Learning factored representation

Followup to: Balancing context with conceptual slippages, Summarizing structure in new labels, Independence of patterns.

Repeated contexts and context transitions become compressed over use, losing variability in their compressed form. Any distinguishing characteristics of particular instances of such repeated contexts can be extracted as separate properties. Commonalities get compressed in a central pattern, and variations become properties of that central pattern. For example, typical objects, such as cups, have certain common characteristics, but properties of a particular cup can be expressed as additional patterns showing where it differs from typicality.

Central pattern of an object extracts mutual information from features describing the object, and as a result remaining patterns of object properties become more independent from each other, given the object pattern. A change in one property of an object doesn’t usually call for changes in other properties, and if it does, the dependent properties should probably again be summarized by a new single property. Individual slippages of object properties don’t affect most of the scene.

Resulting representation shouldn’t be strictly hierarchical, as limiting the representation to a hierarchy significantly reduces its expressive power. Center of a natural category can consist of a collection of interfering patterns, encoding the object’s structure and instantiated depending on context, whereas more rare characteristics are much more independent, given any compatible state of the object’s center.

Learning factored representations of transformations of the scene may result in formation of procedural patterns, with the center of transformation becoming procedure itself, and peripheral variations in transformation’s properties becoming arguments of the procedure.

Episodic and semantic memory

Followup to: Fragments of structure, Summarizing structure in new labels.

In this model, structure restoration can be interpreted as remembering, and context balancing as focusing attention on contextually relevant facts and memories. Depending on the character of restored structure and restoration process, some memories can be considered episodic or semantic. Episodic memory restores a significant part of a single past scene, allowing to situate the restored structure relative to known locations and times. Semantic memory plays out a semantic rule of thumb, filling in a property that can be discerned from the context, and even though this property can have complex structure, it isn’t associated with a particular past scene.

Before an episode is first recalled, the pattern of that episode is unique in the memory. This theoretically allows to recall every single bit of the old episode, there is no ambiguity in the details, for example if an unique label belonging to that episode gets triggered by a cue. But once a part of an episode gets recalled, its content is associated with two episodes: the original one, and the episode of recall. The recalled part, or episodic memory, becomes a weaker cue for the parts of the episode that were not recalled the first time. After many recalls, the episodic memory that gets restored in the context of recall is formed more as a reconstruction of previous episodes of recall, than of the original episode. The details that are not usually recalled get forgotten, and the details that by some reason get distorted during recalls stay distorted in the subsequent recalls. These effects, following from simple considerations about associative memories, are known to occur with human memory, as retrieval-induced forgetting and memory distortion, and can be mimicked by very simple models.

The first episode in agent’s experience to which a rule encoded in semantic memory applies, plays the same role as the original episode of episodic memory. The only difference is in what kind of content gets the emphasis during the recall. Episodic memory retains the relations to many details, even as retrieval-induced forgetting tries to shut them out, with rarity of recall events helping the matter, while semantic memory focuses on few properties and gets applied over and over. This allows to view semantic memory as a special case of episodic memory, that through many cases of recall abstracted out all of the episode-specific details, leaving only what’s usually important in the contexts of recall.

When a fact is learned declaratively, there is an episode in which it’s first stated, but the details of that episode are irrelevant to the fact itself. When the fact is recalled in the future, the recalling process can stop on the fact, without going into the details of the episode in which the fact was learned, even though those details are available. The limited part of episodic memory becomes semantic memory. Alternatively, a fact can be abstracted out as a regularity present in many scenes, without ever being a part of episodic memory, as the only unambiguous inference that follows from cumulative memory of many past episodes.

Another interesting effect is that not every episode can form an episodic memory. If an episode is so ordinary that no cue can uniquely point to it, there is no way to recall it as an episode. When you commute to work a hundredth time, you don’t usually pay attention to details, each action results from a known rule, there is nothing to learn from the process. New memories capture reusable novelty, contexts that are expected to repeat sometime in the future, but did not appear in the past.