Abstract — Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords. I.

Abstract—Can computers have intuition, insights and be creative? Neurocognitive models inspired by the putative processes in the brain show that these mysterious features are a consequence of information processing in complex networks. Intuition is manifested in categorization based on evaluation of similarity, when decision borders are too complex to be reduced to logical rules. It is also manifested in heuristic reasoning based on partial observations, where network activity selects only those path that may lead to solution, excluding all bad moves. Insight results from reasoning at the higher, non-verbal level of abstraction that comes from involvement of the right hemisphere networks forming large “linguistic receptive fields”. Three factors are essential for creativity in invention of novel words: knowledge of word morphology captured in network connections, imagination constrained by this knowledge, and filtering of results that selects most interesting novel words. These principles have been implemented using a simple correlation-based algorithm for autoassociative memory. Results are surprisingly similar to those created by humans. Keywords—Creativity, intuition, insight, brain, language processing, higher cognitive functions, neural modeling.

Abstract—Putative brain processes responsible for understanding language are based on spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. Approximation of this process is of great practical and theoretical interest. Vector model should reflect activations of various concepts in the brain spreading through associative network. Medical ontologies are used to select concepts of specific semantic type and add to each of them related concepts, providing expanded vector representations. The process is constrained by selection of useful extensions for the classification task. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Results show significantly improved clustering and classification accuracy. A practical approach to mapping of associative networks of the brain involved in processing of specific concepts is presented. I.

Abstract. Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed. 1.

Abstract—Neurocognitive model inspired by the putative processes in the brain has been applied to invention of novel words. This domain is proposed as the simplest way to understand creativity using experimental and computational means. Three factors are essential for creativity in this domain: knowledge of the statistical language properties, imagination constrained by this knowledge, and filtering of results that selects most interesting novel words. These principles are implemented using a simple correlation-based algorithm for auto-associative memory that learns the statistical properties of language. Results are surprisingly similar to those created by humans. Perspectives on computational models of creativity are discussed. Keywords—Creativity, brain, language processing, higher cognitive functions, neural modeling.

Brain processes responsible for understanding language are approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. Approximation of this process is of great practical and theoretical interest. Snapshots of activations of various concepts in the brain spreading through associative network may be captured in a vector model. Medical ontologies are used to identify concepts of specific semantic type in the text, and add to each of them related concepts, providing expanded vector representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Results show significantly improved clustering and classification accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts.

Quis ego et qualis ego? Who am I and what kind of man am I? St. Augustine (400 AC) asks an ancient question, that echoes the inscription “Know thyself ” over the Apollo temple in Delphi, and repeats the advice given by Solon, who wrote the first laws for Athenian democracy one thousand years earlier. Similar statements are found in the Mahabharata, the Tao Te Ching, and the Dhammapada, showing that the need for understanding oneself has been articulated from the beginning of philosophical inquiry. These timeless questions were formulated in ancient Greece in terms of nous and psyche, while modern preoccupation with self and consciousness started with Descartes in philosophy and entered the mainstream of science only near the end of the 20 th century (Damasio, 1999). Although our modern conceptualization of the question may be different the essence remains the same. How have such questions been answered in the past and how can we answer them now? Early philosophy looked for simple explanations and focused on the difference between dead and living organisms. How can the growths of plants, the movement of animals, or the movement of thoughts and mental images be explained? No physical difference could be observed at the moment of death, so something immaterial must have left the body. Ancient

years of labeling the subject as unobservable and thus not amenable to rigorous investigation, has initially brought a lot of discussions among the philosophers of mind. Soon other cognitive scientists followed, and now consciousness research is in full swing on many fronts, with its own specialized journals (Consciousness and Cognition, Journal of Consciousness Studies) and series of conferences. In the past few years various attempts to define the problem of consciousness from engineering or computational perspective have emerged. Although detailed brain mechanisms responsible for consciousness are not yet known chances are that it might be possible to build conscious machines using some brain-like architectures. Indeed it has been argued [?] that a system with proper relations between its internals states that control its behavior, able to comment on these states, should behave as

Abstract: Can computers have intuition and insights, and be creative? Neurocognitive models inspired by the putative processes in the brain show that these mysterious features are a consequence of information processing in complex networks. Intuition is manifested in categorization based on evaluation of similarity, when decision borders are too complex to be reduced to logical rules. It is also manifested in heuristic reasoning based on partial observations, where network activity selects only those paths that may lead to solution, excluding all bad moves. Insight results from reasoning at the higher, non-verbal level of abstraction that comes from involvement of the right hemisphere networks forming large “linguistic receptive fields. ” Three factors are essential for creativity in invention of novel words: knowledge of word morphology captured in network connections, imagination constrained by this knowledge, and filtering of results that selects the most interesting novel words. These principles have been implemented using a simple correlation-based algorithm for auto-associative memory. Results are surprisingly similar to those created by humans. One of the objections against computational intelligence considered by Alan Turing in his famous article, “Computing machinery and intelligence,” [1] recalls Lady Lovelace’s objection (written in

Recent developments in cognitive neuroscience radically changed the perspective on understanding human nature. For the first time in history many philosophical questions can be placed on scientific, rather than on philosophical grounds. These questions include understanding of the mind, self, free will, religious and cultural beliefs, morality, politics and social organization. Scientific consensus based on these discoveries is slowly being developed and will have far reaching consequences. Evolutionary perspective explains how homo sapiens has evolved, why do we have specific structures of the body, brain, sensory abilities, and how the mind emerges from embodiment and social interactions. Social neuroscience shows that there is emergent causality: biology determines affective and cognitive abilities, preferences and beliefs, personality, but it is itself influenced by the environment that changes our brains and bodies. All these mechanisms are deeply hidden from ordinary introspection, creating a wrong perception of human nature. Traditional views on human nature are briefly summarized and radical reductionist interpretations of neurobiologists presented, comparing humans to a bag of chemicals. Scientific discoveries cannot be ignored, but their interpretation is not