The processes of learning and memory have traditionally been studied in large experimental organisms (Aplysia, mice, rats and humans), where well‐characterized behaviors are easily tested. Although Drosophila is one of the most experimentally tractable organisms, it has only recently joined the others as a model organism for learning and memory. Drosophila behavior has been studied for over 20 years; however, most of the work in the learning and memory field has focused on initial (...) class='Hi'>learning, because establishing memory in Drosophila has not been as straightforward as in other organisms. A major recent advance in this field has been the development of a training protocol that induces long‐term memory in flies. This made possible experiments that implicated the Drosophila CREB gene as a critical component in the consolidation of long‐term memory, and paves the way for future experiments utilizing the well developed tools in Drosophila. This review will briefly summarize what is known in the field of Drosophila learning and memory to date, and discuss why the unique aspects of this field make traditional approaches difficult and reward the use of alternative paths of experimentation. (shrink)

ces, learning facts and gaining conceptual knowlge, recognizing objects and people, and acquiring ills and habits. Scientific thinking about memory was minated for many years by the assumption that mory is a unitary or monolithic entityRi2;a single ulty of the mind and brain. However, the assumpri of a unitary memory has been challenged by conging evidence from psychology and neuroscience inting toward multiple memory systems that can be sociated from one another. This chapter provides a torical (...) introduction to the issue and summarizes.. (shrink)

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and (...) treatment follow-up. The Hopkins Verbal Learning Test is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo under the Bayesian Regression Models using ‘Stan’ package of R. Comparisons of the fitted models were done using leave-one-out information criteria, Widely applicable information criterion, and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach. (shrink)

Studies of learning in marine invertebrates have yielded new information, implicating protein kinase C and calmodulin‐dependent protein kinase as critical components in pathways for learning and memory that are shared with higher vertebrates. Recent advances correlating in vitro biochemical and biophysical measurements with in vivo learning have begun to elaborate the roles in memory storage for these two kinases, their substrates, and signaling proteins such as calexcitin and calmodulin. Other studies have implicated transcription factors associated (...) with kinases such as the cyclic AMP response element binding protein (CREB). These experiments have resulted in a greater understanding of the role of second messengers, principally calcium and cyclic AMP (cAMP), as vehicles for transforming memory‐specific effects occurring at the membrane into permanent DNA‐mediated cellular and structural changes. (shrink)

This paper discusses the ways in which anesthetic agents can be used to investigate the role of awareness in learning and memory. It reviews research into learning during light, subclinical anesthesia, termedhypesthesia.This research suggests that the effects of anesthetics on implicit and explicit memory are roughly comparable, although implicit memory for simple stimuli may resist the effects of very low doses of anesthetic. In addition, this paper reports experimental data demonstrating that long-term retention of information (...) is prevented by doses of anesthetic that are low enough to permit awareness and even short-term memory of auditory stimuli. Overall, these findings are consistent with the hypothesis that frontal lobe function is particularly sensitive to anesthetics. They raise theoretical and practical questions about the necessity of consciousness for learning and about interpretation of the evidence for learning during surgery under general anesthesia. (shrink)

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion (...) on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and long-term memory retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional “live” learning in classrooms and “virtual” learning through online-based educational technologies. (shrink)

The dynamic developmental theory of attention-deficit/hyperactivity disorder (ADHD) proposes that hypodopaminergic functioning results in anomalous delay-of-reinforcement gradients in ADHD, which in turn might account for many of the observed behavioral and cognitive characteristics. However, hyperdopaminergic functioning might also impair mnemonic representation of codes for spatial, motoric, and reward information and contribute to the purported shorter delay gradients in ADHD.

Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory‐array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter‐free predictions from the models of the rank order of classification difficulty of test strings. The importance (...) of different assumptions regarding the coding of features (How should the absence of a feature be coded? Should single letters or digrams be coded?), the learning rule used (Hebb rule vs. delta rule), and the connectivity (Should features be predicted only by previous features in the string, or by all features simultaneously?) is investigated by determining the performance of the models with and without each assumption. Only one class of connectionist model (the simultaneous delta rule) passes all the tests. It is shown that this class of model can be regarded by abstracting a set of representative but incomplete rules of the grammar. (shrink)

In this Hypothesis and Theory paper, we consider the problem of learning deeply structured knowledge representations in the absence of predefined ontologies, and in the context of long-term learning. In particular, we consider this process as a sequence of re-representation steps, of various kinds. The Information Dynamics of Thinking theory (IDyOT) admits such learning, and provides a hypothetical mechanism for the human-like construction of hierarchical memory, with the provision of symbols constructed by the system that embodies (...) the theory. The combination of long-term learning and meaning construction in terms of symbols grounded in perceptual experience entails that the system, like a human, be capable of memory consolidation, to manage the complex and inconsistent structures that can result from learning of information that becomes more complete over time. Such consolidation changes memory structures, and thus changes their meaning. Therefore, memory consolidation entails re-representation, while re-representation entails changes of meaning. Ultimately, the theory proposes that the processes of learning and consolidation should be considered as repeated re-representation of what is learned. (shrink)

Shors & Matzel set up a straw man, that LTP is a memory storage mechanism, and knock him down without due consideration of the important relations among different levels of organization and analysis regarding LTP, learning, and memory. Assessing these relationships requires analysis and hypotheses linking specific brain regions, neural circuits, plasticity mechanisms, and task demands. The issue addressed by the authors is important, but their analysis is off target.

A consistent thread throughout Johann Gottfried Herder’s thought is his interest in human knowledge and in its origins. Although he never formulated a systematic theory of knowledge, elements of one are disseminated in his writings, from the early manuscript Plato sagte to one of his last works, the periodical Adrastea. Herder assigned a very special function to memory and to the related idea of a recollection of “images,” as they play a pivotal role in the formation of personal identity. (...) He provided an original description of the Platonic theory of recollection, trying to merge ancient and modern metaphysical views and to interpret them from a less metaphysical and more psychological point of view. I then analyze Herder’s notion of memory via another research line, which is basically founded upon the analogy between the childhood of an individual and the infancy of the human race. Finally, I explore Herder’s view that memory and imagination, as “forces” of the soul, can have negative effects on an individual when they are not equally balanced. (shrink)

The phenomenon of ‘Deep Learning,’ which has given us such science-fiction-like innovations as self-driving cars, as well as visual search tools in photographic applications, is a new form, and subset, of ‘Machine Learning’ made possible by very recent innovations in computing. Machine Learning itself has been around for some decades – essentially pattern-recognition software that requires very substantial computing resources, which were, until recently, mostly theoretical and hard to come by. Machine Learning was one avenue of (...) the field of Artificial Intelligence known as Narrow A.I. – the kind of ‘artificial intelligence’ that was strictly limited in scope as a first-steps starting point of what came, as a result, to be known as General A.I. General A.I., known then as simply, ‘Artificial Intelligence’, was the 1950s dream that brought us such things as Robbie the Robot, and more recently C3PO, and The Terminator: the kind of science fiction characters that remain the only manifestations of General Artificial Intelligence. -/- ‘Deep Learning’ also continues engineering’s 1940s trend of using language in a way that I will contest in this paper: a co-opting of words that have been used, in the past, to describe human activities, using them instead to describe what engineers have managed to make machines do. These co-optations reduce the richness of the word, making its referent an algorithm: a flow diagram that represents the bare essentials of what an engineer can understand and reproduce of a human activity; not the human activity itself. This diagram of the ‘engineering possible’ over-simplifies the human activity it tries to depict. With continued usage, the meaning of the word for us today has all-too-often become reduced to what the engineer has newly defined it to mean: something much less than it once was. -/- In this paper I attempt to roll back some of these co-optations, and to re-introduce some of the richness of the words that have been taken by engineering. I examine Turing’s seminal paper on the notion of a thinking machine. I use the philosophical insights of Henri Bergson, especially in his book, Matter and Memory, and the discoveries of neuroscience and complexity scientists. I try to show that the answer to Turing’s question, ‘Can machines think?’ remains a resounding, ‘No!’, and that notions such as ‘deep learning’ are in fact not only an inaccurate use of the very human experience of learning, but degrade the latter in using such a term. (shrink)