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No full textPresentation in the frame of the project "Open science for arts design and music", 19 January 202
Large Language Models Know What To Say But Not When To Speak
No full textExperimentally collected participant-labeled TRPs, collected at the Human Interaction Lab at Tufts University, and released as part of the paper "Large Language Models Know what to Say But Not When to Speak", EMNLP 2024
Perspective matters: An interpretational lens to understand environmental performance indices
No full textEnvironmental performance indices play a crucial role in evaluating countries' environmental efforts and advancing global environmental governance. This study critically examines disparities among prominent climate performance indices, including the Environmental Performance Index (EPI), Climate Change Performance Index (CCPI), and Climate Action Tracker (CAT). We reveal significant divergences in country rankings, particularly between ‘developed’ and ‘least developed’ nations, underscoring how methodological choices profoundly impact outcomes and interpretability. We develop an analytic tool, called EPI-equity, to demonstrate how integrating equity principles can substantially alter performance assessments. We propose a conceptual framework to classify indices based on the perspectives they embody, highlighting how these can shape the interpretation of performance. Our results suggest that the outcomes of environmental performance indices are more likely to concur when they represent similar perspectives. We propose that explicitly articulating the perspective implied by the formalism of performance indices can enhance transparency, guide developers in aligning methodological choices with intended interpretations, and equip users with a clearer understanding of the results. Our analysis highlights the importance of employing multiple indices that encompass a range of perspectives for a comprehensive evaluation of countries' environmental performance. Our adaptable framework provides a structured approach to guide the selection of indices ensuring that they span a broad spectrum of viewpoints. This method mitigates the likelihood of conflicts arising from fragmented worldviews on complex socio-environmental issues
How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task
No full textSequence learning in the serial response time task (SRTT) is one of few learning phenomena where researchers agree that such learning may proceed in the absence of awareness while it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning largely focused on the type of representation that may underlie implicit sequence learning, and whether or not two independent learning systems are necessary to explain qualitative differences between implicit and explicit learning. Using the drift-diffusion model, here we take a cognitive-processes perspective on sequence learning and investigate the cognitive operations that benefit from implicit and explicit sequence learning (e.g., stimulus detection and encoding, response selection, and response execution). To separate the processes involved in expressing implicit versus explicit knowledge, we manipulated explicit sequence knowledge independently of the opportunity to express such knowledge, and analyzed the resulting performance data with a drift-diffusion model to disentangle the contributions of these sub-processes. Results revealed that implicit sequence learning does not affect stimulus processing, but benefits response selection. Moreover, beyond response selection, response execution was affected. Explicit sequence knowledge did not change this pattern if participants worked on probabilistic materials, where it is difficult to anticipate the next response. However, if materials were deterministic, explicit knowledge enabled participants to switch from stimulus-based to plan-based action control, which was reflected in ample changes in the cognitive processes involved in performing the task. First implications for theories of sequence learning, and how the diffusion model may be helpful in future research, are discussed
You are not your mistake: The Power of Excuse and the Wording of Feedback in Examining the Consequences of Essentialist and Behavioral Work Feedback
No full textDemonstration and practical implications of the consequences of the wording of feedback (passive active) and the presence of excuse (present or not present) on the recipient in a vignette study design
Investigating Two Measures of Personality Functioning Impairment Across 36 Countries and 23 Languages: Measurement and Predictive Invariance
No full textIncreased adverse health outcomes in sexual minority populations exposed to stressful childhood experiences: a meta-analysis
No full textStressful childhood experiences (SCEs) are prevalent among sexual and gender minority (SGM) populations and contribute to adverse health outcomes in adulthood. This systematic review and meta-analysis synthesized findings from 64 independent studies published between 2013 and 2022 examining associations between SCEs and adult health outcomes among SGM individuals. Seven outcome domains were identified: psychological health (k = 26), suicide and related behaviors (k = 23), substance use (k = 14), sexual health (k = 13), physical health (k = 5), housing instability (k = 3), and adulthood abuse or victimization (k = 2). Across outcomes, exposure to SCEs was associated with significantly increased odds (31–132%) of adverse health outcomes in adulthood. Because the analyses aggregated outcomes across diverse LGBTQ+ subgroups, additional research is needed to clarify how SCEs uniquely affect distinct sexual and gender minority populations. These findings underscore the importance of assessing SCEs in both research and clinical settings and highlight the need for policies that mitigate their long-term effects on SGM health