Many Labs 2: Investigating Variation in Replicability Across Samples and Settings

We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely highpowered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Instituto de Investigaciones Psicológicas (IIP

Application of the SWAT model for river flow forecasting in Sri Lanka

In the present study, the SWAT model and the Xinanjiang model have been used for daily flow forecasting of the Kalu River upper catchment in Sri Lanka. Kalu River is the second largest river in Sri Lanka and due to heavy rainfalls over the catchment, steep river slopes with narrow valleys in the upper catchment and mild riverbed slopes with wide and flat plains in the middle and lower catchments, the floods in Kalu River basin have become regular. The SWAT model has been used for daily river flow predictions in the Kalu River, and compared with the results obtained using the Xinanjiang model. In this study, the Xinanjiang model has performed slightly better than the SWAT model for forecasting the daily flow of Kalu River. In fact it might be partly attributable due to the poor quality and inadequate data, since the output of the SWAT (distributed model) strictly depends on the quality of input data. In addition, many people in Sri Lanka use well water for their domestic purposes. When considering a catchment as a whole, normally it is a very large area, and therefore it is not possible to record or count all the individual minor scale water utilizations in detail such as small irrigation, animal husbandry in minor scale and industrial water utilizations in minor scale. The cumulative value of such water utilizations might be large. The absence of these data may specially affect the distributed models in water balancing. But the conceptual watershed models (e.g. Xinanjiang model) are capable of adjusting their parameters while calibrating, according to the situation since most of their parameters have no physical background. As a result conceptual watershed models show better performance than distributed models where the catchment characteristics and model inputs are limited or incomplete

Application of models with different types of modelling methodologies for river flow forecasting

Tachikawa, Y.; Vieux, B. E.; Georgakakos, K. P.; Nakakita, E. (Eds.). Weather Radar Information and Distributed Hydrological Modelling: proceedings of Symposium HS03 held during IUGG2003 at Sapporo, Japan, 30 June-11 July 2003. Wallingford, UK: International Association of Hydrological Sciences (IAHS)IAHS Publication 282In the present study, a conceptual watershed model, a distributed watershed model, and an artificial neural network (ANN) have been applied to river flow forecasting in the Kalu River upper catchment in Sri Lanka. The Xinanjiang watershed model has been used as a conceptual watershed model and the SWAT model (Neitsch, 2000) has been used with spatial data as a distributed model. Two types of ANN architectures, namely multi-layer perceptron network (MLP) and radial basis function network (RBF) have been implemented as "black box" type modelling methodology. Based on the application results, it seems that the conceptual watershed model could perform slightly better than the distributed model and the ANN for this watershed. It was clearly noted that the performance of distributed models strictly depends on the quality of input data (Arnold et al., 1998) whereas the performance of conceptual models depends on the calibration (Duan et al., 1992, 1993)

Coherent random lasing controlled by Brownian motion of the active scatterer

The stability of the scattering loop is fundamental for coherent random lasing in a dynamic scattering system. In this work, fluorescence of DPP (N, N-di [3-(isobutyl polyhedral oligomeric silsesquioxanes) propyl] perylene diimide) is scattered to produce RL and we realize the transition from incoherent RL to coherent RL by controlling the Brownian motion of the scatterers (dimer aggregates of DPP) and the stability of scattering loop. To produce coherent random lasers, the loop needs to maintain a stable state within the loop-stable time, which can be determined through controlled Brownian motion of scatterers in the scattering system. The result shows that the loop-stable time is within 5.83 × 10−5 s to 1.61 × 10−4 s based on the transition from coherent to incoherent random lasing. The time range could be tuned by finely controlling the viscosity of the solution. This work not only develops a method to predict the loop-stable time, but also develops the study between Brownian motion and random lasers, which opens the road to a variety of novel interdisciplinary investigations involving modern statistical mechanics and disordered photonics

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity.

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity.

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics