Abstract:Most long-term memories are forgotten. What happens, then, to the changes in neuronal gene expression that were initially required to encode and maintain the memory? Here we show that the decay of recall for long-term sensitization memory in is accompanied both by a form of savings memory (easier relearning) and by persistent transcriptional regulation. A behavioral experiment ( = 14) shows that sensitization training produces a robust long-term sensitization memory, but that recall fades completely within 1 w… Show more
“…The effect size is the 'difference of the difference', or the comparison of the two simple effects. Example from Perez et al (2018): In naive animals, the weak shock produced a 3% decrease in reflex responsiveness. In previously trained animals, the weak shock produced a 23% increase in reflex responsiveness.…”
Section: Effect Sizes Can Be Expressed In Standardized Unitsmentioning
confidence: 99%
“…We now obtain effect sizes of around 2.6 standard deviations. With much larger effect sizes we now use fewer animals (8-12/experiment) to obtain much more consistent results (Conte et al 2017;Perez et al 2018).…”
Section: Effect Sizes Can Be Expressed In Standardized Unitsmentioning
This paper has now been published in the Journal of Undergraduate Neuroscience Eduction: http://www.funjournal.org/wp-content/uploads/2018/04/june-16-e21.pdf?x91298. See also, this record on PubMed and PubMedCentral: https://www.ncbi.nlm.nih.gov/pubmed/30057503. An ongoing reform in statistical practice is to report and interpret effect sizes. This paper provides a short tutorial on effect sizes and some tips on how to help your students think in terms of effect sizes when analyzing data. An effect size is just a quantitative answer to a research question. Effect sizes should always be accompanied by a confidence interval or some other means of expressing uncertainty in generalizing from the sample to the population. Effect sizes are best interpreted in raw scores, but can also be expressed in standardized terms; several popular standardized effect score measures are explained and compared. Training your students to reporting and interpreting effect sizes can help them be better scientists: it will help them think critically about the practical significance of their results, makes uncertainty salient, foster better planning for subsequent experiments, encourage meta-analytic thinking, and can help focus their efforts on optimizing measurement. You can help your students start to think in effect sizes by giving them tools to visualize and translate between different effect size measures, and by tasking them to build a ‘library’ of effect sizes in a research field of interest.
“…The effect size is the 'difference of the difference', or the comparison of the two simple effects. Example from Perez et al (2018): In naive animals, the weak shock produced a 3% decrease in reflex responsiveness. In previously trained animals, the weak shock produced a 23% increase in reflex responsiveness.…”
Section: Effect Sizes Can Be Expressed In Standardized Unitsmentioning
confidence: 99%
“…We now obtain effect sizes of around 2.6 standard deviations. With much larger effect sizes we now use fewer animals (8-12/experiment) to obtain much more consistent results (Conte et al 2017;Perez et al 2018).…”
Section: Effect Sizes Can Be Expressed In Standardized Unitsmentioning
This paper has now been published in the Journal of Undergraduate Neuroscience Eduction: http://www.funjournal.org/wp-content/uploads/2018/04/june-16-e21.pdf?x91298. See also, this record on PubMed and PubMedCentral: https://www.ncbi.nlm.nih.gov/pubmed/30057503. An ongoing reform in statistical practice is to report and interpret effect sizes. This paper provides a short tutorial on effect sizes and some tips on how to help your students think in terms of effect sizes when analyzing data. An effect size is just a quantitative answer to a research question. Effect sizes should always be accompanied by a confidence interval or some other means of expressing uncertainty in generalizing from the sample to the population. Effect sizes are best interpreted in raw scores, but can also be expressed in standardized terms; several popular standardized effect score measures are explained and compared. Training your students to reporting and interpreting effect sizes can help them be better scientists: it will help them think critically about the practical significance of their results, makes uncertainty salient, foster better planning for subsequent experiments, encourage meta-analytic thinking, and can help focus their efforts on optimizing measurement. You can help your students start to think in effect sizes by giving them tools to visualize and translate between different effect size measures, and by tasking them to build a ‘library’ of effect sizes in a research field of interest.
“…We have recently extended these findings by showing that transcriptional changes can also be detected after the decay of recall for a long-term memory (Perez, Patel, Rivota, Calin-jageman, & Calin-Jageman, 2018). Specifically, we used microarray and qPCR to identify changes in gene expression that accompany forgetting of a long-term sensitization memory in Aplysia californica.…”
Section: Introductionmentioning
confidence: 89%
“…This apparent forgetting, however, belies a persistent savings memory, as a brief re-training can produce a long-lasting re-expression of the sensitization memory (Perez et al, 2018).…”
Section: Sensitization Of the Tail-elicited Siphon Withdrawal Reflexmentioning
confidence: 96%
“…This consisted of two moderate shocks (60Hz biphasic DC pulse for 2s at 20ma of constant current) applied to the midline of the tail with a 15-minute rest between the shocks. This protocol is strong enough to produce short-term sensitization in naïve animals, but not sufficient to produce long-term sensitization (Perez et al, 2018;Philips et al, 2006).…”
This is a pre-print of a paper now published in Neurobiology of Learning and Memory: https://doi.org/10.1016/j.nlm.2018.09.007 Most long-term memories are forgotten, becoming progressively less likely to be recalled. Still, some memory fragments may persist beyond forgetting, as savings memory (easier relearning) can persist long after recall has become impossible. What happens to a memory trace during forgetting that makes it inaccessible for recall and yet still effective to spark easier re-learning? We are addressing this question by tracking the transcriptional changes that accompany learning and then forgetting of a long-term sensitization memory in the tail-elicited siphon withdrawal reflex of Aplysia californica. First, we tracked savings memory. We found that even though recall of sensitization fades completely within 1 week of training, savings memory is still robustly expressed at 2 weeks post training. Next, we tracked the time-course of regulation of 11 transcripts we previously identified as potentially being regulated beyond the decay of recall. Remarkably, 3 transcripts still show strong regulation of expression 2 weeks after training and an additional 4 are regulated for at least 1 week. These long-lasting changes in gene expression always began early in the memory process, within 1 day of training. We present a synthesis of our results tracking gene expression changes accompanying sensitization and provide a testable model of how sensitization memory is forgotten.
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