{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T04:12:13Z","timestamp":1729656733553,"version":"3.28.0"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"4-5","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Khoury College of Computer Science"},{"name":"Northeastern University USA"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Biol Cybern"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on others that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking of difficulty for our transforms for human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.<\/jats:p>","DOI":"10.1007\/s00422-023-00968-7","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:02:23Z","timestamp":1686650543000},"page":"331-343","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extreme image transformations affect humans and machines differently"],"prefix":"10.1007","volume":"117","author":[{"given":"Girik","family":"Malik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dakarai","family":"Crowder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ennio","family":"Mingolla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"issue":"11","key":"968_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2012) Slic Superpixels compared to state-of-the-art Superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"968_CR2","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2010) Slic superpixels. Technical report"},{"key":"968_CR3","doi-asserted-by":"crossref","unstructured":"Al-Ali S, Milanova M, Al-Rizzo H, Fox VL (2015) Human action recognition: contour-based and silhouette-based approaches. In: Computer vision in control systems-2, pp 11\u201347. Springer","DOI":"10.1007\/978-3-319-11430-9_2"},{"issue":"5","key":"968_CR4","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1093\/cercor\/4.5.544","volume":"4","author":"T Allison","year":"1994","unstructured":"Allison T, McCarthy G, Nobre A, Puce A, Belger A (1994) Human extrastriate visual cortex and the perception of faces, words, numbers, and colors. Cereb Cortex 4(5):544\u2013554","journal-title":"Cereb Cortex"},{"issue":"12","key":"968_CR5","doi-asserted-by":"publisher","first-page":"1006613","DOI":"10.1371\/journal.pcbi.1006613","volume":"14","author":"N Baker","year":"2018","unstructured":"Baker N, Lu H, Erlikhman G, Kellman PJ (2018) Deep convolutional networks do not classify based on global object shape. PLoS Comput Biol 14(12):1006613","journal-title":"PLoS Comput Biol"},{"key":"968_CR6","doi-asserted-by":"crossref","unstructured":"Ballester P, Araujo R (2016) On the performance of googlenet and alexnet applied to sketches. In: Proceedings of the AAAI conference on artificial intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.10171"},{"key":"968_CR7","first-page":"2556","volume":"34","author":"M Baradad Jurjo","year":"2021","unstructured":"Baradad Jurjo M, Wulff J, Wang T, Isola P, Torralba A (2021) Learning to see by looking at noise. Adv Neural Inf Process Syst 34:2556\u20132569","journal-title":"Adv Neural Inf Process Syst"},{"key":"968_CR8","unstructured":"Bear M, Connors B, Paradiso MA (2020) Neuroscience: Exploring the brain, enhanced edition: exploring the brain, enhanced edition. Jones & Bartlett Learning, ???. https:\/\/books.google.com\/books?id=m-PcDwAAQBAJ"},{"issue":"5\u20136","key":"968_CR9","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.jphysparis.2011.12.001","volume":"106","author":"JA Bednar","year":"2012","unstructured":"Bednar JA (2012) Building a mechanistic model of the development and function of the primary visual cortex. J Physiol Paris 106(5\u20136):194\u2013211","journal-title":"J Physiol Paris"},{"key":"968_CR10","doi-asserted-by":"crossref","unstructured":"Beleznai C, Bischof H (2009) Fast human detection in crowded scenes by contour integration and local shape estimation. In: 2009 IEEE Conference on computer vision and pattern recognition, pp 2246\u20132253","DOI":"10.1109\/CVPR.2009.5206564"},{"issue":"3","key":"968_CR11","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/0010-0285(91)90014-F","volume":"23","author":"I Biederman","year":"1991","unstructured":"Biederman I, Cooper EE (1991) Priming contour-deleted images: evidence for intermediate representations in visual object recognition. Cogn Psychol 23(3):393\u2013419","journal-title":"Cogn Psychol"},{"key":"968_CR12","unstructured":"Brendel W, Bethge M (2019) Approximating cnns with bag-of-local-features models works surprisingly well on imagenet. arXiv preprint arXiv:1904.00760"},{"issue":"46","key":"968_CR13","doi-asserted-by":"publisher","first-page":"10577","DOI":"10.1523\/JNEUROSCI.3726-05.2005","volume":"25","author":"M Carandini","year":"2005","unstructured":"Carandini M, Demb JB, Mante V, Tolhurst DJ, Dan Y, Olshausen BA, Gallant JL, Rust NC (2005) Do we know what the early visual system does? J Neurosci 25(46):10577\u201310597","journal-title":"J Neurosci"},{"issue":"15","key":"968_CR14","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1016\/j.patrec.2013.01.021","volume":"34","author":"AA Chaaraoui","year":"2013","unstructured":"Chaaraoui AA, Climent-P\u00e9rez P, Fl\u00f3rez-Revuelta F (2013) Silhouette-based human action recognition using sequences of key poses. Pattern Recogn Lett 34(15):1799\u20131807","journal-title":"Pattern Recogn Lett"},{"key":"968_CR15","doi-asserted-by":"crossref","unstructured":"Chen X, Xie C, Tan M, Zhang L, Hsieh C-J, Gong B (2021) Robust and accurate object detection via adversarial learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16622\u201316631","DOI":"10.1109\/CVPR46437.2021.01635"},{"key":"968_CR16","doi-asserted-by":"crossref","unstructured":"Corbett JE, Utochkin I, Hochstein S (2023) The pervasiveness of ensemble perception: not just your average review. Cambridge University Press","DOI":"10.1017\/9781009222716"},{"key":"968_CR17","unstructured":"Crowder D, Malik G (2022) Robustness of humans and machines on object recognition with extreme image transformations. CVPR Workshop on What can computer vision learn from visual neuroscience?"},{"key":"968_CR18","first-page":"13073","volume":"33","author":"J Dapello","year":"2020","unstructured":"Dapello J, Marques T, Schrimpf M, Geiger F, Cox D, DiCarlo JJ (2020) Simulating a primary visual cortex at the front of CNNS improves robustness to image perturbations. Adv Neural Inf Process Syst 33:13073\u201313087","journal-title":"Adv Neural Inf Process Syst"},{"key":"968_CR19","first-page":"15595","volume":"34","author":"J Dapello","year":"2021","unstructured":"Dapello J, Feather J, Le H, Marques T, Cox D, McDermott J, DiCarlo JJ, Chung S (2021) Neural population geometry reveals the role of stochasticity in robust perception. Adv Neural Inf Process Syst 34:15595\u201315607","journal-title":"Adv Neural Inf Process Syst"},{"key":"968_CR20","doi-asserted-by":"crossref","unstructured":"De\u00a0Bonet JS, Viola P (1998) Texture recognition using a non-parametric multi-scale statistical model. In: Proceedings. 1998 IEEE computer society conference on computer vision and pattern recognition (Cat. No. 98CB36231), pp 641\u2013647. IEEE","DOI":"10.1109\/CVPR.1998.698672"},{"key":"968_CR21","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition, pp 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"968_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/fncom.2018.00004","volume":"12","author":"Q Dong","year":"2018","unstructured":"Dong Q, Wang H, Hu Z (2018) Commentary: Using goal-driven deep learning models to understand sensory cortex. Front Comput Neurosci 12:4","journal-title":"Front Comput Neurosci"},{"key":"968_CR23","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"issue":"1","key":"968_CR24","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1113\/jphysiol.1991.sp018733","volume":"440","author":"RJ Douglas","year":"1991","unstructured":"Douglas RJ, Martin K (1991) A functional microcircuit for cat visual cortex. J Physiol 440(1):735\u2013769","journal-title":"J Physiol"},{"key":"968_CR25","unstructured":"Edelman S, Intrator N, Poggio T (1997) Complex cells and object recognition"},{"key":"968_CR26","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.cobeha.2017.06.005","volume":"17","author":"AD Ekstrom","year":"2017","unstructured":"Ekstrom AD, Isham EA (2017) Human spatial navigation: Representations across dimensions and scales. Curr Opin Behav Sci 17:84\u201389","journal-title":"Curr Opin Behav Sci"},{"issue":"D1","key":"968_CR27","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1093\/nar\/gkv1208","volume":"44","author":"CG Elsik","year":"2016","unstructured":"Elsik CG, Tayal A, Diesh CM, Unni DR, Emery ML, Nguyen HN, Hagen DE (2016) Hymenoptera genome database: integrating genome annotations in hymenopteramine. Nucleic Acids Res 44(D1):793\u2013800","journal-title":"Nucleic Acids Res"},{"key":"968_CR28","unstructured":"fast.ai, Howard J. Imagenette. https:\/\/github.com\/fastai\/imagenette"},{"issue":"1","key":"968_CR29","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TPAMI.2007.1144","volume":"30","author":"V Ferrari","year":"2007","unstructured":"Ferrari V, Fevrier L, Jurie F, Schmid C (2007) Groups of adjacent contour segments for object detection. IEEE Trans Pattern Anal Mach Intell 30(1):36\u201351","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"968_CR30","doi-asserted-by":"publisher","first-page":"0177385","DOI":"10.1371\/journal.pone.0177385","volume":"12","author":"MR Frank","year":"2017","unstructured":"Frank MR, Cebrian M, Pickard G, Rahwan I (2017) Validating Bayesian truth serum in large-scale online human experiments. PLoS ONE 12(5):0177385","journal-title":"PLoS ONE"},{"key":"968_CR31","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050\u20131059. PMLR"},{"key":"968_CR32","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.conb.2017.08.019","volume":"46","author":"LA Gatys","year":"2017","unstructured":"Gatys LA, Ecker AS, Bethge M (2017) Texture and art with deep neural networks. Curr Opin Neurobiol 46:178\u2013186","journal-title":"Curr Opin Neurobiol"},{"issue":"11","key":"968_CR33","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos R, Jacobsen J-H, Michaelis C, Zemel R, Brendel W, Bethge M, Wichmann FA (2020) Shortcut learning in deep neural networks. Nat Mach Intell 2(11):665\u2013673","journal-title":"Nat Mach Intell"},{"key":"968_CR34","unstructured":"Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W (2019) Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=Bygh9j09KX"},{"key":"968_CR35","unstructured":"Geirhos R, Temme CR, Rauber J, Sch\u00fctt HH, Bethge M, Wichmann FA (2018) Generalisation in humans and deep neural networks. Adv Neural Inform Proc Syst. 31"},{"issue":"13","key":"968_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1167\/7.13.7","volume":"7","author":"MA Georgeson","year":"2007","unstructured":"Georgeson MA, May KA, Freeman TC, Hesse GS (2007) From filters to features: Scale-space analysis of edge and blur coding in human vision. J Vis 7(13):7\u20137","journal-title":"J Vis"},{"issue":"10\u201311","key":"968_CR37","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1016\/S0042-6989(01)00073-6","volume":"41","author":"K Grill-Spector","year":"2001","unstructured":"Grill-Spector K, Kourtzi Z, Kanwisher N (2001) The lateral occipital complex and its role in object recognition. Vision Res 41(10\u201311):1409\u20131422","journal-title":"Vision Res"},{"key":"968_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"968_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"968_CR40","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1016\/S0896-6273(02)01091-7","volume":"36","author":"S Hochstein","year":"2002","unstructured":"Hochstein S, Ahissar M (2002) View from the top: Hierarchies and reverse hierarchies in the visual system. Neuron 36(5):791\u2013804","journal-title":"Neuron"},{"issue":"1","key":"968_CR41","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex. J Physiol 160(1):106","journal-title":"J Physiol"},{"issue":"3","key":"968_CR42","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1113\/jphysiol.1963.sp007079","volume":"165","author":"DH Hubel","year":"1963","unstructured":"Hubel DH, Wiesel TN (1963) Shape and arrangement of columns in cat\u2019s striate cortex. J Physiol 165(3):559","journal-title":"J Physiol"},{"issue":"6","key":"968_CR43","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1152\/jn.1963.26.6.994","volume":"26","author":"DH Hubel","year":"1963","unstructured":"Hubel DH, Wiesel TN (1963) Receptive fields of cells in striate cortex of very young, visually inexperienced kittens. J Neurophysiol 26(6):994\u20131002","journal-title":"J Neurophysiol"},{"key":"968_CR44","doi-asserted-by":"crossref","unstructured":"Kaneko T, Harada T (2020) Noise robust generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8404\u20138414","DOI":"10.1109\/CVPR42600.2020.00843"},{"key":"968_CR45","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.brainres.2009.12.006","volume":"1313","author":"A Keil","year":"2010","unstructured":"Keil A, M\u00fcller MM (2010) Feature selection in the human brain: electrophysiological correlates of sensory enhancement and feature integration. Brain Res 1313:172\u2013184","journal-title":"Brain Res"},{"issue":"5","key":"968_CR46","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s00138-009-0233-8","volume":"22","author":"V Kellokumpu","year":"2011","unstructured":"Kellokumpu V, Zhao G, Pietik\u00e4inen M (2011) Recognition of human actions using texture descriptors. Mach Vis Appl 22(5):767\u2013780","journal-title":"Mach Vis Appl"},{"issue":"5","key":"968_CR47","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/BF00336961","volume":"50","author":"JJ Koenderink","year":"1984","unstructured":"Koenderink JJ (1984) The structure of images. Biol Cybern 50(5):363\u2013370","journal-title":"Biol Cybern"},{"issue":"2","key":"968_CR48","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s00422-021-00870-0","volume":"115","author":"J Koenderink","year":"2021","unstructured":"Koenderink J (2021) The structure of images: 1984\u20132021. Biol Cybern 115(2):117\u2013120","journal-title":"Biol Cybern"},{"issue":"2","key":"968_CR49","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1167\/17.2.7","volume":"17","author":"J Koenderink","year":"2017","unstructured":"Koenderink J, Valsecchi M, van Doorn A, Wagemans J, Gegenfurtner K (2017) Eidolons: Novel stimuli for vision research. J Vis 17(2):7\u20137","journal-title":"J Vis"},{"issue":"7","key":"968_CR50","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1364\/JOSAA.20.001434","volume":"20","author":"TS Lee","year":"2003","unstructured":"Lee TS, Mumford D (2003) Hierarchical Bayesian inference in the visual cortex. JOSA A 20(7):1434\u20131448","journal-title":"JOSA A"},{"issue":"21","key":"968_CR51","doi-asserted-by":"publisher","first-page":"11742","DOI":"10.1073\/pnas.94.21.11742","volume":"94","author":"DM Levi","year":"1997","unstructured":"Levi DM, Sharma V, Klein SA (1997) Feature integration in pattern perception. Proc Natl Acad Sci 94(21):11742\u201311746","journal-title":"Proc Natl Acad Sci"},{"key":"968_CR52","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2017.02.105","volume":"288","author":"D Lin","year":"2018","unstructured":"Lin D, Lin F, Lv Y, Cai F, Cao D (2018) Chinese character captcha recognition and performance estimation via deep neural network. Neurocomputing 288:11\u201319","journal-title":"Neurocomputing"},{"key":"968_CR53","unstructured":"Lindeberg T (2013) Scale-space Theory in Computer Vision, vol 256. Springer"},{"key":"968_CR54","unstructured":"Linsley D, Malik G, Kim J, Govindarajan LN, Mingolla E, Serre T (2021) Tracking without re-recognition in humans and machines. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol 34, pp 19473\u201319486. Curran Associates, Inc., ???. https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/a2557a7b2e94197ff767970b67041697-Paper.pdf"},{"key":"968_CR55","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, W., Yang, Q., Li, B., Yuan, Y.: Towards robust adaptive object detection under noisy annotations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14207\u201314216 (2022)","DOI":"10.1109\/CVPR52688.2022.01381"},{"key":"968_CR56","doi-asserted-by":"crossref","unstructured":"Liu X, Cheng M, Zhang H, Hsieh C-J (2018) Towards robust neural networks via random self-ensemble. In: Proceedings of the European conference on computer vision (ECCV), pp 369\u2013385","DOI":"10.1007\/978-3-030-01234-2_23"},{"key":"968_CR57","unstructured":"Malik G, Linsley D, Serre T, Mingolla E (2021) The challenge of appearance-free object tracking with feedforward neural networks. CVPR Workshop on Dynamic Neural Networks Meet Computer Vision"},{"key":"968_CR58","doi-asserted-by":"publisher","first-page":"979","DOI":"10.3758\/s13423-015-0842-3","volume":"23","author":"A Martin","year":"2016","unstructured":"Martin A (2016) Grapes-grounding representations in action, perception, and emotion systems: how object properties and categories are represented in the human brain. Psychon Bull Rev 23:979\u2013990","journal-title":"Psychon Bull Rev"},{"issue":"7540","key":"968_CR59","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"968_CR60","doi-asserted-by":"crossref","unstructured":"Moon G, Kwon H, Lee KM, Cho M (2021) Integralaction: Pose-driven feature integration for robust human action recognition in videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3339\u20133348","DOI":"10.1109\/CVPRW53098.2021.00372"},{"key":"968_CR61","doi-asserted-by":"crossref","unstructured":"Mori G, Ren X, Efros AA, Malik J (2004) Recovering human body configurations: Combining segmentation and recognition. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004., 2 IEEE","DOI":"10.1109\/CVPR.2004.1315182"},{"issue":"suppl-1","key":"968_CR62","first-page":"658","volume":"39","author":"MC Munoz-Torres","year":"2010","unstructured":"Munoz-Torres MC, Reese JT, Childers CP, Bennett AK, Sundaram JP, Childs KL, Anzola JM, Milshina N, Elsik CG (2010) Hymenoptera genome database: integrated community resources for insect species of the order hymenoptera. Nucleic Acids Res 39(suppl-1):658\u2013662","journal-title":"Nucleic Acids Res"},{"key":"968_CR63","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427\u2013436","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"968_CR64","doi-asserted-by":"crossref","unstructured":"Noury Z, Rezaei M (2020) Deep-captcha: a deep learning based captcha solver for vulnerability assessment. arXiv preprint arXiv:2006.08296","DOI":"10.31219\/osf.io\/km35b"},{"issue":"12","key":"968_CR65","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.tics.2007.09.009","volume":"11","author":"A Oliva","year":"2007","unstructured":"Oliva A, Torralba A (2007) The role of context in object recognition. Trends Cogn Sci 11(12):520\u2013527","journal-title":"Trends Cogn Sci"},{"issue":"7","key":"968_CR66","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/34.56205","volume":"12","author":"P Perona","year":"1990","unstructured":"Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629\u2013639","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"968_CR67","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/TSMCC.2011.2178594","volume":"42","author":"OP Popoola","year":"2012","unstructured":"Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition-a review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865\u2013878","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"19","key":"968_CR68","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1016\/j.visres.2004.04.006","volume":"44","author":"LW Renninger","year":"2004","unstructured":"Renninger LW, Malik J (2004) When is scene identification just texture recognition? Vision Res 44(19):2301\u20132311","journal-title":"Vision Res"},{"issue":"424","key":"968_CR69","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1080\/01621459.1993.10476408","volume":"88","author":"PJ Rousseeuw","year":"1993","unstructured":"Rousseeuw PJ, Croux C (1993) Alternatives to the median absolute deviation. J Am Stat Assoc 88(424):1273\u20131283","journal-title":"J Am Stat Assoc"},{"key":"968_CR70","doi-asserted-by":"crossref","unstructured":"Rusak E, Schott L, Zimmermann RS, Bitterwolf J, Bringmann O, Bethge M, Brendel W (2020) A simple way to make neural networks robust against diverse image corruptions. In: European conference on computer vision, pp 53\u201369. Springer","DOI":"10.1007\/978-3-030-58580-8_4"},{"key":"968_CR71","doi-asserted-by":"crossref","unstructured":"Saisan P, Doretto G, Wu YN, Soatto S (2001) Dynamic texture recognition. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, 2, IEEE","DOI":"10.1109\/CVPR.2001.990925"},{"key":"968_CR72","unstructured":"Schrimpf M, Kubilius J, Hong H, Majaj NJ, Rajalingham R, Issa EB, Kar K, Bashivan P, Prescott-Roy J, Geiger F et al (2020) Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, 407007"},{"key":"968_CR73","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J (2010) Statsmodels: Econometric and statistical modeling with python. In: 9th Python in Science Conference. Vol 57, 61, pp 10-25080","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"968_CR74","doi-asserted-by":"crossref","unstructured":"Shen Y, Ji R, Chen Z, Hong X, Zheng F, Liu J, Xu M, Tian Q (2020) Noise-aware fully webly supervised object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11326\u201311335","DOI":"10.1109\/CVPR42600.2020.01134"},{"key":"968_CR75","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701\u20131708","DOI":"10.1109\/CVPR.2014.220"},{"issue":"4","key":"968_CR76","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/S0959-4388(97)80032-3","volume":"7","author":"K Tanaka","year":"1997","unstructured":"Tanaka K (1997) Mechanisms of visual object recognition: monkey and human studies. Curr Opin Neurobiol 7(4):523\u2013529","journal-title":"Curr Opin Neurobiol"},{"issue":"1\u20132","key":"968_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0010-0277(98)00026-2","volume":"67","author":"MJ Tarr","year":"1998","unstructured":"Tarr MJ, B\u00fclthoff HH (1998) Image-based object recognition in man, monkey and machine. Cognition 67(1\u20132):1\u201320","journal-title":"Cognition"},{"key":"968_CR78","unstructured":"Tolstikhin I, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Steiner AP, Keysers D, Uszkoreit J, Lucic M, Dosovitskiy A (2021) MLP-mixer: An all-MLP architecture for vision. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in neural information processing systems . https:\/\/openreview.net\/forum?id=EI2KOXKdnP"},{"issue":"10","key":"968_CR79","doi-asserted-by":"publisher","first-page":"2744","DOI":"10.1073\/pnas.1513198113","volume":"113","author":"S Ullman","year":"2016","unstructured":"Ullman S, Assif L, Fetaya E, Harari D (2016) Atoms of recognition in human and computer vision. Proc Natl Acad Sci 113(10):2744\u20132749","journal-title":"Proc Natl Acad Sci"},{"key":"968_CR80","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s11263-008-0138-4","volume":"80","author":"Y Wang","year":"2008","unstructured":"Wang Y, Zhu S-C (2008) Perceptual scale-space and its applications. Int J Comput Vision 80:143\u2013165","journal-title":"Int J Comput Vision"},{"issue":"6","key":"968_CR81","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1152\/jn.1963.26.6.1003","volume":"26","author":"TN Wiesel","year":"1963","unstructured":"Wiesel TN, Hubel DH (1963) Single-cell responses in striate cortex of kittens deprived of vision in one eye. J Neurophysiol 26(6):1003\u20131017","journal-title":"J Neurophysiol"},{"issue":"6","key":"968_CR82","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1152\/jn.1963.26.6.978","volume":"26","author":"TN Wiesel","year":"1963","unstructured":"Wiesel TN, Hubel DH (1963) Effects of visual deprivation on morphology and physiology of cells in the cat\u2019s lateral geniculate body. J Neurophysiol 26(6):978\u2013993","journal-title":"J Neurophysiol"},{"issue":"4","key":"968_CR83","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1162\/neco.1991.3.4.498","volume":"3","author":"MA Wilson","year":"1991","unstructured":"Wilson MA, Bower JM (1991) A computer simulation of oscillatory behavior in primary visual cortex. Neural Comput 3(4):498\u2013509","journal-title":"Neural Comput"},{"key":"968_CR84","doi-asserted-by":"crossref","unstructured":"Witkin AP (1987) Scale-space filtering. In: Readings in computer vision, pp 329\u2013332. Elsevier","DOI":"10.1016\/B978-0-08-051581-6.50036-2"},{"key":"968_CR85","doi-asserted-by":"crossref","unstructured":"Xie Q, Luong MT, Hovy E, Le QV (2020) Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10687\u201310698","DOI":"10.1109\/CVPR42600.2020.01070"},{"issue":"3","key":"968_CR86","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nn.4244","volume":"19","author":"DL Yamins","year":"2016","unstructured":"Yamins DL, DiCarlo JJ (2016) Using goal-driven deep learning models to understand sensory cortex. Nat Neurosci 19(3):356\u2013365","journal-title":"Nat Neurosci"},{"key":"968_CR87","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s11263-016-0932-3","volume":"122","author":"Q Yu","year":"2017","unstructured":"Yu Q, Yang Y, Liu F, Song Y-Z, Xiang T, Hospedales TM (2017) Sketch-a-net: a deep neural network that beats humans. Int J Comput Vision 122:411\u2013425","journal-title":"Int J Comput Vision"},{"key":"968_CR88","doi-asserted-by":"crossref","unstructured":"Zhang M, Tseng C, Kreiman G (2020) Putting visual object recognition in context. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12985\u201312994","DOI":"10.1109\/CVPR42600.2020.01300"},{"issue":"1","key":"968_CR89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07882-8","volume":"10","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Firestone C (2019) Humans can decipher adversarial images. Nat Commun 10(1):1\u20139","journal-title":"Nat Commun"},{"issue":"4","key":"968_CR90","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1561\/0600000018","volume":"2","author":"S-C Zhu","year":"2007","unstructured":"Zhu S-C, Mumford D et al (2007) A stochastic grammar of images. Found Trends\u00ae Comput Graph Vis 2(4):259\u2013362","journal-title":"Found Trends\u00ae Comput Graph Vis"},{"key":"968_CR91","doi-asserted-by":"crossref","unstructured":"Zmigrod S, Hommel B (2013) Feature integration across multimodal perception and action: a review. Multisens Res 26(1\u20132):143\u2013157","DOI":"10.1163\/22134808-00002390"}],"container-title":["Biological Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00422-023-00968-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00422-023-00968-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00422-023-00968-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T05:39:17Z","timestamp":1729575557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00422-023-00968-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":91,"journal-issue":{"issue":"4-5","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["968"],"URL":"https:\/\/doi.org\/10.1007\/s00422-023-00968-7","relation":{},"ISSN":["1432-0770"],"issn-type":[{"type":"electronic","value":"1432-0770"}],"subject":[],"published":{"date-parts":[[2023,6,13]]},"assertion":[{"value":"30 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"GM is affiliated to Labrynthe Pvt. Ltd., but this work will not directly benefit Labrynthe.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"IRB#: 22-10-09, dated Oct 11, 2022 from Northeastern University","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Yes.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Yes.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}