Biased Competition between Action Representations

We propose a generalized version of the biased competition account of attention that may be applied to all domains of cognition. Based on our Generalized Biased Competition account, we propose a formal race model of selection of action representations. The model explains how action representations stored in long-term memory are competing for selection based on their match to the current environmental context and their importance weight. We then present results and model fits from three experiments using a recently developed multiple cue paradigm where several attention shifts with different associated reward values are competing. We show that participants were surprisingly efficient at selecting both when the number of cues and the number of possible reward values were increased. Only when we manipulated reward contingencies and knowledge of these, did the participants show suboptimal performance. The new Generalized Biased Competition account can also explain failures of executive control exemplified by goal neglect where instructions fail to influence behavior despite being retrievable. Finally, we argue that our model may provide a unified understanding of intentions, routines, and habits. Specifically, intentions, routines, and habits may be understood as a continuous range of the same fundamental form of action representation but with variation in their strength of long-term memory traces and importance weights.