Simulating the Effect of Environmental Change on Evolving Populations.

This study uses evolutionary simulations to explore the strategies that emerge to enable populations to cope with random environmental changes in situations where lifetime learning approaches are not available to accommodate them. In particular, it investigates how the average magnitude of change per unit time and the persistence of the changes (and hence the resulting autocorrelation of the environmental time series) affect the change tolerances, population diversities, and extinction timescales that emerge. Although it is the change persistence (often discussed in terms of environmental noise color) that has received most attention in the recent literature, other factors, particularly the average change magnitude, interact with this and can be more important drivers of the adaptive strategies that emerge.

Effect of Environmental Change Distribution on Artificial Life Simulations.

It is already well known that environmental variation has a big effect on real evolution, and similar effects have been found in evolutionary artificial life simulations. In particular, a lot of research has been carried out on how the various evolutionary outcomes depend on the noise distributions representing the environmental changes, and how important it is for models to use inverse power-law distributions with the right noise colour. However, there are two distinct factors of relevance-the average total magnitude of change per unit time and the distribution of individual change magnitudes-and misleading results may emerge if those factors are not properly separated.

Imitative and Direct Learning as Interacting Factors in Life History Evolution.

The idea that lifetime learning can have a significant effect on life history evolution has recently been explored using a series of artificial life simulations. These involved populations of competing individuals evolving by natural selection to learn to perform well on simplified abstract tasks, with the learning consisting of identifying regularities in their environment. In reality, there is more to learning than that type of direct individual experience, because it often includes a substantial degree of social learning that involves various forms of imitation of what other individuals have learned before them.

Limiting factors for mapping corpus-based semantic representations to brain activity.

To help understand how semantic information is represented in the human brain, a number of previous studies have explored how a linear mapping from corpus derived semantic representations to corresponding patterns of fMRI brain activations can be learned. They have demonstrated that such a mapping for concrete nouns is able to predict brain activations with accuracy levels significantly above chance, but the more recent elaborations have achieved relatively little performance improvement over the original study. In fact, the absolute accuracies of all these models are still currently rather limited, and it is not clear which aspects of the approach need improving in order to achieve performance levels that might lead to better accounts of human capabilities.

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere.

Lifetime learning as a factor in life history education.

An artificial life approach is taken to explore the effect that lifetime learning can have on the evolution of certain life history traits, in particular the periods of protection that parents offer young, and the age at first reproduction of those young. The study begins by simulating the evolution of simple artificial neural network systems that must learn quickly to perform well on simple classification tasks, and determining if and when extended periods of parental protection emerge. It is concluded that longer periods of parental protection of children do offer clear learning advantages and better adult performance, but only if procreation is not allowed during the protection period.

Extracting semantic representations from word co-occurrence statistics: a computational study.

The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared.

Understanding the emergence of modularity in neural systems.

Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular architectures that are most efficient.

From biological models to the evolution of robot control systems.

Attempts to formulate realistic models of the development of the human oculomotor control system have led to the conclusion that evolutionary factors play a crucial role. Moreover, even rather coarse simulations of the biological evolutionary processes result in adaptable control systems that are considerably more efficient than those designed by human researchers. In this paper I shall describe some of the aspects of these biological models that are likely to be useful for building robot control systems.

Evolving efficient learning algorithms for binary mappings.

Gradient descent training of sigmoidal feed-forward neural networks on binary mappings often gets stuck with some outputs totally wrong. This is because a sum-squared-error cost function leads to weight updates that depend on the derivative of the output sigmoid which goes to zero as the output approaches maximal error. Although it is easy to understand the cause, the best remedy is not so obvious.