Welcome to the website of the Tasmanian Cognition Laboratory.
The primary aim of the Tasmanian Cognition Laboratory (TASCL) is the experimental investigation of cognitive processes that underpin decision making, language, attention, memory, and learning. The TASCL research group has two broad foci: (1) mathematical and computational cognitive models (2) the application of cognitive psychology to applied problems.
The cognitive modelling focus on broadly applicable evidence accumulation and race models that provide a comprehensive account of behaviour, both in terms of responses that are made and the time it takes to make them. We both contribute to the fundamental development of these models and push the boundaries of their application across psychology and the neurosciences. Applied areas of interest are diverse, and include improving the reliability of eyewitness identification evidence; helping people to better evaluate their own memory, learning, and decision making (i.e., metacognition); investigating the cognitive effects of videogame play; understanding how to enhance public support for climate science and associated policy; and complex and high-pressure decision making in defence and Antarctic contexts. Our research is supported by a number of testing suites with high-end data collection PCs, accompanied by office space for graduate students, in both the Psychology Building and Psychology Research Centre, as well as access to high-performance computers for computational modelling.
Publications with associated pdf files can be found in the publications area. In some cases there are also links to associated data and analysis software (see also the Data and Software area of the site). We gratefully acknowledge support from funding bodies, particularly the Australian Research Council (see the Funding area of the site).
The primary aim of the Tasmanian Cognition Laboratory (TASCL) is the experimental investigation of cognitive processes that underpin decision making, language, attention, memory, and learning. The TASCL research group has two broad foci: (1) mathematical and computational cognitive models (2) the application of cognitive psychology to applied problems.
The cognitive modelling focus on broadly applicable evidence accumulation and race models that provide a comprehensive account of behaviour, both in terms of responses that are made and the time it takes to make them. We both contribute to the fundamental development of these models and push the boundaries of their application across psychology and the neurosciences. Applied areas of interest are diverse, and include improving the reliability of eyewitness identification evidence; helping people to better evaluate their own memory, learning, and decision making (i.e., metacognition); investigating the cognitive effects of videogame play; understanding how to enhance public support for climate science and associated policy; and complex and high-pressure decision making in defence and Antarctic contexts. Our research is supported by a number of testing suites with high-end data collection PCs, accompanied by office space for graduate students, in both the Psychology Building and Psychology Research Centre, as well as access to high-performance computers for computational modelling.
Publications with associated pdf files can be found in the publications area. In some cases there are also links to associated data and analysis software (see also the Data and Software area of the site). We gratefully acknowledge support from funding bodies, particularly the Australian Research Council (see the Funding area of the site).
Recent Publications:
In Press:
- Reynolds, A., Garton, R., Kvam, P., Griffin, V., Sauer, J., Osth, A. & Heathcote, A. (accepted 8/3/2020). A dynamic model of deciding not to choose. Journal of Experimental Psychology: General. https://osf.io/zxp5u/ (Supplementary Material).
- Strickland, L., Loft., S. & Heathcote, A. (accepted 2/3/2020). Investigating the effects of ongoing-task bias on prospective memory. Quarterly Journal of Experimental Psychology. (Supplementary Material).
- Reynolds, A., Kvam, P., Osth, A. & Heathcote, A. (accepted 18/2/2020). Correlated racing evidence accumulator models. Journal of Mathematical Psychology.
- Damaso, K., Williams, P. & Heathcote, A. (accepted 16/9/2019). Different types of errors and post-error changes. Psychonomic Bulletin & Review. (Supplementary Material)
- Heathcote, A. (accepted 27/8/2019). What do the rules for the wrong game tell us about how to play the right game? Computational Brain & Behavior.
- Lin, Y. S., & Strickland, L. (accepted 12/8/2019). Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods. The Quantitative Methods for Psychology.
- Gronau, Q. F., Heathcote, A. & Matzke, D. (accepted 3/8/2019). Computing Bayes Factors for evidence-accumulation models using Warp-III Bridge Sampling. Behavior Research Methods. https://psyarxiv.com/9g4et
- van Ravenzwaaij, D., Brown, S.D., Marley, A.J., & Heathcote, A. (accepted 29/7/2019). Accumulating Advantages: A New Conceptualization of Rapid Multiple Choice. Psychological Review. doi: 10.1037/rev0000166
- Osth, A., Racliff, R., Dunn, J., & Heathcote, A. (accepted 22/5/2019). Two processes are not necessary to understand memory deficits. Behavioral and Brain Sciences.
In Print:
2020
- Parker, S., Heathcote, A. & Finkbeiner, M. (2020). Using evidence accumulation modelling to quantify the relative contributions of spatial attention and saccade preparation in perceptual tasks. Journal of Experimental Psychology: Human Perception and Performance, 46, 416-433. (Supplementary Materials).
- Matzke, D., Logan, G. D. & Heathcote, A. (2020). A cautionary note on evidence-accumulation models of response inhibition in the stop-signal paradigm. Computational Brain & Behavior. doi: 10.1007/s42113-020-00075-x
- van Berkel, N., Dennis, S., Zyphur, M., Li, J., Heathcote, A. & Kostakos, V. (2020). Modeling interaction as a complex system. Human-Computer Interaction, https://doi.org/10.1080/07370024.2020.1715221
Book Chapters:
-Strickland, L., Loft, S., & Heathcote, A. (2019). Evidence Accumulation Modeling of Event-Based Prospective Memory. In J. Rummel & M.A. McDaniel (Eds), Current Issues in Memory: Prospective Memory (pp. 78-94). London, United Kingdom: Taylor & Francis.
Technical Reports:
- French, B., Duenser, A., Heathcote, A. (2018). Trust in Automation – A Literature Review. CSIRO Report EP184082. CSIRO, Australia.
For any questions related to this site please email:
contact@tascl.org
In Press:
- Reynolds, A., Garton, R., Kvam, P., Griffin, V., Sauer, J., Osth, A. & Heathcote, A. (accepted 8/3/2020). A dynamic model of deciding not to choose. Journal of Experimental Psychology: General. https://osf.io/zxp5u/ (Supplementary Material).
- Strickland, L., Loft., S. & Heathcote, A. (accepted 2/3/2020). Investigating the effects of ongoing-task bias on prospective memory. Quarterly Journal of Experimental Psychology. (Supplementary Material).
- Reynolds, A., Kvam, P., Osth, A. & Heathcote, A. (accepted 18/2/2020). Correlated racing evidence accumulator models. Journal of Mathematical Psychology.
- Damaso, K., Williams, P. & Heathcote, A. (accepted 16/9/2019). Different types of errors and post-error changes. Psychonomic Bulletin & Review. (Supplementary Material)
- Heathcote, A. (accepted 27/8/2019). What do the rules for the wrong game tell us about how to play the right game? Computational Brain & Behavior.
- Lin, Y. S., & Strickland, L. (accepted 12/8/2019). Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods. The Quantitative Methods for Psychology.
- Gronau, Q. F., Heathcote, A. & Matzke, D. (accepted 3/8/2019). Computing Bayes Factors for evidence-accumulation models using Warp-III Bridge Sampling. Behavior Research Methods. https://psyarxiv.com/9g4et
- van Ravenzwaaij, D., Brown, S.D., Marley, A.J., & Heathcote, A. (accepted 29/7/2019). Accumulating Advantages: A New Conceptualization of Rapid Multiple Choice. Psychological Review. doi: 10.1037/rev0000166
- Osth, A., Racliff, R., Dunn, J., & Heathcote, A. (accepted 22/5/2019). Two processes are not necessary to understand memory deficits. Behavioral and Brain Sciences.
In Print:
2020
- Parker, S., Heathcote, A. & Finkbeiner, M. (2020). Using evidence accumulation modelling to quantify the relative contributions of spatial attention and saccade preparation in perceptual tasks. Journal of Experimental Psychology: Human Perception and Performance, 46, 416-433. (Supplementary Materials).
- Matzke, D., Logan, G. D. & Heathcote, A. (2020). A cautionary note on evidence-accumulation models of response inhibition in the stop-signal paradigm. Computational Brain & Behavior. doi: 10.1007/s42113-020-00075-x
- van Berkel, N., Dennis, S., Zyphur, M., Li, J., Heathcote, A. & Kostakos, V. (2020). Modeling interaction as a complex system. Human-Computer Interaction, https://doi.org/10.1080/07370024.2020.1715221
Book Chapters:
-Strickland, L., Loft, S., & Heathcote, A. (2019). Evidence Accumulation Modeling of Event-Based Prospective Memory. In J. Rummel & M.A. McDaniel (Eds), Current Issues in Memory: Prospective Memory (pp. 78-94). London, United Kingdom: Taylor & Francis.
Technical Reports:
- French, B., Duenser, A., Heathcote, A. (2018). Trust in Automation – A Literature Review. CSIRO Report EP184082. CSIRO, Australia.
For any questions related to this site please email:
contact@tascl.org