The discrepancy between syntax and semantics is a painstaking issue that hinders a better comprehension of the underlying neuronal processes in the human brain. In order to tackle the issue, we at first describe a striking correlation between Wittgenstein's Tractatus, that assesses the syntactic relationships between language and world, and Perlovsky's joint language-cognitive computational model, that assesses the semantic relationships between emotions and “knowledge instinct”. Once established a correlation between a purely logical approach to the language and computable psychological activities, (...) we aim to find the neural correlates of syntax and semantics in the human brain. Starting from topological arguments, we suggest that the semantic properties of a proposition are processed in higher brain's functional dimensions than the syntactic ones. In a fully reversible process, the syntactic elements embedded in Broca's area project into multiple scattered semantic cortical zones. The presence of higher functional dimensions gives rise to the increase in informational content that takes place in semantic expressions. Therefore, diverse features of human language and cognitive world can be assessed in terms of both the logic armor described by the Tractatus, and the neurocomputational techniques at hand. One of our motivations is to build a neuro-computational framework able to provide a feasible explanation for brain's semantic processing, in preparation for novel computers with nodes built into higher dimensions. (shrink)
Have Morsella et al. examined the fundamentals of consciousness? An experiment by Bar et al. has demonstrated the fundamental aspects of conscious and unconscious mechanisms of perception. The mental representations are not crisp and conscious like the perceived objects are, but vague and unconscious. This experiment points to the fundamental function of the neural mechanisms of consciousness in perception. Consciousness is also fundamental for the highest emotions.
There is ample evidence that humans (and other primates) possess a knowledge instinct—a biologically driven impulse to make coherent sense of the world at the highest level possible. Yet behavioral decision-making data suggest a contrary biological drive to minimize cognitive effort by solving problems using simplifying heuristics. Individuals differ, and the same person varies over time, in the strength of the knowledge instinct. Neuroimaging studies suggest which brain regions might mediate the balance between knowledge expansion and heuristic simplification. One region (...) implicated in primary emotional experience is more activated in individuals who use primitive heuristics, whereas two areas of the cortex are more activated in individuals with a strong knowledge drive: one region implicated in detecting risk or conflict and another implicated in generating creative ideas. Knowledge maximization and effort minimization are both evolutionary adaptations, and both are valuable in different contexts. Effort minimization helps us make minor and routine decisions efficiently, whereas knowledge maximization connects us to the beautiful, to the sublime, and to our highest aspirations. We relate the opposition between the knowledge instinct and heuristics to the biblical story of the fall, and argue that the causal scientific worldview is mathematically equivalent to teleological arguments from final causes. Elements of a scientific program are formulated to address unresolved issues. (shrink)
Freedom of will is fundamental to morality, intuition of self, and normal functioning of society. However, science does not provide a clear logical foundation for this idea. This paper considers the fundamental argument against free will, so called reductionism, and why the choice for dualism against monism, follows logically. Then, the paper summarizes unexpected conclusions from recent discoveries in cognitive science. Classical logic turns out not to be a fundamental mechanism of the mind. It is replaced by dynamic logic. Mathematical (...) and experimental evidence are considered conceptually. Dynamic logic counters logical arguments for reductionism. Contemporary science of mind is not reducible; free will can be scientifically accepted along with scientific monism. (shrink)
Logic is a fundamental reason why computational accounts of the mind have failed. Combinatorial complexity preventing computational accounts is equivalent to the Gödelian incompleteness of logic. The mind is not logical, but only logical states and processes in the mind are accessible to subjective consciousness. For this reason, intuitions of psychologists, cognitive scientists, and mathematicians modeling the mind are biased toward logic. This is also true about the changes proposed inAfter Phrenology.
Modeling a complex phenomena such as the mind presents tremendous computational complexity challenges. Modeling field theory (MFT) addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model (also, a problem or some theory) with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called (...) the Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models. (shrink)
The target article by Lindquist et al. considers discrete emotions. This commentary argues that these are but a minor part of human emotional abilities, unifying us with animals. Uniquely human emotions are aesthetic emotions related to the need for the knowledge of cognition, including emotions of the beautiful, cognitive dissonances, and musical emotions. This commentary touches on their cognitive functions and origins.
Modelling a complex phenomenon such as the mind presents tremendous computational complexity challenges. Modelling field theory addresses these challenges in a non-traditional way. The main idea behind MFT is to match levels of uncertainty of the model with levels of uncertainty of the evaluation criterion used to identify that model. When a model becomes more certain, then the evaluation criterion is adjusted dynamically to match that change to the model. This process is called the Dynamic Logic of Phenomena for model (...) construction and it mimics processes of the mind and natural evolution. This paper provides a formal description of DLP by specifying its syntax, semantics, and reasoning system. We also outline links between DLP and other logical approaches. Computational complexity issues that motivate this work are presented using an example of polynomial models. (shrink)