Inferring Cognitive Models from Data using Approximate Bayesian Computation
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1 Inferring Cognitive Models from Data using Approximate Bayesian Computation CHI 2017 Antti Kangasrääsiö 1, Kumaripaba Athukorala 1, Andrew Howes 2, Jukka Corander 3, Samuel Kaski 1, Antti Oulasvirta 4 1 Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland 2 School of Computer Science, University of Birmingham, UK 3 Department of Biostatistics, University of Oslo, Norway 4 Helsinki Institute for Information Technology HIIT, Department of Communications and Networking, Aalto University, Finland Tuesday
2 Main Contribution Parameters of cognitive models can be tuned based on commonly available observation data using Approximate Bayesian Computation (ABC)
3 Examples of Cognitive Models
4 Examples of Cognitive Models Early work: Fitt s law (1954) Speed/accuracy trade-off in pointing tasks
5 Examples of Cognitive Models Early work: Fitt s law (1954) Speed/accuracy trade-off in pointing tasks Modern work: ACT-R (1997) Cognitive tasks with visual elements
6 Examples of Cognitive Models Early work: Fitt s law (1954) Speed/accuracy trade-off in pointing tasks Modern work: ACT-R (1997) Cognitive tasks with visual elements Recent work: Menu search (CHI 2015) Visual search in menus
7 Examples of Cognitive Models Early work: Fitt s law (1954) Speed/accuracy trade-off in pointing tasks Modern work: ACT-R (1997) Cognitive tasks with visual elements Recent work: Menu search (CHI 2015) Visual search in menus Yesterday: A model of visual decision-making by Chen, Starke, Baber & Howes
8 Why Model Human Cognition
9 Why Model Human Cognition Predicting user behavior
10 Why Model Human Cognition Predicting user behavior Explaining cognitive processes
11 Why Model Human Cognition Predicting user behavior Explaining cognitive processes Allowing computer systems to adapt better to the user
12 Model Tuning
13 Model Tuning Goal: select parameters that lead to accurate predictions
14 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters
15 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation
16 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation Acuity and size of peripheral visual field
17 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation Acuity and size of peripheral visual field Reward for completing the task
18 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation Acuity and size of peripheral visual field Reward for completing the task Penalty for wasting one second of time
19 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation Acuity and size of peripheral visual field Reward for completing the task Penalty for wasting one second of time Duration of short-term memory
20 Model Tuning Goal: select parameters that lead to accurate predictions Examples of parameters Duration of a visual fixation Acuity and size of peripheral visual field Reward for completing the task Penalty for wasting one second of time Duration of short-term memory And so on..
21 Characteristics of Modern Cognitive Models
22 Characteristics of Modern Cognitive Models No simple way to tune parameters Highly non-linear model structure, not possible to directly compute best parameter values
23 Characteristics of Modern Cognitive Models No simple way to tune parameters Highly non-linear model structure, not possible to directly compute best parameter values Computing predictions takes time Once we fix the parameter values, the model needs to first learn a behavior strategy
24 Traditional Tuning Methods
25 Traditional Tuning Methods Manual Parameter Tuning Slow: needs human labour No guarantees of optimality
26 Traditional Tuning Methods Manual Parameter Tuning Slow: needs human labour No guarantees of optimality Grid search Might take a very long time No uncertainty estimates
27 Approximate Bayesian Computation (ABC)
28 Approximate Bayesian Computation (ABC) ABC Inference Process
29 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model
30 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions
31 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations
32 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space
33 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space (Repeat until converged)
34 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space (Repeat until converged) Inference Results
35 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space (Repeat until converged) Inference Results Based on the probabilistic model, we can compute an approximate probability distribution over the parameter space
36 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space (Repeat until converged) Inference Results Based on the probabilistic model, we can compute an approximate probability distribution over the parameter space Maximum of the distribution gives us the Maximum Likelihood (ML) estimate
37 Approximate Bayesian Computation (ABC) ABC Inference Process Choose parameter values for the model Simulate predictions Evaluate discrepancy between predictions and observations Use a probabilistic model to estimate discrepancy in different regions of parameter space (Repeat until converged) Inference Results Based on the probabilistic model, we can compute an approximate probability distribution over the parameter space Maximum of the distribution gives us the Maximum Likelihood (ML) estimate Width of distribution corresponds to uncertainty
38 Approximate Bayesian Computation (ABC)
39 Approximate Bayesian Computation (ABC)
40 Approximate Bayesian Computation (ABC)
41 Approximate Bayesian Computation (ABC)
42 Approximate Bayesian Computation (ABC)
43 Approximate Bayesian Computation (ABC)
44 Approximate Bayesian Computation (ABC)
45 Approximate Bayesian Computation (ABC)
46 Approximate Bayesian Computation (ABC)
47 Approximate Bayesian Computation (ABC)
48 Approximate Bayesian Computation (ABC)
49 Approximate Bayesian Computation (ABC)
50 Approximate Bayesian Computation (ABC)
51 Approximate Bayesian Computation (ABC)
52 Approximate Bayesian Computation (ABC)
53 Approximate Bayesian Computation (ABC)
54 Approximate Bayesian Computation (ABC)
55 Approximate Bayesian Computation (ABC)
56 Approximate Bayesian Computation (ABC)
57 Approximate Bayesian Computation (ABC)
58 Approximate Bayesian Computation (ABC)
59 Approximate Bayesian Computation (ABC)
60 Approximate Bayesian Computation (ABC)
61 Approximate Bayesian Computation (ABC)
62 Approximate Bayesian Computation (ABC)
63 Case Study: Visual Search
64 Case Study: Visual Search A recent model for visual search in drop-down menus (Chen et al. CHI 2015)
65 Case Study: Visual Search A recent model for visual search in drop-down menus (Chen et al. CHI 2015) With ABC, the predictions match better to observations
66 Model Development and Individual Modelling with ABC
67 Model Development and Individual Modelling with ABC Fit different models to same dataset This allowed us to estimate how different changes to the model affected the predictions
68 Model Development and Individual Modelling with ABC Fit different models to same dataset This allowed us to estimate how different changes to the model affected the predictions Fit same model to different datasets By fitting models to observations from individual users we were able to improve the predictions over a population level model
69 Resources for Practical Use of ABC
70 Resources for Practical Use of ABC Requirements for ABC
71 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters)
72 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values
73 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset
74 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset Discrepancy measure
75 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset Discrepancy measure Technical Considerations
76 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset Discrepancy measure Technical Considerations There exists programming frameworks for ABC (e.g. ELFI: github.com/elfi-dev/elfi)
77 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset Discrepancy measure Technical Considerations There exists programming frameworks for ABC (e.g. ELFI: github.com/elfi-dev/elfi) The more parameters you infer, the more challenging inference is
78 Resources for Practical Use of ABC Requirements for ABC A simulator model (with tunable parameters) Prior knowledge of reasonable parameter values Observation dataset Discrepancy measure Technical Considerations There exists programming frameworks for ABC (e.g. ELFI: github.com/elfi-dev/elfi) The more parameters you infer, the more challenging inference is Cluster computers offer speedups
79 Take-home Messages Tuning the parameters of cognitive models based on real observations is important for making good predictions Approximate Bayesian Computation (ABC) allows you to do this with only very mild technical requirements ELFI, a Python library for ABC: github.com/elfi-dev/elfi Tech paper: Inverse Reinforcement Learning from Summary Data: arxiv.org/abs/
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