Chaos breeds autonomy: connectionist design between bias and baby-sitting

In connectionism and its offshoots, models acquire functionality through externally controlled learning schedules. This undermines the claim of these models to autonomy. Providing these models with intrinsic biases is not a solution, as it makes their function dependent on design assumptions. Between these two alternatives, there is room for approaches based on spontaneous self-organization. Structural reorganization in adaptation to spontaneous activity is a well-known phenomenon in neural development. It is proposed here as a way to prepare connectionist models for learning and enhance the autonomy of these models.     

The explanatory potential of artificial societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds explanatory strategies differ, and how potential functional explanations can be appraised.     

Downward causation in fluid convection

Recent developments in nonlinear dynamics have found wide application in many areas of science from physics to neuroscience. Nonlinear phenomena such as feedback loops, inter-level relations, wholes constraining and modifying the behavior of their parts, and memory effects are interesting candidates for emergence and downward causation. Rayleigh–Bénard convection is an example of a nonlinear system that, I suggest, yields important insights for metaphysics and philosophy of science. In this paper I propose convection as a model for downward causation in classical mechanics, far more robust and less speculative than the examples typically provided in the philosophy of mind literature. Although the physics of Rayleigh–Bénard convection is quite complicated, this model provides a much more realistic and concrete example for examining various assumptions and arguments found in emergence and philosophy of mind debates. After reviewing some key concepts of nonlinear dynamics, complex systems and the basic physics of Rayleigh–Bénard convection, I begin that examination here by (1) assessing a recently proposed definition for emergence and downward causation, (2) discussing some typical objections to downward causation and (3) comparing this model with Sperry’s examples. The aim of science is not things themselves, as the dogmatists in their simplicity imagine, but the relations among things; outside these relations there is no reality knowable. – Poincaré     

Emergent complexity: What uphill analysis or downhill invention cannot do

In these notes we review emergent phenomena in complex systems, emphasizing ways to identify potential underlying universal mechanisms that generate complexity. The discussion focuses on the emergence of collective behavior in dynamical systems when they are poised near a critical point of a phase transition, either by tuning or by self-organization. We then present the rationale for our proposal that the brain is naturally poised near criticality and review recent results as well as the implications of this view of the functioning brain.     

A Dynamical Systems Approach to Manual Tracking Performance

Manual tracking performance was investigated from the perspective of dynamical systems theory. The authors manipulated the type of visual display, the control system dynamics, and the frequency of the sinusoidal input signal to examine couplings with various phases between the visual signal and control movements. Analyses of the system output amplitude ratio and relative phase showed that participants (N = 24) performed poorly with 90° relative phase coupling. All the couplings became less stable as the movement frequency increased. The authors developed an adaptive oscillator model with linear damping to describe the coupled system consisting of the human performer, the visual display, and the control system dynamics. A geometric account of the stability of performance at different relative phases is also presented.     

Extending Dynamical Systems Theory to Model Embodied Cognition

We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way this formalism can be applied, several neural network models are embedded in a simple model environment. Such phenomena as masking, perceptual ambiguity, and priming are then observed. We also use this formalism to reinterpret examples from the embodiment literature, arguing that it provides for a more thorough analysis of the relevant phenomena.     

Simple games as dynamic, coupled systems: randomness and other emergent properties

From a game theory perspective the ability to generate random behaviors is critical. However, psychological studies have consistently found that individuals are poor at behaving randomly. In this paper we investigated the possibility that the randomness mechanism lies not within the individual players but in the interaction between the players. Provided that players are influenced by their opponent’s past behavior, their relationship may constitute a state of reciprocal causation [Cognitive Science 21 (1998) 461], in which each player simultaneously affects and is affected by the other player. The result of this would be a dynamic, coupled system. Using neural networks to represent the individual players in a game of paper, rock, and scissors, a model of this process was developed and shown to be capable of generating chaos-like behaviors as an emergent property. In addition, it was found that by manipulating the control parameters of the model, corresponding to the amount of working memory and the perceived values of different outcomes, that the game could be biased in favor of one player over the other, an outcome not predicted by game theory. Human data was collected and the results show that the model accurately describes human behavior. The results and the model are discussed in light of recent theoretical advances in dynamic systems theory and cognition.