An interesting review paper recently published in Trends in Cognitive Science, looks into the area of information-seeking from three traditionally separate fields:
- machine learning,
- eye movements in natural behavior,
- and studies of curiosity in psychology and neuroscience.
Although they use different terminology and methods, these three lines of research grapple in fact with strikingly similar questions and propose overlapping mechanisms. Understanding and recognizing recent developments in the area of information seeking from these points of view is of great interest to MindSee and very close to the solution that the project proposes for advancing in the field.
According to the authors, three main themes emerge from the review. First, an understanding of information-seeking requires that we understand how agents monitor their own competence and epistemic states, and specifically how they estimate their uncertainty and generate strategies for reducing that uncertainty.
Second, this question requires that we understand the nature of intrinsic rewards that motivate information-seeking and learning, and may impact cognitive development.
Finally, eye movements are natural indicators of active information searching by the brain. By virtue of their amenability to neurophysiological investigations, the eyes may be an excellent model system for tackling this question, especially if studied in conjunction with computational approaches and the intrinsic reward and cognitive control mechanisms of the brain.
The full paper can be accessed here.