Absolute Versus Relative Forgetting Wixted, John T.
Journal of experimental psychology. Learning, memory, and cognition,
12/2022, Volume:
48, Issue:
12
Journal Article
Peer reviewed
Slamecka and McElree (1983) and Rivera-Lares et al. (2022), like others before them, factorially manipulated the number of learning trials and the retention interval. The results revealed two ...unsurprising main effects: (a) the more study trials, the higher the initial degree of learning, and (b) the longer the retention interval, the more items were forgotten. However, across many experiments, the interaction was not significant, a finding that is often interpreted to mean that the degree of learning is independent of the absolute rate of forgetting (i.e., the absolute number of items forgotten per unit time). Yet there is considerable tension between that interpretation and the fact that forgetting has long been characterized by a power law, according to which the absolute rate of forgetting is not a particularly meaningful measure. When the power function is fit to the same data, the results show that a higher degree of learning results in a lower relative (i.e., proportional) rate of forgetting. This raises an interesting question: which of the two definitions of "forgetting rate" (absolute vs. relative) is theoretically relevant? Here, I make the case that it is the relative rate of forgetting. Theoretically, the explanation of why a higher degree of learning is associated with a lower relative rate of forgetting may be related to why, as observed by Jost (1897) long ago, the passage of time itself is associated with a lower relative rate of forgetting.
Forgetting is removing variables from a logical formula while preserving the constraints on the other variables. In spite of reducing information, it does not always decrease the size of the formula ...and may sometimes increase it. This article discusses the implications of such an increase and analyzes the computational properties of the phenomenon. Given a propositional Horn formula, a set of variables and a maximum allowed size, deciding whether forgetting the variables from the formula can be expressed in that size is Dp-hard in Σ2p. The same problem for unrestricted CNF propositional formulae is D2p-hard in Σ3p.
People can intentionally forget studied material when cued to do so. Corresponding evidence has arisen from studies on item-method directed forgetting, in which participants are asked to forget ...single items directly upon presentation. We measured memory performance of to-be-remembered (TBR) and to-be-forgotten (TBF) items across retention intervals of up to 1 week and fitted power functions of time to the observed recall (Experiment 1) and recognition (Experiment 2) rates. In both experiments and each retention interval condition, memory performance for the TBR items was higher than for the TBF items, supporting the view that directed forgetting effects are lasting. Recall and recognition rates of both TBR and TBF items were well fit by the power function. However, the relative forgetting rates of the two item types differed, with a higher forgetting rate for the TBF than the TBR items. The findings are consistent with the view that TBR and TBF items differ (mainly) in recruitment of rehearsal processes and resulting memory strength.
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) ...or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence of more recent tasks. Over the past few years, a large range of methods and tricks have been introduced to address the continual learning problem, but many have not been fairly and systematically compared under a variety of realistic and practical settings.
To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as Maximally Interfered Retrieval (MIR), iCARL, and GDumb (a very strong baseline) and determine which works best at different memory and data settings as well as better understand the key source of CF; (2) determine if the best online class incremental methods are also competitive in the domain incremental setting; and (3) evaluate the performance of 7 simple but effective tricks such as the ”review” trick and the nearest class mean (NCM) classifier to assess their relative impact. Regarding (1), we observe that iCaRL remains competitive when the memory buffer is small; GDumb outperforms many recently proposed methods in medium-size datasets and MIR performs the best in larger-scale datasets. For (2), we note that GDumb performs quite poorly while MIR – already competitive for (1) – is also strongly competitive in this very different (but important) continual learning setting. Overall, this allows us to conclude that MIR is overall a strong and versatile online continual learning method across a wide variety of settings. Finally for (3), we find that all tricks are beneficial, and when augmented with the “review” trick and NCM classifier, MIR produces performance levels that bring online continual learning much closer to its ultimate goal of matching offline training. Our codes are available at https://github.com/RaptorMai/online-continual-learning.
Employee skills are seen as a main driver of competitive advantages of enterprises. This article provides a state-of-the-art overview of research related to skill management in the field of ...operational research. For this purpose, ‘skill management’ is used as an umbrella term to integrate the different quantitative approaches found in this field. The structured literature review is based on six keywords and includes articles published between 1998 and 2022 in nine major operational research and operational management journals (European Journal of Operational Research, International Journal of Production Research, International Journal of Production Economics, Management Science, Operations Research, Omega, Journal of Operations Management, Production and Operations Management, and Manufacturing & Service Operations Management). Further relevant literature considering skill-related topics and machine scheduling problems is also discussed. The publications included in this review are analyzed in depth with regard to theoretical results on employee skill development. Moreover, a unified notation is introduced that covers different models, machine environments, and objectives. To the best of our knowledge, this is the first review where learning, forgetting, and training aspects are jointly considered. Moreover, the review also highlights substantial research gaps and avenues for future research.
•Review of scheduling models in the field of skill management.•State-of-the art overview of models that include learning, training and forgetting.•In-depth analysis of model with regard to employee skill development.•Unified notation covering different models, machine environments, and objectives.
Pioneering research studies, beginning with those using Drosophila, have identified several molecular and cellular mechanisms for active forgetting. The currently known mechanisms for active ...forgetting include neurogenesis-based forgetting, interference-based forgetting, and intrinsic forgetting, the latter term describing the brain’s chronic signaling systems that function to slowly degrade molecular and cellular memory traces. The best-characterized pathway for intrinsic forgetting includes “forgetting cells” that release dopamine onto engram cells, mobilizing a signaling pathway that terminates in the activation of Rac1/Cofilin to effect changes in the actin cytoskeleton and neuron/synapse structure. Intrinsic forgetting may be the default state of the brain, constantly promoting memory erasure and competing with processes that promote memory stability like consolidation. A better understanding of active forgetting will provide insights into the brain’s memory management system and human brain disorders that alter active forgetting mechanisms.
Davis and Zhong review the neuroscience mechanisms for active forgetting, embedding these within types of forgetting established from experimental psychology. Intrinsic forgetting, one type of active forgetting that chronically erodes memory traces, may be the default state of the brain.
This paper reviews and critically assesses the implications of directed forgetting (DF) research on clinical populations. We begin by reviewing the typical methods and results of the item method and ...list method directed forgetting procedures and provide best practice recommendations for future studies using clinical populations. Next, we note that DF was often interpreted as being due to inhibition, and when clinical populations showed impaired directed forgetting, it was treated as evidence in inhibitory control difficulties. However, inhibition may not be the cause of DF effects, based on current understanding of these cognitive tasks. We instead suggest that item method DF is tied to attentional control, which might include inhibitory mechanisms (or might not). In contrast, list method DF is tied to two forms of memory control: control of mental context (indicated by effective forgetting of List 1), and changes in the strategies used to remember (indicated by better learning of List 2). We review the current state of the clinical DF literature, assess its strength based on our best practice recommendations, and call for more research when warranted.
•Directed forgetting (DF) is often used to study cognitive effects of disorders.•Item-method DF (cue after each item) involves attention control.•List-method DF (cue after each list) likely involves mental context, not inhibition.•Many clinically-oriented studies would benefit from updated contemporary designs.•Some disorders show clear patterns while others need more research.
There is overwhelming evidence in the literature that retrieval practice of studied material can lead to better final recall than restudy of the same material. Far less clear is whether this recall ...benefit is accompanied by reduced subsequent forgetting over time. This study revisited the issue in two experiments by comparing the effects of retrieval practice – with and without feedback –, restudy, and a no-practice condition on recall across different delay intervals ranging between three minutes and several days. We fitted power functions of time to the recall rates of each practice condition and compared relative forgetting rates between conditions. The comparisons showed that relative forgetting was reduced after retrieval practice relative to restudy, the relative forgetting rate after retrieval practice was unaffected by the presence of feedback, and forgetting after restudy did not differ from the no-practice condition. Together with other findings in the literature, the results provide evidence that retrieval practice reduces relative forgetting over time.
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The ...resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern: (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods; and (4) baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage.
Introduction In clinical neuropsychology, the phenomenon of accelerated long-term forgetting (ALF) has advanced to be a marker for subtle but clinically relevant memory problems associated with a ...range of neurological conditions. The normal developmental trajectory of long-term memory, in this case, memory recall after 1 week, and the influence of cognitive variables such as intelligence have not extensively been described, which is a drawback for the use of accelerated long-term forgetting measures in pediatric neuropsychology. Methods In this clinical observation study, we analyzed the normal developmental trajectory of verbal memory recall after 1 week in healthy children and adolescents. We hypothesized that 1-week recall and 1-week forgetting would be age-dependent and correlate with other cognitive functions such as intelligence and working memory. Sixty-three healthy participants between the ages of 8 and 16 years completed a newly developed auditory verbal learning test (WoMBAT) and the WISC-V intelligence test (General Ability Index, GAI). Using these tests, 1 week recall and 1 week forgetting have been studied in relation to GAI, verbal learning performance, and verbal working memory. Results Neither 1-week recall nor 1-week forgetting seems to be age-dependent. They are also not significantly predicted by other cognitive functions such as GAI or working memory. Instead, the ability to recall a previously memorized word list after 7 days seems to depend solely on the initial learning capacity. Conclusion In the clinical setting, this finding can help interpret difficulties in free recall after 7 days or more since they can probably not be attributed to young age or low intelligence.
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