Inferring Cognitive Models from Data using Approximate Bayesian Computation

Kangasrääsiö, Antti; Athukorala, Kumaripaba; Howes, Andrew; Corander, Jukka; Kaski, Samuel; Oulasvirta, Antti

doi:10.1145/3025453.3025576

Cited by 51 publications

(45 citation statements)

References 37 publications

(86 reference statements)

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“…() and later extended by Kangasrääsiö et al. (). This model predicts the visual search behavior (how eyes fixate and move) and task completion times of a person searching for an item from a computer drop‐down menu.…”

Section: Example 2: Computational Rationalitymentioning

confidence: 91%

“…For comparison, Kangasrääsiö et al. () optimized the value of the same parameter in a later study using automatic methods, ending up with a smaller fixation duration around 250 ms, which resulted in a better model fit to observation data.…”

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

“…Kangasrääsiö et al. () used BO to find the parameter values of a cognitive model of visual search in menus. Further, this approach has been used recently in other fields, for example, for parameter tuning biomedical models (Ghassemi, Lehman, Snoek, & Nemati, ) and speech recognition models (Watanabe & Le Roux, ).…”

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

“…Later Kangasrääsiö et al. () used BO and ABC for estimating the maximum of the posterior distribution (MAP estimate) of the parameter values, which improved the model fit. The inference used a dataset collected by Bailly, Oulasvirta, Brumby, and Howes ().…”

Section: Example 2: Computational Rationalitymentioning

confidence: 99%

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Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

et al. 2019

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This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

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Section: Example 2: Computational Rationalitymentioning

confidence: 91%

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

Section: Example 2: Computational Rationalitymentioning

confidence: 99%

See 2 more Smart Citations

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

et al. 2019

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“…For a full specification of a design task, one may need to algorithmically elicit what users "want" or "can" from digitally monitorable traces. This is known as the inverse modeling problem [13]. I discuss probabilistic methods for cognitive models.…”

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confidence: 99%

Optimizing User Interfaces for Human Performance

Oulasvirta

2017

Intelligent Human Computer Interaction

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Abstract. This paper summarizes an invited talk given at the 9th International Conference on Intelligent Human Computer Interaction (December 2017, Paris). Algorithms have revolutionized almost every field of manufacturing and engineering. Is the design of user interfaces the next? This talk will give an overview of what future holds for algorithmic methods in this space. I introduce the idea of using predictive models and simulations of end-user behavior in combinatorial optimization of user interfaces, as well as the contributions that inverse modeling and interactive design tools make. Several research results are presented from gesture design to keyboards and web pages. Going beyond combinatorial optimization, I discuss self-optimizing or "autonomous" UI design agents. Talk SummaryThe possibility of mathematical or algorithmic design of artefacts for human use has been a topic of interest for at least a century. Present-day user-centered design is largely driven by human creativity, sensemaking, empathy, and creation of meaning. The goal of computational methods is to produce a full user interface (e.g., keyboard, menu, web page, gestural input method etc.) that is good or even "best" for human use with some justifiable criteria. Design goals can include increases in speed, accuracy, or reduction in errors or ergonomics issues. Computational methods could speed up the design cycle and improve quality. Unlike any other design method, some computational methods offer a greaterthan-zero chance of finding an optimal design. Computational design offers not only better designs, but a new, rigorous understanding of interface design. Algorithms have revolutionized almost every field of manufacturing and engineering. But why has user interface design remained isolated?The objective of this talk is to outline core technical problems and solution principles in computational UI design, with a particular focus on artefacts designed for human performance. I first outline main approaches to algorithmic user interface (UI) generation. Some main approaches include: (1) use of psychological knowledge to derive or optimize designs [1][2][3], (2) breakdown of complex design problems to constituent decisions [4], (3) formulation of design problems as optimization problems [5], (4) use of design heuristics in objective functions [6], (5) use of psychological models in objective functions [7,8], (6) data-driven

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Towards machines that understand people

Howes,

Jokinen,

Oulasvirta

2023

AI Magazine

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The ability to estimate the state of a human partner is an insufficient basis on which to build cooperative agents. Also needed is an ability to predict how people adapt their behavior in response to an agent's actions. We propose a new approach based on computational rationality, which models humans based on the idea that predictions can be derived by calculating policies that are approximately optimal given human‐like bounds. Computational rationality brings together reinforcement learning and cognitive modeling in pursuit of this goal, facilitating machine understanding of humans.

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Inferring Cognitive Models from Data using Approximate Bayesian Computation

Cited by 51 publications

References 37 publications

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

Optimizing User Interfaces for Human Performance

Towards machines that understand people

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