77,788 research outputs found
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
The Emotivism of Law. Systematic Irrationality, Imagined Orders, and the Spirit of Decision Making
The process of decision making is predictable and irrational according to Daniel Ariely and other economic behaviorists, historians, and philosophers such as Daniel Kahneman or Yuval Noah Harari. Decisions made anteriorly can be, but don’t have to be, present in the actions of a person. Stories and shared belief in myths, especially those that arise from a system of human norms and values and are based on a belief in a “supernatural” order (religion) are important. Because of this, mass cooperation amongst strangers is possible
Incorporating characteristics of human creativity into an evolutionary art algorithm
A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically
The Evolution of Social Contracts
Influential thinkers such as Young, Sugden, Binmore, and Skyrms have developed game-theoretic accounts of the emergence, persistence and evolution of social contracts. Social contracts are sets of commonly understood rules that govern cooperative social interaction within societies. These naturalistic accounts provide us with valuable and important insights into the foundations of human societies. However, current naturalistic theories focus mainly on how social contracts solve coordination problems in which the interests of the individual participants are aligned, not competition problems in which individual interests compete with group interests. In response, I set out to build on those theories and provide a comprehensive naturalistic account of the emergence, persistence and evolution of social contracts. My central claim is that social contracts have culturally evolved to solve cooperation problems, which include both coordination and competition problems. I argue that solutions to coordination problems emerge from “within-group” dynamics, while solutions to competition problems result largely from “between-group” dynamics
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
Creativity and the Brain
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
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