SP ArunProfessor & Chair
Phone : +91 80 2293 3436/3431
E-Mail : sparun[at]iisc.ac.in
Visual perception, object recognition
We recognize objects easily every day, but object recognition is in fact a very difficult problem. Even leading computer algorithms do not match human performance today. Object recognition is not easy for the brain either: a series of cortical areas, taking up ~40% of the brain, is dedicated to vision. But we know very little about the rules by which the brain transforms what we see into what we perceive. What is the nature of this representation? What are the underlying rules?
Our approach to this problem is best understood through an analogy to colour. We see millions of colors but it is well known that color perception is three-dimensional – any color we perceive can be represented using three numbers. Can we do likewise for the millions of shapes we see? Do shapes also reside in a low-dimensional space?
To gain insight into these questions, we perform behavioral experiments in humans and record the electrical activity of neurons from the monkey visual cortex. In the human experiments, we probe the perceptual representation using behavioral tasks such as visual search or categorization. In the monkey experiments, we probe the representation at the level of single neurons in the inferotemporal cortex, an area critical for object recognition. We use both the behavioral and neuronal data to test and validate computational models of object recognition.
Pramod R.T., Katti H and Arun S.P, (2022), Human peripheral blur is optimal for object recognition, Vision Research, 200, : 108083
Arun S.P, (2022), Using compositionality to understand parts in whole objects, European Journal of Neuroscience, Trailblazers in Neuroscience series, in press
Passi A and Arun S.P, (2022), The Bouba–Kiki effect is predicted by sound properties but not speech properties, Attention Perception and Psychophysics, in press
Katti H & Arun SP, (2022), A separable neural code in monkey IT enables perfect CAPTCHA decoding, Journal of Neurophysiology, 127: 869-884, (lay summary on Twitter)
Agrawal A, Nag S, Hari KVS & Arun SP, (2022), Letter processing in upright bigrams predicts reading fluency variations in children, Journal of Experimental Psychology, General, in press, (lay summary on Twitter)
Pramod RT & Arun SP, (2022), Improving Machine Vision using Human Perceptual Representations: The Case of Planar Reflection Symmetry for Object Classification, IEEE Transactions in Pattern Analysis and Machine Intelligence, 44: 228-241, (India Alliance lay summary)
Somraj N, Kashi MS, Arun SP, Soundararajan R, (2022), Understanding the perceived quality of video predictions, Signal Processing, Image Communication, 102:116626
Jacob G, Katti H, Cherian T, Das J, Zhivago KA & Arun SP, (2021), A naturalistic environment to study visual cognition in unrestrained monkeys, eLife, 10: e63816, (lay summary on Twitter)
Jacob G, Pramod RT, Katti H & Arun SP, (2021), Qualitative similarities and differences in visual object representations between brains and deep networks, Nature Communications, 12(1), 1-14.
Jacob G & Arun SP, (2020), How the forest interacts with the trees: Multiscale shape integration explains global and local processing, Journal of Vision, 20:, 1-20
KA Zhivago, LS Sneha, Ranjini Garani, Simran Purokayastha, Naren P Rao, Aditya Murthy, Arun SP, (2020), Perceptual priming can increase or decrease with ageing, Frontiers in Aging Neuroscience
Pramod RT & Arun SP , (2020), Improving Machine Vision using Human Perceptual Representations: The Case of Planar Reflection Symmetry for Object Classification, IEEE Transactions in Pattern Analysis and Machine Intelligence
Agrawal A, Hari KVS & Arun SP, (2020), A compositional neural code in high-level visual cortex can explain jumbled word reading , eLife, 9:e54846
Agrawal A, Hari KVS & Arun SP, (2019), Reading Increases the Compositionality of Visual Word Representations, Psychological Science , 30:, 1707-1723
Katti H & Arun SP, (2019), Are you from North or South India? A hard face classification task reveals systematic representational differences between humans and machines, Journal of Vision, 19(7), 1-1
Katti H, Peleen MV and Arun SP, (2019), Machine vision benefits from human contextual expectations, Scientific Reports, 9:, 2112
Prabaha Gangopadhyay and Jhilik Das, (2019), Do Primates and Deep Artificial Neural Networks Perform Object Categorization in a Similar Manner?, Journal of Neuroscience, 39(6):, 946-948;
Ratan Murty NA & Arun SP, (2018), Multiplicative mixing of object identity and image attributes in single inferior temporal neurons., PNAS, 115:, E3276-85
Pramod RT & Arun SP, (2018), Symmetric objects become special in perception due to generic computations in neurons, Psychological Science, 29:95-109
Katti H, Peelen MV & Arun SP, (2017), How do targets, nontargets and context influence real-world object detection?, Attention Perception and Psychophysics
Vaidhiyan N, Sundaresan R & Arun SP , (2017), Neural dissimilarity indices that predict oddball detection in behaviour, IEEE Transactions on Information Theory,
Ratan Murty NA & Arun SP, (2017), Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex, Journal of Neurophysiology
Ratan Murty NA & Arun SP, (2017), A Balanced Comparison of Object Invariances in Monkey IT Neurons, eNeuro, 4(2), e0333-16
Ratan Murty NA & Arun SP, (2017), Seeing a straight line on a curved surface: Decoupling of patterns from surfaces by single IT neurons, Journal of Neurophysiology , 117: , 104-116
Zhivago KA & Arun SP, (2016), Selective IT neurons are selective along many dimensions, Journal of Neurophysiology, 115: 1512-1520
Pramod RT & Arun SP, (2016), Object attributes combine additively in visual search, Journal of Vision, 1-29
Pramod RT & Arun SP, (2016), Do computational models differ systematically from human object perception?, CVPR
Puneeth NC & Arun SP, (2016), A neural substrate for object permanence in monkey inferotemporal cortex, Scientific Reports, 6:30808
Sunder S & Arun SP, (2016), Look before you seek: Preview adds a fixed benefit to all searches, Journal of Vision, 1-17
Vighneshvel T & Arun SP, (2015), Coding of relative size in monkey inferotemporal cortex, Journal of Neurophysiology, 113:, 2173-2179.
Ratan Murty NA & Arun SP, (2015), Dynamics of 3d view invariance in monkey inferotemporal cortex, Journal of Neurophysiology, 113:, 2180-2194.
Pramod RT & Arun SP, (2014), Features in visual search combine linearly, Journal of Vision, 14(4), 6-6.
Zhivago KA & Arun SP, (2014), Texture discriminability in monkey inferotemporal cortex predicts human texture perception, Journal of Neurophysiology, 112(11), 2745-2755.
Vighneshvel T & Arun SP, (2013), Does linear separability really matter? Complex search is explained by simple search, Journal of Vision, 13(11), 10-10.
Arun SP, (2012), Turning visual search time on its head, Vision Research, 74, 86-92.
Vaidhiyan N, Arun SP & Sundaresan R, (2012), Active sequential hypothesis testing with application to a visual search problem, In 2012 IEEE International Symposium on Information Theory Proceedings (pp. 2201-2205). IEEE.
Mohan K & Arun SP, (2012), Similarity relations in visual search predict rapid visual categorization, Journal of Vision, 12(11), 19-19.
Sripati AP & Olson CR, (2010), Global image similarity in monkey inferotemporal cortex predicts human visual search performance, Journal of Neuroscience, 30(4), 1258-1269.
Sripati AP & Olson CR, (2010), Responses to compound objects in monkey inferotemporal cortex: The whole is equal to the sum of discrete parts, Journal of Neuroscience, 30(23), 7948-7960.
Kim SS, Sripati AP & Bensmaia SJ, (2010), Predicting the timing of spikes evoked by tactile stimulation of the hand. Journal of Neurophysiology, Journal of Neurophysiology, 104(3),, 1484-1496.