Dynamic systems

Efforts to bridge emotion theory with neurobiology can be facilitated by dynamic systems (DS) modeling. DS principles stipulate higher-order wholes emerging from lower-order constituents through bidirectional causal processes – offering a common language for psychological and neurobiological models. After identifying some limitations of mainstream emotion theory, I apply DS principles to emotion–cognition relations. I then present a psychological model based on this reconceptualization, identifying trigger, self-amplification, and self-stabilization phases of emotion-appraisal states, leading to consolidating traits. The article goes on to describe neural structures and functions involved in appraisal and emotion, as well as DS mechanisms of integration by which they interact. These mechanisms include nested feedback interactions, global effects of neuromodulation, vertical integration, action-monitoring, and synaptic plasticity, and they are modeled in terms of both functional inte...

This Monograph aims to contribute to the information processing, the differential, and the developmental modeling of the mind, and to work these into an integrated theory. Toward this aim, a longitudinal study is presented that investigates the relations between processing efficiency, working memory, and problem solving from the age of 8 years to to the age of 16 years. The study involved 113 participants, about equally drawn among 8-, 10-, 12-, and 14-year-olds at the first testing; these participants were tested two more times spaced one year apart. Participants were tested with a large array of tasks addressed to processing efficiency (i.e., speed of processing and inhibition), working memory (in terms of Baddeley's model, phonological storage, visual storage, and the central executive of working memory), and problem solving (quantitative, spatial, and verbal reasoning). Confirmatory factor analysis validated the presence of each of the above dimensions and indicated that the...

Context • This paper is intended for readers familiar with Humberto Maturana’s theory of autopoietic systems and
with the still unresolved debate concerning the existence of non-biological autopoietic systems. Because the seminal
work of the Chilean biologist has not yet been fully and correctly understood in other disciplines, I consider that it
is necessary to offer a more generalized concept of the autopoietic system, derived by implication from Maturana’s
grounding definition. > Problem • The above-mentioned debate is rooted in a deficient application of some rigorous
distinctions, definitions, and epistemological considerations introduced by Maturana when he coined the term “autopoiesis.”
Some researchers think that social or economic organizations could be considered as autopoietic systems of a
higher order because they appear to behave autonomously and be self-organized and self-producing. However, in practice
some precise distinctions would need to be verified through observation in order to claim properly their autopoietic
nature. These distinctions were defined by Varela, Maturana and Uribe in 1974 as a set of six decisional rules (“MV&U
rules”) whereby an observer may possibly justify this stand. My aim is to pinpoint clearly the basic cognitive tasks that
an observer should perform in order to ascertain such a claim. > Method • I accomplish this with a thorough analysis
of the entailments derived from each rule when applied to the most general case – when the observational domain
where the system manifests itself is not specified. A bottom-up approach is used to avoid referring to “apparent autopoietic
behavior” as a starting point distinction (top-down approach): the aim is to distinguish “autopoietic behavior”
as an outcome of more basic distinctions, not as a premise for these. These may be used as abstract tools to facilitate
a rigorous description of observations and lead to precise explanations of the emergence of complex self-generated
dynamic systems. In theory, this conceptual frame is not limited to the macro-molecular domain and may therefore be
applicable to non-biological systems. > Results • According to MV&U rules, the most important distinctions are those
that refer to intra-boundary phenomenology: this focus is necessary to explain how the key processes involved in the
emergence of autopoiesis actually manifest themselves. These explanations are crucial to validating a claim about
the “autopoietic nature” of an observed system. > Implications • This work could help multidisciplinary researchers
to apply properly the theory of autopoietic systems beyond the realm of biology and to settle ongoing debates. It
could also help investigations related to the specifications of software simulation processes for modeling a minimal
artificial autopoietic system. However, the rigorous focus on the role of intra-boundary phenomenology and self-production
of components reveals that our chances of detecting “natural” meta-molecular autopoietic systems are scarce.
> Key words • Autopoiesis, self-production, autonomy, causation structure, artificial life, organizational knowledge.

A brief review of current approaches to active feedback control of the fluctuations arising in turbulent flows is presented, emphasizing the mathematical techniques involved. Active feedback control schemes are categorized and compared by examining the extent to which they are based on the governing flow equations. These schemes are broken down into the following categories: adaptive schemes, schemes based on heuristic physical arguments, schemes based on a dynamical systems approach, and schemes based on optimal control theory applied directly to the Navier-Stokes equations. Recent advances in methods of implementing small scale flow control ideas are also reviewed.

The development of organisms shows an end-state-directedness at various levels of organization. The concept of „self-organization”, arising from the study of physico-chemical processes, has been used for the understanding of the nature and causality of this specific characteristic of organismic development. Based on that concept it is currently attempted to develop computational models of complex biomolecular systems. We believe, however, that essential differences exist between physico-chemical and biological systems, which must be taken into account for future theoretical work. We discuss, e.g., the relationship between static and dynamic quantities, which limits the feasibility of mathematical modeling of the dynamics of organismic development. To grasp the logic of the organismic causality we outline a post-physicalistic concept of organisms based on the idea of an intrinsic nature, which is not static, but rather the process of the production of information.