{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:56:00Z","timestamp":1772150160157,"version":"3.50.1"},"publisher-location":"Cham","reference-count":72,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031637964","type":"print"},{"value":"9783031637971","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63797-1_10","type":"book-chapter","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T23:03:55Z","timestamp":1720566235000},"page":"178-201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Explainable Emotion Decoding for\u00a0Human and\u00a0Computer Vision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8850-090X","authenticated-orcid":false,"given":"Alessio","family":"Borriero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5246-8556","authenticated-orcid":false,"given":"Martina","family":"Milazzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-5183","authenticated-orcid":false,"given":"Matteo","family":"Diano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3085-562X","authenticated-orcid":false,"given":"Davide","family":"Orsenigo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1617-6081","authenticated-orcid":false,"given":"Maria Chiara","family":"Villa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8684-6714","authenticated-orcid":false,"given":"Chiara","family":"DiFazio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-8499","authenticated-orcid":false,"given":"Marco","family":"Tamietto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-6865","authenticated-orcid":false,"given":"Alan","family":"Perotti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2022.200171","volume":"17","author":"N Ahmed","year":"2023","unstructured":"Ahmed, N., Aghbari, Z.A., Girija, S.: A systematic survey on multimodal emotion recognition using learning algorithms. Intell. Syst. Appl. 17, 200171 (2023). https:\/\/doi.org\/10.1016\/j.iswa.2022.200171","journal-title":"Intell. Syst. Appl."},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Akamatsu, Y., Harakawa, R., Ogawa, T., Haseyama, M.: Perceived image decoding from brain activity using shared information of multi-subject fMRI data. IEEE Access 9, 26593\u201326606 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3057800. https:\/\/ieeexplore.ieee.org\/document\/9349437\/","DOI":"10.1109\/ACCESS.2021.3057800"},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Alexander-Bloch, A.F., et al.: On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540\u2013551 (2018). https:\/\/doi.org\/10.1016\/j.neuroimage.2018.05.070","DOI":"10.1016\/j.neuroimage.2018.05.070"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.conb.2019.01.007","volume":"55","author":"DG Barrett","year":"2019","unstructured":"Barrett, D.G., Morcos, A.S., Macke, J.H.: Analyzing biological and artificial neural networks: challenges with opportunities for synergy? Curr. Opin. Neurobiol. 55, 55\u201364 (2019). https:\/\/doi.org\/10.1016\/j.conb.2019.01.007","journal-title":"Curr. Opin. Neurobiol."},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"Barrett, L.F., Bliss-Moreau, E.: Affect as a psychological primitive. In: Advances in Experimental Social Psychology, vol.\u00a041, pp. 167\u2013218. Elsevier (2009). https:\/\/doi.org\/10.1016\/S0065-2601(08)00404-8","DOI":"10.1016\/S0065-2601(08)00404-8"},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.neuroimage.2011.07.037","volume":"59","author":"LB Baucom","year":"2012","unstructured":"Baucom, L.B., Wedell, D.H., Wang, J., Blitzer, D.N., Shinkareva, S.V.: Decoding the neural representation of affective states. Neuroimage 59(1), 718\u2013727 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2011.07.037","journal-title":"Neuroimage"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., Rinzivillo, S.: Benchmarking and Survey of Explanation Methods for Black Box Models (2021). https:\/\/doi.org\/10.48550\/arXiv.2102.13076. arXiv:2102.13076","DOI":"10.48550\/arXiv.2102.13076"},{"key":"10_CR9","doi-asserted-by":"publisher","unstructured":"(Bud)\u00a0Craig, A.D.: How do you feel - now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10(1), 59\u201370 (2009). https:\/\/doi.org\/10.1038\/nrn2555","DOI":"10.1038\/nrn2555"},{"issue":"8","key":"10_CR10","doi-asserted-by":"publisher","first-page":"2608","DOI":"10.1523\/JNEUROSCI.5547-11.2012","volume":"32","author":"AC Connolly","year":"2012","unstructured":"Connolly, A.C., et al.: The representation of biological classes in the human brain. J. Neurosci. 32(8), 2608\u20132618 (2012). https:\/\/doi.org\/10.1523\/JNEUROSCI.5547-11.2012","journal-title":"J. Neurosci."},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1146\/annurev-vision-091517-034202","volume":"4","author":"BR Conway","year":"2018","unstructured":"Conway, B.R.: The organization and operation of inferior temporal cortex. Annu. Rev. Vis. Sci. 4(1), 381\u2013402 (2018). https:\/\/doi.org\/10.1146\/annurev-vision-091517-034202","journal-title":"Annu. Rev. Vis. Sci."},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Cox, R.W., Hyde, J.S.: Software tools for analysis and visualization of fMRI data. NMR Biomed. 10(4\u20135), 171\u2013178 (1997). https:\/\/doi.org\/10.1002\/(SICI)1099-1492(199706\/08)10:4\/5<171::AID-NBM453>3.0.CO;2-L","DOI":"10.1002\/(SICI)1099-1492(199706\/08)10:4\/5<171::AID-NBM453>3.0.CO;2-L"},{"issue":"3","key":"10_CR13","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/TAFFC.2019.2940224","volume":"12","author":"Z Du","year":"2021","unstructured":"Du, Z., Wu, S., Huang, D., Li, W., Wang, Y.: Spatio-temporal encoder-decoder fully convolutional network for video-based dimensional emotion recognition. IEEE Trans. Affect. Comput. 12(3), 565\u2013578 (2021). https:\/\/doi.org\/10.1109\/TAFFC.2019.2940224","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"10_CR14","doi-asserted-by":"publisher","first-page":"2539","DOI":"10.1016\/j.neuroimage.2010.10.007","volume":"54","author":"J Fan","year":"2011","unstructured":"Fan, J., et al.: Involvement of the anterior cingulate and frontoinsular cortices in rapid processing of salient facial emotional information. Neuroimage 54(3), 2539\u20132546 (2011)","journal-title":"Neuroimage"},{"key":"10_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.906290","volume":"16","author":"FV Farahani","year":"2022","unstructured":"Farahani, F.V., Fiok, K., Lahijanian, B., Karwowski, W., Douglas, P.K.: Explainable AI: a review of applications to neuroimaging data. Front. Neurosci. 16, 906290 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.906290","journal-title":"Front. Neurosci."},{"issue":"1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S0031-3203(02)00052-3","volume":"36","author":"B Fasel","year":"2003","unstructured":"Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognit. 36(1), 259\u2013275 (2003)","journal-title":"Pattern Recognit."},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Firat, O., Oztekin, L., Vural, F.T.Y.: Deep learning for brain decoding. In: 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 2784\u20132788. IEEE (2014). https:\/\/doi.org\/10.1109\/ICIP.2014.7025563","DOI":"10.1109\/ICIP.2014.7025563"},{"issue":"7615","key":"10_CR18","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/nature18933","volume":"536","author":"MF Glasser","year":"2016","unstructured":"Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171\u2013178 (2016). https:\/\/doi.org\/10.1038\/nature18933","journal-title":"Nature"},{"key":"10_CR19","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks (2010)"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"Gunes, H., Schuller, B., Pantic, M., Cowie, R.: Emotion representation, analysis and synthesis in continuous space: a survey. In: Face and Gesture 2011, Santa Barbara, CA, USA, pp. 827\u2013834. IEEE (2011). https:\/\/doi.org\/10.1109\/FG.2011.5771357","DOI":"10.1109\/FG.2011.5771357"},{"issue":"2","key":"10_CR21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211735","volume":"14","author":"N Haines","year":"2019","unstructured":"Haines, N., Southward, M.W., Cheavens, J.S., Beauchaine, T., Ahn, W.Y.: Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS ONE 14(2), e0211735 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0211735","journal-title":"PLoS ONE"},{"key":"10_CR22","doi-asserted-by":"publisher","unstructured":"Hanke, M., et al.: Simultaneous fMRI and eye gaze recordings during prolonged natural stimulation - a studyforrest extension (2016). https:\/\/doi.org\/10.1101\/046581","DOI":"10.1101\/046581"},{"issue":"1","key":"10_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.3","volume":"1","author":"M Hanke","year":"2014","unstructured":"Hanke, M., et al.: A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Sci. Data 1(1), 140003 (2014). https:\/\/doi.org\/10.1038\/sdata.2014.3","journal-title":"Sci. Data"},{"issue":"2","key":"10_CR24","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1016\/j.neuroimage.2012.03.016","volume":"62","author":"JV Haxby","year":"2012","unstructured":"Haxby, J.V.: Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage 62(2), 852\u2013855 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.03.016","journal-title":"Neuroimage"},{"issue":"2","key":"10_CR25","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1038\/nrn730","volume":"3","author":"DJ Heeger","year":"2002","unstructured":"Heeger, D.J., Ress, D.: What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3(2), 142\u2013151 (2002). https:\/\/doi.org\/10.1038\/nrn730","journal-title":"Nat. Rev. Neurosci."},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Heinzle, J., et al.: Multivariate decoding of fMRI data: towards a content-based cognitive neuroscience. e-Neuroforum 18(1), 1\u201316 (2012). https:\/\/doi.org\/10.1007\/s13295-012-0026-9","DOI":"10.1007\/s13295-012-0026-9"},{"issue":"7","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1093\/brain\/awg168","volume":"126","author":"J Hornak","year":"2003","unstructured":"Hornak, J.: Changes in emotion after circumscribed surgical lesions of the orbitofrontal and cingulate cortices. Brain 126(7), 1691\u20131712 (2003). https:\/\/doi.org\/10.1093\/brain\/awg168","journal-title":"Brain"},{"issue":"8","key":"10_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0002939","volume":"3","author":"M Jabbi","year":"2008","unstructured":"Jabbi, M., Bastiaansen, J., Keysers, C.: A common anterior insula representation of disgust observation, experience and imagination shows divergent functional connectivity pathways. PLoS ONE 3(8), e2939 (2008). https:\/\/doi.org\/10.1371\/journal.pone.0002939","journal-title":"PLoS ONE"},{"issue":"2","key":"10_CR29","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782\u2013790 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2011.09.015","journal-title":"NeuroImage"},{"key":"10_CR30","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017). arXiv:1412.6980"},{"issue":"2","key":"10_CR31","doi-asserted-by":"publisher","first-page":"401","DOI":"10.3390\/s18020401","volume":"18","author":"B Ko","year":"2018","unstructured":"Ko, B.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018). https:\/\/doi.org\/10.3390\/s18020401","journal-title":"Sensors"},{"key":"10_CR32","unstructured":"Koyamada, S., Shikauchi, Y., Nakae, K., Koyama, M., Ishii, S.: Deep learning of fMRI big data: a novel approach to subject-transfer decoding (2015). arXiv:1502.00093"},{"issue":"11","key":"10_CR33","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1093\/scan\/nsv032","volume":"10","author":"PA Kragel","year":"2015","unstructured":"Kragel, P.A., LaBar, K.S.: Multivariate neural biomarkers of emotional states are categorically distinct. Soc. Cogn. Affect. Neurosci. 10(11), 1437\u20131448 (2015). https:\/\/doi.org\/10.1093\/scan\/nsv032","journal-title":"Soc. Cogn. Affect. Neurosci."},{"issue":"6","key":"10_CR34","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.tics.2016.03.011","volume":"20","author":"PA Kragel","year":"2016","unstructured":"Kragel, P.A., LaBar, K.S.: Decoding the nature of emotion in the brain. Trends Cogn. Sci. 20(6), 444\u2013455 (2016). https:\/\/doi.org\/10.1016\/j.tics.2016.03.011","journal-title":"Trends Cogn. Sci."},{"issue":"1","key":"10_CR35","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1146\/annurev-vision-082114-035447","volume":"1","author":"N Kriegeskorte","year":"2015","unstructured":"Kriegeskorte, N.: Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1(1), 417\u2013446 (2015). https:\/\/doi.org\/10.1146\/annurev-vision-082114-035447","journal-title":"Annu. Rev. Vis. Sci."},{"issue":"5","key":"10_CR36","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.pneurobio.2004.03.006","volume":"72","author":"M Kringelbach","year":"2004","unstructured":"Kringelbach, M.: The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog. Neurobiol. 72(5), 341\u2013372 (2004). https:\/\/doi.org\/10.1016\/j.pneurobio.2004.03.006","journal-title":"Prog. Neurobiol."},{"key":"10_CR37","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol.\u00a025. Curran Associates, Inc. (2012)"},{"key":"10_CR38","doi-asserted-by":"publisher","unstructured":"Kubilius, J., Baeck, A., Wagemans, J., Op\u00a0De\u00a0Beeck, H.P.: Brain-decoding fMRI reveals how wholes relate to the sum of parts. Cortex 72, 5\u201314 (2015). https:\/\/doi.org\/10.1016\/j.cortex.2015.01.020","DOI":"10.1016\/j.cortex.2015.01.020"},{"key":"10_CR39","doi-asserted-by":"publisher","unstructured":"Labs, A., et al.: Portrayed emotions in the movie \u201cForrest Gump\u201d. F1000Research 4, 92 (2015). https:\/\/doi.org\/10.12688\/f1000research.6230.1","DOI":"10.12688\/f1000research.6230.1"},{"issue":"6","key":"10_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.crmeth.2022.100227","volume":"2","author":"S Lee","year":"2022","unstructured":"Lee, S., Bradlow, E.T., Kable, J.W.: Fast construction of interpretable whole-brain decoders. Cell Rep. Methods 2(6), 100227 (2022). https:\/\/doi.org\/10.1016\/j.crmeth.2022.100227","journal-title":"Cell Rep. Methods"},{"issue":"1","key":"10_CR41","doi-asserted-by":"publisher","first-page":"5568","DOI":"10.1038\/s41467-019-13599-z","volume":"10","author":"G Lettieri","year":"2019","unstructured":"Lettieri, G., et al.: Emotionotopy in the human right temporo-parietal cortex. Nat. Commun. 10(1), 5568 (2019). https:\/\/doi.org\/10.1038\/s41467-019-13599-z","journal-title":"Nat. Commun."},{"key":"10_CR42","doi-asserted-by":"publisher","unstructured":"Liang, Y., Liu, B.: Cross-subject commonality of emotion representations in dorsal motion-sensitive areas. Front. Neurosci. 14, 567797 (2020). https:\/\/doi.org\/10.3389\/fnins.2020.567797. https:\/\/www.frontiersin.org\/article\/10.3389\/fnins.2020.567797\/full","DOI":"10.3389\/fnins.2020.567797"},{"issue":"3","key":"10_CR43","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1007\/s42761-023-00215-z","volume":"4","author":"C Lin","year":"2023","unstructured":"Lin, C., Bulls, L.S., Tepfer, L.J., Vyas, A.D., Thornton, M.A.: Advancing naturalistic affective science with deep learning. Affect. Sci. 4(3), 550\u2013562 (2023). https:\/\/doi.org\/10.1007\/s42761-023-00215-z","journal-title":"Affect. Sci."},{"issue":"3","key":"10_CR44","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1017\/S0140525X11000446","volume":"35","author":"KA Lindquist","year":"2012","unstructured":"Lindquist, K.A., Wager, T.D., Kober, H., Bliss-Moreau, E., Barrett, L.F.: The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 35(3), 121\u2013143 (2012). https:\/\/doi.org\/10.1017\/S0140525X11000446","journal-title":"Behav. Brain Sci."},{"issue":"10","key":"10_CR45","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1162\/jocn_a_01544","volume":"33","author":"GW Lindsay","year":"2021","unstructured":"Lindsay, G.W.: Convolutional neural networks as a model of the visual system: past, present, and future. J. Cogn. Neurosci. 33(10), 2017\u20132031 (2021)","journal-title":"J. Cogn. Neurosci."},{"key":"10_CR46","doi-asserted-by":"publisher","unstructured":"Lopes, A.T., De\u00a0Aguiar, E., De\u00a0Souza, A.F., Oliveira-Santos, T.: Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recognit. 61, 610\u2013628 (2017). https:\/\/doi.org\/10.1016\/j.patcog.2016.07.026","DOI":"10.1016\/j.patcog.2016.07.026"},{"key":"10_CR47","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"10_CR48","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.procs.2020.07.101","volume":"175","author":"W Mellouk","year":"2020","unstructured":"Mellouk, W., Handouzi, W.: Facial emotion recognition using deep learning: review and insights. Procedia Comput. Sci. 175, 689\u2013694 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.07.101","journal-title":"Procedia Comput. Sci."},{"key":"10_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","journal-title":"Artif. Intell."},{"issue":"4","key":"10_CR50","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1177\/1073858411416515","volume":"18","author":"DG Mitchell","year":"2012","unstructured":"Mitchell, D.G., Greening, S.G.: Conscious perception of emotional stimuli: brain mechanisms. Neuroscientist 18(4), 386\u2013398 (2012)","journal-title":"Neuroscientist"},{"issue":"2","key":"10_CR51","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1162\/jocn.2008.20024","volume":"20","author":"T Morita","year":"2008","unstructured":"Morita, T., et al.: The role of the right prefrontal cortex in self-evaluation of the face: a functional magnetic resonance imaging study. J. Cogn. Neurosci. 20(2), 342\u2013355 (2008)","journal-title":"J. Cogn. Neurosci."},{"issue":"5","key":"10_CR52","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1093\/scan\/nst011","volume":"9","author":"T Morita","year":"2014","unstructured":"Morita, T., Tanabe, H.C., Sasaki, A.T., Shimada, K., Kakigi, R., Sadato, N.: The anterior insular and anterior cingulate cortices in emotional processing for self-face recognition. Soc. Cogn. Affect. Neurosci. 9(5), 570\u2013579 (2014). https:\/\/doi.org\/10.1093\/scan\/nst011","journal-title":"Soc. Cogn. Affect. Neurosci."},{"issue":"3","key":"10_CR53","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3758\/CABN.3.3.207","volume":"3","author":"FC Murphy","year":"2003","unstructured":"Murphy, F.C., Nimmo-Smith, I., Lawrence, A.D.: Functional neuroanatomy of emotions: a meta-analysis. Cogn. Affect. Behav. Neurosci. 3(3), 207\u2013233 (2003). https:\/\/doi.org\/10.3758\/CABN.3.3.207","journal-title":"Cogn. Affect. Behav. Neurosci."},{"issue":"6","key":"10_CR54","doi-asserted-by":"publisher","first-page":"2682","DOI":"10.1093\/cercor\/bhac235","volume":"33","author":"N Pat","year":"2023","unstructured":"Pat, N., Wang, Y., Bartonicek, A., Candia, J., Stringaris, A.: Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cereb. Cortex 33(6), 2682\u20132703 (2023). https:\/\/doi.org\/10.1093\/cercor\/bhac235","journal-title":"Cereb. Cortex"},{"key":"10_CR55","doi-asserted-by":"publisher","unstructured":"Pikoulis, I., Filntisis, P.P., Maragos, P.: Leveraging semantic scene characteristics and multi-stream convolutional architectures in a contextual approach for video-based visual emotion recognition in the wild. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), Jodhpur, India, pp. 01\u201308. IEEE (2021). https:\/\/doi.org\/10.1109\/FG52635.2021.9666957","DOI":"10.1109\/FG52635.2021.9666957"},{"key":"10_CR56","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1135\u20131144. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"9","key":"10_CR57","doi-asserted-by":"publisher","first-page":"3001","DOI":"10.1007\/s00429-019-01945-2","volume":"224","author":"ET Rolls","year":"2019","unstructured":"Rolls, E.T.: The cingulate cortex and limbic systems for emotion, action, and memory. Brain Struct. Funct. 224(9), 3001\u20133018 (2019)","journal-title":"Brain Struct. Funct."},{"issue":"1","key":"10_CR58","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s41583-020-00395-8","volume":"22","author":"A Saxe","year":"2021","unstructured":"Saxe, A., Nelli, S., Summerfield, C.: If deep learning is the answer, what is the question? Nat. Rev. Neurosci. 22(1), 55\u201367 (2021). https:\/\/doi.org\/10.1038\/s41583-020-00395-8","journal-title":"Nat. Rev. Neurosci."},{"key":"10_CR59","doi-asserted-by":"publisher","DOI":"10.1101\/407007","author":"M Schrimpf","year":"2018","unstructured":"Schrimpf, M., et al.: Brain-score: which artificial neural network for object recognition is most brain-like? Neuroscience (2018). https:\/\/doi.org\/10.1101\/407007","journal-title":"Neuroscience"},{"issue":"1","key":"10_CR60","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.93","volume":"3","author":"A Sengupta","year":"2016","unstructured":"Sengupta, A., et al.: A studyforrest extension, retinotopic mapping and localization of higher visual areas. Sci. Data 3(1), 160093 (2016). https:\/\/doi.org\/10.1038\/sdata.2016.93","journal-title":"Sci. Data"},{"key":"10_CR61","doi-asserted-by":"publisher","unstructured":"Serengil, S.I., Ozpinar, A.: LightFace: a hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp.\u00a01\u20135 (2020). https:\/\/doi.org\/10.1109\/ASYU50717.2020.9259802","DOI":"10.1109\/ASYU50717.2020.9259802"},{"issue":"1","key":"10_CR62","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"issue":"2","key":"10_CR63","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1176\/jnp.23.2.jnp121","volume":"23","author":"FL Stevens","year":"2011","unstructured":"Stevens, F.L.: Anterior cingulate cortex: unique role in cognition and emotion. J. Neuropsychiatry Clin. Neurosci. 23(2), 121\u2013125 (2011)","journal-title":"J. Neuropsychiatry Clin. Neurosci."},{"issue":"39","key":"10_CR64","doi-asserted-by":"publisher","first-page":"16188","DOI":"10.1073\/pnas.1107214108","volume":"108","author":"J Van den Stock","year":"2011","unstructured":"Van den Stock, J., Tamietto, M., Sorger, B., Pichon, S., Gr\u00e9zes, J., de Gelder, B.: Cortico-subcortical visual, somatosensory, and motor activations for perceiving dynamic whole-body emotional expressions with and without striate cortex (V1). Proc. Natl. Acad. Sci. 108(39), 16188\u201316193 (2011)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10_CR65","unstructured":"Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)"},{"key":"10_CR66","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2022.04.019","volume":"492","author":"S Thuseethan","year":"2022","unstructured":"Thuseethan, S., Rajasegarar, S., Yearwood, J.: EmoSeC: emotion recognition from scene context. Neurocomputing 492, 174\u2013187 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.04.019","journal-title":"Neurocomputing"},{"issue":"12","key":"10_CR67","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1162\/jocn.2009.21366","volume":"22","author":"K Vytal","year":"2010","unstructured":"Vytal, K., Hamann, S.: Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis. J. Cogn. Neurosci. 22(12), 2864\u20132885 (2010). https:\/\/doi.org\/10.1162\/jocn.2009.21366","journal-title":"J. Cogn. Neurosci."},{"issue":"4","key":"10_CR68","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1093\/scan\/nsaa057","volume":"15","author":"ME Weaverdyck","year":"2020","unstructured":"Weaverdyck, M.E., Lieberman, M.D., Parkinson, C.: Tools of the trade multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists. Soc. Cogn. Affect. Neurosci. 15(4), 487\u2013509 (2020). https:\/\/doi.org\/10.1093\/scan\/nsaa057","journal-title":"Soc. Cogn. Affect. Neurosci."},{"issue":"3","key":"10_CR69","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nn.4244","volume":"19","author":"DLK Yamins","year":"2016","unstructured":"Yamins, D.L.K., DiCarlo, J.J.: Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19(3), 356\u2013365 (2016). https:\/\/doi.org\/10.1038\/nn.4244","journal-title":"Nat. Neurosci."},{"issue":"2","key":"10_CR70","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","volume":"26","author":"Y Yao","year":"2007","unstructured":"Yao, Y., Rosasco, L., Caponnetto, A.: On early stopping in gradient descent learning. Constr. Approx. 26(2), 289\u2013315 (2007). https:\/\/doi.org\/10.1007\/s00365-006-0663-2","journal-title":"Constr. Approx."},{"key":"10_CR71","doi-asserted-by":"publisher","unstructured":"Yousefnezhad, M., Selvitella, A., Han, L., Zhang, D.: Supervised hyperalignment for multi-subject fMRI data alignment. IEEE Trans. Cogn. Dev. Syst. 13(3), 475\u2013490 (2021). https:\/\/doi.org\/10.1109\/TCDS.2020.2965981. http:\/\/arxiv.org\/abs\/2001.02894, arXiv:2001.02894","DOI":"10.1109\/TCDS.2020.2965981"},{"issue":"1","key":"10_CR72","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2021). https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc. IEEE"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63797-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T23:24:23Z","timestamp":1720567463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63797-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031637964","9783031637971"],"references-count":72,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63797-1_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}