Effective Affective User Interface Design in Games

It is proposed that games, which are designed to generate positive affect, are most successful when they facilitate flow (Csikszentmihalyi 1992). Flow is a state of concentration, deep enjoyment, and total absorption in an activity. The study of games, and a resulting understanding of flow in games can inform the design of nonleisure software for positive affect. The paper considers the ways in which computer games contravene Nielsen’s guidelines for heuristic evaluation (Nielsen and Molich 1990) and how these contraventions impact on flow. The paper also explores the implications for research that stem from the differences between games played on a personal computer and games played on a dedicated console. This research takes important initial steps towards defining how flow in computer games can inform affective design

Fast Entropy Estimation for Natural Sequences

It is well known that to estimate the Shannon entropy for symbolic sequences accurately requires a large number of samples. When some aspects of the data are known it is plausible to attempt to use this to more efficiently compute entropy. A number of methods having various assumptions have been proposed which can be used to calculate entropy for small sample sizes. In this paper, we examine this problem and propose a method for estimating the Shannon entropy for a set of ranked symbolic natural events. Using a modified Zipf-Mandelbrot-Li law and a new rank-based coincidence counting method, we propose an efficient algorithm which enables the entropy to be estimated with surprising accuracy using only a small number of samples. The algorithm is tested on some natural sequences and shown to yield accurate results with very small amounts of data

Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task

The rise and fall of learning: a neural network model of the genetic assimilation of acquired traits

The genetic assimilation of learned behaviour was introduced to the wider evolutionary computation field by the classic simulation of Hinton and Nowlan. Subsequent studies have analysed and extended their initial framework, contributing to the understanding of the often counterintuitive relationship between evolution and learning. We add to this increasing body of literature by presenting an evolving population of neural networks that plainly exhibit the Baldwin effect. Phenotypic plasticity, embodied in the literal learning rate of the neural networks, is evolved along with the network connection weights. Significantly, high levels of plasticity do not cause the population to genetically stagnate once correct behaviour can be learned. Rather, continuing inter-population competition drives the levels of learning down as beneficial behaviour becomes genetically specified. By observing the evolving learning rate of the agent population, and by comparing learned and innate agent responses, we demonstrate the Baldwin effect in its entirety

Developmental motifs reveal complex structure in cell lineages

Many natural and technological systems are complex, with organisational structures that exhibit characteristic patterns, but defy concise description. One effective approach to analysing such systems is in terms of repeated topological motifs. Here, we extend the motif concept to characterise the dynamic behaviour of complex systems by introducing developmental motifs, which capture patterns of system growth. As a proof of concept, we use developmental motifs to analyse the developmental cell lineage of the nematode Caenorhabditis elegans, revealing a new perspective on its complex structure. We use a family of computational models to explore how biases arising from the dynamics of the developmental gene network, as well as spatial and temporal constraints acting on development, contribute to this complex organisation

Towards More Relevant Evolutionary Models: Integrating an Artificial Genome With a Developmental Phenotype

The relationship between the genotype and phenotype of organisms plays a key role in the evolutionary process. While Evolutionary Computation (EC) models have traditionally taken biological inspiration in the design of many key model components (e.g., genetic mutation and crossover, populations under natural selection, etc.), there is a need for more biological input in specifying how a genotype forms a phenotype. There are two powerful theoretical abstractions used in biology for explaining the evolutionary basis of phenotypic development. The first is that there is a sequence of hereditary information (the genotype) passed from one generation to the next. The second is that genes extracted from this sequence interact to form networks of regulation that, when coupled with environmental factors, control the development of an organism (the phenotype). An abstract model of gene regulation exists in the form of the Artificial Genome. This model provides a principled approach to extracting regulatory networks of genes from sequence-level information. L-systems provide a mature framework for modelling developmental phenotypes interacting within environments. This paper takes a step towards integrating these two models, providing a biologically-inspired modelling framework that bridges the chasm between processes occurring in evolutionary timescales, and those occurring within individual lifetimes

Determining the Number of Samples Required to Estimate Entropy in Natural Sequences

Calculating the Shannon entropy for symbolic sequences has been widely considered in many fields. For descriptive statistical problems such as estimating the N-gram entropy of English language text, a common approach is to use as much data as possible to obtain progressively more accurate estimates. However in some instances, only short sequences may be available. This gives rise to the question of how many samples are needed to compute entropy. In this paper, we examine this problem and propose a method for estimating the number of samples required to compute Shannon entropy for a set of ranked symbolic natural events. The result is developed using a modified Zipf-Mandelbrot law and the Dvoretzky-Kiefer-Wolfowitz inequality, and we propose an algorithm which yields an estimate for the minimum number of samples required to obtain an estimate of entropy with a given confidence level and degree of accuracy

Transient phenomena in learning and evolution: genetic assimilation and genetic redistribution

Deacon has recently proposed that complexes of genes can be integrated into functional groups as a result of environmental changes that mask and unmask selection pressures. For example, many animals endogenously synthesize ascorbic acid (vitamin C), but anthropoid primates have only a nonfunctional version of the crucial gene for this pathway. It is hypothesized that the loss of functionality occurred in the evolutionary past when a diet rich in vitamin C masked the effect of the gene, and its loss effectively trapped the animals in a fruit-eating lifestyle. As a result, the complex of abilities that support this lifestyle were evolutionarily bound together, forming a multilocus complex. In this study we use evolutionary computation simulations to explore the thesis that masking and unmasking can transfer dependence from one set of genes to many sets, and thereby integrate the whole complex of genes. We used a framework based on Hinton and Nowlan's 1987 simulation of the Baldwin effect. Additional gene complexes and an environmental parameter were added to their basic model, and the fitness function extended. The simulation clearly demonstrates that the genetic redistribution effect can occur in silico, showing an initial advantage of endogenously synthesized vitamin C, followed by transfer of the fitness contribution to the complex of genes that together allow the acquisition of vitamin C from the environment. As is well known in the modeling community, the Baldwin effect only occurs in simulations when the population of agents is "poised on the brink" of discovering the genetically specified solution. Similarly, the redistribution effect occurs in simulations under specific initial conditions: too little vitamin C in the environment, and its synthesis it is never fully masked; too much vitamin C, and the abilities required to acquire it are not tightly integrated. The Baldwin effect has been hypothesized as a potential mechanism for developing language-specific adaptations like innate universal grammar and other highly modular capacities. We conclude with a discussion of the relevance of genetic assimilation and genetic redistribution to the evolution of language and other cognitive adaptations

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution