Minimizing bugs in cognitive neuroscience programming

Minimizing bugs in cognitive neuroscience programming

INTRODUCTION Rigorous data analysis is a cornerstone of empirical scientific research. In recent years, much attention in the field of cognitive neuroscience has been paid to the issue of correct statistical procedures (e.g., Kriegeskorte et al., 2009; Nieuwenhuis et al., 2011; Kilner, 2013). However, an additional essential aspect of data analysis, which has attracted relatively little attenti...

Evolving structure-function mappings in cognitive neuroscience using genetic programming

A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This...

The Challenges of Cognitive Neuroscience in the 21st Century

A Cognitive Neuroscience Perspective on Memory for Programming Tasks

When faced with frequent interruptions and task-switching, programmers have difficulty keeping relevant task knowledge in their mind. An understanding of how programmers actively manage this knowledge provides a foundation for evaluating cognitive theories and building better tools. Recently, advances in cognitive neuroscience and brain imaging technology has provided new insight into the inner...

DT-MRI Tractography and its Application in Cognitive Neuroscience

Recent advancement of MRI techniques and development of new methods of image analysis have allowed us to study large neural tracts within the human brain. This is based on the principle of diffusion tensor MRI that is similar to that of diffusion-weighted imaging but takes magnitude and direction of the diffusion of water into account. Using this technique we have been able to define large neur...