I think priming is very interesting in the context of semantic processing of text or the world around us, in that a context of a story may also prime us for what comes next. That is, we always build up some kind of expectation of what will happen next and read on happily to see what's really going to occur.
Some time ago, I posted something about latent semantic indexing. It's more or less an indexation of latent semantics. Latent means:
|1.||present but not visible, apparent, or actualized; existing as potential: latent ability.|
|2.||Pathology. (of an infectious agent or disease) remaining in an inactive or hidden phase; dormant.|
|3.||Psychology. existing in unconscious or dormant form but potentially able to achieve expression: a latent emotion.|
|4.||Botany. (of buds that are not externally manifest) dormant or undeveloped.|
So, latent means hidden or dormant. It's about semantic meaning that you can't see inside the text, but use as a key for indexing that meaning or definition. In other posts, I doubted the viability of constructing formal knowledge systems (where knowledge is explicitly documented), due to the huge size and the huge efforts required in defining this knowledge (and the obvious ambiguities and disagreements that go along with it). Other than that, knowledge is also dynamic and changing, not static.
Considering priming thus and binding this with a technique for latent indexing, one could achieve a system where related symbols are primed before they are interpreted. Given different indices for vision, smell, audio and somatosensory information, each specific index could eventually (without saying how) be made to point to the same symbol, thus strengthening the interpretation of the world around a robot or something similar.
Thus, rather than explicitly defining relationships between concepts, consider the possibility of the definition (and growing) of indexed terms which partially trigger related terms (prime the interpreter), as the interpreter moves on to the next data in the stream. This could allow a system to follow a certain context and distinguish relationships of things in different contexts, because the different contexts have different activation profiles of each symbol.
Coupling this with probability algorithms, it would be interesting to see what we find. In fact, using probability is the same as the development of a hypothesis or "what-if" scenario. Whereas a certain relationship does not yet exist, we seek ways to prove the relationship exists by collecting evidence for it.
Some other activities that we learn are subconsciously learned. That is, the action/reaction consequences of throwing an object and having it drop on the floor. If the object is of metal, it probably won't break. If it's made of glass, it'd probably shatter. Those things are not obvious to children, but can quickly be learnt. Glass is transparent, feels a certain way, and there are a number of standard elements which are generally of glass. Plastic looks similar, but makes a different sound. We should aim to prevent dropping the glass on a hard floor. This bit of knowledge is actually a host of different relationships of actions, reactions, properties of objects either visible or audible and by combining these things together, we can reason about a certain outcome.
The psychology book also importantly notes the idea of attention. It specifically states that when attention is not given, performance of analysis, reasoning or control drops significantly. This means that we're able to do only one or two things at a time. One consciously, the other not so. But that it's the entire mind with control, audible and visible verification mechanisms to control the outcome.
The interesting part of this post is that it assumes that symbols as we know them are not named explicitly by natural language, but are somehow coded using an index, which has been organized in such a way that neighboring indexed items become somewhat activated (primed) as well to allow for the resolution of ambiguities. An ambiguity is basically the resolution of two paths of meaning, where the resolution should come by interpreting further input or requesting input from an external source in an attempt to solve it (unless assumptions are made to what it means).
Another thing that drew my attention is that recent strongly primed symbols may be primed strongly in the future independent of its context. This is mostly related to audio signals and related to for example the mentioning of your name. You could be in a pub hearing a buzz, but when your name is called somewhere, you can recognize it immediately within that buzz (thus, the neurons involved in auditory recognition are primed to react to it).
It's probably worthy to extend this theory by developing the model further and considering human actions, reasoning, learning and perception within that model (as opposed to building a network and trying out how it performs). Since it's already very difficult to re-create human abilities using the exact same replicas of biological cells, why not consider simpler acts and verifying parts of this reasoning with such a smaller network?
The first elements of such a network require a clever way of indexing signals and representations. In this way, the indexing mechanism itself is actually a clever heuristic, which may re-index already known symbols and place it in a different space. The indexing mechanism doesn't feel static.