The Narrow Conception of Computational Psychology

One particularly successful approach to modeling within cognitive science is computational psychology. Computational psychology explores psychological processes by building and testing computational models with human data. In this paper, it is argued that a specific approach to understanding computation, what is called the ‘narrow conception’, has problematically limited the kinds of models, theories, and explanations that are offered within computational psychology. After raising two problems for the narrow conception, an alternative, ‘wide approach’ to computational psychology is proposed

Neurons and Symbols: A Manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Modelos y teorías computacionales de la memoria humana: un estado de la cuestión y análisis crítico

Las funciones de la memoria están íntimamente ligadas a todos los procesos cognitivos humanos. El estudio de la memoria humana está sumido en un sinnúmero de teorías y modelos de toda índole, por lo que es necesario aclarar el panorama de estudio para crear mejores aplicaciones en educación y en otras áreas relacionadas. Este artículo sobre modelos y teorías computacionales de la memoria es parte de una línea de investigación en memoria humana que busca crear un estado de la cuestión sobre el peso de los estudios actuales en el área. Se realiza una revisión crítica de 37 artículos sobre computación y memoria utilizando un instrumento de análisis que se ha denominado "tamiz". Se identifican los aspectos más relevantes encontrados en estos artículos y se concluye con unas observaciones generales sobre lo encontrado. Los resultados muestran que hay una gran dependencia a la utilización de la metáfora computacional de la mente para explicar los procesos cognitivos de los seres humanos

Learning and Representing Temporal Knowledge in Recurrent Networks

The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit

Dagstuhl Seminar Proceedings 10302 Learning paradigms in dynamic environments

Abstract We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty. Overview The study of human behaviour is an important part of computer science, artificial intelligence (AI), neural computation, cognitive science, philosophy, psychology and other areas. Among the most prominent tools in the modelling of behaviour are computational-logic systems (classical logic, nonmonotonic logic, modal and temporal logic) and connectionist models of cognition (feedforward and recurrent networks, symmetric and deep networks, self-organising networks). Recent studies in cognitive science, artificial intelligence and evolutionary psychology have produced a number of cognitive models of reasoning, learning and language that are underpinned by computatio