A Time Series Based Point Estimation of Stop Signal Reaction Times

• Activity Number: 192 - Contributed Poster Presentations:SSC
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: SSC
• Author(s): Mohsen Soltanifar* and Keith Knight and Annie Dupuis and Russell Schachar and Michael Escobar
• Companies: University of Toronto, Dalla Lana School of Public Health and University of Toronto, Department of Statistical Sciences and University of Toronto, Dalla Lana School of Public Health and The Hospital for Sick Children and University of Toronto, Dalla Lana School of Public Health
• Keywords: Stop Signal Reaction Times; State Space Models; EM algorithm; Missing Data; Lognormal Distribution

Abstract

The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. In 2017, the authors proposed asymptotically equivalent and larger indexes of mixture SSRT and weighted SSRT to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. To test the hypothesis of no difference between time series based state space estimation of SSRT and Logan 1994 SSRT, two samples of SST data including real data and the simulated data were considered, and State-space missing data EM algorithm was applied for each subject’s SST data, encompassing trial order. Using Logan’s 1994 formulae on ordered SST data, the new state-space SSRT index was calculated. The results for both the real and the simulated data showed that state-space SSRT is significantly larger than Logan’s 1994 SSRT, mixture SSRT, and weighted SSRT. As a conclusion, SSRT indexes based on the information of the preceding trial type are significantly larger than others.