In another place, someone asked me “Will the learning models ever get it right?” I thought that I would post my answer here, and use it as a soapbox to talk about what “Associative learning” is- or should be.
Will Learning Models ever get it right? My response was that they already have. A learning model is a mechanistic, often mathematical, description of the inter-relation of theoretical variables designed to describe how learning occurs. That is, a Learning Model is a precise specification of the syntax of a theory. Such a model is, thus, appropriately grounded by rules that define how those theoretical variables, or constructs, relate to empirical, measurable, events.
These models, at least those coming from Associative Learning theory, already have “it” right because Associative Learning has “it” right.
Every phenomenon that is explained by a principle, exemplified by a model, is a snapshot in time of the operation of many variables. Within that snapshot, for which the model was developed, the model is correct. As more variables are added, changed, or otherwise considered, the explanation may no longer hold. That is simply the way things work. No field of science enjoys the ability to develop a model or theory that will continue to accurately account for the state of things when new variables are added to the snapshot.
Thus, failings of models based on theories found in Associative Learning are not to be taken as failings of the general Associative Learning approach to understanding human psychology. I truly believe that any other perspective on that issue represents an immature or otherwise underdeveloped understanding of the nature of science, explanation, and the operation of the natural world in general.
The “association” and the accompanying representations and responses that are associated, are the proper level of analysis for human and animal behavior. The association has sufficient face validity to be generally understood by the lay public, and it is a sufficient construct to make connections across the fields of psychology. There is no area which does not contain some version of the idea of an “association.” The construct can be further reduced to be described and explained mechanistically, mathematically, or neurobiologically. The “Association” is an appropriate level of analysis in and of itself that can be expanded or reduced depending upon the level of explanation desired.
Associative Learning is a process. It is leverage. A screw jack will lift a car, and a floor jack in a garage will lift a car. Mechanistically they are very different, and that is necessary because they operate in different environments (an analogy to evolution operating on living beings). Nevertheless, the principle by which they operate is the same. It is “leverage.” Though too often unrecognized, Associative Learning is the “leverage” of psychology.
Just as one may study the structure and function of the screw in the screw jack or the hydraulic fluid in the manual floor jack, one can study the implementation of associative learning in various brain mechanisms. Leverage and associative learning may be implemented in different ways, but the principles remain the same. At some point , as the investigation turns from an investigation of leverage to the structure of the screw and how it relates to the handle, the investigation of associative learning in the brain is no longer concerned with associative learning, but the structure of the brain. That associative learning can be reduced does not invalidate it as a valid approach to understanding any more than studying the parts of a jack invalidates the principles of leverage.
One can further reduce the brain to molecules, and those to atoms, and so forth, then the study moves into physics, which has its own set of issues. Regardless of the level of analysis, nature is a chaotic system and cannot be predicted unless A: All the variables are known, B: All the rules specifying the relationships of the variables are known, and C: The starting points of the variables are known. Though impossible, it can be imagined that A: and B: could someday be the state of science, but even with a strong imagination C: shall never be known, thus there will always be error in the prediction of any science.
Learning theory is an approach to understanding, and as an approach it is a correct approach. Learning models are simply the mechanistic descriptions of how the proposed variables inter-relate. What is true for learning theory is true for the models. Does learning theory have “it” right? If “it” is the approach, yes, if “it” is the content, yes, if “it” is a correct description of everything related to behavior the answer is “no.” Will learning theory ever have a correct description of everything related to behavior, the answer is “no,” but as stated earlier, to expect anything else reflects an immature view of the nature of science, explanation, and the operation of the natural world in general.