Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

WiseEye: next generation expandable and programmable camera trap platform for wildlife research

Funding: The work was supported by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1. The work of S. Newey and RJI was part funded by the Scottish Government's Rural and Environment Science and Analytical Services (RESAS). Details published as an Open Source Toolkit, PLOS Journals at: http://dx.doi.org/10.1371/journal.pone.0169758Peer reviewedPublisher PD

Nut production in Bertholletia excelsa across a logged forest mosaic: implications for multiple forest use

Although many examples of multiple-use forest management may be found in tropical smallholder systems, few studies provide empirical support for the integration of selective timber harvesting with non-timber forest product (NTFP) extraction. Brazil nut (Bertholletia excelsa, Lecythidaceae) is one of the world’s most economically-important NTFP species extracted almost entirely from natural forests across the Amazon Basin. An obligate out-crosser, Brazil nut flowers are pollinated by large-bodied bees, a process resulting in a hard round fruit that takes up to 14 months to mature. As many smallholders turn to the financial security provided by timber, Brazil nut fruits are increasingly being harvested in logged forests. We tested the influence of tree and stand-level covariates (distance to nearest cut stump and local logging intensity) on total nut production at the individual tree level in five recently logged Brazil nut concessions covering about 4000 ha of forest in Madre de Dios, Peru. Our field team accompanied Brazil nut harvesters during the traditional harvest period (January-April 2012 and January-April 2013) in order to collect data on fruit production. Three hundred and ninety-nine (approximately 80%) of the 499 trees included in this study were at least 100 m from the nearest cut stump, suggesting that concessionaires avoid logging near adult Brazil nut trees. Yet even for those trees on the edge of logging gaps, distance to nearest cut stump and local logging intensity did not have a statistically significant influence on Brazil nut production at the applied logging intensities (typically 1–2 timber trees removed per ha). In one concession where at least 4 trees ha-1 were removed, however, the logging intensity covariate resulted in a marginally significant (0.09) P value, highlighting a potential risk for a drop in nut production at higher intensities. While we do not suggest that logging activities should be completely avoided in Brazil nut rich forests, when a buffer zone cannot be observed, low logging intensities should be implemented. The sustainability of this integrated management system will ultimately depend on a complex series of socioeconomic and ecological interactions. Yet we submit that our study provides an important initial step in understanding the compatibility of timber harvesting with a high value NTFP, potentially allowing for diversification of forest use strategies in Amazonian Perù

A Large Catalog of Homogeneous Ultra-Violet/Optical GRB Afterglows: Temporal and Spectral Evolution

We present the second Swift Ultra-Violet/Optical Telescope (UVOT) gamma-ray burst (GRB) afterglow catalog, greatly expanding on the first Swift UVOT GRB afterglow catalog. The second catalog is constructed from a database containing over 120,000 independent UVOT observations of 538 GRBs first detected by Swift, the High Energy Transient Explorer 2 (HETE2), the INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL), the Interplanetary Network (IPN), Fermi, and Astro-rivelatore Gamma a Immagini Leggero (AGILE). The catalog covers GRBs discovered from 2005 Jan 17 to 2010 Dec 25. Using photometric information in three UV bands, three optical bands, and a `white' or open filter, the data are optimally co-added to maximize the number of detections and normalized to one band to provide a detailed light curve. The catalog provides positional, temporal, and photometric information for each burst, as well as Swift Burst Alert Telescope (BAT) and X-Ray Telescope (XRT) GRB parameters. Temporal slopes are provided for each UVOT filter. The temporal slope per filter of almost half the GRBs are fit with a single power-law, but one to three breaks are required in the remaining bursts. Morphological comparisons with the X-ray reveal that approximately 75% of the UVOT light curves are similar to one of the four morphologies identified by Evans et al. (2009). The remaining approximately 25% have a newly identified morphology. For many bursts, redshift and extinction corrected UV/optical spectral slopes are also provided at 2000, 20,000, and 200,000 seconds

The interrelated effect of sleep and learning in dogs (Canis familiaris); an EEG and behavioural study

The active role of sleep in memory consolidation is still debated, and due to a large between-species variation, the investigation of a wide range of different animal species (besides humans and laboratory rodents) is necessary. The present study applied a fully non-invasive methodology to study sleep and memory in domestic dogs, a species proven to be a good model of human awake behaviours. Polysomnography recordings performed following a command learning task provide evidence that learning has an effect on dogs’ sleep EEG spectrum. Furthermore, spectral features of the EEG were related to post-sleep performance improvement. Testing an additional group of dogs in the command learning task revealed that sleep or awake activity during the retention interval has both short- and long-term effects. This is the first evidence to show that dogs’ human-analogue social learning skills might be related to sleep-dependent memory consolidation

Ostriches Sleep like Platypuses

Mammals and birds engage in two distinct states of sleep, slow wave sleep (SWS) and rapid eye movement (REM) sleep. SWS is characterized by slow, high amplitude brain waves, while REM sleep is characterized by fast, low amplitude waves, known as activation, occurring with rapid eye movements and reduced muscle tone. However, monotremes (platypuses and echidnas), the most basal (or ‘ancient’) group of living mammals, show only a single sleep state that combines elements of SWS and REM sleep, suggesting that these states became temporally segregated in the common ancestor to marsupial and eutherian mammals. Whether sleep in basal birds resembles that of monotremes or other mammals and birds is unknown. Here, we provide the first description of brain activity during sleep in ostriches (Struthio camelus), a member of the most basal group of living birds. We found that the brain activity of sleeping ostriches is unique. Episodes of REM sleep were delineated by rapid eye movements, reduced muscle tone, and head movements, similar to those observed in other birds and mammals engaged in REM sleep; however, during REM sleep in ostriches, forebrain activity would flip between REM sleep-like activation and SWS-like slow waves, the latter reminiscent of sleep in the platypus. Moreover, the amount of REM sleep in ostriches is greater than in any other bird, just as in platypuses, which have more REM sleep than other mammals. These findings reveal a recurring sequence of steps in the evolution of sleep in which SWS and REM sleep arose from a single heterogeneous state that became temporally segregated into two distinct states. This common trajectory suggests that forebrain activation during REM sleep is an evolutionarily new feature, presumably involved in performing new sleep functions not found in more basal animals

Early Fasting Is Long Lasting: Differences in Early Nutritional Conditions Reappear under Stressful Conditions in Adult Female Zebra Finches

Conditions experienced during early life can have profound effects on individual development and condition in adulthood. Differences in nutritional provisioning in birds during the first month of life can lead to differences in growth, reproductive success and survival. Yet, under natural conditions shorter periods of nutritional stress will be more prevalent. Individuals may respond differently, depending on the period of development during which nutritional stress was experienced. Such differences may surface specifically when poor environmental conditions challenge individuals again as adults. Here, we investigated long term consequences of differences in nutritional conditions experienced during different periods of early development by female zebra finches (Taeniopygia guttata) on measures of management and acquisition of body reserves. As nestlings or fledglings, subjects were raised under different nutritional conditions, a low or high quality diet. After subjects reached sexual maturity, we measured their sensitivity to periods of food restriction, their exploration and foraging behaviour as well as adult resting metabolic rate (RMR). During a short period of food restriction, subjects from the poor nutritional conditions had a higher body mass loss than those raised under qualitatively superior nutritional conditions. Moreover, subjects that were raised under poor nutritional conditions were faster to engage in exploratory and foraging behaviour. But RMR did not differ among treatments. These results reveal that early nutritional conditions affect adult exploratory behaviour, a representative personality trait, foraging and adult's physiological condition. As early nutritional conditions are reflected in adult phenotypic plasticity specifically when stressful situations reappear, the results suggest that costs for poor developmental conditions are paid when environmental conditions deteriorate

Does personality affect premating isolation between locally-adapted populations?

Background: One aspect of premating isolation between diverging, locally-adapted population pairs is female mate choice for resident over alien male phenotypes. Mating preferences often show considerable individual variation, and whether or not certain individuals are more likely to contribute to population interbreeding remains to be studied. In the Poecilia mexicana-species complex different ecotypes have adapted to hydrogen sulfide (H2S)-toxic springs, and females from adjacent non-sulfidic habitats prefer resident over sulfide-adapted males. We asked if consistent individual differences in behavioral tendencies (animal personality) predict the strength and direction of the mate choice component of premating isolation in this system. Results: We characterized focal females for their personality and found behavioral measures of ‘novel object exploration’, ‘boldness’ and ‘activity in an unknown area’ to be highly repeatable. Furthermore, the interaction term between our measures of exploration and boldness affected focal females’ strength of preference (SOP) for the resident male phenotype in dichotomous association preference tests. High exploration tendencies were coupled with stronger SOPs for resident over alien mating partners in bold, but not shy, females. Shy and/or little explorative females had an increased likelihood of preferring the non-resident phenotype and thus, are more likely to contribute to rare population hybridization. When we offered large vs. small conspecific stimulus males instead, less explorative females showed stronger preferences for large male body size. However, this effect disappeared when the size difference between the stimulus males was small. Conclusions: Our results suggest that personality affects female mate choice in a very nuanced fashion. Hence, population differences in the distribution of personality types could be facilitating or impeding reproductive isolation between diverging populations depending on the study system and the male trait(s) upon which females base their mating decisions, respectively

Assessing the Quality of Goal Setting in Behavioural Support for Smoking Cessation and its Association with Outcomes

BACKGROUND: Smoking cessation behavioural support can be effective but practitioners differ markedly in effectiveness, possibly due to variation in the quality of delivery of key behaviour change techniques, such as goal setting (i.e. setting a quit date). OBJECTIVES: This study aimed to (i) develop a reliable method for assessing the quality of practitioners' support in setting quit dates and (ii) assess whether quality predicts initiation of abstinence as a first step to quitting. METHODS: A scale for scoring the quality of goal setting was developed from national guidance documents and applied to 85 transcribed behavioural support sessions. Inter-rater reliability was assessed. Associations between quality scores and quit attempts were assessed. RESULTS: The 10-item scale produced had good inter-rater reliability (Kappa = 0.68). Higher quality goal setting was associated with increased self-reported quit attempts (p < .001; OR = 2.60, 95 % CI 1.54-4.40). The scale components 'set a clear quit date' (χ (2) (2, N = 85) = 22.3, p < .001) and 'within an appropriate timeframe' (χ (2) (2, N = 85) = 15.5, p < .001) were independently associated with quit attempts. CONCLUSIONS: It is possible to reliably assess the quality of goal setting in smoking cessation behavioural support. Higher quality of goal setting is associated with greater likelihood of initiating quit attempts