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GRASS is an open-source geospatial processing engine. With over 400 tools available in the core distribution and an additional 400+ tools available as extensions, GRASS has broad applicability in the Earth Sciences and geomorphometry in particular. In this workshop, we will give an introduction to GRASS and demonstrate some of the geomorphometry tools available in GRASS. Specifically, we will show how to compute stream extraction uncertainty using a workflow adapted from Hengl (2007) [1] and Hengl (2010) [2]. We will begin by downloading publicly available LiDAR data of the Perugia area using GRASS data fetching tools. Then, we will use R’s kriging functions (gstat) to create 100 iterations of a DEM. After exploring some of the stream extraction and flow routing methods available in GRASS, we will extract streams from each of the 100 DEMs to compute stream uncertainty. The workshop will be conducted in a Jupyter Notebook hosted in Google Colab. By the end of the workshop, participants will have hands on experience with: Creating GRASS projects and importing datasets, Adjusting the computational extent and resolution, Creating DEMs from point data using a variety of methods implemented in GRASS and using a stochastic kriging approach in R, Using the R interface for GRASS and R packages with GRASS data, Computing Stream Uncertainty, Developing publication-quality figures with grass.jupyter 

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

The measurement of individual differences in specific cognitive functions has been an important area of study for decades. Often the goal of such studies is to determine whether there are cognitive deficits or enhancements associated with, for example, a specific population, psychological disorder, health status, or age group. The inherent difficulty, however, is that most cognitive functions are not directly observable, so researchers rely on indirect measures to infer an individual’s functioning. One of the most common approaches is to use a task that is designed to tap into a specific function and to use behavioral measures, such as reaction times (RTs), to assess performance on that task. Although this approach is widespread, it unfortunately is subject to a problem of reverse inference: Differences in a given cognitive function can be manifest as differences in RTs, but that does not guarantee that differences in RTs imply differences in that cognitive function. We illustrate this inference problem with data from a study on aging and lexical processing, highlighting how RTs can lead to erroneous conclusions about processing. Then we discuss how employing choice-RT models to analyze data can improve inference and highlight practical approaches to improving the models and incorporating them into one’s analysis pipeline. 

Evidence accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behaviour. EAMs have generated significant theoretical advances in psychology, behavioural economics, and cognitive neuroscience, and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues, and on inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, for relating experimental manipulations to EAM parameters, for planning appropriate sample sizes, and for preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the authors’ substantial collective experience with EAMs. By encouraging good task design practices, and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

Addressing “wicked” problems like urban stormwater management necessitates building shared understanding among diverse stakeholders with the influence to enact solutions cooperatively. Fuzzy cognitive maps (FCMs) are participatory modeling tools that enable diverse stakeholders to articulate the components of a socio-environmental system (SES) and describe their interactions. However, the spatial scale of an FCM is rarely explicitly considered, despite the influence of spatial scale on SES. We developed a technique to couple FCMs with spatially explicit survey data to connect stakeholder conceptualization of urban stormwater management at a regional scale with specific stormwater problems they identified. We used geospatial data and flooding simulation models to quantitatively evaluate stakeholders’ descriptions of location-specific problems. We found that stakeholders used a wide variety of language to describe variables in their FCMs and that government and academic stakeholders used significantly different suites of variables. We also found that regional FCM did not downscale well to concerns at finer spatial scales; variables and causal relationships important at location-specific scales were often different or missing from the regional FCM. This study demonstrates the spatial framing of stormwater problems influences the perceived range of possible problems, barriers, and solutions through spatial cognitive filtering of the system’s boundaries.