Bibliography

Abdulkadir, A., Ronneberger, O., Tabrizi, S. J., & Klöppel, S. (2014). Reduction of confounding effects with voxel-wise gaussian process regression in structural MRI. 2014 International Workshop on Pattern Recognition in Neuroimaging, 1–4.

Abdulrahman, H., & Henson, R. N. (2016). Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for beta-series correlation and multi-voxel pattern analysis. NeuroImage, 125, 756–766.

Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Front. Neuroinform., 8, 14.

Adams, R. B., Jr, Garrido, C. O., Albohn, D. N., Hess, U., & Kleck, R. E. (2016). What facial appearance reveals over time: When perceived expressions in neutral faces reveal stable emotion dispositions. Front. Psychol., 7, 986.

Adams, R. B., Jr, Nelson, A. J., Soto, J. A., Hess, U., & Kleck, R. E. (2012). Emotion in the neutral face: A mechanism for impression formation? Cogn. Emot., 26(3), 431–441.

Adjerid, I., & Kelley, K. (2018). Big data in psychology: A framework for research advancement. Am. Psychol., 73(7), 899–917.

Aliko, S., Huang, J., Gheorghiu, F., Meliss, S., & Skipper, J. I. (2020). A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Sci Data, 7(1), 347.

Alizadeh, S., Jamalabadi, H., Schönauer, M., Leibold, C., & Gais, S. (2017). Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis. Neuroimage, 159, 449–458.

Allefeld, C., Görgen, K., & Haynes, J.-D. (2016). Valid population inference for information-based imaging: From the second-level t-test to prevalence inference. Neuroimage, 141, 378–392.

Allefeld, C., & Haynes, J.-D. (2014). Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated manova. Neuroimage, 89, 345–357.

Allen, E. J., St-Yves, G., Wu, Y., Breedlove, J. L., Dowdle, L. T., & others. (2021). A massive 7T fMRI dataset to bridge cognitive and computational neuroscience. bioRxiv.

Amat, J., Baratta, M. V., Paul, E., Bland, S. T., Watkins, L. R., & Maier, S. F. (2005). Medial prefrontal cortex determines how stressor controllability affects behavior and dorsal raphe nucleus. Nature Neuroscience, 8(3), 365–371.

Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (2001). Intelligenz-Struktur-Test 2000 R (Vol. 2). Hogrefe.

Anderson, M. L. (2016). Précis of after phrenology: Neural reuse and the interactive brain. Behavioral and Brain Sciences, 39.

Andersson, J. L. R., Graham, M. S., Zsoldos, E., & Sotiropoulos, S. N. (2016). Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage, 141, 556–572.

Andersson, J. L. R., Jenkinson, M., Smith, S., & Others. (2007). Non-linear registration, aka spatial normalisation FMRIB technical report TR07JA2. FMRIB Analysis Group of the University of Oxford, 2(1), e21.

Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063–1078.

Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316(1), 29.

Atkinson, D., Hill, D. L., Stoyle, P. N., Summers, P. E., & Keevil, S. F. (1997). Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans. Med. Imaging, 16(6), 903–910.

Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal., 12(1), 26–41.

Babayan, A., Erbey, M., Kumral, D., Reinelt, J. D., Reiter, A. M. F., Röbbig, J., Schaare, H. L., Uhlig, M., Anwander, A., Bazin, P.-L., Horstmann, A., Lampe, L., Nikulin, V. V., Okon-Singer, H., Preusser, S., Pampel, A., Rohr, C. S., Sacher, J., Thöne-Otto, A., … Villringer, A. (2019). A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data, 6, 180308.

Bach, D. R., & Dolan, R. J. (2012). Knowing how much you don’t know: A neural organization of uncertainty estimates. Nature Reviews Neuroscience, 13(8), 572–586.

Baker, N., Lu, H., Erlikhman, G., & Kellman, P. J. (2018). Deep convolutional networks do not classify based on global object shape. PLoS Computational Biology, 14(12), e1006613.

Bangalore, S. S., Prasad, K. M. R., Montrose, D. M., Goradia, D. D., Diwadkar, V. A., & Keshavan, M. S. (2008). Cannabis use and brain structural alterations in first episode schizophrenia—a region of interest, voxel based morphometric study. Schizophr. Res., 99(1), 1–6.

Barman, A., & Dutta, P. (2019). Facial expression recognition using distance and texture signature relevant features. Appl. Soft Comput., 77, 88–105.

Barnes, J., Ridgway, G. R., Bartlett, J., Henley, S. M. D., Lehmann, M., Hobbs, N., Clarkson, M. J., MacManus, D. G., Ourselin, S., & Fox, N. C. (2010). Head size, age and gender adjustment in MRI studies: A necessary nuisance? Neuroimage, 53(4), 1244–1255.

Barrett, L. F. (2012). Emotions are real. Emotion, 12(3), 413.

Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychol. Sci. Public Interest, 20(1), 1–68.

Barrett, L. F., & Satpute, A. B. (2013). Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain. Current Opinion in Neurobiology, 23(3), 361–372.

Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16(7), 419–429.

Barsalou, L. W. (2009). Simulation, situated conceptualization, and prediction. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 1281–1289.

Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of bold fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412–427.

Bastiaansen, J. A., Thioux, M., & Keysers, C. (2009). Evidence for mirror systems in emotions. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1528), 2391–2404.

Bastiani, M., Cottaar, M., Fitzgibbon, S. P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S. N., Jbabdi, S., & Andersson, J. L. R. (2019). Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage, 184, 801–812.

Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370.

Beckmann, C. F., Mackay, C. E., Filippini, N., & Smith, S. M. (2009). Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage, 47, S148.

Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage, 37(1), 90–101.

Bench, S. W., & Lench, H. C. (2019). Boredom as a seeking state: Boredom prompts the pursuit of novel (even negative) experiences. Emotion, 19(2), 242.

Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2018). Facial color is an efficient mechanism to visually transmit emotion. Proc. Natl. Acad. Sci. U. S. A., 115(14), 3581–3586.

Bergen, R. S. van, Ma, W. J., Pratte, M. S., & Jehee, J. F. M. (2015). Sensory uncertainty decoded from visual cortex predicts behavior. Nat. Neurosci., 18(12), 1728–1730.

Berlyne, D. E. (1966). Curiosity and exploration. Science, 153(3731), 25–33.

Berridge, K. C., Robinson, T. E., & Aldridge, J. W. (2009). Dissecting components of reward:“Liking”,“wanting”, and learning. Current Opinion in Pharmacology, 9(1), 65–73.

Betancourt, M. (2017). A conceptual introduction to hamiltonian monte carlo. http://arxiv.org/abs/1701.02434

Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19(12), 2767–2796.

Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008). The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage, 40(2), 644–654.

Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol., 9, 91–121.

Borsboom, D., Maas, H. van der, Dalege, J., Kievit, R., & Haig, B. (2020). Theory construction methodology: A practical framework for theory formation in psychology.

Brand, A., Allen, L., Altman, M., Hlava, M., & Scott, J. (2015). Beyond authorship: Attribution, contribution, collaboration, and credit. Learned Publishing, 28(2), 151–155.

Braver, T. S., Krug, M. K., Chiew, K. S., Kool, W., Westbrook, J. A., Clement, N. J., Adcock, R. A., Barch, D. M., Botvinick, M. M., Carver, C. S., & others. (2014). Mechanisms of motivation–cognition interaction: Challenges and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 443–472.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). SSO Schweiz. Monatsschr. Zahnheilkd., 16(3), 199–231.

Brinkman, L., Todorov, A., & Dotsch, R. (2017). Visualising mental representations: A primer on noise-based reverse correlation in social psychology. European Review of Social Psychology, 28(1), 333–361.

Brodtmann, A., Puce, A., Darby, D., & Donnan, G. (2009). Regional fMRI brain activation does correlate with global brain volume. Brain Research, 1259, 17–25.

Brooks, J. A., Stolier, R. M., & Freeman, J. B. (2018). Stereotypes bias visual prototypes for sex and emotion categories. Soc. Cogn., 36(5), 481–493.

Brosch, T., Bar-David, E., & Phelps, E. A. (2013). Implicit race bias decreases the similarity of neural representations of black and white faces. Psychological Science, 24(2), 160–166.

Bryant, D., & Howard, A. (2019). A comparative analysis of Emotion-Detecting AI systems with respect to algorithm performance and dataset diversity. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 377–382.

Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., Weber, J., & Ochsner, K. N. (2014). Cognitive reappraisal of emotion: A meta-analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990.

Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci., 14(5), 365–376.

Bzdok, D. (2017). Classical statistics and statistical learning in imaging neuroscience. Front. Neurosci., 11, 543.

Carlson, T. A., & Wardle, S. G. (2015). Sensible decoding. Neuroimage, 110, 217–218.

Carr, L., Iacoboni, M., Dubeau, M.-C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences, 100(9), 5497–5502.

Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. J. Pers. Soc. Psychol., 67(2), 319–333.

Chang, C., Cunningham, J. P., & Glover, G. H. (2009). Influence of heart rate on the BOLD signal: The cardiac response function. Neuroimage, 44(3), 857–869.

Chekroud, A. M., Ward, E. J., Rosenberg, M. D., & Holmes, A. J. (2016). Patterns in the human brain mosaic discriminate males from females. Proc. Natl. Acad. Sci. U. S. A., 113(14), E1968.

Chen, C., Crivelli, C., Garrod, O. G., Schyns, P. G., Fernández-Dols, J.-M., & Jack, R. E. (2018). Distinct facial expressions represent pain and pleasure across cultures. Proceedings of the National Academy of Sciences, 115(43), E10013–E10021.

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., Lin, C., Initiative, A. D. N., & others. (2012). Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage, 60(1), 59–70.

Cichy, R. M., & Kaiser, D. (2019). Deep neural networks as scientific models. Trends Cogn. Sci., 23(4), 305–317.

Citron, F. M., Gray, M. A., Critchley, H. D., Weekes, B. S., & Ferstl, E. C. (2014). Emotional valence and arousal affect reading in an interactive way: Neuroimaging evidence for an approach-withdrawal framework. Neuropsychologia, 56, 79–89.

Cohn, J. F., Ambadar, Z., & Ekman, P. (2007). Observer-based measurement of facial expression with the facial action coding system. The Handbook of Emotion Elicitation and Assessment, 1(3), 203–221.

Cohn, J., & Kanade, T. (2007). Use of automated facial image analysis for measurement of emotion expression. Handbook of Emotion Elicitation and Assessment, 222–238.

Cordaro, D. T., Sun, R., Keltner, D., Kamble, S., Huddar, N., & McNeil, G. (2018). Universals and cultural variations in 22 emotional expressions across five cultures. Emotion, 18(1), 75–93.

Corradi-Dell’Acqua, C., Tusche, A., Vuilleumier, P., & Singer, T. (2016). Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex. Nature Communications, 7(1), 1–12.

Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proc. Natl. Acad. Sci. U. S. A., 114(38), E7900–E7909.

Cowen, A. S., Keltner, D., Schroff, F., Jou, B., Adam, H., & Prasad, G. (2021). Sixteen facial expressions occur in similar contexts worldwide. Nature, 589(7841), 251–257.

Craddock, R. C., Holtzheimer, P. E., 3rd, Hu, X. P., & Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magn. Reson. Med., 62(6), 1619–1628.

Craddock, R. C., James, G. A., Holtzheimer III, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928.

Craig, A. D., & Craig, A. (2009). How do you feel–now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10(1).

Craig, B. M., Koch, S., & Lipp, O. V. (2017). The influence of social category cues on the happy categorisation advantage depends on expression valence. Cogn. Emot., 31(7), 1493–1501.

Craig, B. M., & Lipp, O. V. (2018). The influence of multiple social categories on emotion perception. J. Exp. Soc. Psychol., 75, 27–35.

Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.-O., Chupin, M., Benali, H., Colliot, O., & Alzheimer’s Disease Neuroimaging Initiative. (2011). Automatic classification of patients with alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage, 56(2), 766–781.

Cummins, R. (2000). How does it work?“ Versus” what are the laws?“: Two conceptions of psychological explanation. Explanation and Cognition, 117–144.

Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9(2), 179–194.

Darwin, C. (1872). The expression of the emotions in man and animals, new york: D. Appleton and Company.

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.

Decety, J. (2011). Dissecting the neural mechanisms mediating empathy. Emotion Review, 3(1), 92–108.

De Corte, K., Buysse, A., Verhofstadt, L. L., Roeyers, H., Ponnet, K., & Davis, M. H. (2007). Measuring empathic tendencies: Reliability and validity of the dutch version of the interpersonal reactivity index. Psychologica Belgica, 47(4), 235–260.

Del Giudice, M., Lippa, R. A., Puts, D. A., Bailey, D. H., Bailey, J. M., & Schmitt, D. P. (2016). Joel et al.’s method systematically fails to detect large, consistent sex differences. Proc. Natl. Acad. Sci. U. S. A., 113(14), E1965.

Delis, I., Chen, C., Jack, R. E., Garrod, O. G. B., Panzeri, S., & Schyns, P. G. (2016). Space-by-time manifold representation of dynamic facial expressions for emotion categorization. J. Vis., 16(8), 14–14.

Demetriou, L., Kowalczyk, O. S., Tyson, G., Bello, T., Newbould, R. D., & Wall, M. B. (2018). A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI. Neuroimage, 176, 404–416.

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255.

Dennett, D. C. (2006). The frame problem of AI. Philosophy of Psychology: Contemporary Readings, 433, 67–83.

Denny, B. T., Kober, H., Wager, T. D., & Ochsner, K. N. (2012). A meta-analysis of functional neuroimaging studies of self-and other judgments reveals a spatial gradient for mentalizing in medial prefrontal cortex. Journal of Cognitive Neuroscience, 24(8), 1742–1752.

Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968–980.

Deska, J. C., Lloyd, E. P., & Hugenberg, K. (2018). The face of fear and anger: Facial width-to-height ratio biases recognition of angry and fearful expressions. Emotion, 18(3), 453–464.

Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage, 53(1), 1–15.

Dhollander, T., Raffelt, D., & Connelly, A. (2016). Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. ISMRM Workshop on Breaking the Barriers of Diffusion MRI, 5, 5.

Diekhof, E. K., Kaps, L., Falkai, P., & Gruber, O. (2012). The role of the human ventral striatum and the medial orbitofrontal cortex in the representation of reward magnitude–an activation likelihood estimation meta-analysis of neuroimaging studies of passive reward expectancy and outcome processing. Neuropsychologia, 50(7), 1252–1266.

Dinga, R., Schmaal, L., Penninx, B. W. J., Veltman, D. J., & Marquand, A. F. (2020). Controlling for effects of confounding variables on machine learning predictions. In bioRxiv (p. 2020.08.17.255034).

Dixon, L. (1999). Dual diagnosis of substance abuse in schizophrenia: Prevalence and impact on outcomes. Schizophr. Res., 35 Suppl, S93–100.

Douaud, G., Smith, S., Jenkinson, M., Behrens, T., Johansen-Berg, H., Vickers, J., James, S., Voets, N., Watkins, K., Matthews, P. M., & James, A. (2007). Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain, 130(Pt 9), 2375–2386.

Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI. Trends Cogn. Sci., 20(6), 425–443.

Dubois, J., Galdi, P., Han, Y., Paul, L. K., & Adolphs, R. (2018). Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. Personality Neuroscience, 1.

Dukart, J., Schroeter, M. L., Mueller, K., Initiative, A. D. N., & Others. (2011). Age correction in dementia–matching to a healthy brain. PLoS One, 6(7), e22193.

Ebersole, C. R., Atherton, O. E., Belanger, A. L., Skulborstad, H. M., Allen, J. M., Banks, J. B., Baranski, E., Bernstein, M. J., Bonfiglio, D. B. V., Boucher, L., Brown, E. R., Budiman, N. I., Cairo, A. H., Capaldi, C. A., Chartier, C. R., Chung, J. M., Cicero, D. C., Coleman, J. A., Conway, J. G., … Nosek, B. A. (2016). Many labs 3: Evaluating participant pool quality across the academic semester via replication. J. Exp. Soc. Psychol., 67, 68–82.

Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82(397), 171–185.

Egner, T., Ely, S., & Grinband, J. (2010). Going, going, gone: Characterizing the time-course of congruency sequence effects. Front. Psychol., 1, 154.

Eigenhuis, A., Kamphuis, J. H., & Noordhof, A. (2013). Development and validation of the dutch brief form of the multidimensional personality questionnaire (MPQ-BF-NL). Assessment, 20(5), 565–575.

Ekman, P., Freisen, W. V., & Ancoli, S. (1980). Facial signs of emotional experience. J. Pers. Soc. Psychol., 39(6), 1125–1134.

Ekman, P., & Friesen, W. V. (1976). Measuring facial movement. Environmental Psychology and Nonverbal Behavior, 1(1), 56–75.

Ekman, P., & Keltner, D. (1997). Universal facial expressions of emotion. Segerstrale U, P. Molnar P, Eds. Nonverbal Communication: Where Nature Meets Culture, 27–46.

Ekman, P., Sorenson, E. R., & Friesen, W. V. (1969). Pan-cultural elements in facial displays of emotion. Science, 164(3875), 86–88.

Elk, M. van, & Snoek, L. (2020). The relationship between individual differences in gray matter volume and religiosity and mystical experiences: A preregistered voxel-based morphometry study. Eur. J. Neurosci., 51(3), 850–865.

Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and Emotion, 30(2), 111–116.

Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One, 12(9), e0184661.

Esteban, O., Blair, R., Markiewicz, C. J., Berleant, S. L., Moodie, C., Ma, F., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., Poldrack, R. A., & Gorgolewski, K. J. (2017). Poldracklab/fmriprep: 1.0.0 (Version 1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.1095198

Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., Ye, Z., Salo, T., Valabregue, R., Amlien, I. K., Liem, F., Jacoby, N., Stojić, H., Cieslak, M., Urchs, S., … Gorgolewski, K. J. (2020). Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nat. Protoc., 15(7), 2186–2202.

Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., & others. (2019). FMRIPrep: A robust preprocessing pipeline for functional mri. Nature Methods, 16(1), 111–116.

Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nat. Methods, 16(1), 111–116.

Ethofer, T., Van De Ville, D., Scherer, K., & Vuilleumier, P. (2009). Decoding of emotional information in voice-sensitive cortices. Current Biology, 19(12), 1028–1033.

Etzel, J. A., Valchev, N., & Keysers, C. (2011). The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines. Neuroimage, 54(2), 1159–1167.

Floresco, S. B. (2015). The nucleus accumbens: An interface between cognition, emotion, and action. Annual Review of Psychology, 66, 25–52.

Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage, Supplement 1(47), S102.

Forstmann, B. U., & Wagenmakers, E.-J. (2015). An introduction to Model-Based cognitive neuroscience (B. U. Forstmann & E.-J. Wagenmakers, Eds.). Springer, New York, NY.

Fölster, M., Hess, U., & Werheid, K. (2014). Facial age affects emotional expression decoding. Front. Psychol., 5, 30.

Franken, I. H. A., Muris, P., & Rassin, E. (2005). Psychometric properties of the dutch bis/bas scales. J. Psychopathol. Behav. Assess., 27(1), 25–30.

Franklin, R. G., Adams, R. B., Steiner, T. G., & Zebrowitz, L. A. (2019). Reading the lines in the face: The contribution of angularity and roundness to perceptions of facial anger and joy. Emotion, 19(2), 209–218.

Friesen, W., & Ekman, P. (1978). Facial action coding system: A technique for the measurement of facial movement. Palo Alto, 3.

Friesen, W. V., & Ekman. (1983). EMFACS-7: Emotional facial action coding system. Unpublished Manuscript, University of California at San Francisco, 2(36), 1.

Frigg, R., & Hartmann, S. (2020). Models in Science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2020). Metaphysics Research Lab, Stanford University.

Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.

Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences, 8(9), 396–403.

Ganzetti, M., Wenderoth, N., & Mantini, D. (2016). Intensity inhomogeneity correction of structural MR images: A Data-Driven approach to define input algorithm parameters. Front. Neuroinform., 10, 10.

Gazendam, F. J., Kamphuis, J. H., Eigenhuis, A., Huizenga, H. M. H., Soeter, M., Bos, M. G. N., Sevenster, D., & Kindt, M. (2015). Personality predicts individual variation in fear learning: A multilevel growth modeling approach. Clin. Psychol. Sci., 3(2), 175–188.

Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11), 665–673.

Gelder, B. de, Van den Stock, J., Meeren, H. K., Sinke, C. B., Kret, M. E., & Tamietto, M. (2010). Standing up for the body. Recent progress in uncovering the networks involved in the perception of bodies and bodily expressions. Neuroscience & Biobehavioral Reviews, 34(4), 513–527.

Gelfert, A. (2016). How to do science with models: A philosophical primer. Springer, Cham.

Gescheider, G. A. (2013). Psychophysics: The fundamentals. Psychology Press.

Gewin, V. (2016). Data sharing: An open mind on open data. Nature, 529(7584), 117–119.

Gilbert, S. J., Swencionis, J. K., & Amodio, D. M. (2012). Evaluative vs. Trait representation in intergroup social judgments: Distinct roles of anterior temporal lobe and prefrontal cortex. Neuropsychologia, 50(14), 3600–3611.

Gill, D., Garrod, O. G. B., Jack, R. E., & Schyns, P. G. (2014). Facial movements strategically camouflage involuntary social signals of face morphology. Psychol. Sci., 25(5), 1079–1086.

Gilron, R., Rosenblatt, J. D., & Mukamel, R. (2016). Addressing the" problem" of temporal correlations in mvpa analysis. 2016 International Workshop on Pattern Recognition in Neuroimaging (Prni), 1–4.

Gilron, R., Rosenblatt, J., Koyejo, O., Poldrack, R. A., & Mukamel, R. (2017). What’s in a pattern? Examining the type of signal multivariate analysis uncovers at the group level. Neuroimage, 146, 113–120.

Glezerman, M. (2016). Yes, there is a female and a male brain: Morphology versus functionality. Proceedings of the National Academy of Sciences, 113(14), E1971–E1971.

Glover, G. H., Li, T.-Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 44(1), 162–167.

Goldstein, J. M., Seidman, L. J., Horton, N. J., Makris, N., Kennedy, D. N., Caviness, V. S., Jr, Faraone, S. V., & Tsuang, M. T. (2001). Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. Cereb. Cortex, 11(6), 490–497.

Golman, R., & Loewenstein, G. (2015). Curiosity, information gaps, and the utility of knowledge. Information Gaps, and the Utility of Knowledge (April 16, 2015), 96–135.

Good, C. D., Johnsrude, I., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001a). Cerebral asymmetry and the effects of sex and handedness on brain structure: A voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage, 14(3), 685–700.

Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001b). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14(1 Pt 1), 21–36.

Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform., 5, 13.

Gorgolewski, K., Esteban, O., Schaefer, G., Wandell, B., & Poldrack, R. (2017). OpenNeuro—a free online platform for sharing and analysis of neuroimaging data. Organization for Human Brain Mapping. Vancouver, Canada, 1677.

Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data, 3, 160044.

Gorgolewski, K. J., Esteban, O., Ellis, D. G., Notter, M. P., Ziegler, E., Johnson, H., Hamalainen, C., Yvernault, B., Burns, C., Manhães-Savio, A., Jarecka, D., Markiewicz, C. J., Salo, T., Clark, D., Waskom, M., Wong, J., Modat, M., Dewey, B. E., Clark, M. G., … Ghosh, S. (2017). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. 0.13.1.

Gorgolewski, K. J., Esteban, O., Ellis, D. G., Notter, M. P., Ziegler, E., Johnson, H., Hamalainen, C., Yvernault, B., Burns, C., Manhães-Savio, A., Jarecka, D., Markiewicz, C. J., Salo, T., Clark, D., Waskom, M., Wong, J., Modat, M., Dewey, B. E., Clark, M. G., … Ghosh, S. (2017). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1 (Version 0.13.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.581704

Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J.-B., & others. (2015). NeuroVault. Org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in Neuroinformatics, 9, 8.

Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J.-B., Yarkoni, T., & Margulies, D. S. (2015). NeuroVault.org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform., 9, 8.

Gottlieb, J., & Oudeyer, P.-Y. (2018). Towards a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.

Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: Computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–593.

Görgen, K., Hebart, M. N., Allefeld, C., & Haynes, J.-D. (2017). The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. Neuroimage.

Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. Neuroimage, 48(1), 63–72.

Groen, I. I., Greene, M. R., Baldassano, C., Fei-Fei, L., Beck, D. M., & Baker, C. I. (2018). Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior. Elife, 7.

Groot, A. D. de. (1961). An introduction to Model-Based cognitive neuroscience. Mouton, ’s-Gravenhage.

Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–496.

Gruber, M. J., & Ranganath, C. (2019). How curiosity enhances hippocampus-dependent memory: The prediction, appraisal, curiosity, and exploration (pace) framework. Trends in Cognitive Sciences, 23(12), 1014–1025.

Guan, J., Ryali, C. K., & Angela, J. Y. (2018). Computational modeling of social face perception in humans: Leveraging the active appearance model. bioRxiv.

Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 16(4), 789–802.

Guggenmos, M., Sterzer, P., & Cichy, R. M. (2018). Multivariate pattern analysis for MEG: A comparison of dissimilarity measures. Neuroimage, 173, 434–447.

Gulban, O. F., Nielson, D., Poldrack, R., Lee, J., Gorgolewski, C., Vanessasaurus, & Ghosh, S. (2019). Poldracklab/pydeface: V2.0.0.

Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999). Sex differences in brain gray and white matter in healthy young adults: Correlations with cognitive performance. J. Neurosci., 19(10), 4065–4072.

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1), 389–422.

Güçlü, U., & Gerven, M. A. J. van. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci., 35(27), 10005–10014.

Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intell. Syst., 24(2), 8–12.

Hariri, A. R., Bookheimer, S. Y., & Mazziotta, J. C. (2000). Modulating emotional responses: Effects of a neocortical network on the limbic system. Neuroreport, 11(1), 43–48.

Harris, C. R., Millman, K. J., Walt, S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M. H. van, Brett, M., Haldane, A., Del Rı́o, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362.

Harris, S., Sheth, S. A., & Cohen, M. S. (2008). Functional neuroimaging of belief, disbelief, and uncertainty. Annals of Neurology, 63(2), 141–147.

Harvey, A. K., Pattinson, K. T. S., Brooks, J. C. W., Mayhew, S. D., Jenkinson, M., & Wise, R. G. (2008). Brainstem functional magnetic resonance imaging: Disentangling signal from physiological noise. J. Magn. Reson. Imaging, 28(6), 1337–1344.

Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303(5664), 1634–1640.

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

Haxby, J. V. (2012). Multivariate pattern analysis of fMRI: The early beginnings. Neuroimage, 62(2), 852–855.

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

Haynes, J.-D. (2015). A primer on pattern-based approaches to fMRI: Principles, pitfalls, and perspectives. Neuron, 87(2), 257–270.

Hebart, M. N., & Baker, C. I. (2017). Deconstructing multivariate decoding for the study of brain function. Neuroimage.

Hebart, M. N., Bankson, B. B., Harel, A., Baker, C. I., & Cichy, R. M. (2018). The representational dynamics of task and object processing in humans. Elife, 7.

Hebart, M. N., Zheng, C. Y., Pereira, F., & Baker, C. I. (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nat Hum Behav, 4(11), 1173–1185.

Hess, U., Adams, R. B., Jr, Grammer, K., & Kleck, R. E. (2009). Face gender and emotion expression: Are angry women more like men? J. Vis., 9(12), 19.1–8.

Hess, U., Adams, R. B., Jr, & Kleck, R. E. (2009). The face is not an empty canvas: How facial expressions interact with facial appearance. Philos. Trans. R. Soc. Lond. B Biol. Sci., 364(1535), 3497–3504.

Hess, U., Adams, R. B., & Kleck, R. E. (2009). The categorical perception of emotions and traits. Soc. Cogn., 27(2), 320–326.

Hoekstra, H. A., Ormel, H., & De Fruyt, F. (1996). Persoonlijkheidsvragenlijsten: NEO-pi-r & neo-ffi. Swets & Zeitlinger.

Holdgraf, C. R., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017). Encoding and decoding models in cognitive electrophysiology. Front. Syst. Neurosci., 11, 61.

Hoogeveen, S., Snoek, L., & Elk, M. van. (2020). Religious belief and cognitive conflict sensitivity: A preregistered fMRI study. Cortex, 129, 247–265.

Höfling, T. T. A., Gerdes, A. B. M., Föhl, U., & Alpers, G. W. (2020). Read my face: Automatic facial coding versus psychophysiological indicators of emotional valence and arousal. Front. Psychol., 11, 1388.

Hsee, C. K., & Ruan, B. (2016). The pandora effect: The power and peril of curiosity. Psychological Science, 27(5), 659–666.

Hsu, A., Borst, A., & Theunissen, F. E. (2004). Quantifying variability in neural responses and its application for the validation of model predictions. Network, 15(2), 91–109.

Huntenburg, J. M. (2014). Evaluating nonlinear coregistration of BOLD EPI and t1w images [PhD thesis]. Freie Universität Berlin; pure.mpg.de.

Hunter. (2007). Matplotlib: A 2D graphics environment. IEEE Ann. Hist. Comput., 9, 90–95.

Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76(6), 1210–1224.

Ince, R. A. A., Kay, J. W., & Schyns, P. G. (2020). Bayesian inference of population prevalence. In bioRxiv (p. 2020.07.08.191106).

Ivanova, A. A., Schrimpf, M., Anzellotti, S., Zaslavsky, N., & others. (2021). Is it that simple? Linear mapping models in cognitive neuroscience. bioRxiv.

Izard, C. E. (1994). Innate and universal facial expressions: Evidence from developmental and cross-cultural research. Psychol. Bull., 115(2), 288–299.

Jack, R., Crivelli, C., & Wheatley, T. (2017). Data-Driven methods to diversify knowledge of human psychology. Trends Cogn. Sci.

Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R. (2009). Cultural confusions show that facial expressions are not universal. Curr. Biol., 19(18), 1543–1548.

Jack, R. E., Garrod, O. G. B., & Schyns, P. G. (2014). Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Curr. Biol., 24(2), 187–192.

Jack, R. E., Garrod, O. G. B., Yu, H., Caldara, R., & Schyns, P. G. (2012). Facial expressions of emotion are not culturally universal. Proc. Natl. Acad. Sci. U. S. A., 109(19), 7241–7244.

Jack, R. E., & Schyns, P. G. (2015). The human face as a dynamic tool for social communication. Curr. Biol., 25(14), R621–34.

Jack, R. E., Sun, W., Delis, I., Garrod, O. G. B., & Schyns, P. G. (2016). Four not six: Revealing culturally common facial expressions of emotion. J. Exp. Psychol. Gen., 145(6), 708–730.

Jack, R., & Schyns, P. G. (2017). Toward a social psychophysics of face communication. Annu. Rev. Psychol., 68, 269–297.

Jaeger, B., Oud, B., Williams, T., Krumhuber, E., Fehr, E., & Engelmann, J. B. (2020). Can people detect the trustworthiness of strangers based on their facial appearance?

Jaeger, B., Sleegers, W., Stern, J., Penke, L., & Jones, A. (2020). The accuracy and meta-accuracy of personality impressions from faces.

Jahfari, S., Waldorp, L., Ridderinkhof, K. R., & Scholte, H. S. (2015). Visual information shapes the dynamics of corticobasal ganglia pathways during response selection and inhibition. J. Cogn. Neurosci., 27(7), 1344–1359.

Jamalabadi, H., Alizadeh, S., Schönauer - Human brain …, M., & 2016. (2016). Classification based hypothesis testing in neuroscience: Below‐chance level classification rates and overlooked statistical properties of linear parametric classifiers. Wiley Online Library.

Jenkinson, M. (2003). Fast, automated, n-dimensional phase-unwrapping algorithm. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 49(1), 193–197.

Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841.

Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. Neuroimage, 62(2), 782–790.

Jepma, M., Verdonschot, R. G., Van Steenbergen, H., Rombouts, S. A., & Nieuwenhuis, S. (2012). Neural mechanisms underlying the induction and relief of perceptual curiosity. Frontiers in Behavioral Neuroscience, 6, 5.

Jeurissen, B., Leemans, A., & Sijbers, J. (2014). Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI. Med. Image Anal., 18(7), 953–962.

Jimura, K., & Poldrack, R. A. (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50(4), 544–552.

Joel, D., & Fausto-Sterling, A. (2016). Beyond sex differences: New approaches for thinking about variation in brain structure and function. Philos. Trans. R. Soc. Lond. B Biol. Sci., 371(1688), 20150451.

Johnstone, T., Ores Walsh, K. S., Greischar, L. L., Alexander, A. L., Fox, A. S., Davidson, R. J., & Oakes, T. R. (2006). Motion correction and the use of motion covariates in multiple-subject fMRI analysis. Hum. Brain Mapp., 27(10), 779–788.

Jolly, E., & Chang, L. J. (2019). The flatland fallacy: Moving beyond low–dimensional thinking. Top. Cogn. Sci.

Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T.-y., & Camerer, C. F. (2009). The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963–973.

Kashdan, T. B., & Silvia, P. J. (2009). Curiosity and interest: The benefits of thriving on novelty and challenge. Oxford Handbook of Positive Psychology, 2, 367–374.

Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T. U., Sebold, M., Manjaly, Z.-M., Pruessmann, K. P., & Stephan, K. E. (2017). The PhysIO toolbox for modeling physiological noise in fMRI data. J. Neurosci. Methods, 276, 56–72.

Kay, K. N. (2017). Principles for models of neural information processing. Neuroimage.

Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452(7185), 352–355.

Kay, K. N., Winawer, J., Mezer, A., & Wandell, B. A. (2013). Compressive spatial summation in human visual cortex. J. Neurophysiol., 110(2), 481–494.

Kellen, D. (2019). A model hierarchy for psychological science. Computational Brain & Behavior, 2(3), 160–165.

Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med., 76(5), 1574–1581.

Keltner, D., Sauter, D., Tracy, J., & Cowen, A. (2019). Emotional expression: Advances in basic emotion theory. J. Nonverbal Behav., 43(2), 133–160.

Kendall, M. G., & Stuart, A. (1973). Functional and structural relationship. The Advanced Theory of Statistics, 2, 399–343.

Keysers, C., & Gazzola, V. (2014). Dissociating the ability and propensity for empathy. Trends in Cognitive Sciences, 18(4), 163–166.

Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain it cortical representation. PLoS Computational Biology, 10(11), e1003915.

Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88(3), 449–460.

Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Chaibub Neto, E., & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLoS Comput. Biol., 13(2), e1005350.

Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahnı́k, Š., Batra, R., Berkics, M., Bernstein, M. J., Berry, D. R., Bialobrzeska, O., Binan, E. D., Bocian, K., Brandt, M. J., Busching, R., … Nosek, B. A. (2018). Many labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490.

Ko, B. C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18(2).

Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behaviour, 3(6), 587–595.

Koolschijn, P. C. M. P., Geurts, H. M., Leij, A. R. van der, & Scholte, H. S. (2015). Are autistic traits in the general population related to global and regional brain differences? J. Autism Dev. Disord., 45(9), 2779–2791.

Kostro, D., Abdulkadir, A., Durr, A., Roos, R., Leavitt, B. R., Johnson, H., Cash, D., Tabrizi, S. J., Scahill, R. I., Ronneberger, O., Klöppel, S., & Track-HD Investigators. (2014). Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing. Neuroimage, 98, 405–415.

Kriegeskorte, N. (2015). Deep neural networks: A new framework for modeling biological vision and brain information processing. Annu Rev Vis Sci, 1, 417–446.

Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.

Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience: The dangers of double dipping. Nature Neuroscience, 12(5), 535.

Krishnan, A., Woo, C.-W., Chang, L. J., Ruzic, L., Gu, X., López-Solà, M., Jackson, P. L., Pujol, J., Fan, J., & Wager, T. D. (2016). Somatic and vicarious pain are represented by dissociable multivariate brain patterns. Elife, 5, e15166.

Krumhuber, E. G., Kappas, A., & Manstead, A. S. R. (2013). Effects of dynamic aspects of facial expressions: A review. Emot. Rev., 5(1), 41–46.

Kumar, M., Ellis, C. T., Lu, Q., Zhang, H., Capotă, M., Willke, T. L., Ramadge, P. J., Turk-Browne, N. B., & Norman, K. A. (2020). BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis. PLoS Comput. Biol., 16(1), e1007549.

Kunz, M., Meixner, D., & Lautenbacher, S. (2019). Facial muscle movements encoding pain—a systematic review. Pain, 160(3), 535.

Kveraga, K., Boshyan, J., Adams Jr, R. B., Mote, J., Betz, N., Ward, N., Hadjikhani, N., Bar, M., & Barrett, L. F. (2015). If it bleeds, it leads: Separating threat from mere negativity. Social Cognitive and Affective Neuroscience, 10(1), 28–35.

Lage-Castellanos, A., Valente, G., Formisano, E., & De Martino, F. (2019). Methods for computing the maximum performance of computational models of fMRI responses. PLoS Computational Biology, 15(3), e1006397.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and anovas. Frontiers in Psychology, 4, 863.

Lamm, C., Decety, J., & Singer, T. (2011). Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. Neuroimage, 54(3), 2492–2502.

Lamm, C., & Majdandžić, J. (2015). The role of shared neural activations, mirror neurons, and morality in empathy–a critical comment. Neuroscience Research, 90, 15–24.

Lang, P. J. (2005). International affective picture system (iaps): Affective ratings of pictures and instruction manual. Technical Report.

Lang, P. J., & Bradley, M. M. (2010). Emotion and the motivational brain. Biological Psychology, 84(3), 437–450.

Lang, P. J., Bradley, M. M., Cuthbert, B. N., & others. (1997). International affective picture system (iaps): Technical manual and affective ratings. NIMH Center for the Study of Emotion and Attention, 1, 39–58.

LaRocque, J. J., Lewis-Peacock, J. A., Drysdale, A. T., Oberauer, K., & Postle, B. R. (2013). Decoding attended information in short-term memory: An EEG study. J. Cogn. Neurosci., 25(1), 127–142.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Lefevre, C. E., Etchells, P. J., Howell, E. C., Clark, A. P., & Penton-Voak, I. S. (2014). Facial width-to-height ratio predicts self-reported dominance and aggression in males and females, but a measure of masculinity does not. Biol. Lett., 10(10), 20140729.

Legrand, D., & Ruby, P. (2009). What is self-specific? Theoretical investigation and critical review of neuroimaging results. Psychological Review, 116(1), 252.

Lench, H. C., Flores, S. A., & Bench, S. W. (2011). Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: A meta-analysis of experimental emotion elicitations. Psychological Bulletin, 137(5), 834.

Levy, D. J., & Glimcher, P. W. (2012). The root of all value: A neural common currency for choice. Current Opinion in Neurobiology, 22(6), 1027–1038.

Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods, 264, 47–56.

Lien, J. J., Kanade, T., Cohn, J. F., & Ching-Chung Li. (1998). Automated facial expression recognition based on FACS action units. Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 390–395.

Lieshout, L. L. van, Vandenbroucke, A. R., Müller, N. C., Cools, R., & Lange, F. P. de. (2018). Induction and relief of curiosity elicit parietal and frontal activity. Journal of Neuroscience, 38(10), 2579–2588.

Lindeløv, J. K. (2019). Common statistical tests are linear models. https://lindeloev.github.io/tests-as-linear/.

Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., & Barrett, L. F. (2016). The brain basis of positive and negative affect: Evidence from a meta-analysis of the human neuroimaging literature. Cerebral Cortex, 26(5), 1910–1922.

Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012). The brain basis of emotion: A meta-analytic review. The Behavioral and Brain Sciences, 35(3), 121.

Litman, J. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793–814.

Liu, M., Duan, Y., Ince, R. A. A., Chen, C., Garrod, O. G. B., Schyns, P., & Jack, R. (2020). Facial expressions of emotion categories are embedded within a dimensional space of valence-arousal.

Liu, X., Hairston, J., Schrier, M., & Fan, J. (2011). Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 35(5), 1219–1236.

Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75.

Long, B., Yu, C. P., & Konkle, T. (2017). A mid-level organization of the ventral stream. bioRxiv.

Lüders, E., Steinmetz, H., & Jäncke, L. (2002). Brain size and grey matter volume in the healthy human brain. Neuroreport, 13(17), 2371–2374.

Maas, H. L. J. van der, Snoek, L., & Stevenson, C. E. (2021). How much intelligence is there in artificial intelligence? A 2020 update. Intelligence, 87.

Magnotta, V. A., Friedman, L., & FIRST BIRN. (2006). Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN multicenter imaging study. J. Digit. Imaging, 19(2), 140–147.

Marchewka, A., Żurawski, Ł., Jednoróg, K., & Grabowska, A. (2014). The nencki affective picture system (naps): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behavior Research Methods, 46(2), 596–610.

Marvin, C. B., & Shohamy, D. (2016). Curiosity and reward: Valence predicts choice and information prediction errors enhance learning. Journal of Experimental Psychology: General, 145(3), 266.

Matsumoto, D., Keltner, D., Shiota, M. N., O’Sullivan, M., & Frank, M. (2008). Facial expressions of emotion. Handbook of Emotions., 3rd Ed., 3, 211–234.

McCarthy, P. (2021). FSLeyes (Version 1.0.1). Zenodo. https://doi.org/10.5281/zenodo.4704476

McCrae, R. R., & Costa, P. T., Jr. (1987). Validation of the five-factor model of personality across instruments and observers. J. Pers. Soc. Psychol., 52(1), 81–90.

McElreath, R. (2020). Statistical rethinking: A bayesian course with examples in R and STAN. CRC Press.

McGrath, J., Saha, S., Chant, D., & Welham, J. (2008). Schizophrenia: A concise overview of incidence, prevalence, and mortality. Epidemiol. Rev., 30, 67–76.

McKinney, W., & Others. (2011). Pandas: A foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9).

Medford, N., & Critchley, H. D. (2010). Conjoint activity of anterior insular and anterior cingulate cortex: Awareness and response. Brain Structure and Function, 214(5-6), 535–549.

Mendes, N., Oligschläger, S., Lauckner, M. E., Golchert, J., Huntenburg, J. M., Falkiewicz, M., Ellamil, M., Krause, S., Baczkowski, B. M., Cozatl, R., Osoianu, A., Kumral, D., Pool, J., Golz, L., Dreyer, M., Haueis, P., Jost, R., Kramarenko, Y., Engen, H., … Margulies, D. S. (2019). A functional connectome phenotyping dataset including cognitive state and personality measures. Sci Data, 6, 180307.

Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5-6), 655–667.

Mileva, V. R., Cowan, M. L., Cobey, K. D., Knowles, K. K., & Little, A. C. (2014). In the face of dominance: Self-perceived and other-perceived dominance are positively associated with facial-width-to-height ratio in men. Pers. Individ. Dif., 69, 115–118.

Milham, M. P., Banich, M. T., & Barad, V. (2003). Competition for priority in processing increases prefrontal cortex’s involvement in top-down control: An event-related fMRI study of the stroop task. Brain Res. Cogn. Brain Res., 17(2), 212–222.

Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., Griffanti, L., Douaud, G., Okell, T. W., Weale, P., Dragonu, I., Garratt, S., Hudson, S., Collins, R., Jenkinson, M., … Smith, S. M. (2016). Multimodal population brain imaging in the UK biobank prospective epidemiological study. Nat. Neurosci., 19(11), 1523–1536.

Misaki, M., Kim, Y., Bandettini, P. A., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage, 53(1), 103–118.

Mischkowski, D., Crocker, J., & Way, B. M. (2016). From painkiller to empathy killer: Acetaminophen (paracetamol) reduces empathy for pain. Social Cognitive and Affective Neuroscience, 11(9), 1345–1353.

Montepare, J. M., & Dobish, H. (2003). The contribution of emotion perceptions and their overgeneralizations to trait impressions. J. Nonverbal Behav., 27(4), 237–254.

Moshontz, H., Campbell, L., Ebersole, C. R., IJzerman, H., Urry, H. L., Forscher, P. S., Grahe, J. E., McCarthy, R. J., Musser, E. D., Antfolk, J., Castille, C. M., Evans, T. R., Fiedler, S., Flake, J. K., Forero, D. A., Janssen, S. M. J., Keene, J. R., Protzko, J., Aczel, B., … Chartier, C. R. (2018). The psychological science accelerator: Advancing psychology through a distributed collaborative network. Adv Methods Pract Psychol Sci, 1(4), 501–515.

Mumford, J. A., Davis, T., & Poldrack, R. A. (2014). The impact of study design on pattern estimation for single-trial multivariate pattern analysis. Neuroimage, 103, 130–138.

Murayama, K. (2018). Psychological science agenda| june 2018. Psychological Science.

Murayama, K., FitzGibbon, L., & Sakaki, M. (2019). Process account of curiosity and interest: A reward-learning perspective. Educational Psychology Review, 31(4), 875–895.

Murugappan, M., & Mutawa, A. (2021). Facial geometric feature extraction based emotional expression classification using machine learning algorithms. PLoS One, 16(2), e0247131.

Naselaris, T., Allen, E., & Kay, K. (2021). Extensive sampling for complete models of individual brains. Current Opinion in Behavioral Sciences, 40, 45–51.

Naselaris, T., & Kay, K. N. (2015). Resolving ambiguities of MVPA using explicit models of representation. Trends Cogn. Sci., 19(10), 551–554.

Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and decoding in fMRI. Neuroimage, 56(2), 400–410.

Nastase, S. A., Goldstein, A., & Hasson, U. (2020). Keep it real: Rethinking the primacy of experimental control in cognitive neuroscience. Neuroimage, 222, 117254.

Neri, P., Parker, A. J., & Blakemore, C. (1999). Probing the human stereoscopic system with reverse correlation. Nature, 401(6754), 695–698.

Neth, D., & Martinez, A. M. (2009). Emotion perception in emotionless face images suggests a norm-based representation. J. Vis., 9(1), 5.1–11.

Newell, A. (1973). You can’t play 20 questions with nature and win: Projective comments on the papers of this symposium. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press.

Ngo, G., Khosla, M., Jamison, K., Kuceyeski, A., & others. (2021). Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network. bioRxiv.

Nichols, T., Brett, M., Andersson, J., Wager, T., & Poline, J.-B. (2005). Valid conjunction inference with the minimum statistic. Neuroimage, 25(3), 653–660.

Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., Proal, E., Thirion, B., Van Essen, D. C., White, T., & Yeo, B. T. T. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci., 20(3), 299–303.

Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25.

Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014). A toolbox for representational similarity analysis. PLoS Comput. Biol., 10(4), e1003553.

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(9), 424–430.

Núñez, R., Allen, M., Gao, R., Miller Rigoli, C., Relaford-Doyle, J., & Semenuks, A. (2019). What happened to cognitive science? Nat Hum Behav, 3(8), 782–791.

O’Brien, L. M., Ziegler, D. A., Deutsch, C. K., Frazier, J. A., Herbert, M. R., & Locascio, J. J. (2011). Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods. Psychiatry Res., 193(2), 113–122.

Ojala, M., & Garriga, G. C. (2010). Permutation tests for studying classifier performance. J. Mach. Learn. Res., 11(Jun), 1833–1863.

Onderwijsindeling, S. (2016). Standard educational classification. Den Haag/Heerlen, Netherlands: Centraal Bureau Voor de Statistiek [Statistics Netherlands].

Oosterhof, N. N., & Todorov, A. (2009). Shared perceptual basis of emotional expressions and trustworthiness impressions from faces. Emotion, 9(1), 128–133.

Oosterwijk, S. (2017a). Choosing the negative: A behavioral demonstration of morbid curiosity. PloS One, 12(7), e0178399.

Oosterwijk, S. (2017b). CurioVal preregistered fMRI analyses. OSF. osf.io/gdtk9

Oosterwijk, S., & Barrett, L. F. (2014). Embodiment in the construction of emotion experience and emotion understanding. Routledge Handbook of Embodied Cognition. New York: Routledge, 250–260.

Oosterwijk, S., Lindquist, K. A., Adebayo, M., & Barrett, L. F. (2016). The neural representation of typical and atypical experiences of negative images: Comparing fear, disgust and morbid fascination. Social Cognitive and Affective Neuroscience, 11(1), 11–22.

Oosterwijk, S., Lindquist, K. A., Anderson, E., Dautoff, R., Moriguchi, Y., & Barrett, L. F. (2012). States of mind: Emotions, body feelings, and thoughts share distributed neural networks. NeuroImage, 62(3), 2110–2128.

Oosterwijk, S., Mackey, S., Wilson-Mendenhall, C., Winkielman, P., & Paulus, M. P. (2015). Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems. Social Neuroscience, 10(3), 294–307.

Oosterwijk, S., Snoek, L., Rotteveel, M., Barrett, L. F., & Scholte, H. S. (2017). Shared states: Using MVPA to test neural overlap between self-focused emotion imagery and other-focused emotion understanding. Soc. Cogn. Affect. Neurosci., 12(7), 1025–1035.

Oosterwijk, S., Snoek, L., Tekoppele, J., Engelbert, L. H., & Scholte, H. S. (2020). Choosing to view morbid information involves reward circuitry. Sci. Rep., 10(1), 15291.

Parkinson, C., Liu, S., & Wheatley, T. (2014). A common cortical metric for spatial, temporal, and social distance. Journal of Neuroscience, 34(5), 1979–1987.

Parmley, M., & Cunningham, J. G. (2014). She looks sad, but he looks mad: The effects of age, gender, and ambiguity on emotion perception. J. Soc. Psychol., 154(4), 323–338.

Parra, L. C., Spence, C. D., Gerson, A. D., & Sajda, P. (2005). Recipes for the linear analysis of EEG. Neuroimage, 28(2), 326–341.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., & others. (2011). Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12, 2825–2830.

Peelen, M. V., Atkinson, A. P., & Vuilleumier, P. (2010). Supramodal representations of perceived emotions in the human brain. Journal of Neuroscience, 30(30), 10127–10134.

Peelen, M. V., & Downing, P. E. (2007). Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations. Trends Cogn. Sci., 11(1), 4–5.

Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods, 51(1), 195–203.

Pessoa, L., Gutierrez, E., Bandettini, P., & Ungerleider, L. (2002). Neural correlates of visual working memory: fMRI amplitude predicts task performance. Neuron, 35(5), 975–987.

Pinto, Y., Leij, A. R. van der, Sligte, I. G., Lamme, V. A. F., & Scholte, H. S. (2013). Bottom-up and top-down attention are independent. J. Vis., 13(3), 16.

Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63.

Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: Data sharing in neuroimaging. Nat. Neurosci., 17(11), 1510–1517.

Popov, V., Ostarek, M., & Tenison, C. (2018). Practices and pitfalls in inferring neural representations. NeuroImage, 174, 340–351.

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154.

Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84, 320–341.

Powers, D. M. (2011). Evaluation: From precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv Preprint arXiv:2010.16061.

Pruim, R. H., Mennes, M., Rooij, D. van, Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-aroma: A robust ica-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267–277.

Pulvermüller, F., & Fadiga, L. (2010). Active perception: Sensorimotor circuits as a cortical basis for language. Nature Reviews Neuroscience, 11(5), 351–360.

Punitha, A., & Geetha, M. K. (2013). Texture based emotion recognition from facial expressions using support vector machine. Int. J. Comput. Appl., 80(5), 1–5.

Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., & Lawrence, N. D. (2009). Dataset shift in machine learning. The MIT Press.

Ramakrishnan, K., Scholte, H. S., Groen, I. I. A., Smeulders, A. W. M., & Ghebreab, S. (2014). Visual dictionaries as intermediate features in the human brain. Front. Comput. Neurosci., 8, 168.

Rao, A., Monteiro, J. M., Mourao-Miranda, J., & Alzheimer’s Disease Initiative. (2017). Predictive modelling using neuroimaging data in the presence of confounds. Neuroimage, 150, 23–49.

Raven, J. (2000). The raven’s progressive matrices: Change and stability over culture and time. Cogn. Psychol., 41(1), 1–48.

Raven, J., Court, J. H., & Raven, J. C. (1998). Manual for raven’s progressive matrices and vocabulary scales.

Reggio, G. (1982). Koyaanisqatsi. Institute for Regional Education/American Zoetrope.

Rimé, B., Delfosse, C., & Corsini, S. (2005). Emotion fascination: Responses to viewing pictures of september 11 attacks. Cognition & Emotion, 19(6), 923–932.

Ringach, D., & Shapley, R. (2004). Reverse correlation in neurophysiology. Cogn. Sci., 28(2), 147–166.

Ritchie, J. B., Kaplan, D. M., & Klein, C. (2017). Decoding the brain: Neural representation and the limits of multivariate pattern analysis in cognitive neuroscience. Br. J. Philos. Sci.

Rooij, I. van, & Baggio, G. (2021). Theory before the test: How to build High-Verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci., 1745691620970604.

Rosenblatt, J. D. (2016). Multivariate revisit to “sex beyond the genitalia”. Proc. Natl. Acad. Sci. U. S. A., 113(14), E1966–7.

Rosenblatt, J. D., Vink, M., & Benjamini, Y. (2014). Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation. Neuroimage, 84, 113–121.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. Int. J. Comput. Vis., 115(3), 211–252.

Rütgen, M., Seidel, E.-M., Silani, G., Riečansky, I., Hummer, A., Windischberger, C., Petrovic, P., & Lamm, C. (2015). Placebo analgesia and its opioidergic regulation suggest that empathy for pain is grounded in self pain. Proceedings of the National Academy of Sciences, 112(41), E5638–E5646.

Saad, Z. S., Reynolds, R. C., Jo, H. J., Gotts, S. J., Chen, G., Martin, A., & Cox, R. W. (2013). Correcting brain-wide correlation differences in resting-state FMRI. Brain Connect., 3(4), 339–352.

Sabatinelli, D., Fortune, E. E., Li, Q., Siddiqui, A., Krafft, C., Oliver, W. T., Beck, S., & Jeffries, J. (2011). Emotional perception: Meta-analyses of face and natural scene processing. Neuroimage, 54(3), 2524–2533.

Sahani, M., & Linden, J. F. (2003). How linear are auditory cortical responses? Adv. Neural Inf. Process. Syst., 125–132.

Said, C. P., Sebe, N., & Todorov, A. (2009). Structural resemblance to emotional expressions predicts evaluation of emotionally neutral faces. Emotion, 9(2), 260–264.

Sakaki, M., Yagi, A., & Murayama, K. (2018). Curiosity in old age: A possible key to achieving adaptive aging. Neuroscience & Biobehavioral Reviews, 88, 106–116.

Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in python using PyMC3. PeerJ Comput. Sci., 2, e55.

Samanez-Larkin, G. R., & Knutson, B. (2015). Decision making in the ageing brain: Changes in affective and motivational circuits. Nature Reviews Neuroscience, 16(5), 278–289.

Sato, W., & Yoshikawa, S. (2007). Enhanced experience of emotional arousal in response to dynamic facial expressions. J. Nonverbal Behav., 31(2), 119–135.

Satpute, A. B., Kragel, P. A., Barrett, L. F., Wager, T. D., & Bianciardi, M. (2019). Deconstructing arousal into wakeful, autonomic and affective varieties. Neuroscience Letters, 693, 19–28.

Schalk, J. van der, Hawk, S. T., Fischer, A. H., & Doosje, B. (2011). Moving faces, looking places: Validation of the amsterdam dynamic facial expression set (ADFES). Emotion, 11(4), 907–920.

Schäfer, T., & Schwarz, M. A. (2019). The meaningfulness of effect sizes in psychological research: Differences between Sub-Disciplines and the impact of potential biases. Front. Psychol., 10, 813.

Scholte, H. S. (2018). Fantastic DNimals and where to find them. Neuroimage, 180(Pt A), 112–113.

Schoppe, O., Harper, N. S., Willmore, B. D. B., King, A. J., & Schnupp, J. W. H. (2016). Measuring the performance of neural models. Front. Comput. Neurosci., 10, 10.

Schyns, P. G., Gosselin, F., & Smith, M. L. (2009). Information processing algorithms in the brain. Trends in Cognitive Sciences, 13(1), 20–26.

Scott, D. W. (1979). On optimal and data-based histograms. Biometrika, 66(3), 605–610.

Sedgwick, P. (2013). Analysing case-control studies: Adjusting for confounding. BMJ: British Medical Journal, 346.

Seijdel, N., Tsakmakidis, N., Haan, E. H. F. de, Bohte, S. M., & Scholte, H. S. (2020). Depth in convolutional neural networks solves scene segmentation. PLoS Comput. Biol., 16(7), e1008022.

Sepehrband, F., Lynch, K. M., Cabeen, R. P., Gonzalez-Zacarias, C., Zhao, L., D’Arcy, M., Kesselman, C., Herting, M. M., Dinov, I. D., Toga, A. W., & Clark, K. A. (2018). Neuroanatomical morphometric characterization of sex differences in youth using statistical learning. Neuroimage, 172, 217–227.

Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 1286–1291.

Silvia, P. J. (2008). Interest—the curious emotion. Current Directions in Psychological Science, 17(1), 57–60.

Singer, T. (2012). The past, present and future of social neuroscience: A european perspective. Neuroimage, 61(2), 437–449.

Singer, T., Critchley, H. D., & Preuschoff, K. (2009). A common role of insula in feelings, empathy and uncertainty. Trends in Cognitive Sciences, 13(8), 334–340.

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A., 106(31), 13040–13045.

Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., & Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31(4), 1487–1505.

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23 Suppl 1, S208–19.

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

Smith, S. M., & Nichols, T. E. (2018). Statistical challenges in “big data” human neuroimaging. Neuron, 97(2), 263–268.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020a). AOMIC-ID1000 NeuroVault.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020b). AOMIC-ID1000 OpenNeuro.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020c). AOMIC-PIOP1 NeuroVault.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020d). AOMIC-PIOP1 OpenNeuro.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020e). AOMIC-PIOP2 NeuroVault.

Snoek, L., Miesen, M. van der, Beemsterboer, T., Van Der Leij, A., Eigenhuis, A., & Scholte, H. S. &. (2020f). AOMIC-PIOP2 OpenNeuro.

Snoek, L., Miesen, M. M. van der, Beemsterboer, T., Leij, A. van der, Eigenhuis, A., & Steven Scholte, H. (2021). The amsterdam open MRI collection, a set of multimodal MRI datasets for individual difference analyses. Sci Data, 8(1), 85.

Snoek, L., Miletić, S., & Scholte, H. S. (2019). How to control for confounds in decoding analyses of neuroimaging data. Neuroimage, 184, 741–760.

Snoek, L., Mittenbühler, M., Jack, R., Schyns, P., Fischer, A., & Scholte, H. S. (n.d.). Explainable models of facial movements predict emotion perception behavior.

Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). STAI manual for the State-Trait anxiety inventory. Consulting Psychologists Press.

Spreng, R. N., Mar, R. A., & Kim, A. S. (2009). The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: A quantitative meta-analysis. Journal of Cognitive Neuroscience, 21(3), 489–510.

Spunt, R. P., & Lieberman, M. D. (2012). An integrative model of the neural systems supporting the comprehension of observed emotional behavior. Neuroimage, 59(3), 3050–3059.

Stelzer, J., Buschmann, T., Lohmann, G., Margulies, D. S., Trampel, R., & Turner, R. (2014). Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping. Frontiers in Neuroscience, 8, 66.

Streiner, D. L. (2006). Building a better model: An introduction to structural equation modelling. Can. J. Psychiatry, 51(5), 317–324.

Tamir, M. (2016). Why do people regulate their emotions? A taxonomy of motives in emotion regulation. Personality and Social Psychology Review, 20(3), 199–222.

Thorstenson, C. A., Elliot, A. J., Pazda, A. D., Perrett, D. I., & Xiao, D. (2018). Emotion-color associations in the context of the face. Emotion, 18(7), 1032–1042.

Tjur, T. (2009). Coefficients of determination in logistic regression Models—A new proposal: The coefficient of discrimination. Am. Stat., 63(4), 366–372.

Todd, M. T., Nystrom, L. E., & Cohen, J. D. (2013). Confounds in multivariate pattern analysis: Theory and rule representation case study. Neuroimage, 77, 157–165.

Todorov, A., Baron, S. G., & Oosterhof, N. N. (2008). Evaluating face trustworthiness: A model based approach. Soc. Cogn. Affect. Neurosci., 3(2), 119–127.

Toisoul, A., Kossaifi, J., Bulat, A., Tzimiropoulos, G., & Pantic, M. (2021). Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nature Machine Intelligence, 3(1), 42–50.

Tosh, C., Greengard, P., Goodrich, B., Gelman, A., Vehtari, A., & Hsu, D. (2020). The piranha problem: Large effects swimming in a small pond. http://www.stat.columbia.edu/~gelman/research/unpublished/piranhas.pdf; stat.columbia.edu.

Tottenham, N., Tanaka, J. W., Leon, A. C., McCarry, T., Nurse, M., Hare, T. A., Marcus, D. J., Westerlund, A., Casey, B. J., & Nelson, C. (2009). The NimStim set of facial expressions: Judgments from untrained research participants. Psychiatry Res., 168(3), 242–249.

Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 202, 116137.

Treiber, J. M., White, N. S., Steed, T. C., Bartsch, H., Holland, D., Farid, N., McDonald, C. R., Carter, B. S., Dale, A. M., & Chen, C. C. (2016). Characterization and correction of geometric distortions in 814 diffusion weighted images. PLoS One, 11(3), e0152472.

Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2017). Approaches to analysis in model-based cognitive neuroscience. J. Math. Psychol., 76(B), 65–79.

Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging, 29(6), 1310–1320.

Uddin, L. Q., Iacoboni, M., Lange, C., & Keenan, J. P. (2007). The self and social cognition: The role of cortical midline structures and mirror neurons. Trends in Cognitive Sciences, 11(4), 153–157.

Unkelbach, C., Fiedler, K., Bayer, M., Stegmüller, M., & Danner, D. (2008). Why positive information is processed faster: The density hypothesis. Journal of Personality and Social Psychology, 95(1), 36.

Van der Ploeg, H. M. (1980). Validity of the Zelf-Beoordelings-Vragenlijst (a dutch version of the spielberger State-Trait anxiety inventory). Ned. Tijdschr. Psychol., 35(4), 243–249.

Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Ugurbil, K., & WU-Minn HCP Consortium. (2013). The WU-Minn human connectome project: An overview. Neuroimage, 80, 62–79.

Van Haren, N. E., Cahn, W., Hulshoff Pol, H. E., & Kahn, R. S. (2013). Confounders of excessive brain volume loss in schizophrenia. Neurosci. Biobehav. Rev., 37(10 Pt 1), 2418–2423.

Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. Neuroimage, 48(3), 564–584.

Varoquaux, G. (2018). Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage, 180(Pt A), 68–77.

Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage, 145(Pt B), 166–179.

Varoquaux, G., & Thirion, B. (2014). How machine learning is shaping cognitive neuroimaging. Gigascience, 3, 28.

Veraart, J., Fieremans, E., & Novikov, D. S. (2016). Diffusion MRI noise mapping using random matrix theory. Magn. Reson. Med., 76(5), 1582–1593.

Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., & Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. Neuroimage, 142, 394–406.

Veraart, J., Sijbers, J., Sunaert, S., Leemans, A., & Jeurissen, B. (2013). Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls. Neuroimage, 81, 335–346.

Vorst, H. (2010). Intelligentie structuur test (IST). Hogrefe.

Waarde, J. A. van, Scholte, H. S., Oudheusden, L. J. B. van, Verwey, B., Denys, D., & Wingen, G. A. van. (2014). A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol. Psychiatry, 20, 609.

Wacholder, S., Silverman, D. T., McLaughlin, J. K., & Mandel, J. S. (1992). Selection of controls in case-control studies. III. Design options. Am. J. Epidemiol., 135(9), 1042–1050.

Wagenmakers, E.-J., Wetzels, R., Borsboom, D., Maas, H. L. J. van der, & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspect. Psychol. Sci., 7(6), 632–638.

Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A., & Ochsner, K. N. (2008). Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron, 59(6), 1037–1050.

Walther, A., Nili, H., Ejaz, N., Alink, A., Kriegeskorte, N., & Diedrichsen, J. (2016). Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage, 137, 188–200.

Wang, S., Peterson, D. J., Gatenby, J. C., Li, W., Grabowski, T. J., & Madhyastha, T. M. (2017). Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI. Front. Neuroinform., 11, 17.

Waskom, M., Botvinnik, O., Ostblom, J., Lukauskas, S., Hobson, P., MaozGelbart, Gemperline, D. C., Augspurger, T., Halchenko, Y., Cole, J. B., Warmenhoven, J., Ruiter, J. de, Pye, C., Hoyer, S., Vanderplas, J., Villalba, S., Kunter, G., Quintero, E., Bachant, P., … Evans, C. (2020). Mwaskom/seaborn: V0.10.0 (january 2020).

Waskom, M. L. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021.

Waytz, A., & Mitchell, J. P. (2011). Two mechanisms for simulating other minds: Dissociations between mirroring and self-projection. Current Directions in Psychological Science, 20(3), 197–200.

Weber, S. C., Kahnt, T., Quednow, B. B., & Tobler, P. N. (2018). Frontostriatal pathways gate processing of behaviorally relevant reward dimensions. PLoS Biology, 16(10), e2005722.

Wegrzyn, M., Vogt, M., Kireclioglu, B., Schneider, J., & Kissler, J. (2017). Mapping the emotional face. How individual face parts contribute to successful emotion recognition. PLoS One, 12(5), e0177239.

Weichwald, S., Meyer, T., Özdenizci, O., Schölkopf, B., Ball, T., & Grosse-Wentrup, M. (2015). Causal interpretation rules for encoding and decoding models in neuroimaging. Neuroimage, 110, 48–59.

Westfall, J., Nichols, T. E., & Yarkoni, T. (2016). Fixing the stimulus-as-fixed-effect fallacy in task fMRI. Wellcome Open Research, 1.

Westfall, J., & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. PloS One, 11(3), e0152719.

Wierzba, M., Riegel, M., Pucz, A., Leśniewska, Z., Dragan, W. Ł., Gola, M., Jednoróg, K., & Marchewka, A. (2015). Erotic subset for the nencki affective picture system (naps ero): Cross-sexual comparison study. Frontiers in Psychology, 6, 1336.

Wiggers, M. (1982). Judgments of facial expressions of emotion predicted from facial behavior. J. Nonverbal Behav., 7(2), 101–116.

Wilson-Mendenhall, C. D., Barrett, L. F., Simmons, W. K., & Barsalou, L. W. (2011). Grounding emotion in situated conceptualization. Neuropsychologia, 49(5), 1105–1127.

Windhager, S., Schaefer, K., & Fink, B. (2011). Geometric morphometrics of male facial shape in relation to physical strength and perceived attractiveness, dominance, and masculinity. Am. J. Hum. Biol., 23(6), 805–814.

Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92, 381–397.

Wood, K. H., Wheelock, M. D., Shumen, J. R., Bowen, K. H., Ver Hoef, L. W., & Knight, D. C. (2015). Controllability modulates the neural response to predictable but not unpredictable threat in humans. NeuroImage, 119, 371–381.

Woolgar, A., Golland, P., & Bode, S. (2014). Coping with confounds in multivoxel pattern analysis: What should we do about reaction time differences? A comment on todd, nystrom & cohen 2013. Neuroimage, 98, 506–512.

Woolgar, A., Thompson, R., Bor, D., & Duncan, J. (2011). Multi-voxel coding of stimuli, rules, and responses in human frontoparietal cortex. Neuroimage, 56(2), 744–752.

Woolrich, M. (2008). Robust group analysis using outlier inference. Neuroimage, 41(2), 286–301.

Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multilevel linear modelling for fmri group analysis using bayesian inference. Neuroimage, 21(4), 1732–1747.

Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal autocorrelation in univariate linear modeling of fmri data. Neuroimage, 14(6), 1370–1386.

Worsley, K. J. (2001). Statistical analysis of activation images. Functional MRI: An Introduction to Methods, 14(1), 251–270.

Wu, M. C.-K., David, S. V., & Gallant, J. L. (2006). Complete functional characterization of sensory neurons by system identification. Annu. Rev. Neurosci., 29, 477–505.

Wurm, M. F., & Lingnau, A. (2015). Decoding actions at different levels of abstraction. Journal of Neuroscience, 35(20), 7727–7735.

Xie, X., & Lam, K.-M. (2009). Facial expression recognition based on shape and texture. Pattern Recognit., 42(5), 1003–1011.

Xu, T., White, J., Kalkan, S., & Gunes, H. (2020). Investigating bias and fairness in facial expression recognition. Computer Vision – ECCV 2020 Workshops, 506–523.

Xu, T., Zhan, J., Garrod, O. G., Torr, P. H., Zhu, S.-C., Ince, R. A., & Schyns, P. G. (2018). Deeper interpretability of deep networks. arXiv Preprint arXiv:1811.07807.

Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624.

Yarkoni, T. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical Power—Commentary on vul et al. (2009). Perspect. Psychol. Sci., 4(3), 294–298.

Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670.

Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psychol. Sci., 1745691617693393.

Yu, H., Garrod, O. G. B., & Schyns, P. G. (2012). Perception-driven facial expression synthesis. Comput. Graph., 36(3), 152–162.

Yu-Feng, Z., Yong, H., Chao-Zhe, Z., Qing-Jiu, C., Man-Qiu, S., Meng, L., Li-Xia, T., Tian-Zi, J., & Yu-Feng, W. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 29(2), 83–91.

Zaki, J., & Ochsner, K. N. (2012). The neuroscience of empathy: Progress, pitfalls and promise. Nature Neuroscience, 15(5), 675–680.

Zaki, J., Wager, T. D., Singer, T., Keysers, C., & Gazzola, V. (2016). The anatomy of suffering: Understanding the relationship between nociceptive and empathic pain. Trends in Cognitive Sciences, 20(4), 249–259.

Zebrowitz, L. A. (2017). First impressions from faces. Curr. Dir. Psychol. Sci., 26(3), 237–242.

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med., 15(11), e1002683.

Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging, 20(1), 45–57.

Zuckerman, M. (1979). Sensation seeking. Beyond the optimal level of arousal. L. Erlbaum Associates.

Zuckerman, M., & Litle, P. (1986). Personality and curiosity about morbid and sexual events. Personality and Individual Differences, 7(1), 49–56.