /82

2 Structure of the Lecture Motivation & Definition : Cognitive Architectures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 2/82

3 Computational Models Psychological experiments and their analysis lead to theories about human behavior Want to apply those results for development, improvement or evaluation of technical systems Example: Optimize user interface to respect user s limitation in memory capacity Need computational models which produce quantitative estimates of human behavior and performance Develop cognitive models using language of mathematics and algorithms 3/82

4 Example of a computational cognitive model Retrospectively perceived time is different from real time: A task with a lot of idle time and many context changes ( things happen ) is perceived as longer as a task without Derive formula which calculates perceived duration of a task: ED TT ( A B ) AT R TT estimated duration fitting parameters TT = total time AT = active time number of context changes (simplified from Dzaack et al., 2006) 4/82

5 Architectures instead of individual models Psychological experiments and models usually explain isolated effects of human behavior (e.g. in memory) Problem: For complex tasks, individual models should interact! Example: Emotions influence memory e.g. items stimulated with high emotional arousal are more easily memorized Solution: A cognitive architecture combines individual models provides means for information exchange Another advantage is the provision of a common modeling paradigm and language Simplifies the development and connection of new models 5/82

6 Definition: Cognitive Architecture A cognitive architecture specifies the underlying infrastructure for an intelligent agent. (Langley, 2009) *is+ a computational modeling platform for cognitive tasks (Taatgen, 2006) Only those architectures are valid that are cognitively plausible, i.e. which correspond to human behavior An architecture provides a small set of structures and modeling approaches is able to cover a wide range of tasks and domains Comes with an implementation that supports the creation of specific models for a certain task 6/82

7 Cognitive Architectures for Computer Scientists Provides both theoretical background and implementation Defines a programming paradigm (i.e. in what terms is the process of cognition specified?) Defines an API for the definition of specific models Data structures to represent knowledge, information Programs to model cognition and decision processes Runtime environment to execute and evaluate models (optional) Interface to other programs or devices to have the model actually influence its environment (e.g. by pressing buttons on a user interface) 7/82

8 Applications Evaluation of Man-Machine-Interfaces Simulated user interacts with the interface Enables designer to identify bottlenecks, misplaced GUI-elements, Cognitive Tutoring systems System can predict if learner still has unused mental capacity Reinforcement of associations can support learning Neuropsychology (Interpretation of fmri data) Cognitive architectures are often (in some abstract way) related to the structure of the human brain Can help to interprete and predict fmri data All three examples require the existence of a complete architecture in contrast to isolated models! 8/82

9 Cognitive Architectures: A Selection A number of different architectures exists: ACT-R (Anderson, since 1980s) Soar (Laird/Newell, 1983) PSI (Dörner, since 1990s) Co-Jack (Ritter, 2008) Differences in modeling focus, modeling paradigms, maturity Provide a programming interface to develop own models A model reflects one specific task described with the modeling language provided by the architecture 9/82

10 Structure of the Lecture Motivation & Definition : Cognitive Architektures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 10/82

11 Requirements according to Newell In the 1980s, cognitive psychologist Newell criticized the development of the time: researchers focused too much on isolated aspects of cognition in laboratory experiments (this is actually still an issue) defined a famous list of criteria a cognitive architecture has to fulfill to be considered a mature representation of the human mind Newell also suggests how a cognitive architecture can be evaluated according to these criteria Be careful: Newell also promotes his own architecture (SOAR)! 11/82

12 Requirement 1: Flexible Behavior Computational Universality Despite the lack of evolutionary anticipation, humans are able to solve complicated tasks air traffic control computer programming Does not mean that all tasks are equally simple to solve that there are no specialized facilities for some cognitive functions Evaluation: Formally prove Turing completeness Status: Neural Networks for example are known to be Turing complete (as opposed to earlier proposals ( perceptrons ) of neural computation) 12/82

13 Requirement 2: Real-time performance Humans can solve difficult tasks given limited time A cognitive architecture should reproduce this performance Constraint also applies to learning (e.g. the time and effort it takes to learn a new concept or skill) Evaluation: Easy for specific cases of human cognition, impossible to prove in the general case Status: For simple models, this is trivial due to available computational power. However, artificial learning and nontrivial decision making still requires vast resources. 13/82

14 Requirement 3: Adaptive Behavior Humans perform computation which optimally serves their needs Concerns the basic processes of an architecture as well as the whole system Evaluation: Requires real-world tasks to create realistic needs and constraints. Status: Generic mechanisms are available. However, their general feasibility is unknown. 14/82

15 Requirement 4: Vast Knowledge Base Humans have vast amounts of general and specialized knowledge at hand Not all information is equally reliable or relevant, which has to be handled when knowledge base becomes large Evaluation: Assess how a model scales (in terms of runtime, focus, rating of information) with a growing knowledge base Status: Large databases for many domains exist, their integration however is lacking 15/82

16 Requirement 5: Dynamic Behavior Real-World is not like solving a puzzle like the Towers of Hanoi The world changes in often unpredictable ways People make mistakes and recover from them Evaluation: Again, very hard to judge in laboratory environments. Insert models based on the architecture into real or synthetic (but sophisticated) environments Status: Cases of successful integration exist (simulated pilots in a large-scale Air Force simulation together with human pilots) 16/82

17 Requirement 6: Knowledge Integration Humans have the capacity for intellectual combination Architectures need to reproduce this capacity System must not be set up to anticipate special cases of this capacity! Evaluation: Define test cases of hallmarks of daily (!) human capacity for intellectual combination : inference, induction, metaphor, analogy Status: Architectures are still pre-programmed to perform intellectual combination 17/82

18 Requirement 7: Natural Language Understand and produce natural human language parsing, comprehension, inference and relating current text to past knowledge Evaluation: Common human tests for language comprehension, i.e. reading texts and answering questions on it Status: Works for tiny domains or on very superficial level 18/82

19 Requirement 8: Consciousness Self-Awareness Causes or explains subliminal perception, implicit learning and meta-cognitive processes One of the more controversial requirements (as even stated by Newell himself) May be epiphenomenal (i.e. only a secondary effect) Evaluation: Very difficult. Status: Not directly addressed in any major cognitive architecture 19/82

20 Requirement 9: Learning Human ability to acquire competences Takes place on many different levels (Squire, 1992): Semantic Memory Episodic Memory Skills Priming Conditioning Evaluation: Cognitive Architecture must be able to expose all different kinds of learning Status: Various models for learning of different types were proposed 20/82

21 Requirement 10: Development No functional requirement but a constraint Human cognition does not emerge full-blown but unfolds gradually due to growth of the organism and exposure to experience Any reasonable cognitive architecture should reproduce this behavior Evaluation: No precise characterization of gradual human-like development available Status: Theories for gradual development exist for certain architectures but are of uncertain validity 21/82

22 Requirement 11: Evolution Human cognitive abilities must have arisen through evolutionary history Evaluation:? Status: No architecture provides any mechanisms which simulate evolution 22/82

23 Requirement 12: Brain This last constraint requires neural implementation of cognition The architecture has to contain brain-like structures or support mappings of certain components to areas of the brain Evaluation: finding correlations between model computations and fmri data (or other brain-imaging methods) Status: Some models offer initial indications of this relation 23/82

24 Structure of the Lecture Motivation & Definition : Cognitive Architectures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 24/82

25 Classic Cognitive Architecture Stimulus Attention Perception Thought process A stimulus (e.g. a symbol on the screen) appears The stimulus catches the human s attention The stimulus is perceived and encoded The encoded stimulus is subject to a thought process The thought process results in a decision The decision leads to a response (e.g. pressing a button) Decision Response 25/82

26 Does this model capture all aspects of cognition? Experiment: Read the following text! PARIS IN THE THE SPRING 26/82

27 Top-Down vs. Bottom-Up Did you read: Paris in the spring? Check the slide again! Bottom Up Processing: From raw stimulus to decision (as presented in the architecture) Top Down Processing: Cognition driven by previous knowledge and expectations Most human cognition is a mixture of both types of processing Experiment by Bruner et al. (1951): Participants saw playing cards very briefly The set of cards included black hearts (in a normal set, all hearts are red) Participants claimed to see purple or brown hearts Literal blending of black color (from bottom-up process) and red color (from top-down process)! 27/82

28 Parallelelism and Modularization Bottom-up and top-down processing have to occur in parallel Bottom-up processing begins with stimulus perception At the same time, top-down processing involves memory retrieval etc. Process might involve several passes and re-evaluation caused by feedback loops between both modes A serial architecture does not support parallelism Also need to split the monolithic thought process to a create a finer modularization 28/82

29 Connectionist vs. Rule-Based Architectures In a rule-based architecture, the process of cognition is represented as a set of rules Rules determine how input is processed, how actions are executed, how cognitive tasks are solved Rules can be designed manually and (to a certain degree) learned from experience A connectionist architecture is composed of a network of very small computational units, called neurons Inspired by the neuronal structure of the human brain Massive parallel computing Behavior of the network is usually not designed but learned Behavior is less predictable More on neural networks in a later lecture! 29/82

30 Rule-based Architectures A rule encodes a bit of knowledge on how information can be processed (procedural knowledge) A rule is typically of the form IF THEN The IF part describes on which types of cognitive states the rule can be applied The THEN part describes how the cognitive state is transformed if the rule is executed Simplified example (integer addition by counting fingers ): IF number1=x AND number2 =Y AND counter=z AND Z<Y THEN number1=retrieve_successor(x), counter=retrieve_successor(z) 30/82

31 Connectionism based on Neurons Quelle: Wikimedia One basic building block: A neuron with weighted inputs (from other neurons) and an activation function that controls whether the neuron fires Huge networks can be constructed by attaching the activation output of one neuron to the input of other neurons 31/82

32 Feed-Forward Neural Networks Stimulus (e.g. visual) Response (e.g. verbal, manual) Paradigm: Parallel Distributed Processing (PDP) 32/82

33 Learning in Neural Networks Neural networks can learn to produce desired behavior There exist two different learning paradigms for NN: Backpropagation: Maintain a set of learning examples of desired stimulus-response pairs Do iteratively: Feed stimulus into tentative network, observe response and calculate error (usually difference between observed and expected response) Derive error function after network weights, adjust weights in direction of error gradient Dominant learning rule for feed-forward networks Hebbian Learning Rule: What fires together, wires together Learning rule: Adjust weight between neurons which are often stimulated together 33/82

34 Critisism of Connectionism Brain-like? Anderson: Modern form of Behaviorism Reminder: Behaviorism means No modeling/explanation of mental processes predict direct mapping from stimulus to response Neural networks in practice are often essentially black boxes due to their complexity and vast number of parameters Backpropagation learning algorithm only takes input and output into account But: Network topology can be designed according to examples from nature, by human experts or with clever algorithms 34/82

35 Symbolic vs. Subsymbolic Symbolic: Elements of computation have semantic meaning (e.g. words, mathematical symbols, ) Subsymbolic: No such meaning, e.g. statistically derived values Symbolic computation allows well-defined behavior and provability of certain properties Subsymbolic computation is less human-interpretable, may account for fuzzyness of complex real-world problems Correlates with the Connectionist vs. Rule-Based Dimension: Weights of a neural network are subsymbolic in nature But: Topology of the network can convey meaning Often, we find hybrid architectures with symbolic computing on more abstract levels and subsymbolic computing on lower levels 35/82

36 Symbolic computing with Neural Networks An early connectionist model by McClelland and Rumelhart to model language processing: 36/82

37 Structure of the Lecture Motivation & Definition : Cognitive Architectures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 37/82

38 Example: ACT-R 38/82

39 Cognitive Architectures: ACT-R as an example Developed since the 80s by John Anderson at CMU Programmed and programmable in LISP Hybrid architecture: Symbolic processing at higher levels, subsymbolic processing at lower levels Modular structure structures the system in certain components : visual, motor, memory, planning, Modules can be modified and replaced, new modules can be added to the system 39/82

40 Chunks Main building block for information exchange between modules Chunks encode declarative knowledge (e.g. facts) A chunk has a type and attributes which can be atomic values or other chunks Chunks are transferred between modules by copying them from and to buffers Most modules have an input and an output buffer Declarative Module ( Memory ) In C chunk describing request C1 C3 C2 C4 C5 C6 Out C3 chunk matching request 40/82

41 Example of a Chunk chunk type definition (chunk-type property object attribute value ) chunk instance (c1 ISA property object shark another chunk as attribute attribute dangerous value true atomic attribute ) attribute-value pairs 41/82

42 Simplified Example of Data Flow Task: Solve an equation given on a computer screen by typing the solution on a keyboard 1. Visual module: perceives equation on screen. It is encoded as a chunk and made available for other modules. 2. The chunk encoding (part of) the equation is copied to the input module of the declarative module to retrieve the solution. 3. When the equation is more complex, tentative results are stored in the imaginary module. 4. When the solution is derived, it is copied to the input buffer of the manual module to type in the solution. 42/82

43 Production Rules What defines which actions take place when? Production rules contain the procedural knowledge of the model and determine its actions A new action is selected whenever the state of the model changes External stimuli Processing of a submodule finished Production rules has preconditions and bindings A production rule is valid for execution if its preconditions are satisfied When an production rule fires, its bindings are executed Preconditions usually define patterns concerning the content of various buffer modules Bindings usually move chunks between buffers 43/82

44 Goals and Goal Stack The goal buffer maintains the current goal of the model In most models, this does not describe completely different intentions, but the internal state of a task Example: Goals for the task Towers of Hanoi : G1: Move tower from position A to position C G2: Move subtower from position A to position B Structure induces a stack of goals and subgoals Determines which production rules are valid (they check in their preconditions) A B C 44/82

45 Utility What to do if more than one action is valid for execution? Action selection based on utility: From all valid actions, use the one with the highest utility How is utility defined? U i = P i G - C i U i = Utility of production i P i = Probability that the current goal is achieved when executing i G = Value of current goal C i = Cost of executing i (usually duration) P i and C i are learned from observation in multiple repetitions Extension: Reinforcement Learning ( later lecture on RL) 45/82

46 Example Model: Counting (chunk-type count-order first second) (chunk-type add arg1 arg2 sum count) (add-dm (a ISA count-order first 0 second 1) (b ISA count-order first 1 second 2) (add-goal ISA add arg1 5 arg2 2)) (goal-focus add-goal) 46/82

47 Example Model: Main Production Rules (P increment-sum =goal> ISA add sum =sum count =count - arg2 =count =retrieval> ==> =goal> ISA count-order first =count second =newsum count =newsum +retrieval> isa count-order first =count) 47/82 (P increment-count =goal> ISA add sum =sum =retrieval> ==> =goal> count =count ISA count-order first =count second =newcount count =newcount +retrieval> isa count-order first =sum)

48 Example Model: How does it work? Goal: Add arg1 and arg2 Maintain two temporary variables count and sum Sum: starts with arg1 and is incremented until it reaches arg1+arg2 Count: starts with 0 and is incremented until it reaches arg2 (which terminates the operation) Models how addition works for small children ( counting fingers ) How can we model the instantaneous addition process of adults? 48/82

49 Learning of new Production Rules An initial set of production rules is specified by the modeler ACT-R can learn new production rules by composing existing ones For each pair of subsequent productions, ACT-R generates a new production which has the effect of both This is especially useful if one rule retrieves a chunk and the other processes it, simplified example: Goal G: Decide whether a number is odd (press O ) or even (press E ) Rule A: IF Goal=G and number X is attended THEN fetch chunk (is-even num=x) Rule B: IF Goal=G and has retrieved chunk (is-even num=x val=v) THEN press button V When presented with Goal=G and X=7, ACT-R will compose the rule: RULE AB: IF Goal=G and number 7 is attended THEN press button O 49/82

50 Declarative Module The declarative module represents short-term and long-term memory Each chunk is associated with an activation value that describes its estimated utility and relevance Activation determines retrieval probability and retrieval duration for a chunk Activation is composed of several components: Base activation: Depends on frequency and recency of presentation of the chunk Noise: Random activation, sudden inspiration Spreading activation: Activation which leaks from associated chunks (two chunks are associated if one is an attribute of the other) 50/82

51 Timings Important for applications and evaluation of models ACT-R is strong at predicting the duration of operations on a millisecond-level Example Declarative Module: Retrieval time depends on the activation of the item: Example Procedural Module: Production rule selection is no conscious process T Fe i Implicitly applying the PDP paradigm: All decisions for rule evaluation are made in parallel Rule selection and execution takes a fixed amount of time (50ms), independent of the number of rules, the utility distribution etc. A i activation latency factor 51/82

52 ACT-R and the Brain Certain brain regions have been identified with certain tasks Visual cortex Motor cortex ACT-R is structured into functional modules Define a mapping between modules and brain regions Activity in a module should correspond to brain activity in the associated region Precise timings produced by ACT-R help in the mapping process 52/82

53 ACT-R and the Brain Mapping of ACT-R modules to regions of the brain (from: Anderson et al., A central circuit of the mind, 2008) 53/82

54 Timings for Module Activity Exact timings for module activity as predicted by ACT-R (from: Anderson et al., A central circuit of the mind, 2008) 54/82

55 fmri and the BOLD Effect The Blood Oxygenation Level Dependent (BOLD) effect in a nutshell: Active neurons in the brain have an increased demand of oxygen which is supplied through the blood Functional magnetic resonance imaging (fmri) makes this effect visible with a very high spatial resolution 55/82

56 Mapping from ACT-R to fmri (a) Demand of the aural module in the described task Measured by the proportion of time the module is active in a 1.5s window 56/82

57 Mapping from ACT-R to fmri (b) BOLD response as a function of time after a stimulus 57/82

58 Mapping from ACT-R to fmri (c) Prediction = Convolution of demand with BOLD response function Compare with observed experimental data 58/82

59 Evaluation of Mapping between ACT-R and fmri Works for very (very!) controlled experiments For example, people have to use the correct strategy for perceiving and processing the equation Can only measure activity on a module level Temporal resolution of fmri not high enough for fine-grained analysis 59/82

60 Application: User Interface Evaluation Model-based Evaluation of Cell Phone Menu Interaction (Ritter et al., 2004) Goal: Evaluate the user interface of a mobile phone by using a cognitive architecture If this approach is reliable, further evaluation can be performed with the model Improvements can rapidly be tested without real user interaction 60/82

61 Application: User Interface Evaluation Participants in the study were given the task to navigate through the menu of a cell phone Participants were trained before the experiments to familiarize them with the interface and avoid errors Automatically annotate an image of the cell phone to determine the position, color and function of its buttons The visual module and the motor module can interact with the simulated cell phone Cognitive model can now perform exactly the same operations as the human 61/82

62 User Interface Evaluation: Results User data ACT-R model Fitt s law* X GOMS (another cognitive architecture) > One scrolling operation * Fitt s law is a traditional and still very popular method which predicts timing as a function of the distance between two buttons. It is beaten here by both cognitive modeling approaches. 62/82

63 ACT-R critisism Large set of arbitrary parameters ACT-R 6.0 has ~50 parameters Only for a few guidelines on how to set them exist Very large degree of freedom Can create arbitrary behavior Not state-of-the-art from software engineering point of view LISP code of varying quality Does not obey all quality criteria of maintainable software (documentation, information hiding, ) ACT-R does not model individual differences well Different strategies have to be coded and selected manually Concepts like emotion or workload are not reflected 63/82

64 Strengths and Weaknesses of ACT-R ACT-R is strong in the prediction of low-level operational tasks: Memory retrieval Information processing Task execution Exact prediction of timings on a fine scale Goals are usually pre-defined by the modeler, i.e. goal selection is trivial (goals define status of a task, not distinct tasks between which the model has to decide) No expression of concepts like motivation 64/82

65 Structure of the Lecture Motivation & Definition : Cognitive Architectures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 65/82

66 Psi Developed by the German Psychologist Dietrich Dörner The main work describes a steam-powered entity that lives in an environment where it has to find energy and water Implementation by Bach (2003), also used to control micro robots 66/82

67 The world of micropsi 67/82

68 Main Properties of Psi Mainly connectionist approach: Memory, perceptional patterns and action scripts are all encoded in a large network structure (not regarded in detail here) Individual concepts like emotion and motivation Those individual traits create individual, variable behavior Does not concentrate on exact timings of low-level actions Not suited for fine-grained analysis of human behavior The original theory was developed by Dietrich Dörner Java-based implementation and a rephrasing of the theory (prob. more readable for CS students) by Joscha Bach 68/82

69 Perception in Psi 69/82

70 Action Schemata in Psi Walk Walk 1 Walk 2 GoWest Check Action schemata combine atomic actors to larger macros organized in a network structure Activation spreads through the network, indicating the next macro to expand or actor to execute 70/82

71 PSI s Motivational System The entity has a set of predefined motivational urges An urge measures the mismatch between a target value of a demand and the current value Urges are meant to be as general and orthogonal as possible Goals of the agent are associated with the satisfaction of an urge max min hunger thirst integrity 71/82

72 PSI s Motivational System Physiological Urges Food, water, physical integrity Satisfied by consumption of resources Social Urges External legitimity (acceptance, gratification) Internal legitimity (fulfillment of social norms) Cognitive Urges Reduction of uncertainty Satisfied by exploration Measured by mismatch of expected and observed outcome of actions Competence Ability to reach a goal 72/82

73 Action selection in PSI Motive: Urge together with a goal that satisfies this urge Example: The urge hunger together with an opportunity to eat is a motive An agents maintains several motives, but only one is active at a time (depending on the strength of the urge and the estimated chance of realization) A motive activates associated goal states from long-term memory A planning process is executed to determine an action sequence that transforms the current state to a goal state For planning, actions are represented as triplets: (precondition, motor schema, postcondition) In which state it is valid Action script Expected resulting state (represented as network) 73/82

74 Modulation of Cognition Urges modify the cognition process by adjusting certain parameters (modulators) which influence planning: Activation readiness for action even at high energy costs increases when strong urges (e.g. potentially vital demands) arise Resolution level depth and width of the planning space at the cost of processing speed Level of detail in perception an memory retrieval drops with increasing urge levels Selection threshold Readiness to switch to a new motive (a high value supports flexible behavior but has the danger of oscilating between conflicting strategies) Value depends on the motive and its strength 74/82

75 Emotion Model Emotions are not in an independent sub-system but an intrinsic aspect of cognition A change of a demand is represented as a pleasure or distress signal, used as reinforcement signals to evaluate executed actions An emotion in Psi theory is the configuration of Modulators Pleasure/Distress Cognitive Urges Not a fixed set of prototypical emotions but a continuous, multi-dimensional emotion space This models the phenomenological qualities of emotion (i.e. its observable effects), not the experience of emotion (which requires reflective capabilities) 75/82

76 Examples of Emotion in Psi The following emotions are not explicitly modeled in the Psi theory but are examples for known phenomena that can be observed for a Psi agent Surprise, relief, disappointment: related to evaluations of matches of anticipated events Social emotions (e.g. shame) are related to social urges Anger is characterized by the failure to attain a goal (i.e. a high displeasure signal) and a low resolution level The latter is also typical for fear, combined with a high certainty urge and a high selection threshold Psi does not have primary emotions but not directed attitutes, like love, hate, envy 76/82

77 Overview of the Action Selection in Psi 77/82

78 Evaluation of the Psi Model Evaluation of the Psi is more fuzzy than typical ACT-R evaluations Detje (2003): Have humans an Psi agents explore the same virtual island over 1 hour Task: Survive, collect certain items Humans and agents perform comparable in number of visited locations, collected items, approached objects Differences in exploration behavior: Humans explore visited locations in detail, agents try to visit more different locations Dörner (1999): Simulated overcrowding on the simulated island showed emergence of effects known for humans 78/82

79 Structure of the Lecture Motivation & Definition : Cognitive Architectures Requirements for cognitive architectures Types of cognitive architectures Example 1: ACT-R Example 2: Psi Conclusion 79/82

80 Conclusion Cognitive architectures try to model the whole process of human cognition from perception over processing and planning to action execution Currently, every architecture has its own focus: ACT-R concentrates on fine grained timing of operational task execution Psi concentrates on concepts like motivation, emotion, planning There exist many, many other architectures SOAR: developed by Alan Newell (Co-)Jack: systematic modulation of cognition 80/82

81 Literature Anderson, Lebiere: The Newell Test for a theory of cognition, 2003 Analysis of Newell s criteria for a cognitive architecture Anderson, Bothell, Byrne, Gouglass: An Integrated Theory of the Mind, 2004 Introduction to the ACT-R theory Bach, Dörner, Vuine: Psi and MicroPsi A Novel Approach to Modeling Emotion and Cognition in a Cognitive Architecture, 2006 Overview over the PSI architecture and the micro-psi implementation 81/82

82 Student Research at the CSL Want to estimate mental workload of the user Model the human mind as queuing network Calculate throughput and determine bottlenecks Available as study-, diploma-, bachelor- or master thesis Necessary: Good programming skills (pref. Java) and interest in researching cognitive models Contact: 82/82