The paper explains implicit memory research to those not knowledgeable about implicit memory. First, I define implicit and explicit memory. Second, I describe phenomena of implicit memory known as priming. Different kinds of priming are discussed in the context of a multiple memory systems account of implicit memory. Third, I compare the strengths and the weaknesses of the memory systems approach and the processing approach to explain implicit memory. The need for a comprehensive theory of memory is discussed. Last, I discuss three important issues for future research: the role of consciousness in implicit and explicit retrieval, comparisons among memory systems and cognitive processing approaches, and procedual memory as a point of contact of body and mind.
Cognitive psychology has focussed mainly on conscious and strategic information processing, but there are many examples for which knowledge is obtained unconsciously and without strategy. Studies on implicit learning show that feature correlations in an environment are attained without conscious effort through examples. This article explains basic experimental paradigms in the study area, and examines factors that affect implicit learning. As learning is affected by study conditions, instructions and structure of stimuli, some effort is needed to integrate the stimuli perceptually for implicit learning. This paper also discusses the abstractness and implicitness of knowledge. Abstractness of knowledge is questionable, but some knowledge over each example is obviously attained. Simulation studies, most of which are based on connectionist approaches, have succeeded in simulating human behavior regarding implicit learning, and so cast doubt on the abstractness of knowledge. Studies about implicit learning provide information about the other side of human cognition. Together with connectionist approaches, they provide valuable knowledge to related areas such as episode-semantic distinction of memory and induction.
As a framework for analyzing the effects of task structures in problem solving, a probabilistic model of problem solving is formulated by introducing “probabilities of using problem representations.” The effects of “undermining hypotheses by data (or evidence)” in probability updating tasks are experimentally examined by measuring the probabilities of using problem representations. “Undermining” here means both “direct undermining by data” and “indirect undermining via the likelihood function of which value is zero.” The experimental analysis shows that (1) undermining is a strong obstacle to the Bayesian solutions of the probability updating tasks, and (2) there exist differences between the direct and the indirect undermining effects. A mathematical model, named “Probability Flow Model,” is made which expresses how the probabilities of using problem representations depend upon the general tendencies of human information use. This Probability Flow Model is experimentally validated. The differences between the direct and the indirect undermining effects are examined on the basis of the Probability Flow Model. The analysis shows that the differences are due to the differences in the degree of realization of the general tendencies of human information use. An interpretation of the differences in the degree of realization of the general tendencies is given from the viewpoint of how to relate a datum to hypotheses in solving the probability updating tasks. A new approach to human inductive reasoning, in which there has been no theoretical progress during the last two decades, is also suggested from the viewpoint of belief fixation and belief perseverance. It is an old custom that the classical statistics in Neyman-Pearson school is used in psychological data analyses, although its application to them is unreasonable. In this paper, Bayesian statistics is adopted because of its appropriateness to psychological data.
We propose a computational model of emotion using the mechanism of the immune system. This model is inspired by the observation that emotion is similar to the immune system, in that each is a result of a self-defense system that is capable of adapting to the environment. The main component of this model is a body, in which many cells undergo Brownian Motion. Each cell has energy and characteristics that correspond to one of the five ego states in transactional analysis(Berne, 1964). The cells' state as a whole is its emotional state. Some cells are activated when antigens invade the body, and they work to sweep away those antigens. This process changes the emotional state. We implemented this model, and the results of experiments indicate that the model has the following two characteristics, which other models have not been able to achieve: (1) Emotional states change continuously. (2) Emotional states change in response to stimuli (antigens) according to the state at that moment and its past changes. We implemented a dialogue system using this model and found that the system can imitate human dialogues.
We describe a 3-D object recognition system from gray-scale images. It uses “subjective contours” as well as luminance edges (physical contours). In this paper, we suppose two kinds of subjective contours based on psychological and physiological findings: (1) Subjective contours that are automatically generated as by-products in the lower level processes of visual system. Alignment and proximity of physical contours and line ends promote generating such contours. The edge detection process involved in this kind of subjective contours are modeled by BCS neural network (Grossberg & Carpenter, 1985) in our system. (2) Subjective contours that represent a hypothesis of segmentation with volumes in the higher level process. The volumes have primitive shapes and they are components of object models. We used geons (Biederman, 1987) for describing object models. Higher level process groups both physical and subjective contours that are detected in the lower level process into the most probable geons, by found features such as line junctions and curvatures of lines. We describe the idea of recognition system that combines lower and higher level processes. We applied it to partially shaded images, arrangement of lines and partly degraded image.
For semantics of Japanese complex sentence which represents psychological causality, we introduce a new pragmatic role called observer for the purpose of clarifying the constraints among semantic roles of subordinate and the subject of main clauses. In this paper, we are concerned mainly with Japanese complex sentences whose subordinate clause is connected by conjuntive particle NODE or NONI.