Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories

A Methodology for Developing Computational Implementations of Scientific Theories

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”Computer programs have become a popular representation for scientific theories, particularly for implementing models or simulations of observed phenomena. Expressing a theory as an executable computer program provides many benefits, including: making all processes concrete, supporting the development of specific models, and hence enabling quantitative predictions to be derived from the theory. However, as implementations of scientific theories, these computer programs will be subject to change and modification. As programs change, their behaviour will also change, and ensuring continuity in the scientific value of the program is difficult. We propose a methodology for developing computer software implementing scientific theories. This methodology allows the developer to continuously change and extend their software, whilst alerting the developer to any changes in its scientific interpretation. We introduce tools for managing this development process, as well as for optimising the developed models

Evolving structure-function mappings in cognitive neuroscience using genetic programming

A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints, for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems. (This article does not exactly replicate the final version published in the Journal of Swiss Psychology. It is not a copy of the original published article and is not suitable for citation.

Computational scientific discovery in psychology

Scientific discovery is a driving force for progress, involving creative problem-solving processes to further our understanding of the world. Historically, the process of scientific discovery has been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyse data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories, and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This review addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically

Heuristic Search of Heuristics

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. All rights are reserved by the Publisher. his is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1007/978-3-031-47994-6How can we infer the strategies that human participants adopt to carry out a task? One possibility, which we present and discuss here, is to develop a large number of strategies that participants could have adopted, given a cognitive architecture and a set of possible operations. Subsequently, the (often many) strategies that best explain a dataset of interest are highlighted. To generate and select candidate strategies, we use genetic programming, a heuristic search method inspired by evolutionary principles. Specifically, combinations of cognitive operators are evolved and their performance compared against human participants’ performance on a specific task. We apply this methodology to a typical decision-making task, in which human participants were asked to select the brighter of two stimuli. We discover several understandable, psychologically-plausible strategies that offer explanations of participants’ performance. The strengths, applications and challenges of this methodology are discussed

Scientific discovery reloaded

The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The ‘philosophical matrix’ moulded by logical empiricism and analytical tradition has been challenged by the ‘friends of discovery’, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery (like the deductive, cognitive, and evolutionary), but also new ways of practicing it in a rational and more systematic way. Ampliative rules, methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a ‘scientific discovery revolution’: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity

Evolving process-based models from psychological datausing genetic programming

The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving models. We apply this system to the delayed match-to-sample task. We show how the challenge of bloating may be addressed by extending the fitness function to include measures of cognitive performance

Genetic Programming for Developing Simple Cognitive Models

©2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models

Architectural authorship in generative design

The emergence of evolutionary digital design methods, relying on the creative generation of novel forms, has transformed the design process altogether and consequently the role of the architect. These methods are more than the means to aid and enhance the design process or to perfect the representation of finite architectural projects. The architectural design philosophy is gradually transcending to a hybrid of art, engineering, computer programming and biology. Within this framework, the emergence of designs relies on the architect- machine interaction and the authorship that each of the two shares. This work aims to explore the changes within the design process and to define the authorial control of a new breed of architects- programmers and architects-users on architecture and its design representation. For the investigation of these problems, this thesis is to be based on an experiment conducted by the author in order to test the interaction of architects with different digital design methods and their authorial control over the final product. Eventually, the results will be compared and evaluated in relation to the theoretic views. Ultimately, the architect will establish his authorial role

Cognitivism and Innovation in Economics - Two Lectures

This issue of the Department W.P. reproduces two lectures by Professor Loasby organized by the CISEPS (Centre for Interdisciplinary Studies in Economics, Psychology and the Social Sciences at Bicocca) in collaboration with the IEP, the Istituto di Economia Politica of the Bocconi University in Milan. The first lecture was delivered at the University of Milano-Bicocca on 13 October 2003 and the second was staged the day after at the Bocconi University. The lectures are reproduced here together with a comment by dr. Stefano Brusoni of Bocconi and SPRU. Two further comments were presented at the time by Professor Richard Arena of the University of Nice and by Professor Pier Luigi Sacco of the University of Venice. Both of them deserve gratitude for active participation to the initiative. Unfortunately it has not been possible to include their comments in the printed form. In these lectures Brian Loasby opens under the title of Psychology of Wealth (a title echoing a famous essay by Carlo Cattaneo) and he develops an argument in cognitive economics which is based on Hayek’s theory of the human mind with significant complements and extensions, mainly from Smith and Marshall. The second lecture provides a discussion on organization and the human mind. It can be read independently although it is linked to the former. Indeed, in Professor Loasby’s words, “the psychology of wealth leads to a particular perspective on this problem of organization”. The gist of the argument lies in the need to appreciate the significance of an appropriate “balance between apparently conflicting principles: the coherence, and therefore the effectiveness, of this differentiated system requires some degree of compatibility between its elements, but the creation of differentiated knowledge and skills depends on the freedom to make idiosyncratic patterns by thinking and acting in ways which may be radically different from those of many other people”. This dilemma of compatibility vs. independence can find solution in a variety of contexts, as Loasby’s analysis shows. In his comments Richard Arena had focussed on the rationality issues, so prominent in Loasby’s text. For example, he had suggested that the cleavage between rational choice equilibrium and evolutionary order offers ground to new forms of self-organization. Pier Luigi Sacco had emphasized that Loasby’s approach breaks new ground on the economics of culture and paves the way to less simplistic conceptions of endogenous growth than is suggested by the conventional wisdom of current models. Unfortunately, as hinted above, is has proved impossible to include those comments in the present booklet along with Loasby’s lectures. A special obligation must be recorded to Dr. Stefano Brusoni, who has prepared a written version of his own comment which has been printed in this booklet and can be offered to the reader. Among other participants Roberto Scazzieri, of the University of Bologna, Tiziano Raffaelli, of the University of Pisa, Luigino Bruni of Bicocca, Riccardo Cappellin of Rome ‘Tor Vergata’ and others were able to offer significant comments during the two sessions of the initiative. The organizers are particularly grateful to Professor Brian Loasby for the active and generous support of the initiative. Together with our colleagues and students we have been able to admire his enthusiasm and intellectual creativity in treating some of the more fascinating topics of contemporary economics.