December 2016

Albert Bandura finds three main substructures in the human agency structure. First of all, we must have a motive to do something. Secondly, we need to know how to come to the result, and, finally, each action has something what the psychology calls the affect. Hereby we use this word to speak of something that goes beyond the strict rationality. It is, above all, emotions, feelings, and even such romantic things as hope, courage, or emotional outbursts. All these things have a remarkable characteristic that any serious science of humanity gets perplexed at an attempt to give a precise definition to any of these words. Indeed, affective states are situations when we rather feel than understand. The question what the role of the emotions in the efficiency increase of our actions is still open.

Executive functions (EFs; also called executive control or cognitive control) refer to a family of top-down mental processes needed when you have to concentrate and pay attention, when going on automatic or relying on instinct or intuition would be ill-advised, insufficient, or impossible (Burgess & Simons 2005, Espy 2004, Miller&Cohen 2001).Using EFs is effortful; it is easier to continue doing what you have been doing than to change, it is easier to give into temptation than to resist it, and it is easier to go on “automatic pilot” than to considerwhat to do next.

Within striatal circuits the neuromodulator dopamine is considered by many  to act as a learning signal, controlling neural plasticity (60). One of the main  lines of evidence has come from recordings of midbrain dopamine neurons  whose axons project to wide regions of forebrain (61)—including the striatum,  which has a very high density of dopamine receptors. In macaque monkeys such  cells often show brief (~0.1 s) increases in firing rate, particularly to unexpected  rewards, or to unexpected cues that signal upcoming rewards (reviewed in  62). The ability of dopamine cells to ignore fully expected rewards and shift  firing to cues that predict rewards has a strong resemblance to “error signals”  in certain formal learning theories (63) and certain computational models of  “reinforcement learning”(64). Both classes of model refer to situations in which  the extent of associative learning is controlled by a simple signal that provides  feedback on the overall success or failure of an action or expectation. Because  the signal does not provide detailed information on the exact nature of any errors  committed, it is sometimes described as a “critic” rather than a “teacher” (65).