Abstract
Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering, and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n = 307) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analyzed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, and Biology), current researcher status (undergraduate student, PhD student, and post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods vs. non-methods (Psychology/Biology) groups was especially striking for questions related to signal analysis and linear algebra, two areas particularly relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programs in cognitive neuroscience institutions. We will provide practical suggestions on how to achieve this.
Highlights
Cognitive neuroscientists use physical measurements of neural activity and behavior to study information processing in mind and brain
We present results from an online survey of methods skills from 307 participants, mostly students and post-doctoral researchers working in the cognitive neurosciences
Our results suggest that there is room for improvement with respect to the methods skills among junior researchers in cognitive neuroscience, that researchers are aware of this, and that there is strong demand for more skills-oriented training opportunities
Summary
Cognitive neuroscientists use physical measurements of neural activity and behavior to study information processing in mind and brain. Human Cognitive Neuroscience Training and electro-/magnetoencephalography (EEG/MEG) have become standard tools for cognitive neuroscientists. These techniques produce large amounts of data reflecting changes in metabolism or electrical activity in the brain (Huettel et al, 2009; Supek and Aine, 2014). Researchers involved in this work come from diverse backgrounds in psychology, cognitive science, medicine, biology, physiology, engineering, computer science, physics, mathematics, etc. This poses challenges with respect to the acquisition of the appropriate scientific computing and data analysis skills, as well as for communication among researchers from very different scientific backgrounds
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
