Machine Learning Approaches to Cognitive Parameter Acquisition
Terran LaneUniversity of New Mexico
Chris Forsythe, Patrick Xavier
Sandia National Labs{jcforsy,pgxavie}@sandia.gov
Sandia’s Cognitive Modeling Framework
Computational models of human decision-makers
Models attention, perceptual cues, situational awareness, decision making
Based on oscillatory models of activation Spreading activation networks and feedback
loops between functional elements Applications -- data analysis, security, tutoring… Bottleneck: models hand-built/tuned
Expensive and slow!
The Big PictureW
orld
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Automated Model Acquisition High predictive accuracy
87% correct prediction of operator’s interpretation of scenario (incl. relevance)
91% correct in recognizing situation only Insights into operator decision-making process Models are task & user specific
Only 26% overlap between users Large effort in building and tuning models
Project goal: (semi-)automate acquisition of parameters, network topologies, etc.
Prediction accuracy secondary concern
Roles for Machine Learning
Parameter acquisition Interconnection weights Activation levels Oscillator frequencies
Network topologies Inter-cue spreading activation network Cue <-> situation relations Feedbacks
Cues and situation identification
Parameter AcquisitionW
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Parameter Acquisition: Issues
Superficially supervised learning Observe features/cues and operator actions;
induce params (find Θ s.t. fΘ:CA) Similar to ANN backprop, EM, etc. Many effective, well understood techniques
Problem: not just high-likelihood params Actually want params used by human operator Much harder – observable stimuli don’t directly
reflect operator’s internal state Cognitive plausibility constraint
Parameter Acquisition: Approaches
Additional instrumentation Measure characteristics of operator Biometrics – eye tracking, MEG, etc. Expensive, not widespread Maybe not informative to params anyway
Utility elicitation techniques Software queries user about why decisions
were made / state of attention Picks questions to maximally improve model Emulates expert knowledge engineer
Network Topology InductionW
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Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
L=137
Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
L=137 L=238
Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
L=137 L=238 L=493
Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
L=137 L=238 L=493 L=318
Topology Induction: Issues
Find structure of interconnections between variables (I.e., cues, situations)
Much harder than parameter acquisition Formally, maximum likelihood/MAP search
through all possible networks
L=137 L=238 L=493 L=318
Topology Induction: Approaches
Principles of structure search well understood Gradient ascent, annealing, genetic search,
constrained search, etc. Difficult in practice
Computationally intractable Resulting models very sensitive to data Spurious likelihood spikes low confidence
models Compounded by cognitive plausibility constraint Can get leverage from cognitive plausibility,
though
Cue and Situation IdentificationW
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Cue and Situation Identification: Issues
Discern cues and whole environmental situations employed by user
Related to constructive feature induction, nonlinear projection identification, relational learning, etc.
Search across all possible nodes/relations
N=2 N=3
Cue and Situations: Approaches
Cutting-edge ML problem Direct elicitation is probably most promising
approach Formulating search space/uncertainty reduction
not straightforward Even user interface is difficult (naming synthetic
nodes/relations)
Conclusions
Decrease time/effort/cost to construct and tune cognitive model
Constrained to correspond to human’s internal model Both bane and boon to automated model
construction Insights into operator’s mental state/decision-
making process Requires/drives novel ML algorithms Future work: all of it…
Questions?