Active learning strategies in a spatial concept learning game
Todd Matthew Gureckis, Douglas Benjamin Markant · eScholarship (California Digital Library) · 2009
Effective learning often involves actively querying the environment for information that disambiguates potential hypotheses. However, the space of observations available in any situation can vary greatly in potential “informativeness. ” In this report, we study participants ’ ability to gauge the information value of potential observations in a cognitive search task based on the children’s game Battleship. Participants selected observations to disambiguate between a large number of potential game configurations subject to information-collection costs and penalties for making errors in a test phase. An “ideallearner” model is developed to quantify the utility of possible observations in terms of the expected gain in points from knowing the outcome of that observation. The model was used as a tool for measuring search efficiency, and for classifying various types of information collection decisions. We find that participants are generally effective at maximizing gain relative to their current state of knowledge and the constraints of the task. In addition, search behavior shifts between an slower, but more efficient “exploitive ” mode of local search and a faster, less efficient pattern of “exploration.” Traditional experimental approaches to human learning tend to emphasize passive learning situations. For example, in a typical concept learning task, subjects are presented with examples one at a time, the order of which are selected by the experimenter (often at random and with exhaustive sampling of the training set). However, this procedure ignores the fact that real-world learning often requires learners to actively create their own learning experiences by constructing revealing queries or engaging in exploration of unknown contingencies