RESUMEN
This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.
Asunto(s)
Algoritmos , Inteligencia Artificial , Memoria , Modelos Teóricos , Redes Neurales de la Computación , Dinámicas no Lineales , Reconocimiento de Normas Patrones Automatizadas/métodos , Biomimética/métodos , Análisis por Conglomerados , Simulación por Computador , Retroalimentación , Estadística como AsuntoRESUMEN
Tracking behavior with a virtual spider and a neutral target is compared in fearful and nonfearful subjects. Head-tracking in virtual environments appears to be a scale-free behavior with long-range fractal-like patterns. Moreover, these fractal patterns change according to what the target affords the tracker and the level of behavioral avoidance manifested by the subjects. Results are interpreted in terms of ecological psychology and nonlinear dynamics, and implications for virtual reality (VR) psychology are outlined.
Asunto(s)
Movimientos de la Cabeza , Trastornos Fóbicos/fisiopatología , Trastornos Fóbicos/psicología , Desempeño Psicomotor , Arañas , Interfaz Usuario-Computador , Adulto , Animales , Enfermedad Crónica , Gráficos por Computador , Simulación por Computador , Ambiente , Miedo/psicología , Femenino , Humanos , Masculino , Percepción de Movimiento , Reproducibilidad de los Resultados , Autoevaluación (Psicología) , Sensibilidad y Especificidad , Índice de Severidad de la Enfermedad , Resultado del TratamientoRESUMEN
The goal of this article is to propose a new cognitive model that focuses on bottom-up learning of explicit knowledge (i.e., the transformation of implicit knowledge into explicit knowledge). This phenomenon has recently received much attention in empirical research that was not accompanied by a corresponding work effort in cognitive modeling. The new model is called TEnsor LEarning of CAusal STructure (TELECAST). In TELECAST, implicit processing is modeled using an unsupervised connectionist network (the Joint Probability EXtractor: JPEX) while explicit (causal) knowledge is implemented using a Bayesian belief network (which is built online using JPEX). Every task is simultaneously processed explicitly and implicitly and the results are integrated to provide the model output. Here, TELECAST is used to simulate a causal inference task and two serial reaction time experiments.