Can theories of mental imagery, conscious mental contents, developed within cognitive science throw light on the obscure (but culturally very significant) concept of imagination? Three extant views of mental imagery are considered: quasi-pictorial, description, and perceptual activity theories. The first two face serious theoretical and empirical difficulties. The third is (for historically contingent reasons) little known, theoretically underdeveloped, and empirically untried, but has real explanatory potential. It rejects the “traditional ” symbolic computational view of mental contents, but is compatible with recent situated cognition and active vision approaches in robotics. This theory is developed and elucidated. Three related key aspects of imagination (non-discursiveness, creativity, and seeing as) raise difficulties for the other theories. Perceptual activity theory presents imagery as non-discursive and relates it closely to seeing as. It is thus well placed to be the basis for a general theory of imagination and its role in creative thought.

A simple model of self-organised learning with no classical (Hebbian) reinforcement is presented. Synaptic connections involved in mistakes are depressed. The model operates at a highly adaptive, probably critical, state reached by extremal dynamics similar to that of recent evolution models. Thus, one might think of the mechanism as synaptic Darwinism. It is widely believed that learning in the brain resides in alterations of synaptic efficacy. Without exception, contemporary formulations of such learning follows Hebb’s ideas [1] of reinforcement: synaptic connections among neurons excited during a a given firing pattern are strengthened by a process of long term potentiation (LTP). However, long term synaptic depression (LTD) in the mammalian brain is almost as prevalent as potentiation, but there appears to be little or no understanding of its functional role. Working hypotheses covers a wide range, where depression is given always an auxiliary function to potentiation [2]. A recent review [3], reflecting the current variety of ideas regarding the functional role of LTD, speculates: “Although it is conceivable that LTP is

Endless invention, endless experiment, Brings knowledge of motion, but not of stillness; Knowledge of speech, but not of silence; Knowledge of words, and ignorance of the Word. All our knowledge brings us nearer to our ignorance, all our ignorance brings us nearer to death, but nearness to death no nearer to God. Where is the Life we have lost in living? Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? T. S. Eliot, The Rock “Long range power low correlation ” (LRC) is defined as a maximal propagation distance of the effect of some disturbance within a system found in many systems that can be represented as strings of symbols. LRC between characters has been identified also in natural language texts. The aim of this paper is to show that long- range power law correlation can be also found in computer programs meaning that some common laws hold for both natural language texts and computer programs. This fact enables one to draw parallels between these two different types of human writings and on the other hand enables to measure the differences between them.

Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and “fuzzy”. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, according to its dimensions. The ENG model analysis shows that the net evolves as a scale-free graph, and justifies in a deeply physical sense- the term “gas ” here used.