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Brain. Conscious and unconscious mechanisms of cognition, emotions, and language.

Perlovsky L, Ilin R - Brain Sci (2012)

Bottom Line: Fundamental reasons for CC are related to the Gödel's difficulties of logic, a most fundamental mathematical result of the 20th century.Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities.The review discusses cognitive functions of the beautiful, sublime, music.

View Article: PubMed Central - PubMed

Affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, MA 02129, USA. leonid@seas.harvard.edu.

ABSTRACT
Conscious and unconscious brain mechanisms, including cognition, emotions and language are considered in this review. The fundamental mechanisms of cognition include interactions between bottom-up and top-down signals. The modeling of these interactions since the 1960s is briefly reviewed, analyzing the ubiquitous difficulty: incomputable combinatorial complexity (CC). Fundamental reasons for CC are related to the Gödel's difficulties of logic, a most fundamental mathematical result of the 20th century. Many scientists still "believed" in logic because, as the review discusses, logic is related to consciousness; non-logical processes in the brain are unconscious. CC difficulty is overcome in the brain by processes "from vague-unconscious to crisp-conscious" (representations, plans, models, concepts). These processes are modeled by dynamic logic, evolving from vague and unconscious representations toward crisp and conscious thoughts. We discuss experimental proofs and relate dynamic logic to simulators of the perceptual symbol system. "From vague to crisp" explains interactions between cognition and language. Language is mostly conscious, whereas cognition is only rarely so; this clarifies much about the mind that might seem mysterious. All of the above involve emotions of a special kind, aesthetic emotions related to knowledge and to cognitive dissonances. Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities. The review discusses cognitive functions of the beautiful, sublime, music.

No MeSH data available.


Related in: MedlinePlus

Finding “smile” and “frown” patterns in noise, an example of dynamic logic operation: (a) true “smile” and “frown” patterns are shown without noise; (b) actual image available for recognition (signals are below noise, signal-to-noise ratio is between ½ and ¼, 100 times lower than usually considered necessary); (c) an initial fuzzy blob-model, the vagueness corresponds to uncertainty of knowledge; (d) through (h) show improved models at various steps of dynamic logic (DL) (equation A3 are solved in 22 steps). Between stages (d) and (e) the algorithm tried to fit the data with more than one model and decided that it needs three blob-models to “understand” the content of the data. There are several types of models: One uniform model describing noise (it is not shown) and a variable number of blob-models and parabolic models, which number, location, and curvature are estimated from the data. Until about stage (g) the algorithm “thought” in terms of simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (h), when similarity (equation A1) stopped increasing.
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brainsci-02-00790-f001: Finding “smile” and “frown” patterns in noise, an example of dynamic logic operation: (a) true “smile” and “frown” patterns are shown without noise; (b) actual image available for recognition (signals are below noise, signal-to-noise ratio is between ½ and ¼, 100 times lower than usually considered necessary); (c) an initial fuzzy blob-model, the vagueness corresponds to uncertainty of knowledge; (d) through (h) show improved models at various steps of dynamic logic (DL) (equation A3 are solved in 22 steps). Between stages (d) and (e) the algorithm tried to fit the data with more than one model and decided that it needs three blob-models to “understand” the content of the data. There are several types of models: One uniform model describing noise (it is not shown) and a variable number of blob-models and parabolic models, which number, location, and curvature are estimated from the data. Until about stage (g) the algorithm “thought” in terms of simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (h), when similarity (equation A1) stopped increasing.

Mentions: The purpose of this section is to illustrate the DL perception processes, multiple simulators running in parallel as described above. We use a simple example, still unsolvable by other methods, (mathematical details are omitted, they could be found in [51]). In this example, DL searches for patterns in noise. Finding patterns below noise can be an exceedingly complex problem. If an exact pattern shape is not known and depends on unknown parameters, these parameters should be found by fitting the pattern model to the data. However, when the locations and orientations of patterns are not known, it is not clear which subset of the data points should be selected for fitting. A standard approach for solving this kind of problem, which has already been mentioned, is multiple hypotheses testing [20]; this algorithm exhaustively searches all logical combinations of subsets and models and is not practically useful because of CC. Nevertheless, DL successfully find the patterns under noise. In the current example, we are looking for “smile” and “frown” patterns in noise shown in Figure 1a without noise, and in Figure 1b with noise, as actually measured. Object signals are about 2–3 times below noise and cannot be seen by human visual system (it is usually considered that human visual system is better than any algorithm for perception of objects, therefore we emphasize that DL exceeds performance of human perception in this case because DL models work well with random noise, while human perception was not optimized by evolution for this kind of signals).


Brain. Conscious and unconscious mechanisms of cognition, emotions, and language.

Perlovsky L, Ilin R - Brain Sci (2012)

Finding “smile” and “frown” patterns in noise, an example of dynamic logic operation: (a) true “smile” and “frown” patterns are shown without noise; (b) actual image available for recognition (signals are below noise, signal-to-noise ratio is between ½ and ¼, 100 times lower than usually considered necessary); (c) an initial fuzzy blob-model, the vagueness corresponds to uncertainty of knowledge; (d) through (h) show improved models at various steps of dynamic logic (DL) (equation A3 are solved in 22 steps). Between stages (d) and (e) the algorithm tried to fit the data with more than one model and decided that it needs three blob-models to “understand” the content of the data. There are several types of models: One uniform model describing noise (it is not shown) and a variable number of blob-models and parabolic models, which number, location, and curvature are estimated from the data. Until about stage (g) the algorithm “thought” in terms of simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (h), when similarity (equation A1) stopped increasing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061812&req=5

brainsci-02-00790-f001: Finding “smile” and “frown” patterns in noise, an example of dynamic logic operation: (a) true “smile” and “frown” patterns are shown without noise; (b) actual image available for recognition (signals are below noise, signal-to-noise ratio is between ½ and ¼, 100 times lower than usually considered necessary); (c) an initial fuzzy blob-model, the vagueness corresponds to uncertainty of knowledge; (d) through (h) show improved models at various steps of dynamic logic (DL) (equation A3 are solved in 22 steps). Between stages (d) and (e) the algorithm tried to fit the data with more than one model and decided that it needs three blob-models to “understand” the content of the data. There are several types of models: One uniform model describing noise (it is not shown) and a variable number of blob-models and parabolic models, which number, location, and curvature are estimated from the data. Until about stage (g) the algorithm “thought” in terms of simple blob models, at (g) and beyond, the algorithm decided that it needs more complex parabolic models to describe the data. Iterations stopped at (h), when similarity (equation A1) stopped increasing.
Mentions: The purpose of this section is to illustrate the DL perception processes, multiple simulators running in parallel as described above. We use a simple example, still unsolvable by other methods, (mathematical details are omitted, they could be found in [51]). In this example, DL searches for patterns in noise. Finding patterns below noise can be an exceedingly complex problem. If an exact pattern shape is not known and depends on unknown parameters, these parameters should be found by fitting the pattern model to the data. However, when the locations and orientations of patterns are not known, it is not clear which subset of the data points should be selected for fitting. A standard approach for solving this kind of problem, which has already been mentioned, is multiple hypotheses testing [20]; this algorithm exhaustively searches all logical combinations of subsets and models and is not practically useful because of CC. Nevertheless, DL successfully find the patterns under noise. In the current example, we are looking for “smile” and “frown” patterns in noise shown in Figure 1a without noise, and in Figure 1b with noise, as actually measured. Object signals are about 2–3 times below noise and cannot be seen by human visual system (it is usually considered that human visual system is better than any algorithm for perception of objects, therefore we emphasize that DL exceeds performance of human perception in this case because DL models work well with random noise, while human perception was not optimized by evolution for this kind of signals).

Bottom Line: Fundamental reasons for CC are related to the Gödel's difficulties of logic, a most fundamental mathematical result of the 20th century.Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities.The review discusses cognitive functions of the beautiful, sublime, music.

View Article: PubMed Central - PubMed

Affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, MA 02129, USA. leonid@seas.harvard.edu.

ABSTRACT
Conscious and unconscious brain mechanisms, including cognition, emotions and language are considered in this review. The fundamental mechanisms of cognition include interactions between bottom-up and top-down signals. The modeling of these interactions since the 1960s is briefly reviewed, analyzing the ubiquitous difficulty: incomputable combinatorial complexity (CC). Fundamental reasons for CC are related to the Gödel's difficulties of logic, a most fundamental mathematical result of the 20th century. Many scientists still "believed" in logic because, as the review discusses, logic is related to consciousness; non-logical processes in the brain are unconscious. CC difficulty is overcome in the brain by processes "from vague-unconscious to crisp-conscious" (representations, plans, models, concepts). These processes are modeled by dynamic logic, evolving from vague and unconscious representations toward crisp and conscious thoughts. We discuss experimental proofs and relate dynamic logic to simulators of the perceptual symbol system. "From vague to crisp" explains interactions between cognition and language. Language is mostly conscious, whereas cognition is only rarely so; this clarifies much about the mind that might seem mysterious. All of the above involve emotions of a special kind, aesthetic emotions related to knowledge and to cognitive dissonances. Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities. The review discusses cognitive functions of the beautiful, sublime, music.

No MeSH data available.


Related in: MedlinePlus