Dr Yi-Shin Lin
I recently joined the Tasmanian Cognition Laboratory, upon completing my PhD training at the University of Birmingham (UK). My thesis examines the role of attentional template on search decision-making. I approach the question through a recently emerged method: hierarchical Bayesian models and evidence accumulation models. This work helps to better understand how different pre-set search goals (either a concrete image or an abstract idea), influence decision rate or criteria.
I received my PhD in Experimental Psychology from the University of Birmingham, my MA from the City University of New York, and BSc from National Taiwan University. My professional training expands far afield to sleep science, data mining and information science. I have accumulated experience in R and C programming and specifically in cognitive models using Bayesian statistics. My current research focuses on cognitive models and their application in understanding human cognition.
I received my PhD in Experimental Psychology from the University of Birmingham, my MA from the City University of New York, and BSc from National Taiwan University. My professional training expands far afield to sleep science, data mining and information science. I have accumulated experience in R and C programming and specifically in cognitive models using Bayesian statistics. My current research focuses on cognitive models and their application in understanding human cognition.
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Position
Postdoctoral Research Fellow in Cognitive Psychology Faculty / Division School of Medicine Division of Psychology yishinlin001@gmail.com Location University of Tasmania School of Medicine Division of Psychology Private Bag 30 Sandy Bay, Tasmania 7005 |
Qualifications
- BSc, National Taiwan University - MA, The City University of New York - PhD, University of Birmingham Fields of Research - Visual Search - Perceptual Decision Making - Cognitive Modelling |
Journal Publications
In Press
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.
Heathcote, A., Lin, Y-S, Reynolds, A., Strickland, L., Gretton, M. & Matzke, D. (2018, online). Dynamic models of choice. Behavior Research Methods. DOI:10.3758/s13428-018-1067-y
Lin, Y. S., & Heathcote, A (in preparation). ggdmc: An R package for hierarchical Bayesian evidence accumulation models, using differential evolution Markov Chain Monte Carlo Sampler. Retrieved from https://github.com/TasCL/ggdmc
In Print
2019
Lin, Y-S., Heathcote, A. & Holmes, W.R. (2019). Parallel probability density approximation. Behavior Research Methods, 51, 2777–2799. doi: 10.3758/s13428-018-1153-1
2015
Lin, Y. S., Heinke, D., & Humphreys, G. W. (2015). Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77(3), 985-1010.
In Press
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.
Heathcote, A., Lin, Y-S, Reynolds, A., Strickland, L., Gretton, M. & Matzke, D. (2018, online). Dynamic models of choice. Behavior Research Methods. DOI:10.3758/s13428-018-1067-y
Lin, Y. S., & Heathcote, A (in preparation). ggdmc: An R package for hierarchical Bayesian evidence accumulation models, using differential evolution Markov Chain Monte Carlo Sampler. Retrieved from https://github.com/TasCL/ggdmc
In Print
2019
Lin, Y-S., Heathcote, A. & Holmes, W.R. (2019). Parallel probability density approximation. Behavior Research Methods, 51, 2777–2799. doi: 10.3758/s13428-018-1153-1
2015
Lin, Y. S., Heinke, D., & Humphreys, G. W. (2015). Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77(3), 985-1010.