Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Putting some order in person memory: memory for (serial) order in impression formation

American Psychological Association (PsycINFO Classification Categories and Codes) 2340 Cognitive Processes; 2343 Learning & Memory; 3000 Social Psychology; 3040 Social Perception and CognitionThe present work examines the representation and retrieval of order information in person memory. The study of memory for serial order has been absent from the research on the underling memory processes of impression formation, which has been focusing exclusively on item information. In this work we argue that our understanding of person memory is incomplete without an account for order and item information representation and retrieval. According to a chaining hypothesis, we predicted that the organizational processes involved in impression formation would hinder the ability to represent order by means of associations between items in successive positions. The first three experiments indicated, contradicting our hypothesis, that when people form impressions they are able to represent, retrieve and use order information for order judgements and (serial) recall. The two following studies, experiment 4 and 5, directly manipulated the associations that were built in memory when people formed impressions, to understand whether order information representation was based on associations between items that appeared in successive serial positions. Results showed that the ability to use order information was unaffected by changes in the structure of non-serial inter-item associations, which suggests that order representation is not derived from mere serial associations. Experiment 6, the last from the set of experiments reported here, suggested that the representation of order information is less dependent on episodic memory, in contrast to item information. The findings from this set of 6 experiments suggested, firstly, that when people form impressions they are able to reconstruct serial order (even when such order has no meaning), and secondly, that order representation in person memory seem not to be derived from the inter-item associations formed at encoding. Finally, an ordinal proposal for the representation and use of order in person memory is discussed.O objectivo central do presente trabalho é o estudo da representação e recuperação da informação de ordem em memória de pessoas. A memória de ordem serial tem permanecido fora da investigação sobre os processos mnésicos subjacentes à formação de impressões, investigação esta que se tem centrado exclusivamente na informação de item. Argumentamos que o conhecimento sobre memória de pessoas não pode ser completo sem que haja uma compreensão dos processos envolvidos na representação e recuperação da informação de ordem. De acordo com a hipótese de chaining, os processos que caracterizam a formação de impressões prejudicam o estabelecimento de associações entre itens em posições sucessivas, interferindo com a representação da informação de ordem. As três primeiras experiências sugerem, contrariamente ao esperado, que quando as pessoas formam impressões estão a representar informação de ordem, que pode ser utilizada em tarefas de julgamento e recordação. Nas experiências 4 e 5 manipulámos directamente as associações que se formam durante a codificação, quando as pessoas formam impressões, tentando perceber se a representação de ordem se basearia em associações entre itens em posições seriais sucessivas. Os resultados indicam que, independentemente da mudança na densidade associativa da rede, a capacidade de os participantes acederem e utilizarem informação de ordem não é afectada. Estes dados sugerem que a representação da informação não acontece pela mera associação de itens em posições sucessivas. A experiência 6 sugere que a representação da informação de ordem, em contraste com a informação de item, depende menos da memória episódica. Este conjunto de resultados sugere (i) que quando as pessoas formam impressões são capazes de reconstruir a ordem e (ii) que a representação da informação de ordem em memória de pessoas não é dependente das associações que se estabelecem entre os itens, durante a codificação. Finalmente, uma proposta ordinal para a representação e recuperação da ordem em memória de pessoas é discutida.The present work was sponsored by a Doctoral Grant (Ref. SFRH/BD/23748/2005) of the Science and Technology Foundation (FCT), Portugal, and the Program POCI2010, which is funded by the Portuguese Ministry of Science, Technology and Higher Education, and the European Social Fund (Community Support Framework III)

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Model of models -- Part 1

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

A workshop on the gathering of information for problem formulation

Issued as Quarterly progress reports no. [1-5], Proceedings and Final contract report, Project no. G-36-651Papers presented at the Workshop/Symposium on Human Computer Interaction, March 26 and 27, 1981, Atlanta, G