Dr Yi-Shin Lin
- Position: Research Fellow
- Areas of expertise: Cognitive Psychology; Mathematical modelling; Behavioural research methods; Bayesian inference; Evidence-accumulation models. Software engineering.
- Email: Y.S.Lin@leeds.ac.uk
- Location: Room 8.238 Driving Simulator, Physics Research Deck
- Website: Software | Notes on Cognitive modelling | Twitter | Googlescholar | ORCID
I joined the Institute for Transport Studies in April 2020. I received my PhD (2015) and MA (2007) in Experimental Psychology and Cognitive Neuroscience from the University of Birmingham and the City University of New York. I studied in Psychology for BSc in the National Taiwan University (2002).
- Research fellow in Human and AI cognition in COMMOTIONS project
My research with the Human Factor and Safety (HFS) Group in the Institute of Transport Studies (ITS) focusses on road safety. In particular, together with the colleagues in HFS, we study the road user behaviours, in particular, how they interact how the interaction might evolve into traffic collisions, etc. In a conference proceeding, we reported a study in the lane-merging behaviours in drivers by applying a machined-learned model, dubbed the Long Short-Term Memory (LSTM), recurrent neural-network (Srinivasan et al, 2021). This finding goes hand-in-hand with our new cognitive model that applying the basic principle of utility maximisation, discrete motor primitives, (sensory) evidence accumulation to explain pedestrian and drivers behaviours in traffic interaction (Lin et al., 2022).
My research in the last couple of years span across human vision, decision-making, Bayesian computation, and computational modelling. I began my PhD project with an interest in examining the process of visual search, and designed a computational model to analyse the distribution data in the two-choice visual search task. I then studied how the Bayesian modelling might help to explain the distributiona data. This resulted in a hierarchical Weibull distribution model to fit the RT data in a series of visual search experiments. This work contributes to the field, by showing that one can infer the processing of visual search by examining different bands (e.g., fast vs. slow RTs) in the RT distributions. I published this software on GitHub and documented it in the paper “Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework” (2015) when the open science movement was still in its early time.
During my time with the Tasmanian Cognition Laboratory, I led two projects developing new statistical methods using Bayesian computation. In the first project I designed a new version of the complex decision-making model, Piecewise Linear Ballistic Accumulator model. This version harnesses parallel heterogeneous computation using GPU-based CUDA and the C++ language. I also created a tool to fit complex decision-making models which were previously unfeasible. I have shared the result of this work also in my GitHub and documented its details in the paper, “Parallel probability density approximation” (2018). My second project was a generic solution for conducting hierarchical Bayesian modelling. I implemented one Bayesian sampling method, population-based Markov Chain Monte Carlo (MCMC), which is particularly effective in cognitive models featuring correlated parameters. The source code is available also in my GitHub. This work was summarised in a paper, titled “Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods”.
A selection of the projects I have contributed with the data and code in the OSF.