AI Article Synopsis
- Brewin and Andrews (2016) argue that the percentage of people susceptible to false childhood memories is likely over 15%, contradicting the idea that this is an "upper bound."
- False memories can develop quickly, even during brief and low-pressure interviews, and become increasingly vivid with repeated questioning.
- The authors also express concerns regarding the integrity of the peer review process in this area of research.
Article Abstract
Brewin and Andrews (2016) propose that just 15% of people, or even fewer, are susceptible to false childhood memories. If this figure were true, then false memories would still be a serious problem. But the figure is higher than 15%. False memories occur even after a few short and low-pressure interviews, and with each successive interview, they become richer, more compelling, and more likely to occur. It is therefore dangerously misleading to claim that the scientific data provide an "upper bound" on susceptibility to memory errors. We also raise concerns about the peer review process.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5248607 | PMC |
| http://dx.doi.org/10.1002/acp.3265 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers.
Sensors (Basel)
December 2024
CommSensLab-UPC, Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain.
Interferometric radiometers operating at L-band, such as ESA's SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors' fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions.
View Article and Find Full Text PDFUnlabelled: While visual working memory (WM) is strongly associated with reductions in occipitoparietal 8-12 Hz alpha power, the role of 4-7 Hz frontal midline theta power is less clear, with both increases and decreases widely reported. Here, we test the hypothesis that this theta paradox can be explained by non-oscillatory, aperiodic neural activity dynamics. Because traditional time-frequency analyses of electroencephalopgraphy (EEG) data conflate oscillations and aperiodic activity, event-related changes in aperiodic activity can manifest as task-related changes in apparent oscillations, even when none are present.
View Article and Find Full Text PDFThe relationship between electric field strength induced by electroconvulsive therapy and cognitive and antidepressant outcomes.
Neuropsychopharmacology
January 2025
Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands.
Electroconvulsive therapy (ECT) is an effective treatment for depression but is often associated with cognitive side effects. In patients, ECT-induced electric field (E-field) strength across brain regions varies significantly due to anatomical differences, which may explain individual differences in cognitive side effects. We examined the relationship between regional E-field strength and change in verbal fluency score (i.
View Article and Find Full Text PDFApproaches for extracting daily dosage from free-text prescription signatures in heart failure with reduced ejection fraction: a comparative study.
JAMIA Open
February 2025
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.
Objective: To compare various methods for extracting daily dosage information from prescription signatures (sigs) and identify the best performers.
Materials And Methods: In this study, 5 daily dosage extraction methods were identified. Parsigs, RxSig, Sig2db, a large language model (LLM), and a bidirectional long short-term memory (BiLSTM) model were selected.
Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.
Heliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!