Dataset Information

Rats answer an unexpected question after incidental encoding.

ABSTRACT: A fundamental aspect of episodic memory is that retrieval of information can occur when encoding is incidental and memory assessment is unexpected. These features are difficult to model in animals because behavioral training likely gives rise to well-learned expectations about the sequence of events. Thus, the possibility remains that animals may solve an episodic memory test by using well-learned semantic rules without remembering the episode at memory assessment. Here we show that rats can answer an unexpected question after incidental encoding in a hippocampal-dependent manner, consistent with the use of episodic memory. Rats were initially trained to report about a recent event (food versus no food) and separately searched for food where there was no expectation of being asked about the presence of food. To test episodic memory, we gave rats the opportunity to incidentally encode the presence or absence of food and unexpectedly asked them to report about the recent event. Temporary inactivation of the CA3 region of the hippocampus with bilateral infusions of lidocaine selectively eliminated the ability of rats to answer the unexpected, but not the expected, question. Our studies suggest that rats remember an earlier episode after incidental encoding based upon hippocampal-dependent episodic memory.

SUBMITTER: Zhou W

PROVIDER: S-EPMC3376895 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rats answer an unexpected question after incidental encoding.

Zhou Wenyi W Hohmann Andrea G AG Crystal Jonathon D JD

Current biology : CB 20120524 12

A fundamental aspect of episodic memory is that retrieval of information can occur when encoding is incidental and memory assessment is unexpected. These features are difficult to model in animals because behavioral training likely gives rise to well-learned expectations about the sequence of events. Thus, the possibility remains that animals may solve an episodic memory test by using well-learned semantic rules without remembering the episode at memory assessment. Here we show that rats can ans ...[more]

PMID: 22633809

Similar Datasets

Project description:Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.

Project description:IntroductionThroughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minorities, were disproportionately impacted by illness, hospitalization, and death. Equity in clinical trials means that the participants in clinical trials represent the people who are most likely to have the health condition and need the treatment that the trial is testing. Infodemiology approaches examining user conversations on social media platforms have the potential to elucidate specific barriers and challenges related to clinical trial participation, including among racial and ethnic minority populations.Materials and methodsThe study retrospectively collected and analyzed user question and answer posts from Quora in October 2021 using an inductive content coding approach. We also examined user's publicly available profile metadata to identify racial and ethnic minority users to capture their experiences, attitudes, topics, and discussions of barriers to COVID-19 vaccine trials.ResultsA total of 1073 questions and 7479 answers were collected. A total of 763 questions and 2548 answers were identified as related to COVID-19 vaccine clinical trials. The majority of these online interactions focused on asking questions and sharing knowledge and opinions about COVID-19 vaccine trials, including major topics related to: (a) interpreting whether clinical trial results could be trusted; (b) questions about vaccine efficacy and safety; (c) understanding trial design, regulatory considerations, and vaccine platforms; and (d) questions about trial enrollment, length, and adequate representation. Additionally, four major barriers discussed included: (i) disagreement from users regarding whether clinical trials require representation from different racial and ethnic minorities; (ii) concerns regarding the safety of trials when participating; (iii) lack of knowledge on how to participate in a trial; and (iv) questions of whether participants could withdraw from a trial to access an approved COVID-19 vaccine.ConclusionsOur study found active user discussions related to COVID-19 vaccine clinical trials on Quora, including those specific to minority health topics and those posted by a smaller group of self-identified racial and ethnic minority online users. Results from this study can help identify barriers to participation among the general public and underrepresented groups while also supporting the design of future outreach strategies to help with recruitment and inclusive trial participation.

Dataset Information

Rats answer an unexpected question after incidental encoding.

Publications

Rats answer an unexpected question after incidental encoding.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets