Editing Classical conditioning (section)

====Element-based models====
The Rescorla-Wagner model treats a stimulus as a single entity, and it represents the associative strength of a stimulus with one number, with no record of how that number was reached. As noted above, this makes it hard for the model to account for a number of experimental results. More flexibility is provided by assuming that a stimulus is internally represented by a collection of elements, each of which may change from one associative state to another. For example, the similarity of one stimulus to another may be represented by saying that the two stimuli share elements in common. These shared elements help to account for stimulus generalization and other phenomena that may depend upon generalization. Also, different elements within the same set may have different associations, and their activations and associations may change at different times and at different rates. This allows element-based models to handle some otherwise inexplicable results.

=====The SOP model=====
A prominent example of the element approach is the "SOP" model of Wagner.<ref>{{cite book |vauthors=Wagner AR |date=1981 |chapter=SOP: A model of automatic memory processing in animal behavior. |veditors=Spear NE, Miller RR |title=Information processing in animals: Memory mechanisms |pages=5–47 |location=Hillsdale, NJ |publisher=Erlbaum |isbn=978-1-317-75770-2}}</ref> The model has been elaborated in various ways since its introduction, and it can now account in principle for a very wide variety of experimental findings.<ref name="Bouton_2016"/> The model represents any given stimulus with a large collection of elements. The time of presentation of various stimuli, the state of their elements, and the interactions between the elements, all determine the course of associative processes and the behaviors observed during conditioning experiments.

The SOP account of simple conditioning exemplifies some essentials of the SOP model. To begin with, the model assumes that the CS and US are each represented by a large group of elements. Each of these stimulus elements can be in one of three states: 
* primary activity (A1) - Roughly speaking, the stimulus is "attended to." (References to "attention" are intended only to aid understanding and are not part of the model.)
* secondary activity (A2) - The stimulus is "peripherally attended to."
* inactive (I) – The stimulus is "not attended to."
Of the elements that represent a single stimulus at a given moment, some may be in state A1, some in state A2, and some in state I.

When a stimulus first appears, some of its elements jump from inactivity I to primary activity A1. From the A1 state they gradually decay to A2, and finally back to I. Element activity can only change in this way; in particular, elements in A2 cannot go directly back to A1. If the elements of both the CS and the US are in the A1 state at the same time, an association is learned between the two stimuli. This means that if, at a later time, the CS is presented ahead of the US, and some CS elements enter A1, these elements will activate some US elements. However, US elements activated indirectly in this way only get boosted to the A2 state. (This can be thought of the CS arousing a memory of the US, which will not be as strong as the real thing.) With repeated CS-US trials, more and more elements are associated, and more and more US elements go to A2 when the CS comes on. This gradually leaves fewer and fewer US elements that can enter A1 when the US itself appears. In consequence, learning slows down and approaches a limit. One might say that the US is "fully predicted" or "not surprising" because almost all of its elements can only enter A2 when the CS comes on, leaving few to form new associations.

The model can explain the findings that are accounted for by the Rescorla-Wagner model and a number of additional findings as well. For example, unlike most other models, SOP takes time into account. The rise and decay of element activation enables the model to explain time-dependent effects such as the fact that conditioning is strongest when the CS comes just before the US, and that when the CS comes after the US ("backward conditioning") the result is often an inhibitory CS. Many other more subtle phenomena are explained as well.<ref name="Bouton_2016"/>

A number of other powerful models have appeared in recent years which incorporate element representations. These often include the assumption that associations involve a network of connections between "nodes" that represent stimuli, responses, and perhaps one or more "hidden" layers of intermediate interconnections. Such models make contact with a current explosion of research on [[Artificial neural network|neural networks]], [[artificial intelligence]] and [[machine learning]].{{Citation needed|date=July 2021}}