Language and cognition interaction neural mechanisms

Abstract

How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language "ready-made" and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a "teacher." A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's "language prewired brain" built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures.

Figure 1

Dynamic logic operation example, finding cognitively related events in noise, in EEG signals. The searched processes are shown in Figure 1 at the bottom row. These events are “phase cones,” circular events expanding or contracting in time (horizontal direction t, each time step is 5 ms); in this case, two expanding and one contracting events are simulated as measured by an array of 64 × 64 sensors. Direct search through all combinations of models and data leads to complexity of approximately M ^N = 10^10,000, a prohibitive computational complexity. The models and conditional similarities for this case are described in details in [44], a uniform model for noise (not shown), expanding and contracting cones for the cognitive events. The first 5 rows illustrate dynamic logic convergence from a single vague blob at iteration 2 (row 1, top) to closely estimated cone events at iteration 200 (row 5); we did not attempt to reduce the number of iterations in this example; the number of computer operations was about 10¹⁰. Thus, a problem that was not solvable due to CC becomes solvable using dynamic logic.

Figure 2

Learning situations; white dots show present objects, and black dots correspond to absent objects. Vertical axes show 1000 objects, and horizontal axes show 10 situations each containing 10 relevant objects and 40 ransom one; in addition, there are 5000 “clutter” situations containing only random objects; (a) shows situations sorted along horizontal axis; hence, there are horizontal lines corresponding to relevant objects (right half contains only random noise); (b) shows the same situations in random order, which looks like random noise.

Figure 3

(a) shows DL initiation (random) and the first three iterations; the vertical axis shows objects, and the horizontal axis shows models (from 1 to 20). The problem is approximately solved by the third iteration. This is illustrated in (b), where the error is shown on the vertical error. The correct situations are chosen by minimizing the error. The error does not go to 0 for numerical reasons as discussed in [55].

Figure 4

Parallel hierarchies of language and cognition consist of lower-level concepts (like situations consist of objects). A set of objects (or lower-level concepts) relevant to a situation (or higher-level concept) should be learned among practically infinite number of possible random subsets (as discussed, larger than the Universe). No amount of experience would be sufficient for learning useful subsets from random ones. The previous section overcame combinatorial complexity of learning, given that the sufficient information is present. However, theories of mathematical linguistics offer no explanation where this information would come from.

Figure 5

Developing meanings by connecting language and cognition requires motivation, in other words, emotions. If language emotionality is too weak, language is disconnected from the world, meanings are lost, and cultures disintegrate. If language emotionality is too strong, connections could not evolve and cultures stagnate. Is it possible to keep the balance?