scholarly journals Artificial Neural Network Simulations of Human Learning Suggest the Presence of Metastable Attractors in Visual Memory

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Philippe Chassy ◽  
Frederic Surre
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Visual Memory ◽  
Human Performance ◽  
Cognitive Architecture ◽  
Stable State ◽  
Human Learning ◽  
Learning Curves ◽  
Long Term Memory

The attractor hypothesis states that knowledge is encoded as topologically-defined, stable configurations of connected cell assemblies. Irrespective to its original state, a network encoding new information will thus self-organize to reach the necessary stable state. To investigate memory structure, a multimodular neural network architecture, termed Magnitron, has been developed. Magnitron is a biologically-inspired cognitive architecture that simulates digit recognition. It implements perceptual input, human visual long-term memory in the ventral visual pathway and, to a lesser extent, working memory processes. To test the attractor hypothesis a Monte Carlo simulation of 10,000 individuals has been run. Each simulated learner was trained in recognizing the ten digits from novice to expert stage. The results replicate several features of human learning. First, they show that random connectivity in long-term visual memory accounts for novices’ performance. Second, the learning curves revealed that Magnitron simulates the well-known psychological power law of practice. Third, after learning took place, performance departed from chance level and reached a minimum target of 95% of correct hits; hence simulating human performance in children (i.e., when digits are learned). Magnitron also replicates biological findings. In line with research using voxel-based morphometry, Magnitron showed that matter density increases while training is taken place. Crucially, the spatial analysis of the connectivity patterns in long-term visual memory supported the hypothesis of a stable attractor. The significance of these results regarding memory theory is discussed.

Knowledge and performance in action

2004 ◽  
Vol 8 (4) ◽  
pp. 38-53 ◽  
Author(s):  
Thomas Schack
Keyword(s):  
High Performance ◽  
Human Performance ◽  
Cognitive Architecture ◽  
Long Term Memory ◽  
Object Knowledge ◽  
Training Techniques ◽  
Action Knowledge ◽  
And Performance ◽  
High Level

This article addresses the functional links between knowledge and performance in human activity. Starting with the evolutionary roots of knowledge and activity, it shows how the combination of adaptive behavior and knowledge storage has formed over various stages of evolution. The cognitive architecture of human actions is discussed against this background, and it is shown how knowledge is integrated into action control. Then, methodological issues in the study of action knowledge are considered, and an experimental method is presented that can be used to assess the structure of action knowledge in long‐term memory. This method is applied in studies on the relation between object knowledge and performance in mechanics and between movement knowledge and performance in high‐performance sportswomen. These studies show how experts’ knowledge systems can be assessed, and how this may contribute to the optimization of human performance. In high‐level experts, these representational frameworks were organized in a highly hierarchical tree‐like structure, were remarkably similar between individuals, and matched well the functional demands of the task. In comparison, the action representations in low‐level performers were organized less hierarchically, were more variable between persons, and were not so well in accordance with functional demands. These results support the hypothesis that voluntary actions are planned, executed, and stored in memory directly by way of representations of their anticipated perceptual effects. The method offers new possibilities to investigate knowledge structures. Based on such results it is possible to improve performance via special training‐techniques. This paper fulfils an identified research need concerning the interaction of knowledge and performance and offers new perspectives for future forms of knowledge management.

scholarly journals Memory and Perception-based Facial Image Reconstruction

2017 ◽  
Author(s):  
Chi-Hsun Chang ◽  
Dan Nemrodov ◽  
Andy C. H. Lee ◽  
Adrian Nestor
Keyword(s):  
Image Reconstruction ◽  
Visual Memory ◽  
Long Term Memory ◽  
Facial Features ◽  
Identity Recognition ◽  
Facial Identity ◽  
Face Images ◽  
Computer Based ◽  
Novel Applications

AbstractVisual memory for faces has been extensively researched, especially regarding the main factors that influence face memorability. However, what we remember exactly about a face, namely, the pictorial content of visual memory, remains largely unclear. The current work aims to elucidate this issue by reconstructing face images from both perceptual and memory-based behavioural data. Specifically, our work builds upon and further validates the hypothesis that visual memory and perception share a common representational basis underlying facial identity recognition. To this end, we derived facial features directly from perceptual data and then used such features for image reconstruction separately from perception and memory data. Successful levels of reconstruction were achieved in both cases for newly-learned faces as well as for familiar faces retrieved from long-term memory. Theoretically, this work provides insights into the content of memory-based representations while, practically, it opens the path to novel applications, such as computer-based ‘sketch artists’.

Memristor-Based Neural Network Circuit of Long-term Memory

2021 ◽  
Author(s):  
Juntao Han ◽  
Junwei Sun ◽  
Xiao Xiao ◽  
Peng Liu
Keyword(s):  
Neural Network ◽  
Long Term Memory ◽  
Term Memory

Neural Network Structure for Spatio-Temporal Long-Term Memory

2012 ◽  
Vol 23 (6) ◽  
pp. 971-983 ◽  
Author(s):  
V. A. Nguyen ◽  
J. A. Starzyk ◽  
Wooi-Boon Goh ◽  
D. Jachyra
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Long Term Memory ◽  
Term Memory ◽  
Neural Network Structure ◽  
Spatio Temporal

Long-Term Memory Performance in Adult ADHD

2016 ◽  
Vol 21 (4) ◽  
pp. 267-283 ◽  
Author(s):  
Timo Skodzik ◽  
Heinz Holling ◽  
Anya Pedersen
Keyword(s):  
Visual Memory ◽  
Adult Adhd ◽  
Memory Performance ◽  
Meta Analysis ◽  
Long Term Memory ◽  
Term Memory ◽  
Memory Tests ◽  
Memory Acquisition ◽  
Meta Analyses

Objective: Memory problems are a frequently reported symptom in adult ADHD, and it is well-documented that adults with ADHD perform poorly on long-term memory tests. However, the cause of this effect is still controversial. The present meta-analysis examined underlying mechanisms that may lead to long-term memory impairments in adult ADHD. Method: We performed separate meta-analyses of measures of memory acquisition and long-term memory using both verbal and visual memory tests. In addition, the influence of potential moderator variables was examined. Results: Adults with ADHD performed significantly worse than controls on verbal but not on visual long-term memory and memory acquisition subtests. The long-term memory deficit was strongly statistically related to the memory acquisition deficit. In contrast, no retrieval problems were observable. Conclusion: Our results suggest that memory deficits in adult ADHD reflect a learning deficit induced at the stage of encoding. Implications for clinical and research settings are presented.

scholarly journals Is working memory inherently more ‘precise’ than long-term memory? Extremely high fidelity visual long-term memories for frequently encountered objects

2019 ◽  
Author(s):  
Annalise Miner ◽  
Mark Schurgin ◽  
Timothy F. Brady
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Visual Memory ◽  
Memory Systems ◽  
Long Term Memory ◽  
Continuous Measure ◽  
High Fidelity ◽  
Term Memory ◽  
Naturalistic Setting

Long-term memory is often considered easily corruptible, imprecise and inaccurate, especially in comparison to working memory. However, most research used to support these findings relies on weak long-term memories: those where people have had only one brief exposure to an item. Here we investigated the fidelity of visual long-term memory in more naturalistic setting, with repeated exposures, and ask how it compares to visual working memory fidelity. Using psychophysical methods designed to precisely measure the fidelity of visual memory, we demonstrate that long-term memory for the color of frequently seen objects is as accurate as working memory for the color of a single item seen 1 second ago. In particular, we show that repetition greatly improves long-term memory, including the ability to discriminate an item from a very similar item ('fidelity'), in both a lab setting (Exps. 1-3) and a naturalistic setting (brand logos, Exp. 4). Overall our results demonstrate the impressive nature of visual long-term memory fidelity, which we find is even higher fidelity than previously indicated in situations involving repetitions. Furthermore, our results suggest that there is no distinction between the fidelity of visual working memory and visual long-term memory, but instead both memory systems are capable of storing similar incredibly high fidelity memories under the right circumstances. Our results also provide further evidence that there is no fundamental distinction between the ‘precision’ of memory and the ‘likelihood of retrieving a memory’, instead suggesting a single continuous measure of memory strength best accounts for working and long-term memory.

scholarly journals Cognitive aging and verbal labeling in continuous visual memory

2020 ◽  
Vol 48 (7) ◽  
pp. 1196-1213
Author(s):  
Alicia Forsberg ◽  
Wendy Johnson ◽  
Robert H. Logie
Keyword(s):  
Older Adults ◽  
Cognitive Aging ◽  
Visual Memory ◽  
Memory Performance ◽  
Age Groups ◽  
Long Term Memory ◽  
Younger Adults ◽  
Healthy Older Adults ◽  
Age Related

Abstract The decline of working memory (WM) is a common feature of general cognitive decline, and visual and verbal WM capacity appear to decline at different rates with age. Visual material may be remembered via verbal codes or visual traces, or both. Souza and Skóra, Cognition, 166, 277–297 (2017) found that labeling boosted memory in younger adults by activating categorical visual long-term memory (LTM) knowledge. Here, we replicated this and tested whether it held in healthy older adults. We compared performance in silence, under instructed overt labeling (participants were asked to say color names out loud), and articulatory suppression (repeating irrelevant syllables to prevent labeling) in the delayed estimation paradigm. Overt labeling improved memory performance in both age groups. However, comparing the effect of overt labeling and suppression on the number of coarse, categorical representations in the two age groups suggested that older adults used verbal labels subvocally more than younger adults, when performing the task in silence. Older adults also appeared to benefit from labels differently than younger adults. In younger adults labeling appeared to improve visual, continuous memory, suggesting that labels activated visual LTM representations. However, for older adults, labels did not appear to enhance visual, continuous representations, but instead boosted memory via additional verbal (categorical) memory traces. These results challenged the assumption that visual memory paradigms measure the same cognitive ability in younger and older adults, and highlighted the importance of controlling differences in age-related strategic preferences in visual memory tasks.

scholarly journals Long-Term Memory Search across the Visual Brain

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Milan Fedurco
Keyword(s):  
Visual Cortex ◽  
Visual Memory ◽  
Signal Transmission ◽  
Memory Search ◽  
Postsynaptic Membrane ◽  
Blood Oxygenation ◽  
Multisensory Perception ◽  
Long Term Memory ◽  
Oxygenation Level

Signal transmission from the human retina to visual cortex and connectivity of visual brain areas are relatively well understood. How specific visual perceptions transform into corresponding long-term memories remains unknown. Here, I will review recent Blood Oxygenation Level-Dependent functional Magnetic Resonance Imaging (BOLD fMRI) in humans together with molecular biology studies (animal models) aiming to understand how the retinal image gets transformed into so-called visual (retinotropic) maps. The broken object paradigm has been chosen in order to illustrate the complexity of multisensory perception of simple objects subject to visual —rather than semantic— type of memory encoding. The author explores how amygdala projections to the visual cortex affect the memory formation and proposes the choice of experimental techniques needed to explain our massive visual memory capacity. Maintenance of the visual long-term memories is suggested to require recycling of GluR2-containingα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPAR) andβ2-adrenoreceptors at the postsynaptic membrane, which critically depends on the catalytic activity of the N-ethylmaleimide-sensitive factor (NSF) and protein kinase PKMζ.

A Self-Organizing Neural Network to Approach Novelty Detection

2012 ◽  
pp. 262-282
Author(s):  
Marcelo Keese Albertini ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Architecture ◽  
Novelty Detection ◽  
Analytical Models ◽  
New Concepts ◽  
Normal Behavior ◽  
Regression Techniques ◽  
Self Organizing

Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results.

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