Krishna Prasad Miyapuram

Assistant Professor, Cognitive Science

My research integrates the cognitive processes of learning and decision making mechanisms in humans. Projects that are currently ongoing include behavioral and neural correlates of statistical learning, perceptual and value-based decision making. A wide range of experimental and analytic techniques such as psychophysics, eye tracking, brain imaging methodologies (EEG, fMRI) and computational modelling (Reinforcement learning, Bayesian approaches) are used. With machine learning approaches applied to structural MRI images, I am working on early detection of Alzeimhers’ disease.


Ph.D. Cognitive Neuroscience, University of Cambridge (2004 – 2008)
M. Tech. Artificial Intelligence (2002- 2004) &
M.Sc. Electronics (1998-2000) University of Hyderabad

Professional and teaching experience

Assistant Professor, Indian Institute of Technology, Gandhinagar, (Oct 2012 to present)
Postdoctoral fellow, Center for Mind/Brain sciences, University of Trento, Italy (2011-2012 )
Cognitive Psychologist/Neuroscientist. Unilever R &D,Vlaardingen,The Netherlands (2008-2011)
Visiting Researcher, Core Research for Evolutionary Science & Technology, Kyoto, Japan (2003)
Visiting Researcher, Exploratory Research for Advanced Technology, Kyoto, Japan (2000/1) Research Assistant (Indo-Japanese Project – fMRI), University of Hyderabad, India (2000-2002)

Representative publications

  • Chawla, M., & Miyapuram, K. P. (2018). Context-Sensitive Computational Mechanisms of Decision Making. Journal of Experimental Neuroscience.
  • Goyal, S., Miyapuram, K.P. (2017). Risk Attitude in Gain and Loss Domain With and Without Feedback: A Study on Indian Population, Society for Judgement and Decision Making, Vancouver, November 10-14, 2017.
  • Viraj Mavani, Shanmuganathan Raman, Krishna P. Miyapuram:
    Facial Expression Recognition Using Visual Saliency and Deep Learning. ICCV Workshops 2017: 2783-2788
  • Chawla, M., & Miyapuram, K. P. (2016, September). Common neural coding across domains of decision making identified by meta-analysis. In Front. Neuroinform. Conference Abstract: Neuroinformatics.
  • Devu Mahesan, Manisha Chawla, Krishna P. Miyapuram:
    The Effect of Reward Information on Perceptual Decision-Making. ICONIP (4) 2016: 156-163
  • Goyal, S., Miyapuram, K. P., & Lahiri, U. (2015, November). Predicting Consumer's Behavior Using Eye Tracking Data. In Soft Computing and Machine Intelligence (ISCMI), 2015 Second International Conference on (pp. 126-129). IEEE.
  • Manisha Chawla, Krishna P. Miyapuram:
    Influence of Previous Choice and Outcome in a Two-Alternative Decision-Making Task.ICONIP (2) 2015: 467-474
  • M. Chawla, and K.P. Miyapuram, Comparison of meta-analysis approaches for neuroimaging studies of reward processing: A case study. IJCNN 2015: 1-5 {Talk}
  • Miyapuram, K.P., Pamnani, U., Doya, K., Bapi, R.S. Inter Subject Correlation of Brain Activity  during Visuo-Motor Sequence Learning, 21st International Conference on Neural Information Processing (ICONIP2014).


  • Neeraj Kumar, Jaison A. Manjaly, K.P. Miyapuram. (2014) Feedback about Action Performed can Alter the Sense of Self Agency, Frontiers in Psychology (Consciousness Research), 5:145.
  • E.H. Zandstra, K.P. Miyapuram, P.N. Tobler. (2013). Understanding consumer decisions using behavioural economics. Progress in Brain Research, 202:197-211.
  • K.P. Miyapuram, V.S.C. Pammi.  (2013). Understanding Decision Neuroscience – A multidisciplinary perspective and neural substrates. Progress in Brain Research, 202:239-66.
  • K.P. Miyapuram, P.N. Tobler, L. Gregorios-Pippas, W. Schultz. (2012) BOLD responses in reward regions to hypothetical and imaginary monetary rewards. NeuroImage 59(2):1692-1699.
  • V.S.C. Pammi*, K.P. Miyapuram*, Ahmed, K. Samejima, R.S. Bapi, K. Doya. (2012). Changing the Structure of Complex Visuo-motor Sequences Selectively Activates the Fronto-Parietal Network. NeuroImage 59(2):1180-1189. {* = Equal contribution}
  • R.S. Bapi, K.P. Miyapuram, F.X. Graydon, K. Doya. (2006). fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences. NeuroImage, 32(2):714-727. {Editor’s Choice Award for the year 2006}