References

CGG+12

Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y. C., Abdi, H., and Haxby, J. V. The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32(8):2608–2618, February 2012.

CS03

Cox, D. D. and Savoy, R. L. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19(2 Pt 1):261–270, 2003.

DM04

Delorme, A. and Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1):9–21, March 2004.

DK17

Diedrichsen, J. and Kriegeskorte, N. Representational models: a common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS computational biology, 13(4):e1005508, 2017.

EGSKM98

Edelman, S., Grill-Spector, K., Kushnir, T., and Malach, R. Toward direct visualization of the internal shape representation space by fMRI. Psychobiology, 26(4):309–321, 1998.

HHS+09a

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., and Pollmann, S. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1):37–53, 2009. doi:10.1007/s12021-008-9041-y.

HHS+09b

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J., and Pollmann, S. PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in neuroinformatics, 3:3, 2009.

HMGorgen+14

Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., and Bießmann, F. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage, 87:96–110, 2014.

HGF+01

Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., and Pietrini, P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539):2425–2430, September 2001.

KOP16

Kaiser, D., Oosterhof, N. N., and Peelen, M. V. The neural dynamics of attentional selection in natural scenes. Journal of neuroscience, 36(41):10522–10528, 2016.

KD14

King, J.-R. and Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends in cognitive sciences, 2014.

KGB06

Kriegeskorte, N., Goebel, R., and Bandettini, P. Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10):3863–3868, March 2006.

KMR+08

Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., and Bandettini, P. A. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6):1126–1141, December 2008.

LW04

Ledoit, O. and Wolf, M. Honey, i shrunk the sample covariance matrix. The Journal of Portfolio Management, 30(4):110–119, 2004. doi:10.3905/jpm.2004.110.

LTO+14

Leske, S., Tse, A., Oosterhof, N. N., Hartmann, T., Müller, N., Keil, J., and Weisz, N. The strength of alpha and beta oscillations parametrically scale with the strength of an illusory auditory percept. NeuroImage, 88:69–78, March 2014.

MBK09

Mur, M., Bandettini, P. A., and Kriegeskorte, N. Revealing representational content with pattern-information fMRI–an introductory guide. Social cognitive and affective neuroscience, 4(1):101–109, 2009.

NPDH06

Norman, K. A., Polyn, S. M., Detre, G. J., and Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in cognitive sciences, 10(9):424–430, 2006. doi:10.1016/j.tics.2006.07.005.

OFMS11

Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011:156869, 2011.

OCH16

Oosterhof, N. N., Connolly, A. C., and Haxby, J. V. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave. Frontiers in Neuroinformatics, 2016. doi:10.3389/fninf.2016.00027.

OTD12

Oosterhof, N. N., Tipper, S. P., and Downing, P. E. Visuo-motor imagery of specific manual actions: A multi-variate pattern analysis fMRI study. NeuroImage, 63(1):262–271, October 2012.

OWDD11

Oosterhof, N. N., Wiestler, T., Downing, P. E., and Diedrichsen, J. A comparison of volume-based and surface-based multi-voxel pattern analysis. NeuroImage, 56(2):593–600, May 2011.

OWD+10

Oosterhof, N. N., Wiggett, A. J., Diedrichsen, J., tipper, S. P., and Downing, P. E. Surface-based information mapping reveals crossmodal vision-action representations in human parietal and occipitotemporal cortex. Journal of neurophysiology, 104(2):1077–1089, August 2010.

PWD06

Peelen, M. V., Wiggett, A. J., and Downing, P. E. Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion. Neuron, 49(6):815–822, 2006. doi:doi:10.1016/j.neuron.2006.02.004.

PMB09

Pereira, F., Mitchell, T., and Botvinick, M. Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1):S199–S209, 2009. doi:10.1016/j.neuroimage.2008.11.007.

SN09

Smith, S. M. and Nichols, T. E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1):83–98, 2009.

SCT13

Stelzer, J., Chen, Y., and Turner, R. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65(C):69–82, January 2013.