Connecting functional brain imaging and Parallel Distributed Processing

References

• Arthurs, O. J., & Boniface, S. (2002). How well do we understand the neural origins of the fMRI BOLD signal? Trends in Neurosciences, 25(1), 27–31. doi:10.1016/S0166-2236(00)01995-0
   PubMed Web of Science ®Google Scholar
• Behrmann, M., & Plaut, D. C. (2013). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in Cognitive Sciences, 17, 210–219. doi:10.1016/j.tics.2013.03.007
   PubMed Web of Science ®Google Scholar
• Bulthé, J., De Smedt, B., & Op de Beeck, H. P. (2014). Format-dependent representations of symbolic and non-symbolic numbers in the human cortex as revealed by multi-voxel pattern analyses. NeuroImage, 87, 311–322. doi:10.1016/j.neuroimage.2013.10.049
   PubMed Web of Science ®Google Scholar
• Carlson, T. A., Schrater, P., & He, S. (2003). Patterns of activity in the categorical representations of objects. Journal of Cognitive Neuroscience, 15, 704–717. doi:10.1162/jocn.2003.15.5.704
   PubMed Web of Science ®Google Scholar
• Cox, C. R., & Rogers, T. T. Taking distributed representations seriously. Manuscript submitted for publication
   Google Scholar
• Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., & Poldrack, R. A. (2014). What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. NeuroImage, 97, 271–283. doi:10.1016/j.neuroimage.2014.04.037
   PubMed Web of Science ®Google Scholar
• Dehaene, S., & Cohen, L. (1997). Cerebral pathways for calculation: Double dissociation between rote verbal and quantitative knowledge of arithmetic. Cortex, 33, 219–250. doi:10.1016/S0010-9452(08)70002-9
   PubMed Web of Science ®Google Scholar
• Feredoes, E., Tononi, G., & Postle, B. R. (2007). The neural bases of the short-term storage of verbal information are anatomically variable across individuals. The Journal of Neuroscience, 27, 11003–11008. doi:10.1523/JNEUROSCI.1573-07.2007
   PubMed Web of Science ®Google Scholar
• Glezer, L. S., Jiang, X., & Riesenhuber, M. (2009). Evidence for highly selective neuronal tuning to whole words in the “visual word form area.” Neuron, 62, 199–204. doi:10.1016/j.neuron.2009.03.017
   PubMed Web of Science ®Google Scholar
• Gopnik, A., & Wellman, H. M. (1994). The theory theory. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind: Domain specificity in cognition and culture (pp. 257–293). New York, NY: Cambridge University Press.
   Google Scholar
• Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: Neural models of stimulus-specific effects. Trends in Cognitive Sciences, 10(1), 14–23. doi:10.1016/j.tics.2005.11.006
   PubMed Web of Science ®Google Scholar
• Henson, R. N. A., & Rugg, M. D. (2003). Neural response suppression, haemodynamic repetition effects, and behavioural priming. Neuropsychologia, 41, 263–270. doi:10.1016/S0028-3932(02)00159-8
   PubMed Web of Science ®Google Scholar
• Jimura, K., & Poldrack, R. A. (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50, 544–552. doi:10.1016/j.neuropsychologia.2011.11.007
   PubMed Web of Science ®Google Scholar
• Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience, 17, 4302–4311.
   PubMed Web of Science ®Google Scholar
• Kriegeskorte, N. (2009). Relating population-code representations between man, monkey, and computational models. Frontiers in Neuroscience, 3, 363–373. doi:10.3389/neuro.01.035.2009
   PubMedGoogle Scholar
• Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103, 3863–3868. doi:10.1073/pnas.0600244103
   PubMed Web of Science ®Google Scholar
• Logothetis, N. K., & Wandell, B. A. (2004). Interpreting the BOLD signal. Annual Review of Physiology, 66, 735–769. doi:10.1146/annurev.physiol.66.082602.092845
   PubMed Web of Science ®Google Scholar
• Martin, A., & Chao, L. L. (2001). Semantic memory and the brain: Structure and processes. Current Opinion in Neurobiology, 11, 194–201. doi:10.1016/S0959-4388(00)00196-3
   PubMed Web of Science ®Google Scholar
• Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424–430. doi:10.1016/j.tics.2006.07.005
   PubMed Web of Science ®Google Scholar
• Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1 Suppl 1), S199–S209. doi:10.1016/j.neuroimage.2008.11.007
   PubMed Web of Science ®Google Scholar
• Piazza, M., Pinel, P., Le Bihan, D., & Dehaene, S. (2007). A magnitude code common to numerosities and number symbols in human intraparietal cortex. Neuron, 53, 293–305. doi:10.1016/j.neuron.2006.11.022
   PubMed Web of Science ®Google Scholar
• Pinker, S. (1991). Rules of language. Science, 253, 530–535. doi:10.1126/science.1857983
   PubMed Web of Science ®Google Scholar
• Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103(1), 56–115. doi:10.1037/0033-295X.103.1.56
   PubMed Web of Science ®Google Scholar
• Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data?. Trends in Cognitive Sciences, 10, 59–63.
   PubMed Web of Science ®Google Scholar
• Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron, 72, 692–697.
   PubMed Web of Science ®Google Scholar
• Poldrack, R. A., Halchenko, Y. O., & Hanson, S. J. (2009). Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20, 1364–1372. doi:10.1111/j.1467-9280.2009.02460.x
   PubMed Web of Science ®Google Scholar
• Posner, M. I., Petersen, S. E., Fox, P. T., & Raichle, M. E. (1988). Localization of cognitive operations in the human brain. Science, 240, 1627–1631. doi:10.1126/science.3289116
   PubMed Web of Science ®Google Scholar
• Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435, 1102–1107. doi:10.1038/nature03687
   PubMed Web of Science ®Google Scholar
• Rao, N., Cox, C., Nowak, R., & Rogers, T. T. (2013). Sparse overlapping sets lasso for multitask learning and its application to fMRI analysis. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 26 (pp. 2202–2210). Curran Associates. Retrieved from http://papers.nips.cc/paper/4891-sparse-overlapping-sets-lasso-for-multitask-learning-and-its-application-to-fmri-analysis.pdf
   Google Scholar
• Riggall, A. C., & Postle, B. R. (2012). The relationship between working memory storage and elevated activity as measured with functional magnetic resonance imaging. The Journal of Neuroscience, 32, 12990–12998. doi:10.1523/JNEUROSCI.1892-12.2012
   PubMed Web of Science ®Google Scholar
• Rish, I., Cecchi, G. A., Heuton, K., Baliki, M. N., & Apkarian, A. V. (2012). Sparse regression analysis of task-relevant information distribution in the brain. SPIE, 8314, 831412–831418. doi:10.1117/12.911318
   Google Scholar
• Rissman, J., Eliassen, J. C., & Blumstein, S. E. (2003). An event-related fMRI investigation of implicit semantic priming. Journal of Cognitive Neuroscience, 15, 1160–1175.
   PubMed Web of Science ®Google Scholar
• Rogers, T. T., & McClelland, J. L. (2014). Parallel distributed processing at 25: Further explorations in the microstructure of cognition. Cognitive Science, 38, 1024–1077. doi:10.1111/cogs.12148
   PubMed Web of Science ®Google Scholar
• Rumelhart, D. E., McClelland, J. L., & Hinton, G. E. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press.
   Google Scholar
• Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel Distributed Processing: Explorations in the microstructure of cognition (Vols. 1–2, Vol. 1). Cambridge, MA: MIT Press.
   Google Scholar
• Seidenberg, M. S., & Plaut, D. C. (2014). Quasiregularity and its discontents: The legacy of the past tense debate. Cognitive Science, 38, 1190–1228. doi:10.1111/cogs.12147
   PubMed Web of Science ®Google Scholar
• Shinkareva, S. V., Mason, R. A., Malave, V. L., Wang, W., Mitchell, T. M., & Just, M. A. (2008). Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLoS ONE, 3(1), e1394. doi:10.1371/journal.pone.0001394
   Google Scholar
• Smolensky, P. (1986). Neural and conceptual interpretations of parallel distributed processing models. Boulder, CO: Colorado University at Boulder Department of Computer Science.
   Google Scholar
• Tahmasebi, A. M., Davis, M. H., Wild, C. J., Rodd, J. M., Hakyemez, H., Abolmaesumi, P., & Johnsrude, I. S. (2012). Is the link between anatomical structure and function equally strong at all cognitive levels of processing? Cerebral Cortex, 22, 1593–1603. doi:10.1093/cercor/bhr205
   PubMed Web of Science ®Google Scholar
• Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309–318. doi:10.1016/j.tics.2006.05.009
   PubMed Web of Science ®Google Scholar
• Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science, 4, 274–290.
   Web of Science ®Google Scholar
• Wang, X., Hutchinson, R., & Mitchell, T. M. (2004). Training fMRI classifiers to detect cognitive states across multiple human subjects. In S. Thrun, L. K. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems 16 (pp. 709–716). Cambridge, MA: MIT Press. Retrieved from http://papers.nips.cc/paper/2449-training-fmri-classifiers-to-detect-cognitive-states-across-multiple-human-subjects.pdf
   Google Scholar
• Wheatley, T., Weisberg, J., Beauchamp, M. S., & Martin, A. (2005). Automatic priming of semantically related words reduces activity in the Fusiform Gyrus. Journal of Cognitive Neuroscience, 17, 1871–1885.
   PubMed Web of Science ®Google Scholar
• White, C. N., & Poldrack, R. A. (2013). Using fMRI to constrain theories of cognition. Perspectives on Psychological Science, 8(1), 79–83. doi:10.1177/1745691612469029
   Web of Science ®Google Scholar