The framework of "plateaus, dips, and leaps" shines light on periods when individuals may be inventing new methods of skilled performance. We begin with a review of the role "performance plateaus" have played in (a) experimental psychology, (b) human-computer interaction, and (c) cognitive science. We then reanalyze two classic…

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed…

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other…

This paper brings the intellectual tools of cognitive science to bear on resolving the ''paradox of the active user'' ["Interfacing Thought: Cognitive Aspects of Human--Computer Interaction," Cambridge, MIT Press, MA, USA]--the persistent use of inefficient procedures in interactive tasks by experienced or even expert users when demonstrably more…

Constraints and dependencies among the elements of embodied cognition form patterns or microstrategies of interactive behavior. Hard constraints determine which microstrategies are possible. Soft constraints determine which of the possible microstrategies are most likely to be selected. When selection is non-deliberate or automatic the least…