Rumination Derails Reinforcement Learning With Possible Implications for Ineffective Behavior

How does rumination affect reinforcement learning—the ubiquitous process by which people adjust behavior after error to behave more effectively in the future? In a within-subjects design ( N = 49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly, this impairment could not be attributed to decreased attentional breadth (quantified using a decay parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention) but not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.

Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior

How does rumination affect reinforcement learning—the ubiquitous process by which we adjust behavior after error in order to behave more effectively in the future? In a within-subject design (n=49), we tested whether experimentally induced rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly this impairment could not be attributed to decreased attentional breadth (quantified using a “decay” parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention), yet not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers

Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.

Expanded job scope model and turnover intentions: A moderated mediation model of Core-Self Evaluation and job involvement

Existing study was conducted to make a combined examination of the mediating role of (a) Job involvement in linking expanded job scope model (EJSM) with turnover intentions and (b) investigate how the relationship among EJSM and turnover intention is conditional based on the level of Core Self-Evaluation (CSE) in employees.700 questionnaires were circulated among the employees of education and financial sector which yields 490 returns achieving a response rate of 70%. After initial data screening 420 complete responses were available for analyses. The results exhibit that Job involvement (JI) mediates the relationships between EJCM and turnover intentions. The results of the moderated mediation depict that JI mediates the relationships between job scope and high level of CSE in employees. The outcomes delivered valuable understandings for managers and consultants, especially to Human Resource professionals who are trying to facilitate the workforce in challenging working environment through improved job design. The businesses may encourage high level of employee involvement through redesigned job scope in presence of high order personality characteristics which helps to reduce turnover intentions. This paper contributed in the literature of job design in three different ways. First, existing research makes theoretical contribution by adding new dimension in existing JSM which is flexible work time. Second, it describes how dynamic work settings may refine employees’ abilities and behaviors. Third, the research deals with a unique view in research of job design by combining personality as a moderator (i.e., CSE).

Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model

Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.

Global maps of ecosystem functional properties with the SCOPE model on Google earth engine Sentinel-2 composites

<p>To monitor ecosystems at large spatial scale multiple data sources are needed. We developed a methodology to simulate ecosystem functional properties (EFPs): light use efficiency (LUE), water use efficiency (WUE), and evaporative fraction (EF) with Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model at global scale using weather and optical satellite data.</p><p>EFPs, metrics that integrate ecosystem processes and environmental conditions, are calculated from ecosystem fluxes: gross primary productivity (GPP), sensible (H) and latent (LE) heat flux. These fluxes were simulated by SCOPE from weather parameters and plant traits (leaf area index (LAI), leaf chlorophyll content (Cab)). The weather data was taken from ECMWF ERA5-Land dataset, the plant traits were retrieved with look-up table (LUT) from Sentinel-2 Level 2 composites, exported from Google Earth engine at 10 km resolution.</p><p>LUT retrieval was optimized on a synthetic dataset to reach acceptable quality for the key drivers of GPP flux: LAI (R<sup>2</sup> = 0.75) and Cab (R<sup>2</sup> = 0.62). The global retrieved LAI showed some discrepancies with MODIS LAI product MCD15, especially in forest regions (RMSE = 1.73 m<sup>2</sup> m<sup>-2</sup>). As a consequence, SCOPE-simulated GPP was lower in those regions, compared to MODIS GPP product (MYD17) (RMSE = 0.81 kgC m<sup>-2</sup> yr<sup>-1</sup>). SCOPE-simulated heat fluxes were compared to ECMWF energy flux from ERA5-Land dataset (RMSE<sub>H</sub> = 35.4 W m<sup>-2</sup>, RMSE<sub>LE</sub> = 41.6 W m<sup>-2</sup>). EFPs validation is in progress.</p><p>The discrepancies in LAI can be explained by the fact that we did not use plant functional type information during LUT retrieval, in contrast to the MODIS algorithm. Significant overestimation of LE in dry areas is the consequence of the absence of water balance routine in SCOPE model. We consider SCOPE to be a promising tool for optical and weather data fusion.</p><p><em>The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.</em></p>

Exploring the scattering and reabsorption of chlorophyll fluorescence: implications for remote sensing of photosynthesis

<p>In recent years, technological progress in high-resolution field spectrometers have enabled the use of alternative tracer for constraining ecosystem-scale photosynthesis, i.e. sun-induced fluorescence (SIF). The principle underlying the use of SIF as a proxy of gross primary productivity (GPP) is based on the fact that the light energy absorbed by chlorophyll molecules can proceed into three different pathways: photochemistry, heat dissipation, and chlorophyll fluorescence. Since these processes directly compete for the same excitation energy, measurements of SIF and non-photochemical quenching (NPQ) are expected to provide information on photosynthetic performance.</p><p>However, SIF signal measured at the leaf level or beyond is affected by several processes, including wavelength dependent scattering and reabsorption, which may need to be considered when linking SIF data and photosynthetic CO<sub>2</sub> assimilation.</p><p>To address this question, we conducted a multi-scale and multi-technique study that considered measurements of photosynthetic (GPP), optical (SIF, reflectance - R and transmittance - T), physiological (NPQ) and biophysical (the amount of absorbed photosynthetically active radiation - APAR) parameters of two soybean varieties: the MinnGold mutant, characterized by significantly reduced chlorophyll content (Chl), and the wild type, non-Chl deficient Eiko. We further used the “Soil-Canopy Observation Photosynthesis and Energy fluxes” (SCOPE) model to investigate the reabsorption and scattering of SIF. The measured leaf R, T and SIF and top-of-the-canopy R were used to retrieve biochemical and structural parameters of both varieties by inversion of the SCOPE model, while its forward mode was used to determine and correct for the scattering and reabsorption of SIF at both leaf and canopy level.</p><p>Our study revealed that despite the large difference in Chl content (the ratio of Chl between MinnGold and Eiko was nearly 1:5), similar leaf and canopy photosynthesis rates were maintained in the Chl‐deficient mutant. This phenomenon was captured neither by traditional spectral vegetation indices related to canopy greenness, nor by SIF measured in-situ. However, the modelling simulations revealed that when correcting for leaf and canopy scattering and reabsorption processes both varieties presented similar SIF yield (SIF/APAR). Furthermore, field measurements showed that APAR and NPQ in MinnGold were lower than in Eiko. This together explains the similar measured GPP and simulated SIF yield between the two varieties, and indicates that interpretation and application of SIF as a GPP tracer requires understanding and quantification of all these processes.</p>

Drought stress detection with a coupled AgroC-SCOPE model

<p>Photosynthesis is the cornerstone of all life on earth. Light energy, captured by chlorophyll, fuels photosynthesis. As an excess of absorbed light leads to harmful products, the excess light is either dissipated as heat or it is re-emitted in the atmosphere. The latter pathway results in a weak, but very specific spectral signal, right from the heart of the photosynthetic apparatus, called chlorophyll fluorescence. Recent advancements in spectrometry have allowed the retrieval of fluorescence with remote sensing. Given its close link to photosynthesis, it has the potential of informing crop growth models. The aim of this study is to estimate the stress parameter of the crop growth model AgroC by incorporating remotely-sensed sun-induced chlorophyll fluorescence (SIF) data. The radiative transfer model SCOPE converts the leaf-level fluorescence obtained from AgroC to canopy-scale SIF. In case of a stress, the SIF at 760 nm decreases, while the SIF at 687 nm shows a more complex relationship to stress. Comparing the modelled canopy-scale and observed SIF provides information on the plant water stress status, allowing a more precise estimate of the photosynthetic activity. Downstream, this leads to a better estimation of the plant growth, as well as a better estimation of the carbon and water fluxes. A field campaign is conducted over a sugar beet field in Merzenhausen, Germany, in which the fluorescence was measured alongside the water and carbon fluxes. As the fluorescence provides an additional constraint on the photosynthesis, the AgroC-SCOPE model is expected to provide significantly better estimates of the carbon fluxes compared to the AgroC model. The results of the coupled AgroC-SCOPE model will be presented at this meeting. This study provides information on the link between drought stress and fluorescence. An approach similar to the one proposed in this study will allow detecting drought stress at the regional to global scale with FLEX data.</p>