The ins and outs of unpacking the black box: Understanding motivation using a multi-level approach.

Abstract

Although higher-level constructs often fail to explain the mechanisms underlying motivation, we argue that purely mechanistic approaches have limitations. Lower-level neural data help us identify "biologically plausible" mechanisms, while higher-level constructs are critical to formulate measurable behavioral outcomes when constructing computational models. Therefore, we propose that a multi-level, multi-measure approach is required to fully unpack the black box of motivated behavior.