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Query: UMLS:C0598853 (forgetting)
 3,232 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

How does experience modify what we store in long-term memory? Is it an effect of unattended experience or does it require supervision? What role is played by temporal correlations in the input stream? We present a plastic recurrent network in which memory of faces is initially embedded and then, in the absence of supervision, the presentation of temporally correlated faces drastically changes long-term memory. We model and interpret the results of recent experiments and provide predictions for future testing. The stimuli are frames of a morphing film, interpolating between two memorized faces: If the temporal order of presentation of the frame stimuli is random, then the structure of memory is basically unaffected by synaptic plasticity (memory preservation). If the temporal order is sequential, then all image frames are classified as the same memory (memory collapse). The empirical findings are reproduced in the simulated dynamics of the network, in which the evolution of neural activity is conditioned by the associated synaptic plasticity (learning). The results are captured by theoretical analysis, which leads to predictions concerning the critical parameters of the stimuli; a third phase is identified in which memory is erased (forgetting).
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PMID:Impact of spatiotemporally correlated images on the structure of memory. 1736 Jun 79

How are faces forgotten? Studies examining forgetting in visual working memory (VWM) typically use simple visual features; however, in ecological scenarios, VWM typically contains complex objects. Given their significance in everyday functioning and their visual complexity, here we investigated how upright and inverted faces are forgotten within a few seconds, focusing on the raw errors that accompany such forgetting and examining their characteristics. In three experiments we found that longer retention intervals increased the size of errors. This effect was mainly accounted for by a larger proportion of random errors - suggesting that forgetting of faces reflects decreased accessibility of the memory representations over time. On the other hand, longer retention intervals did not modulate the precision of recall - suggesting that forgetting does not affect the precision of accessible memory representation. Thus, when upright and inverted faces are forgotten there is a complete failure to access them or a complete collapse of their memory representation. In contrast to the effect of retention interval (i.e., forgetting), face inversion led to larger errors that were mainly associated with decreased precision of recall. This effect was not modulated by the duration of the retention interval, and was observed even when memory was not required in the task. Therefore, upright faces are remembered more precisely compared to inverted ones due to perceptual, rather than mnemonic processes.
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PMID:The Rapid Forgetting of Faces. 3010 Aug 94

Biological neural network models whereby brains make minds help to understand autonomous adaptive intelligence. This article summarizes why the dynamics and emergent properties of such models for perception, cognition, emotion, and action are explainable, and thus amenable to being confidently implemented in large-scale applications. Key to their explainability is how these models combine fast activations, or short-term memory (STM) traces, and learned weights, or long-term memory (LTM) traces. Visual and auditory perceptual models have explainable conscious STM representations of visual surfaces and auditory streams in surface-shroud resonances and stream-shroud resonances, respectively. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. Even if it makes some accurate classifications, they are not explainable and thus cannot be used with confidence. Deep Learning shares these problems with the back propagation algorithm, whose computational problems due to non-local weight transport during mismatch learning were described in the 1980s. Deep Learning became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning. ART is a self-organizing production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning and only locally computable quantities, to rapidly classify large non-stationary databases without experiencing catastrophic forgetting. ART classifications and predictions are explainable using the attended critical feature patterns in STM on which they build. The LTM adaptive weights of the fuzzy ARTMAP algorithm induce fuzzy IF-THEN rules that explain what feature combinations predict successful outcomes. ART has been successfully used in multiple large-scale real world applications, including remote sensing, medical database prediction, and social media data clustering. Also explainable are the MOTIVATOR model of reinforcement learning and cognitive-emotional interactions, and the VITE, DIRECT, DIVA, and SOVEREIGN models for reaching, speech production, spatial navigation, and autonomous adaptive intelligence. These biological models exemplify complementary computing, and use local laws for match learning and mismatch learning that avoid the problems of Deep Learning.
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PMID:A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action. 3267 45