Connections and Abstractions: Blending Epistemologies of Love and Separation in Environmental Education

Understanding the epistemological dimension of the subject–object dichotomy is crucial for environmental learning. Contrasting the epistemologies of separation and love, Arthur Zajonc argues that learning is seriously limited unless we focus more attention on fostering deep connections of respect, love, and participation with the objects we study. Although an attitude of detachment and objectivity is sometimes appropriate, understanding such things as social justice and the environment demand an approach that softens the sharp dichotomy between knower and thing–to–be–known. While largely agreeing with Zajonc, we emphasize that the epistemologies of separation and love should not be seen as wholly distinct or unrelated. A deep understanding of ourselves and the world around us depends upon a shifting back and forth between these approaches, though this will, admittedly, not be susceptible to any strict set of methodological rules. Learning depends upon not only understanding how to use these two epistemologies, but, importantly, learning how to shift between them with ease. Furthermore, we suggest that Zajonc’s use of the dual concepts of the logic of discovery and of justification to illustrate his two epistemologies can be made more descriptively accurate and prescriptively useful by noticing that in the process of learning—of discovering—investigators can and do move fluidly between seeking detached objectivity and connectedness. We embrace a broad pedagogical approach to environmental education consistent with Zajonc’s view and that is place–based and multi– and trans– disciplinary. This includes a rejection of the priority of science over the humanities, a narrowing of the gap between knower and thing–to–be–known, and a move away from attempts to excessively abstract from particulars to generalities and law

Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling

Over the last decade, the Bayesian estimation of evidence-accumulation models has gainedpopularity, largely due to the advantages afforded by the Bayesian hierarchical framework.Despite recent advances in the Bayesian estimation of evidence-accumulation models,model comparison continues to rely on suboptimal procedures, such as posterior parameterinference and model selection criteria known to favor overly complex models. In this paperwe advocate model comparison for evidence-accumulation models based on the Bayesfactor obtained via Warp-III bridge sampling. We demonstrate, using the Linear BallisticAccumulator (LBA), that Warp-III sampling provides a powerful and flexible approachthat can be applied to both nested and non-nested model comparisons, even in complexand high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-usesoftware implementation of the Warp-III sampler and outline a series of recommendationsaimed at facilitating the use of Warp-III sampling in practical applications

A cautionary note on evidence-accumulation models of response inhibition in the stop-signal paradigm

The stop-signal paradigm is a popular procedure to investigate responseinhibition–the ability to stop ongoing responses. It consists of a choice responsetime (RT) task that is occasionally interrupted by a stop stimulussignaling participants to withhold their response. Performance in the stopsignalparadigm is often formalized as race between a set of go runners triggeredby the choice stimulus and a stop runner triggered by the stop signal.We investigated whether evidence-accumulation processes, which have beenwidely used in choice RT analysis, can serve as the runners in the stop-signalrace model and support the estimation of psychologically meaningful parameters.We examined two types of the evidence-accumulation architectures:the racing Wald model (Logan, Van Zandt, Verbruggen, & Wagenmakers, 2014) and a novel proposal based on the Lognormal race (Heathcote & Love,2012). Using a series of simulation studies and fits to empirical data, wefound that these models are not measurement models in the sense that thedata-generating parameters cannot be recovered in realistic experimentaldesigns

RNA polymerase V-dependent small RNAs in Arabidopsis originate from small, intergenic loci including most SINE repeats.

In plants, heterochromatin is maintained by a small RNA-based gene silencing mechanism known as RNA-directed DNA methylation (RdDM). RdDM requires the non-redundant functions of two plant-specific DNA-dependent RNA polymerases (RNAP), RNAP IV and RNAP V. RNAP IV plays a major role in siRNA biogenesis, while RNAP V may recruit DNA methylation machinery to target endogenous loci for silencing. Although small RNA-generating regions that are dependent on both RNAP IV and RNAP V have been identified previously, the genomic loci targeted by RNAP V for siRNA accumulation and silencing have not been described extensively. To characterize the RNAP V-dependent, heterochromatic siRNA-generating regions in the Arabidopsis genome, we deeply sequenced the small RNA populations of wild-type and RNAP V null mutant (nrpe1) plants. Our results showed that RNAP V-dependent siRNA-generating loci are associated predominately with short repetitive sequences in intergenic regions. Suppression of small RNA production from short repetitive sequences was also prominent in RdDM mutants including dms4, drd1, dms3 and rdm1, reflecting the known association of these RdDM effectors with RNAP V. The genomic regions targeted by RNAP V were small, with an estimated average length of 238 bp. Our results suggest that RNAP V affects siRNA production from genomic loci with features dissimilar to known RNAP IV-dependent loci. RNAP V, along with RNAP IV and DRM1/2, may target and silence a set of small, intergenic transposable elements located in dispersed genomic regions for silencing. Silencing at these loci may be actively reinforced by RdDM

Cognitive workload measurement and modeling under divided attention

Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver's cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention