scholarly journals Hemispheric specialization for visual words is shaped by attention to sublexical units during initial learning

2015 ◽  
Vol 145-146 ◽  
pp. 23-33 ◽  
Author(s):  
Yuliya N. Yoncheva ◽  
Jessica Wise ◽  
Bruce McCandliss
Author(s):  
Latife Yazigi ◽  
Cristiane Seixas Duarte ◽  
Jacqueline Santoantonio ◽  
Andrés Eduardo Aguirre Antúnez ◽  
Antonio Carlos Pacheco ◽  
...  

1975 ◽  
Vol 20 (10) ◽  
pp. 778-780 ◽  
Author(s):  
JOSEPH E. BOGEN

2018 ◽  
Vol 11 (4) ◽  
pp. 329-341
Author(s):  
Erick Francisco Quintas Conde ◽  
Adriana Oliveira de Santana Lucena ◽  
Rosenir Maria da Silva ◽  
Alberto Filgueiras ◽  
Allan Pablo Lameira ◽  
...  

2020 ◽  
Author(s):  
Robert Calin-Jageman ◽  
Irina Calin-Jageman ◽  
Tania Rosiles ◽  
Melissa Nguyen ◽  
Annette Garcia ◽  
...  

[[This is a Stage 2 Registered Report manuscript now accepted for publication at eNeuro. The accepted Stage 1 manuscript is posted here: https://psyarxiv.com/s7dft, and the pre-registration for the project is available here (https://osf.io/fqh8j, 9/11/2019). A link to the final Stage 2 manuscript will be posted after peer review and publication.]] There is fundamental debate about the nature of forgetting: some have argued that it represents the decay of the memory trace, others that the memory trace persists but becomes inaccessible due to retrieval failure. These different accounts of forgetting lead to different predictions about savings memory, the rapid re-learning of seemingly forgotten information. If forgetting is due to decay, then savings requires re-encoding and should thus involve the same mechanisms as initial learning. If forgetting is due to retrieval failure, then savings should be mechanistically distinct from encoding. In this registered report we conducted a pre-registered and rigorous test between these accounts of forgetting. Specifically, we used microarray to characterize the transcriptional correlates of a new memory (1 day after training), a forgotten memory (8 days after training), and a savings memory (8 days after training but with a reminder on day 7 to evoke a long-term savings memory) for sensitization in Aplysia californica (n = 8 samples/group). We found that the re-activation of sensitization during savings does not involve a substantial transcriptional response. Thus, savings is transcriptionally distinct relative to a newer (1-day old) memory, with no co-regulated transcripts, negligible similarity in regulation-ranked ordering of transcripts, and a negligible correlation in training-induced changes in gene expression (r = .04 95% CI [-.12, .20]). Overall, our results suggest that forgetting of sensitization memory represents retrieval failure.


2020 ◽  
Author(s):  
Robert Calin-Jageman ◽  
Irina Calin-Jageman ◽  
Tania Rosiles ◽  
Melissa Nguyen ◽  
Annette Garcia ◽  
...  

[[This is a Stage 1 Registered Report manuscript. The project was submitted for review to eNeuro. Upon revision and acceptance, this version of the manuscript was pre-registered on the OSF (9/11/2019, https://osf.io/fqh8j) (but due to an oversight not posted as a preprint until July 2020). A Stage 2 manuscript is now posted as a pre-print (https://psyarxiv.com/h59jv) and is under review at eNeuro. A link to the final Stage 2 manuscript will be added when available.]]There is fundamental debate about the nature of forgetting: some have argued that it represents the decay of the memory trace, others that the memory trace persists but becomes inaccessible due to retrieval failure. These different accounts of forgetting make different predictions about savings memory, the rapid re-learning of seemingly forgotten information. If forgetting is due to decay then savings requires re-encoding and should thus involve the same mechanisms as initial learning. If forgetting is due to retrieval-failure then savings should be mechanistically distinct from encoding. In this registered report we conducted a pre-registered and rigorous test between these accounts of forgetting. Specifically, we used microarray to characterize the transcriptional correlates of a new memory (1 day from training), a forgotten memory (8 days from training), and a savings memory (8 days from training but with a reminder on day 7 to evoke a long-term savings memory) for sensitization in Aplysia californica (n = 8 samples/group). We find that the transcriptional correlates of savings are [highly similar / somewhat similar / unique] relative to new (1-day-old) memories. Specifically, savings memory and a new memory share [X] of [Y] regulated transcripts, show [strong / moderate / weak] similarity in sets of regulated transcripts, and show [r] correlation in regulated gene expression, which is [substantially / somewhat / not at all] stronger than at forgetting. Overall, our results suggest that forgetting represents [decay / retrieval-failure / mixed mechanisms].


2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.


2021 ◽  
Vol 48 ◽  
pp. 100918
Author(s):  
Yuhan Chen ◽  
Michelle Slinger ◽  
J. Christopher Edgar ◽  
Luke Bloy ◽  
Emily S. Kuschner ◽  
...  

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