Stucky, B. | Search bioRxiv

bioRxiv2024

We are the sensors of consciousness! A review and analysis on how awakenings during sleep influence dream recall.

PurposeSince the 1930s, researchers have awakened people from different stages of sleep to record what they have experienced. While some aspects, including asking whether participants had dreams or thoughts before awakening, largely remain the same, others, such as the method of awakening, vary greatly. In addition, it is often assumed that the influence of participant characteristics, such as personality traits, motivation, memory, and attention, is reduced by collecting experiences immediately after they occur, rather than through delayed morning recall. However, the extent to which these variables influence dream recall upon awakening has not yet been thoroughly investigated. Materials and methodsTo explore possible contextual and individual influences, this review analyzed 69 awakening studies conducted between 2000 and 2024 and utilized the DREAM database. Differences between sleep stages were quantified and experiences analyzed using the categories with recall, without recall, and no report. ResultsSimilar levels of null reports were found in NREM stage 2 and stage 3. Significant factors affecting dream recall included the method of awakening (lower recall with an alarm compared to calling the participants name), the number of study days (reduced recall for multiple days) and the sleep environment (higher recall at home compared to the laboratory), along with participant characteristics beyond age, sex and study design. Recall rates from NREM sleep are particularly sensitive to the method of awakening and interindividual differences. ConclusionBoth the awakening procedure and participant characteristics influence the amount of reported sleep experiences, which can impact study outcomes, such as the identification of neural correlates of consciousness. Therefore, greater emphasis needs to be placed on how experiences are collected and on participant characteristics, such as openness to experience or familiarity with different states of consciousness. Plain Language SummaryHave you ever wondered how often you dream while you sleep? We reviewed 69 studies where people were woken up to share their experiences. They reported dreaming most of the time, even in deep sleep. You might ask, "Why dont I remember much in the morning then?" Well, maybe you forgot your dream because a lot of time has passed. These studies capture the experience right after it happens, but even then, some people remember dreams more than others. This could be because they are more focused on their dreams. How you wake up also matters. If someone shakes you awake, you may forget your dream, but if they gently call your name, you have a better chance of remembering it. You might also fail to recognize more subtle experiences, such as a sense of calm. So perhaps sleep is not as unconscious as we thought, leaving much to be explored in future studies.

neuroscience↗

bioRxiv2019

Methods for broad-scale plant phenology assessments using citizen scientists’ photographs

Broad-scale plant flowering phenology data has predominantly come from geographically and taxonomically restricted monitoring networks. However, platforms such as iNaturalist, where citizen scientists upload photographs and curate identifications, provide a promising new source of data. Here we develop a general set of best practices for scoring iNaturalist digital records supporting downstream re-use in phenology studies. We focus on a case study group, Yucca, because it has showy flowers and is well documented on iNaturalist. Additionally, drivers of Yucca phenology are not well-understood despite need for Yucca to synchronize flowering with obligate moth pollinators. Finally, evidence of anomalous flowering events have been recently reported, but the extent of those events is unknown. We use best-practices approach to annotate nearly 9,000 Yucca iNaturalist records, and compare the spatiotemporal coverage of this dataset with other broad-scale monitoring resources. Our findings demonstrate that iNaturalist provides unique phenology information, including delineation of extents of unusual flowering events. We also determine if unusual early flowering events impact later, typical flowering periods. Finally, we adapt a plant phenology global knowledge-store to integrate iNaturalist annotation results, supporting broadest reuse. Our approach has application to other plant groups, leveraging rapidly increasing data resources from iNaturalist to study phenology.

ecology↗