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.

** NEW ** Postdoctoral openings under Cognitive Science Research Initiative ***

The PI is interested in supporting applications for postdoctoral fellowships for the year 2017-18 at the interface of cognitive science and computational modelling in the following projects

1) Early detection of Alzheimers disease using machine learning approaches: The candidate should have prior experience of Functional or structural MRI analysis and good computational and programming skills, willingness to learn and apply machine learning methods

2) Computational modelling of emotion and decision making: The candidate should have prior experience in psychology and neuroscience of decision making and be comfortable with drift diffusion models, Bayesian models, and machine learning approaches in modelling emotion recognition through empirical cognitive psychological methods

3) Behavioural analysis of road safety behaviour: The candidate should have very good skills of Big data analytics together with solid background in experimental psychology.



Principal Investigator, Statistical Learning of Category Information: A neuroimaging Investigation, DST Cognitive Science Research Initiative, 2013-2016

Principal Investigator, Segmentation of action sequences, IITGN Intramural research funding, 2013-2016

Principal Investigator, Integration of Perceptual and Value based decision making: A Cognitive and Computational Approach, DST Cognitive Science Research Initiative, 2015-2017.

Post doctoral Fellow

Dr. Dyutiman Mukhopadhyay

The Relevance Of 9th Rasa In The Generation Of Aesthetic Delight: A Neuroaesthetic Approach Using EEG And Eyetracking, DST Cognitive Science Research Initiative, Postdoctoral fellowship, 2014-2016.

Doctoral Students

Manisha Chawla – Context Sensitive Computational Models of Decision making, 2012/13 – date

Shruti Goyal – Social influences on decision making, 2014 – date

Sujata Sinha – Computational neurosciene 2015-date

Anvita Gopal – Temporal judgement and decision making 2015-date

Devendra Mani Tripathi – Computational Approaches for Early detection of Alzheimers disease, 2013 – 2015

Masters Students

Narmadha Nagaraj – Role of reward Information and feedback emotions in decision making

Bharatesh Rayappa – Individualised Brain atlases for early detection of Alzheimers disease.

Devu Mahesan – Integration of Perceptual and Value based decision making 2014-2016

Rakhi – Relationship between impulsivity and food choice 2014-16

Abhishek Ghatraj – Influence of regularity of auditory tones in motor sequence learning 2014-16

Dr. Ashwani Kumar Mishra – Voxel based Morphometry of Alzheimers disease neuroimaging initiative  data, 2013-15

Ujjval Pamnani – Computational modelling of two-alternative decision making, 2013-15

Research interests

Predictive Coding, Neuroeconomics, Decision Making, Motor Sequence Learning, Consumer Psychology, Olfaction, Brain Imaging, Machine Learning for fMRI (Multi-Variate Pattern Analysis), Artificial Intelligence

Broad Research Topics

  • Computational approaches in Brain Imaging

This topic covers Cognitive Neuroscience investigations through functional brain imaging (EEG & fMRI) focussed on attention, perception, mental imagery, action, discrete and continuous movements, learning and decision making. Modalities include visual, auditory and motor. Particular focus will be on model based fMRI e.g. reinforcement learning, meta analysis approaches e.g. activation likelihood estimation, functional connectivity approaches, Resting state and default mode networks, Bayesian modelling, multivariate pattern analysis using machine learning algorithms.

  • Integrated approaches in Learning

This broad topic covers multifaceted investigation of learning phenomenon such as visuomotor sequence learning, associative learning, habit formation, reinforcement learning, category learning, statistical learning, information theory, language & artificial grammar learning, rote learning, observational learning, and learning dynamics in games. Interfaces with other related areas such as effect of learning on attention, memory, decision making, and music and emotional modulation are studied.

  • Reward, Decision making, Game theory & Neuroeconomics

This topic focuses on classical and instrumental conditioning approaches for reinforcement learning. Particular emphasis is on computational modelling of reinforcement learning integrated with decision making. Empirical work will focus on Game theory and interactive learning in Gaming situations and social decision making.

  • Consumer behaviour & Neuromarketing

This topic focuses on comparative studies of consumers in different economic markets and cultural backgrounds in terms of brand loyalty, distributor owned brands, economic modelling of purchase behaviour, prediction of consumer decisions, dynamics underlying consumer decision making particularly in single vs multi attribute/conjoint decisions. Techniques employed include eye tracking, emotion prediction through facial recognition, data mining text analysis for keywords, mouse tracking, approach and avoidance behaviour, change of mind in decision making. A second focus of this topic is on brain imaging studies for neuromarketing focussed.


Ph.D. 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

  • 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}
  • M. Chawla, M. Mesa and K.P. Miyapuram,  Graph Clustering for Large-Scale Text-Mining of Brain Imaging Studies. WCI 2015: 163-168 {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.
  • Chawla, M., & Miyapuram, K. P. Meta-analysis of functional neuroimaging data. In Second International Conference on Image Information Processing (ICIIP), 2013 (pp. 256-260). IEEE.
  • K.P. Miyapuram, P.N. Tobler, W. Schultz , Predicting the imagined contents using brain activation, NCVPRIPG 2013, IEEE
  • 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}
  • V. Singh, K.P. Miyapuram, R.S. Bapi. Detection of Cognitive States from fMRI Data Using Machine Learning Techniques. International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, 2007, pp. 587-592.
  • K.P. Miyapuram, R.S. Bapi, V.S.C. Pammi, Ahmed, K. Doya. Hierarchical Chunking during Learning of Visuomotor Sequences. IEEE Proceedings of International Joint Conference on Neural Networks (IJCNN), Canada, 2006, pp. 249-253.
  • 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}
  • R.S. Bapi, V.S.C. Pammi, K.P. Miyapuram, Ahmed. (2005). Investigation of sequence processing: A cognitive and computational neuroscience perspective. Current Science, 89(10):1690–1698.

Professional memberships

Elected fellow (Life), Cambridge commonwealth society
(past) IEEE, Society for Neuroscience, USA
British Neuroscience Association
Indian Academy of Neuroscience

Professional services

Review Editor: Frontiers in Movement Science & Sport Psychology; Frontiers in Decision Neuroscience

Adhoc peer reviewer: Journal of Cognitive Neuroscience, Brain Research, Appetite, Psychological studies, Journal of Economic Psychology, Human brain mapping. Cerebral Cortex

Reviewer for International Joint Conference on Neural Networks 2007, 2009, Cognitive Science Society 2009, EuroCogSci 2011, Organization for Human Brain Mapping 2011, 2012,  ICIEIS 2011, Int. Conf. Cognitive Modeling 2012, 2015.