Language Generation (Human Cognitive Functions)

This is the last posting of explaining the figure on human cognitive functions. Today's topic is Language Generation.

Language Generation is the process in which a network of association (or a semantic network) is mapped to syntactic structure (phrase structure).  The choice of a part of the network to be uttered leads to the generation of a syntactic structure and a linear phonetic representation.  Generated phonetic representation (image) may be 'parsed back' to semantic/pragmatic representation so that its effects are evaluated.  The process is thought to be a special case of planning, though the processing of syntactic structure would be automatically processed in a specialized module as in the case of parsing.

Language Understanding (Human Cognitive Functions)

This is the 7th posting of explaining the figure on human cognitive functions. Today's topic is Parsing (Language Understanding).

As parsing seems to be done in the neo-cortex in human language processing and it apparently requires temporal pattern learning, it is thought to be the function of THC.  On the other hand, parsing is automatically done in a specialized module (see The Modularity of Mind by J. Fodor).   The output of parsing yields the representation of situations (if language understanding is successful), which is thought to be a network of association (or a semantic network).
Part of the representation of situations would be learned through interaction with the exterior world by means of perception and motion, and relation with language expression learned in the process of language acquisition.  We acquire more abstract concepts without direct correspondence with objects in the exterior world by expanding the basic semantics that is innate or acquired by early interaction with the world.

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Planning (Human Cognitive Functions)

This is the 6th posting of explaining the figure on human cognitive functions. Today's topic is Planning.

Planning, which comes after Imagining Situations, is thought to be the imagining process to construct the representation of action sequences evaluated (with the past (learned) experience) as good enough for a given goal representation. If an action sequence is not evaluated as preferable, then another sequence would be imagined by some backtracking mechanism.
Planning is in most cases done consciously and is thought to involve Working Memory (or the executive function).  While WM is omitted from the figure, any cognitive function would involve it if the process is carried out reflectively or consciously.

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Imagining Situations (Human Cognitive Functions)

This is the 5th posting of explaining the figure on human cognitive functions. Today's topic is Imagination.

The line Imagining Situations coming from Association is in fact the major function of association, which presupposes Perceiving Situations discussed earlier, where it is suggested that situations may be represented as sequences of association.  Since THC (Temporal Hierarchical Categorizer) can associate temporal patterns with other temporal patterns, if sequences of association representing situation are given to THC as input, it will be able to associate the representation of a situation with another one.  Here, the input is not sensory but it is the representation of situations so that THC constitutes a recurrent loop.
Situations to be imagined are not only past situations but also ‘imaginary’ situations created by combining parts of (past) situations.

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Episodic Memory (Human Cognitive Functions)

This is the 4th posting of explaining the figure on human cognitive functions. Today's topic is Episodic Memory.

Episodic memory comes next to Perceiving Situations, for an episode is an individual situation, thereby the relation between Perceiving Situations and Episodic Memory parallels with that between Object Category Recognition and Individual Object Recognition mentioned above.  Objects and relations within an episode should be individuals.
While the types of recognition previously discussed can be learned ‘stochastically,’ episodic memory requires one-shot memorizing.  In the brain, situations to be memorized would be chosen by clues such as novelty so that signals necessary for memorizing them would be provided (perhaps with long-term potentiation in the hippocampus).

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Perceiving Situations (Human Cognitive Functions)

This is the third posting of explaining the figure on human cognitive functions. Today's topic is the perception of situations.

Perceiving Situations is another function of THC (the Temporal Hierarchical Categorizer).  A small animal may perceive, for example, a situation in which it should hide itself upon detecting a moving spot in the sky.   A human being may recall the situation type of Fire upon sensing the smell of burning.   The representation of a Situation normally contains those of objects, their features and relations among them (as in some formal semantic theories).  For example, the representation (frame) of the Eating situation contains the representations of Eater, Food and the (Eating) relation between them.   As the representation of a situation is a combination of its elements, they are normally constructed ad hoc.
While the representation of a situation can be complex, it does not have to be represented at the same time (synchronously) but can be represented dynamically by associating its components one by one.
Dynamic representation can be applied to perception such as complex visual scene perception or multi-modal perceptual integration, where information would be bound by a series of association (see Information Binding, the dotted line from Association in the figure).

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Object Recognition (Human Cognitive Functions)

This is the second posting of explaining the figure on human cognitive functions. Today's topic is object recognition.

2a Object Category Recognition

The line Object Category Recognition is rooted in Temporal Hierarchical Categorizer (THC) in the figure.  This is a function of THC to recognize exterior objects.  It is pattern recognition, but its realization would not be easy.  To think of vision, an object would hardly enter into the visual field again with the previous conditions; an object would have various orientations, be under various lighting conditions and have focused images in various parts of the retina.   As multi-layered neural networks such as deep learning networks are achieving some good results with object category recognition, one would say the brain works in a similar way.  However, artificial learning mechanisms (including deep learning) normally require extensive tuning and it is not clear how is the brain tuned in learning yet.

2b Individual Object Recognition

Individual Object Recognition comes next to Object Category Recognition.  The function recognizes an individual, e.g., Tom after getting acquainted with him.  Unlike a category, an individual does not have instances and does not have different values for its feature at the same time (Leibniz’ law).  A human being may inherently has the concept of individuals or may learn it by observing physical objects traversing in time-space.  In any case, the mechanism of individual object recognition is different from category recognition.

2c Belief in External Objects

Belief in External Objects also comes next to Object Category Recognition.  The function recognizes things as being exterior.  Philosophically speaking, nobody can be completely sure that there are things beyond the mental realm.   However, if an agent recognizes (the category of) ‘external’ objects, learns how to interact with them and thereby gets into ‘healthy’ relation with them, then it could be said to believe in the external objects (with the intentional stance).

2d Belief in Other Minds

The line Belief in Other Minds (Theory of Mind) comes next.  This belief is the recognition of other people as having the mind similar to the believer itself.  While Theory of Mind (ToM) may partly be learned from interaction with others, considering the fact that not every human being has ToM, some part of it would be genetic (mirror neurons would be part of it).

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A Figure of Human Cognitive Functions

In this and the subsequent postings, I'm going to present a simplified and organized view of major human cognitive functions with an illustration (mind map -- the figure below) to serve for AGI designs.  (The branches in the map indicates cognitive functions and branching indicates their specialization.)

In this page, I'll explain the path from sensory input to motor output via pattern recognizers and association (the blue arrows 1a & 1b).

1a Temporal Hierarchical Categorizers

Temporal Hierarchical Categorizers (THC, hereafter) are pattern recognizers that classify (categorize) sensory input.

• THC’s Categorizing function is acquired through supervised or unsupervised learning, so that a Categorizer may acquire categories of sensory input by itself.  In last decades, various (computational) neural networks have been proposed to explain automatic categorizing of input data, suggesting that biological neural networks may have such a function.
• THC is Hierarchical.  While many of proposed neural network models were hierarchical, a new kind of hierarchical models called 'Deep Learning' has made successes in categorization tasks in recent years.  The importance of hierarchy in neo-cortical modeling has been also emphasized in the books by J. Hawkins and R. Kurzweil (the term THC, of course, echoes Hawkins’s Temporal Hierarchy Memory).  For pattern recognition in mammalian cerebra is carried out by the hierarchy of neo-cortices, the Categorizer is thought to be hierarchical.
• THC is Temporal; for animals or robots to interact with the environment, the patterns they have to deal with are time series.  If a pattern recognizer is to be modeled as a neural network (model), a recurrent network would do the job.  The figure's reference to the diencephalon suggests a circuitry involving the part of the brain constitutes recurrent networks.

1b Association

Now let’s look at the Association part of the figure.  One of the most elementary associations would be that from sensory output to motor output, which associates a sensory pattern (or category) with motor pattern (or category).  Association is not pattern recognition but involves the recollection of patterns within a modality or between modalities, where the patterns are the ones recognized by pattern recognizers.  This means association appropriates the function of pattern recognizers.
The arrow titled ‘Associating Features’ from Association to THC in the figure suggests that there is association of lower feature patterns with higher patterns (e.g., when we imagine a flower, we recall the color and shape).  In real brains, this function would be assumed by massive efferent connections.

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