Epistemic Action Increases With Skill
Paul P. Maglio, David Kirsh · 1996
On most accounts of expertise, as agents increase their skill, they are assumed to make fewer mistakes and to take fewer redundant or backtracking actions. Contrary to such accounts, in this paper we present data collected from people learning to play the videogame Tetris which show that as skill increases,the proportion of game actions that are later undone by backtracking also increases. Nevertheless, we also found that as game skill increases, players speed up as predicted by the power law of practice. We explain the observed increase in backtracking as the result of an interactive search process in which agentinternal and agent-external actions are interleaved, making the cognitive computation more efficient (i.e., faster). We refer to external actions which simplify an agent's computation as epistemic actions. Introduction In this paper, we present experimental data which runs counter to an assumption that underlies most theories of skill learning: that more skilled agents take ...