Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

© Peter C. R. Lane, Fernand Gobet. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC 3.0)Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the ‘speciated non-dominated sorting genetic algorithm’ for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.Peer reviewedFinal Published versio

Is Experts' Knowledge Modular?

This paper explores, both with empirical data and with computer simulations, the extent to which modularity characterises experts' knowledge. We discuss a replication of Chase and Simon's (1973) classic method of identifying 'chunks', i.e., perceptual patterns stored in memory and used as units. This method uses data about the placement of pairs of items in a memory task and consists of comparing latencies between these items and the number and type of relations they share. We then compare the human data with simulations carried out with CHREST, a computer model of perception and memory. We show that the model, based upon the acquisition of a large number of chunks, accounts for the human data well. This is taken as evidence that human knowledge is organised in a modular fashion

Is Experts' Knowledge Modular?

This paper explores, both with empirical data and with computer simulations, the extent to which modularity characterises experts' knowledge. We discuss a replication of Chase and Simon's (1973) classic method of identifying 'chunks', i.e., perceptual patterns stored in memory and used as units. This method uses data about the placement of pairs of items in a memory task and consists of comparing latencies between these items and the number and type of relations they share. We then compare the human data with simulations carried out with CHREST, a computer model of perception and memory. We show that the model, based upon the acquisition of a large number of chunks, accounts for the human data well. This is taken as evidence that human knowledge is organised in a modular fashion

Is attentional discounting in value-based decision making magnitude sensitive?

Choices in value-based decision making are affected by the magnitude of the alternatives (i.e. the summed values of the options). Magnitude sensitivity has been instrumental in discriminating between computational models of choice. Smith and Krajbich [(2019a). Gaze amplifies value in decision making. Psychological Science, 30(1), 116–128. https://doi.org/10.1177/0956797618810521] have shown that the attentional drift-diffusion model (aDDM) can account for magnitude sensitivity. This is because the discount parameter on the value of the nonfixated alternative ensures faster choices for high-magnitude alternatives, even in the case of high-magnitude equal alternatives compared to low-magnitude equal alternatives. Their result highlights the importance of visual fixations as a mechanism for magnitude sensitivity. This rationale relies on the untested assumption that the discount parameter is constant across magnitude levels. However, the discount parameter could vary as a function of the magnitude of the alternatives in unpredicted ways; this would suggest that the ability of the aDDM to account for magnitude sensitivity has been misinterpreted by previous research. Here, we reanalyse previous datasets and we directly test whether attentional discounting scales with the magnitude of the alternatives. Our analyses show that attentional discounting does not vary with magnitude. This result further strengthens the aDDM and the role that visual fixations could play as an explanation of magnitude sensitivity

Cognitive training: a field in search of a phenomenon

Considerable research has been carried out in the last two decades on the putative benefits of cognitive training on cognitive function and academic achievement. Recent metaanalyses summarising the extent empirical evidence have resolved the apparent lack of consensus in the field and led to a crystal-clear conclusion: the overall effect of far transfer is null, and there is little to no true variability between the types of cognitive training. Despite these conclusions, the field has maintained an unrealistic optimism about the cognitive and academic benefits of cognitive training, as exemplified by a recent article (Green et al., 2019). We demonstrate that this optimism is due to the field neglecting the results of meta-analyses and largely ignoring the statistical explanation that apparent effects are due to a combination of sampling errors and other artifacts. We discuss recommendations for improving cognitive training research, focusing on making results publicly available, using computer modelling, and understanding participants’ knowledge and strategies. Given that the available empirical evidence on cognitive training and other fields of research suggests that the likelihood of finding reliable and robust far-transfer effects is low, research efforts should be redirected to near transfer or other methods for improving cognition

On-the-fly simplification of genetic programming models

The last decade has seen amazing performance improvements in deep learning. However, the black-box nature of this approach makes it difficult to provide explanations of the generated models. In some fields such as psychology and neuroscience, this limitation in explainability and interpretability is an important issue. Approaches such as genetic programming are well positioned to take the lead in these fields because of their inherent white box nature. Genetic programming, inspired by Darwinian theory of evolution, is a population-based search technique capable of exploring a highdimensional search space intelligently and discovering multiple solutions. However, it is prone to generate very large solutions, a phenomenon often called “bloat”. The bloated solutions are not easily understandable. In this paper, we propose two techniques for simplifying the generated models. Both techniques are tested by generating models for a well-known psychology experiment. The validity of these techniques is further tested by applying them to a symbolic regression problem. Several population dynamics are studied to make sure that these techniques are not compromising diversity – an important measure for finding better solutions. The results indicate that the two techniques can be both applied independently and simultaneously and that they are capable of finding solutions at par with those generated by the standard GP algorithm – but with significantly reduced program size. There was no loss in diversity nor reduction in overall fitness. In fact, in some experiments, the two techniques even improved fitness

Cognitive training does not enhance general cognition

Due to potential theoretical and societal implications, cognitive training has been one of the most influential topics in psychology and neuroscience. The assumption behind cognitive training is that one’s general cognitive ability can be enhanced by practicing cognitive tasks or intellectually demanding activities. The hundreds of studies published so far have provided mixed findings and systematic reviews have reached inconsistent conclusions. To resolve these discrepancies, we carried out several meta-analytic reviews. The results are highly consistent across all the reviewed domains: minimal effect on domain-general cognitive skills. Crucially, the observed between-study variability is accounted for by design quality and statistical artefacts. The cognitive-training program of research has showed no appreciable benefits, and other more plausible practices to enhance cognitive performance should be pursued

Working memory training in typically developing children: a meta-analysis of the available evidence

The putative effectiveness of working memory (WM) training at enhancing cognitive and academic skills is still ardently debated. Several researchers have claimed that WM training fosters not only skills such as visuospatial WM and short-term memory (STM), but also abilities outside the domain of WM, such as fluid intelligence and mathematics. Other researchers, while acknowledging the positive effect of WM training on WM-related cognitive skills, are much more pessimistic about the ability of WM training to improve other cognitive and academic skills. In other words, the idea that far-transfer-that is, the generalization of a set of skills across two domains only loosely related to each other-may take place in WM training is still controversial. In this meta-analysis, the authors focused on the effects of WM training on cognitive and academic skills (e.g., fluid intelligence, attention/inhibition, mathematics, and literacy) in typically developing (TD) children (aged 3 to 16). Whereas WM training exerted a significant effect on cognitive skills related to WM training (g = 0.46), little evidence was found regarding far-transfer effects (g = 0.12). Moreover, the size of the effects was inversely related to the quality of the design (i.e., random allocation to the groups and presence of an active control group). Results suggest that WM training is ineffective at enhancing TD children's cognitive or academic skills and that, when positive effects are observed, they are modest at best. Thus, in line with other types of training, far-transfer rarely occurs and its effects are minimal

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems