Jaime S Ide1, Sien Hu2, Sheng Zhang2, Angela J Yu3, Chiang-shan R Li4. 1. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. 2. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA. 3. Department of Cognitive Science, University of California, San Diego , La Jolla, CA 92093, USA. 4. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06520, USA. Electronic address: chiang-shan.li@yale.edu.
Abstract
BACKGROUND: Cocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework. METHODS: A "sequential effect" is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject's belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p < 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups. RESULTS: By comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p < 0.003), as well as showing less behavioral adjustment in response to P(stop) (p < 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC. CONCLUSIONS: Cocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction.
BACKGROUND:Cocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework. METHODS: A "sequential effect" is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject's belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p < 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups. RESULTS: By comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p < 0.003), as well as showing less behavioral adjustment in response to P(stop) (p < 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC. CONCLUSIONS:Cocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction.
Authors: Sarah R Bednarski; Sheng Zhang; Kwang-Ik Hong; Rajita Sinha; Bruce J Rounsaville; Chiang-shan R Li Journal: Drug Alcohol Depend Date: 2011-06-23 Impact factor: 4.492
Authors: Chiang-Shan R Li; Xi Luo; Rajita Sinha; Bruce J Rounsaville; Kathleen M Carroll; Robert T Malison; Yu-Shin Ding; Sheng Zhang; Jaime S Ide Journal: Drug Alcohol Depend Date: 2010-02-16 Impact factor: 4.492
Authors: Peter Manza; Sien Hu; Jaime S Ide; Olivia M Farr; Sheng Zhang; Hoi-Chung Leung; Chiang-shan R Li Journal: J Psychopharmacol Date: 2016-01-11 Impact factor: 4.153
Authors: Jaime S Ide; Simon Zhornitsky; Herta H Chao; Sheng Zhang; Sien Hu; Wuyi Wang; John H Krystal; Chiang-Shan R Li Journal: Biol Psychiatry Cogn Neurosci Neuroimaging Date: 2018-05-03
Authors: Sheng Zhang; Sien Hu; Rajita Sinha; Marc N Potenza; Robert T Malison; Chiang-Shan R Li Journal: Neuroimage Clin Date: 2016-08-04 Impact factor: 4.881
Authors: Jianping Hu; Sien Hu; Julianna R Maisano; Herta H Chao; Sheng Zhang; Chiang-Shan R Li Journal: Front Hum Neurosci Date: 2016-11-03 Impact factor: 3.169
Authors: Ju-Yu Yen; Yi-Chun Yeh; Peng-Wei Wang; Tai-Ling Liu; Yun-Yu Chen; Chih-Hung Ko Journal: Int J Environ Res Public Health Date: 2017-12-25 Impact factor: 3.390
Authors: Yihe Zhang; Jaime S Ide; Sheng Zhang; Sien Hu; Nikola S Valchev; Xiaoying Tang; Chiang-Shan R Li Journal: Neuroscience Date: 2017-06-13 Impact factor: 3.590