Neural mechanisms of proactive and reactive inhibitory control

<p>PhD Thesis - Utrecht University</p> <p>September 8, 2011</p> <p> </p> <p>The overall aim of this thesis was to increase the understanding of the neural mechanisms underlying proactive inhibition (preparing for stopping) and reactive inhibition (stopping outright) and how these mechanisms are affected in schizophrenia. First, our findings suggest that the rIFC is involved in reactive inhibition only, whereas the SMC and the striatum are engaged both in proactive and reactive inhibition. Therefore, our findings appear to challenge the common view that the whole neural network involved in outright stopping is recruited in anticipation of stopping. Second, our results provide insight into the mechanism underlying reactive inhibition, indicating that the rIFC exerts inhibitory control over M1 via a cortico-basal ganglia pathway that includes the SMC and the right striatum. Third, our findings suggest that reduced proactive inhibition in schizophrenia is associated with striatal dysfunction, possibly reflecting striatal dopaminergic abnormalities.</p

Best Practices in Scientific Computing - Discussion of Wilson et al., PLos Biology, 2014

Discussion of the following paper + concrete examples in MATLAB:<br><br>Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., ... & Waugh, B. (2014). Best practices for scientific computing. PLoS Biol, 12(1), e1001745.<br

Stop-signal anticipation task (SSAT) - stimulus-presentation code

Stimulus presentation code for the stop-signal anticipation task (Zandbelt & Vink, PLoS ONE, 2010; Zandbelt et al., Biol Psychiatry, 2011; Zandbelt et al., J Cogn Neurosci, 2013

Modeling response inhibition

Presentation on models of response inhibition for the course ‘Cognitive control’ of the MSc program Cognitive Neuroscience at Radboud University. Topics addressed include background on response inhibition, the independent race model of the stop-signal task, and sequential sampling models of response inhibition.<br

Presentation of McKiernan et al., eLife, 2016 + open science debate

Presentation for lab meeting of Roshan Cools' lab in Feb 2017, discussing the following paper:<br><br>McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., … Yarkoni, T. (2016). How open science helps researchers succeed. ELife, 5, e16800. https://doi.org/10.7554/eLife.16800<br><br>The paper was followed by a debate on open science, featuring six participants debating three propositions:<br>1. Enganging in open science will boost my career<br>2. Enganging in open science will accelerate my research<br>3. Enganging in open science will improve the quality of my research<br

Cognitive modeling

Introductory presentation on cognitive modeling for the course ‘Cognitive control’ of the MSc program Cognitive Neuroscience at Radboud University. It addresses basic questions, such as 'What is a model?', 'Why use models?', and 'How to use models?'<br

Exerting cognitive control

Ignite talk for Donders Sessions on "Mechanisms of the will" on Exerting cognitive control (April 20, 2017). It provides a (very) brief overview of the main behavioral findings, cognitive and neural mechanisms, and current debates on proactive and reactive inhibitory control as measured with the stop-signal task.<br

Moving forward: Transparency, Openness, and Reproducibility in Our Lab - Handout for Workshop Activity

Handout/Assignment developed as part of Workshop on Open Science, organized for lab retreat of Roshan Cools' lab (July 2016).<br><br>The activity started with a presentation on outlining the problems of a lack of transparency, openness, and reproducibility in science and what various stakeholders are doing to address this (available here: <br><div><a href="https://doi.org/10.6084/m9.figshare.4877480.v1">https://doi.org/10.6084/m9.figshare.4877480.v1</a>). <br></div><div><br></div><div>The presentation was followed by a breakout session centered on the question: what can we, as a team, do to become<br>more transparent, open, and reproducible? In small teams, participants discussed the opportunities, challenges, and feasibility of six open science practices:<br></div><div>1. pre-registration, <br></div><div>2. stopping questionable research practices, <br></div><div>3. data sharing, <br></div><div>4. code review</div><div>5. version control</div><div>6. replication projects<br></div><div><br></div><div>The final part of the workshop was a central discussion, in which teams motivated their choices and explored how the lab could benefit from open science practices.<br></div

exgauss: a MATLAB toolbox for fitting the ex-Gaussian distribution to response time data

<p>A MATLAB toolbox for fitting the ex-Gaussian distribution to response time data. It also includes a function for plotting the empirical observations and model predictions as histogram/probability density function and empirical distribution function/cumulative distribution function.</p

Cognitive mechanisms of the defer-speedup and date-delay framing effects in intertemporal choice - data management plan

This data management plan describes a dataset that will be generated to address the following research question: How does the way in which time is described influence intertemporal choice? The dataset will consist of task performance data of a maximum of 192 (but potentially fewer) human participants (one dataset of the defer-speedup framing effect and one dataset of the date-delay framing effect). The approximate total size of this dataset will be less than 1 Gigabyte. This dataset may be useful for behavioral economists, experimental psychologists, and cognitive neuroscientists interested in intertemporal choice and framing effects