Reduced mind-wandering in Mild Cognitive Impairment: Testing the spontaneous retrieval deficit hypothesis

© American Psychological Association, 2018. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: http://dx.doi.org/10.1037/neu0000457Objective: Research on early cognitive markers of Alzheimer's disease (AD) is primarily focused on declarative episodic memory tests that involve deliberate and effortful/strategic processes at retrieval. The present study tested the spontaneous retrieval deficit hypothesis, which predicts that people with amnestic mild cognitive impairment (aMCI), who are at increased risk of developing AD, are particularly impaired on tasks that rely on spontaneous retrieval processes. Method: Twenty-three participants with aMCI and 25 healthy controls (HC) completed an easy vigilance task and thought probes (reporting what was going through their mind), which were categorized as spontaneous thoughts about the past (i.e., involuntary memories), current situation, and future (i.e., spontaneous prospection). Results: Participants with aMCI reported significantly fewer spontaneous thoughts or mind-wandering than HC. This effect was driven by significantly fewer involuntary memories, although groups did not differ in the number of current and future thoughts. Conclusions: Findings provide strong support for the spontaneous retrieval deficit hypothesis. Implications for research on mind-wandering and the default network, early cognitive markers of the disease, and our theoretical understanding of the nature of cognitive deficits in AD are discussed.Peer reviewe

Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state of the art methods and highlight opportunities for future research in this area. Our dataset is shared with the research community: https://github.com/Sai90000/ScientificHypothesisEvidencing.git

Personalized medicine—a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension

The United Nations Sustainable Development Goals in Systems Engineering: Eliciting sustainability requirements

This paper discusses a PhD research project testing the hypothesis that using the United Nations Sustainable Development Goals(SDG) as explicit inputs to drive the Software Requirements Engineering process will result in requirements with improved sustainability benefits. The research has adopted the Design Science Research Method (DSRM) [21] to test a process named SDG Assessment for Requirements Elicitation (SDGARE). Three DSRM cycles are being used to test the hypothesis in safety-critical, highprecision, software-intensive systems in aerospace and healthcare. Initial results from the first two DSRM cycles support the hypothesis. However, these cycles are in a plan-driven (waterfall) development context and future research agenda would be a similar application in an Agile development context.Comment: 7th International Conference on ICT for Sustainability (ICT4S2020), June 21--26, 2020, Bristol, United Kingdom. ACM has non-exclusive licence to publis

Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state-of-the-art benchmarks and highlight opportunities for future research in this area. The dataset is available at https://github.com/Sai90000/ScientificHypothesisEvidencing.gi

Insights into Assessing the Genetics of Endometriosis

Endometriosis is a complex disease arising from the interplay between multiple genetic and environmental factors. The genetic variants potentially underlying the hereditary component of endometriosis have been widely investigated through hypothesis-driven candidate gene studies, an approach that generally has proven to be inherently difficult and problematic for a number of reasons. Recently, through major collaborative efforts in the endometriosis research field, hypothesis-free genome-wide approaches have started to provide new insights into potential pathways leading to development of endometriosis, as well as highlighting the phenotypic heterogeneity of the condition. This review summarizes the most recent studies investigating the genetic variation contributing to endometriosis, with a particular focus on genome-wide approaches, and discusses promising future directions of genetic research

The spectro-contextual encoding and retrieval theory of episodic memory.

The spectral fingerprint hypothesis, which posits that different frequencies of oscillations underlie different cognitive operations, provides one account for how interactions between brain regions support perceptual and attentive processes (Siegel etal., 2012). Here, we explore and extend this idea to the domain of human episodic memory encoding and retrieval. Incorporating findings from the synaptic to cognitive levels of organization, we argue that spectrally precise cross-frequency coupling and phase-synchronization promote the formation of hippocampal-neocortical cell assemblies that form the basis for episodic memory. We suggest that both cell assembly firing patterns as well as the global pattern of brain oscillatory activity within hippocampal-neocortical networks represents the contents of a particular memory. Drawing upon the ideas of context reinstatement and multiple trace theory, we argue that memory retrieval is driven by internal and/or external factors which recreate these frequency-specific oscillatory patterns which occur during episodic encoding. These ideas are synthesized into a novel model of episodic memory (the spectro-contextual encoding and retrieval theory, or "SCERT") that provides several testable predictions for future research

From introspections, brain scans, and memory tests to the role of social context:advancing research on interaction and learning

The goal of this epilogue is to use the methodological contributions of the studies presented in this special issue as a starting point for suggestions about methodology in conducting future interaction research. As is the case in most developing fields, interaction research develops methods internally as it continually borrows and extends techniques used in other disciplines and revitalizes older techniques by adding new or different angles unique to interaction. Interaction researchers have also begun to forge relationships in new areas (e.g., by working with psychologists and developing working memory [WM] tests). This sort of cooperation is an important step in the drive to uncover more information about the relationship between interaction and learning. As several contributors to this special issue have noted, the most recent advances in methodology have been driven by questions about how interaction works (as opposed to whether it works). In turn, some of the methodological innovations discussed here will also ultimately allow new questions to be asked. Indeed, the relationship between questions (i.e., suggestions about what needs to be investigated next) and methods (i.e., plans for how to carry out such investigations) is particularly close in interaction research, which is a relatively new but vibrant and quickly developing area. Consequently, this epilogue considers both methods and questions conjointly, beginning with a discussion of methodological issues in the most recent theorizing about the interaction hypothesis

Elevational and local climate variability predicts thermal breadth of mountain tropical tadpoles

The climate variability hypothesis posits that increased environmental thermal variation should select for thermal generalists, while stable environments should favor thermal specialists. This hypothesis has been tested on large spatial scales, such as latitude and elevation, but less so on smaller scales reflective of the experienced microclimate. Here, we estimated thermal tolerance limits of 75 species of amphibian tadpoles from an aseasonal tropical mountain range of the Ecuadorian Andes, distributed along a 3500 m elevational range, to test the climatic variability hypothesis at a large (elevation) and a small (microhabitat) scale. We show how species from less variable thermal habitats, such as lowlands and those restricted to streams, exhibit narrower thermal tolerance breadths than highland and pond-dwelling species respectively. Interestingly, while broader thermal tolerance breadths at large scales are driven by higher cold tolerance variation (heat-invariant hypothesis), at local scales they are driven by higher heat tolerance variation. This contrasting pattern may result from divergent selection on both thermal limits to face environmental thermal extremes at different scales. Specifically, within the same elevational window, exposure to extreme maximum temperatures could be avoided through habitat shifts from temporary ponds to permanent ponds or streams, while minimum peak temperatures remained invariable between habitats but steadily decreased with elevation. Therefore an understanding of the effects of habitat conversion is crucial for future research on resilience to climate change