This site is devoted to Neuro-Information Science, an emerging area concerned with applications of cognitive neuroscience in information science.

The progress made in neuroscience in the last two decades allows one to expect that neuroscience will contribute to other disciplines (such as Information Science) by providing a richer account of user cognition than that which is obtained from any other source. In addition, new technology developments in fMRI (functional magnetic resonance imaging) in eyetracking (e.g., embedding high-resolution eye-tracking hardware in consumer laptops) and in EEG (electroencephalography - e.g., low-cost consumer-level devices) raise the possibility of introducing these devices into the home environment as novel ways of collecting data about users and in support of new interaction modalities. Neuro-Information Science is an emerging field that aims to take advantage of advances in cognitive neuroscience and neuroimaging techniques and apply them to answering Information Science questions. The general motivation is a belief that an increasing familiarity with brain function should eventually lead to better Information Science theories.

  • Objectivity – Improve the measurement by complementing current sources of data with evidence from brain imaging data that can be taken as being objective and not subject to biases (e.g., subjectivity bias).
  • Localization – Localize brain areas associated with information science constructs (such as information relevance, browsing vs. search).
  • Measurement of unobservable variables (e.g., mental load, beliefs, emotions), and verification of hypothesized links between these variables and observable behavior (e.g., information source selection, choice of search tactic, stopping search) by mapping them on brain areas and their activation.
  • Verification – Verify if information seeking behaviors considered as distinct are using distinct or similar brain areas.
  • Precision - Add precision to information science models.


  • Expertise – the use of neuroscience tool requires expertise that goes beyond typical information science education and will thus require teaming up with neuroscientists.
  • Cost – neuroscience tools can be very expensive  In particular, fMRI equipment is particularly expensive (several million dollars) and thus conducting fMRI experiments requires access to neuroimaging centers and cooperation with neuroscientists.
  • Awareness – the use of neuroimaging techniques in information science is new. Making progress will require increasing awareness of this new sub-area in the information science and information retrieval communities and gaining acceptance of funding agencies.
  • Limitations – neuroimaging techniques come with their own limitations. We need to be aware that finding correlations between brain activities and information-behavior does not equate establishing causality (Ramsey et al., 2010) and that reverse inference (inferring engagement of a cognitive process from the activation of a brain region) should be used with great caution (Poldrack, 2006). 

The list of opportunities have been inspired by the following articles:
  • Camerer, C. F., Loewenstein, G., & Prelec, D. (2004). Neuroeconomics: Why Economics Needs Brains. Scandinavian Journal of Economics, 106(3), 555–579.
  • Dimoka, A., Pavlou, P. A., & Davis, F. (2010). NeuroIS: The Potential of Cognitive Neuroscience for Information Systems Research. INFORMATION SYSTEMS RESEARCH. 1-18.
Limitations draw upon the following articles:
  • Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63. doi:10.1016/j.tics.2005.12.004
  • Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C. (2010). Six problems for causal inference from fMRI. NeuroImage, 49(2), 1545–1558. doi:10.1016/j.neuroimage.2009.08.065.

The site is created and maintained by Jacek Gwizdka [website] [publications] [citations]