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Record W3136781525 · doi:10.1201/9781315207117-3

Stress, Memory, Forgetting and What, Lymnaea Can Tell us About a Stressful World

2021· book-chapter· en· W3136781525 on OpenAlexaffabout
Ken Lukowiak

Bibliographic record

VenueApple Academic Press eBooks · 2021
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsForgettingPsychologyLymnaeaNeuroscienceStress (linguistics)Cognitive psychologyCognitive scienceBiologyPhilosophyEcologyLinguistics

Abstract

fetched live from OpenAlex

68The ability of animals to learn and remember during their lifetime allows them to successfully adapt to various environmental stressors. Stress modulates (either enhancing or diminishing) the ability to learn and the ability to form memory and the ability to recall memory. We have attempted to use environmentally relevant stressors (e.g., crowding, low levels of calcium, predator detection and thermal shock) to determine how the various stressors change the ability to learn, form memory and to recall that memory. It is difficult, if not impossible, to predict ahead of actually doing the experiment to say with any certainty how a specific stressor will alter memory formation and its recall. It is even more difficult to predict how a combination of stressors alters these cognitive events. Identical stressor stimuli affect different strains of Lymnaea (e.g., smart vs. average) differently. It may be that one of the “costs” of being “smart” is a poor ability to handle stress. The birth of modern neuroscience occurred in the 1950s. A number of seminal events associated with scientists and clinicians from the Montreal area played important but sometimes forgotten roles in establishing what we now call Neuroscience. One event was the brain surgery performed on a patient known as HM that lead to Brenda Milner’s team observations on human memory, which ultimately showed that specific neural circuits were necessary for different forms of declarative and non-declarative memory (Milner et al., 1998). Those observations formed the basis of many experiments that ultimately led to our present understanding of the molecular events occurring in specific neurons, which are necessary for memory formation. However, the techniques and knowledge needed to undertake those studies at the neuronal and circuit level depended in large measure on the realization that molluscs possess large, identifiable neurons, which controlled interesting, tractable behaviors. A second and a third event were the ideas put forward by Donald Hebb in the 1950s (the Hebb synapse (Hebb, 1949) and the inverted U shape function regarding stress and memory formation; Fig. 3.1). Interestingly, this latter idea is most often attributed to a paper by Yerkes and Dodson in 1908; but in reality Hebb (Hebb, 1954) conceived the notion of what is now commandingly referred to as the Yerkes–Dodson law (Lukowiak et al., 2015). Foundational studies that were also necessary for Neuroscience to become a “science” were occurring in France and Monaco led by Tauc (1954) and Arvanitaki and Chalazonitis (1955), respectively, using the central nervous system (CNS) of the sea hare (genus Aplysia). These studies 69laid the groundwork for the use of molluscan model systems to investigate the causal neuronal mechanisms of learning and memory. Building on these earlier studies, Kandel and Tauc’s (1965) discovery of heterosynaptic facilitation in a molluscan preparation laid the experimental groundwork for hypotheses developed subsequently to explain the “Hebb synapse” and the neuronal basis of learning and the subsequent formation of long-lasting memory. Ultimately, the research performed by a multitude of investigators using a wide variety of molluscan preparations culminated in Eric Kandel being awarded the Nobel Prize for Medicine and Physiology in 2000 “for the discoveries concerning signal transduction in the nervous system” (Kandel, 2001). In this chapter, I will tell two main stories. One a compelling (I hope) series of stories of how learning, memory, and forgetting are all altered by environmentally relevant stressors; and, two, why it is important to understand the neuroecology of the model system one works with.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.280
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2021
Admission routes2
Has abstractyes

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