Reading hidden intentions in the human brain.

By John-Dylan Haynes, Katsuyuki Sakai, Geraint E. Rees, Sam Joseph Gilbert, ChrisD . Frith, Richard E. Passingham. Current Biology, 2007

689 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Epidemiology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/47e7a6fb1c137404d3e8271a4796e5fa3568aaca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Epidemiology

In Experimental and Cognitive Psychology

Citations in the corpus

Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “could decode cognitive states, such as the target of selective attention (Kamitani and Tong, 2005), and the intention to perform one task rather than another (Haynes et al., 2007).”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, neutral): “Thus, the application of SLR could prove as useful as SVM, which has been gaining popularity in several recent fMRI studies (Cox and Savoy, 2003; Mitchell et al., 2004; LaConte et al., 2005; Kamitani and Tong, 2005, 2006; Haynes et al., 2007 and so on).”
The expected value of control: An integrative theory of anterior cingulate cortex function (2013, neutral): “…2012; Durstewitz et al., 2010; Johnston et al., 2007; Matsuzaka et al., 2012; Womelsdorf et al., 2010), task sets (Forstmann et al., 2006; Haynes et al., 2007), and specific actions (Hampton and O’Doherty, 2007; Isomura et al., 2003; for reviews see Morecraft and Tanji, 2009; Rushworth…”
Human volition: towards a neuroscience of will (2008, background): “The anterior medial prefrontal cortex might hold intentions during a delay period between the initial decision regarding what to do and the later moment at which the action is execute”
Searchlight analysis: Promise, pitfalls, and potential (2013, methodology): “The first searchlight analyses used the Mahalanobis distance as the similarity measure for information mapping, but a widely adopted variation is to use machine learning algorithms, often support vector machines (SVMs), instead (Haynes et al., 2007; Kriegeskorte and Bandettini, 2007b).”
Encoding and decoding in fMRI (2010, background): “, 2009), intention (Haynes et al., 2007), cognitive control (Esterman et al.”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “One limitation in many pattern-based fMRI studies is that only a small set of different cognitive states is considered, for example, which of several images a person is viewing (Haxby et al., 2001) or which of two intentions a person is holding (Haynes et al., 2007).”
The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “As mentioned above, rather than running a decoding analysis on individual images, it has become quite common to use single trial estimates of data (Mumford et al., 2012) or even estimates that combine multiple trials within one run (Haynes et al., 2007).”
Improved mapping of information distribution across the cortical surface with the support vector machine (2008, methodology): “Previous fMRI studies that mapped distribution of information have used the " search light " approach (Haynes et al., 2007).”
Decoding neural representational spaces using multivariate pattern analysis. (2014, methodology): “Representational similarity analysis (RSA) (Kriegeskorte et al. 2008a) analyzes the geometry of representations Connolly et al. 2012a, on a u s on l ), whereas MVP classiﬁcation may ﬁnd that the classiﬁcation accuracy is equivalent in those regions.”
Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns (2008, positive): “Furthermore, it simplifies the interpretation of the results as the analysis is restricted to voxels showing neurophysiologically understood responses (but see Haynes et al., 2007 and Discussion).”
Motivation and the Goal Theory of Current Concerns (2011, background): “Thus, when Haynes et al. (2007) asked people to choose between two tasks and to hold that intention for a few seconds before acting on it, the investigators were able to “read” which task these participants were choosing from differential fMRI patterns in the medial prefrontal cortex.”
The neural basis of visual body perception (2007, methodology): “Also, whole-brain applications of MVPA, which apply an analysis 'searchlight' across the cortex to investigate the information content of different brain region”
A spatio-temporal support vector machine searchlight for fMRI analysis (2011, methodology): “[3] extend the approach in [2] by applying an SVM within each searchlight location.”
Deconstructing multivariate decoding for the study of brain function (2017, methodology): “One simple approach for getting at the difference in univariate and multivariate responses is comparing results of two analyses directly, for example by demonstrating a significant result withmultivariate decoding but a null result with classical univariate analysis (for early studies, see e.g. Eger”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Although the distinction between higher-order and lowerorder functions is not clear cut, by convention BMIs are called cognitive if they work in the domains of cognitive states (348, 886), executive functions (349), decision making (21, 335, 343), memory (62, 63), attention (35, 417), and language (”