Adaptive Tests of Cognitive Models
Lead Research Organisation:
University College London
Department Name: Psychology
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Berry CJ
(2012)
Models of recognition, repetition priming, and fluency: exploring a new framework.
in Psychological review
Maarten Speekenbrink (Author)
(2010)
Adaptive design for model discrimination
Maarten Speekenbrink (Author)
(2011)
Adaptive design for model discrimination
Maarten Speekenbrink (Author)
(2009)
Adaptive tests for model discrimination
Maarten Speekenbrink (Author)
(2010)
Adaptive design for model discrimination
Maarten Speekenbrink (Author)
(2011)
Discriminating between ecological, Bayesian, and dual-process theories of JDM
Maarten Speekenbrink (Author)
(2010)
Adaptive design for model discrimination
Magda Osman (Author)
(2009)
Prediction vs. control : which is best for learning about a dynamic environment?
Magda Osman (Author)
(2011)
Controlling stable and unstable dynamic decision making environments
Osman M
(2011)
Cue utilization and strategy application in stable and unstable dynamic environments
in Cognitive Systems Research
| Description | We have developed a method to adaptively design experiments to discriminate between competing formal models of behaviour and cognition. There are at least two factors which make identifying the model that best describes human behaviour difficult: (1) competing models often make highly similar predictions, and (2) due to individual differences, situations in which the models do make different predictions vary from individual to individual. To solve these problems, our method adapts the design to a participant during the experiment, designing each consecutive experimental trial to optimally discriminate between a set of competing models. The method relies on Sequential Monte Carlo (SMC) estimation of the models and chooses trials which are expected to provide the most information about the best model in the set of models under consideration. As SMC is a general technique not tied to a particular class of models, out method is widely applicable. We have applied our adaptive design method in simulated and actual experiments on category learning and decision-making and developed SMC algorithms to estimate specific models in these areas. This has shown clear benefits of the methods, obtaining conclusive results at a fraction of the trials normally required. |
| Exploitation Route | The methods developed can be used in any area of industry which is concerned with discriminating between (statistical) models. The methods developed can be used in any area of science and industry which is concerned with discriminating between (statistical) models. We are continuing our work in this area and are also exploring the potential use in an educational context. |
| Sectors | Communities and Social Services/Policy,Education,Healthcare |
| Title | SMCS4: An R package with S4 classes and methods for Sequential Monte Carlo estimation. |
| Description | An R package with S4 classes and methods for Sequential Monte Carlo estimation. |
| Type Of Technology | Software |
| Year Produced | 2011 |
| Open Source License? | Yes |
| Impact | NA |
| URL | http://smcs4.r-forge.r-project.org/ |
| Description | Active learning |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | Lecture describing optimal information search, including adaptive optimal design for model discrimination. This generated an interesting discussion with students. Students wrote interesting essays on this topic. |
| Year(s) Of Engagement Activity | 2012 |
| URL | http://www.ljdm.info/talks_all.php |
| Description | Adaptive design for model discrimination |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Other academic audiences (collaborators, peers etc.) |
| Results and Impact | Talk lead to interesting and useful discussion Talk inspired other researchers to think about adaptive design |
| Year(s) Of Engagement Activity | 2011 |
| Description | Discriminating between competing models |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited presentation at Decision Technology, 29 February 2012 Section not completed |
| Year(s) Of Engagement Activity | 2012 |
| Description | Seminar on Bayesian adaptive experimental design |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Professional Practitioners |
| Results and Impact | People from various departments at Queen Mary University attended this talk in a series on Bayesian methods and the talk increased understanding of, and interest in, Bayesian optimal experimental design methods. |
| Year(s) Of Engagement Activity | 2015 |
| Description | Workshop on Optimizing Experimental Designs: Theory, Practice, and Applications |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The workshop was held at the Annual Meeting of the Cognitive Science Society in Pasadena and was organized around two specific goals: (1) to educate the cognitive science community about optimal experimental design (OED) and (2) to bring practitioners together who use it to share and showcase their latest work with the community. Both goals were reached. |
| Year(s) Of Engagement Activity | 2015 |
| URL | https://sites.google.com/site/oedworkshop/home |