93,351 research outputs found
Session 5: Development, Neuroscience and Evolutionary Psychology
Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog
Information Based Hierarchical Brain Organization/Evolution from the Perspective of the Informational Model of Consciousness
Introduction: This article discusses the brain hierarchical organization/evolution as a consequence of the information-induced brain
development, from the perspective of the Informational Model of Consciousness.
Analysis: In the frame of the Informational Model of Consciousness, a detailed info-neural analysis ispresented, concerning the specific
properties/functions of the informational system of the human body composed by the Center of Acquisition and Storing of Information, Center of
Decision and Command, Info-Emotional Center, Maintenance Informational System, Genetic Transmission System, Info Genetic Generator and Info-
Connection center, in relation with the neuro-connected brain areas, with a special attention to the Info-Connection and its specific properties.
Besides a meticulous analysis of the info-connections/neuro-functions of these centers, a special attention was paid to limbic/cingulate cortex
activities. Defined as a trust/confidence center, additional features are highlighted in correlation with the activity of the anterior cingulate cortex,
consisting in the intervention/moderation of amygdala emotional signals, conflicting opposite YES/NO data and error elimination in the favor of the
organism adaptation/survival, the intervention in the certainty/uncertainty balance to select a suitable pro-life information (antientropic effect), in
moderation of pain and in the stimulation of the empathic inter-human relations/communication. Representing the correspondence between the
informational subsystems and the brain area map, itis shown that the up/down integration of information by epigenetic mechanisms and the down/
up evolution are correlated.
Results: The analysis of the functions of the anterior cingulate opens new gates of investigations concerning the involved intimate mechanisms
at the level of cell microstructure, specifically on the compatibility with quantum assisted processes admitted by the Informational Model of
Consciousness and the quantum-based models The discussion on the information integration/codification by epigenetic mechanisms shows that
this process starts from the superior levels of brain conscious info-processing areas and progressively advances to the automatic/autonomic inferior
levels ofthe informational system, under insistent/repetitive cues/stress conditions, pointing out an hierarchical functional/anatomical structure of
the brain organization. Additional arguments are discussed, indicating thatthe down/up progressive scale representation is a suggestive illustration
of the brain evolution, induced/assisted/determined by information, accelerated at humans by the antientropic functions of the Info-Connection
center.
Conclusions: The hierarchical organization of the brain is a consequence of the integration process of information, defining its development
accordingly to the adaptation requirements for survival during successive evolution stages of the organism, information playing a determinant/key
role
An evolutionary behavioral model for decision making
For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud
of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud
the same evolutionary mechanism is able to assemble quite\ud
complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies
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Evolving structure-function mappings in cognitive neuroscience using genetic programming
A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems.
(This article does not exactly replicate the final version published in the Journal of Swiss Psychology. It is not a copy of the original published article and is not suitable for citation.
Intrinsic Motivation Systems for Autonomous Mental Development
Exploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
From Physical Aggression to Verbal Behavior: Language Evolution and Self-Domestication Feedback Loop
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
The dimensions of personality in humans and other animals: A comparative and evolutionary perspective
This paper considers the structure and proximate mechanisms of personality in humans and other animals. Significant similarities were found between personality structures and mechanisms across species in at least two broad traits: Extraversion and Neuroticism. The factor space tapped by these personality dimensions is viewed as a general integrative framework for comparative and evolutionary studies of personality in humans and other animals. Most probably, the cross-species similarities between the most broad personality dimensions like Extraversion and Neuroticism as well as other Big Five factors reflect conservative evolution: constrains on evolution imposed by physiological, genetic and cognitive mechanisms. Lower-order factors, which are more species- and situation-specific, would be adaptive, reflecting correlated selection on and trade-offs between many traits
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Spring School on Language, Music, and Cognition: Organizing Events in Time
The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session.
Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations
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