Dissociating mental states related to doing nothing by means of fMRI pattern classification

Libet's legacy: A primer to the neuroscience of volition

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Re-imagining motor imagery: building bridges between cognitive neuroscience and sport psychology

One of the most remarkable capacities of the mind is its ability to simulate sensations, actions, and other types of experience. A mental simulation process that has attracted recent attention from cognitive neuroscientists and sport psychologists is motor imagery or the mental rehearsal of actions without engaging in the actual physical movements involved. Research on motor imagery is important in psychology because it provides an empirical window on consciousness and movement planning, rectifies a relative neglect of non-visual types of mental imagery, and has practical implications for skill learning and skilled performance in special populations (e.g., athletes, surgeons). Unfortunately, contemporary research on motor imagery is hampered by a variety of semantic, conceptual, and methodological issues that prevent cross-fertilization of ideas between cognitive neuroscience and sport psychology. In this paper, we review these issues, suggest how they can be resolved, and sketch some potentially fruitful new directions for inter-disciplinary research in motor imagery.