SCiP Awards

2024: Dr. Alex Paxton, University of Conneticut

Dr. Alexandra Paxton’s research significantly advances our understanding of human behavior, data ethics, and scientific accessibility. Her research on interpersonal coordination dynamics challenges the traditional focus on cooperation by examining understudied contexts like conflict. Using multimodal approaches—integrating speech, language, movement, and emotion—she reveals how these interactions unfold across various media, such as face-to-face and text-based communication. This research is especially relevant in today’s polarized society and provides valuable insights for improving dialogue in contentious discussions. Her theoretical models offer a deeper understanding of how context shapes human interaction, enhancing the science of communication and social behavior.

Dr. Alexandra Paxton is also a prominent voice in data ethics. Her scholarship emphasizes ethical, transparent, and person-centered practices in human-derived, large-scale data research. Throughout her career, she has explored the implications of data collection and use, advocating for research practices that honor the dignity and rights of participants. For instance, Dr. Paxton has proposed an expansion of the foundational principles of the Belmont Report aimed to address challenges in big data research, including consent, privacy, and the potential for re-identification. Through this research, Dr. Paxton is helping to establish a framework for ethical research that promotes the responsible use of data and ensures the protection of individuals’ rights, even as technological capabilities evolve.

Further, Dr. Paxton is committed to making data-intensive sciences more inclusive and accessible. She develops open-source tools and resources aimed at lowering barriers for researchers, particularly those from less technical backgrounds or historically underrepresented groups in STEM. Her dedication to equity is reflected in her mentorship: over 75% of her undergraduate research assistants identify as members of underrepresented or historically excluded groups. She actively engages with the McNair Scholar Program and the Work-Study Research Assistantship Program at UConn, encouraging broader participation in research and supporting students’ professional development.

Importantly, Dr. Paxton’s work has direct implications for policy and practice. She applies her research to promote prosocial outcomes and reduce divisiveness in real-world settings, such as improving conflict outcomes through interventions that move beyond traditional methods. Her interdisciplinary approach, leadership in data ethics, and dedication to diversity and inclusion position her as an influential figure in the field, with a tangible impact on scientific practice and society. Overall, Dr. Paxton’s accomplishments embody the principles of open science, equitable research, and transformative scholarship, making her an ideal candidate for the FABBS Early Career Impact Award.

2021: Dr. Brendan Johns, McGill University

Dr. Brendan Johns is an Assistant Professor in the Department of Psychology at McGill University. The goal of his research is to redefine the field of computational cognitive science through trailblazing cognitive model development grounded in machine learning and big data methodologies. Dr. Johns’ research capitalizes on today’s incredible technology to collect human behavior at scale. The range and types of data that can now be assembled is unprecedented, from controlled crowdsourcing to data mining social media. Big data analytics and machine learning offers new opportunities to provide insights across psychology, and the research undertaken by Dr. Johns is an example of the power and promise of this research area.

Dr. Johns conducts cutting-edge theoretical and applied research. The goal of the theoretical research prong is to understand the statistical underpinnings of the natural language environment and to determine how humans learn from this information. The applied research prong uses the resulting knowledge-based intelligence systems to generate cognitive technologies that can be widely deployed, such as the development of new automated clinical and educational computational tools. The ultimate aim of his research is to understand the computational underpinnings of human cognition and to use this knowledge to integrate cognitive computations into disruptive technology development.

This research has resulted in 47 published articles, many in top journals in his field, including Psychological Review, Cognitive Psychology, Psychonomic Bulletin & Review, and Behavior Research Methods, among others. His research is funded from a 5-year Discovery Grant from the Natural Sciences and Engineering Research Council of Canada, among other funding sources. Dr. Johns received numerous research awards during his time as a graduate student, such as the Marr prize from the Cognitive Science Society for best student paper. After receiving his Ph.D. he continued receiving research awards, as he won the article of the year award in the Canadian Journal of Experimental Psychology in 2015 from the the Canadian Psychological association, as well as a member-select speaker award from the Psychonomic Society in 2017.

Dr. Johns has also provided service to his field. He is currently an associate editor for the journal Behavior Research Methods and is on the editorial board at Canadian Journal of Experimental Psychology. He is currently on the steering committee of the Society for Computation in Psychology and was also a previous member from 2016-2019.

Update: Dr. Johns also won the Early Career Award from the Psychonomic Society!

2019: Dr. Laura Allen, University of New Hampshire

A prominent aim of Dr. Allen’s research is to investigate the higher-level cognitive skills that are required for successful text comprehension and production, as well as the ways in which performance in these domains can be enhanced through strategy instruction and training. She has conducted a number of studies to understand how individual differences in cognitive skills and knowledge relate to performance on reading comprehension and writing assessments. This research has revealed a number of characteristics of successful readers and writers, such as their ability to generate inferences, their knowledge of vocabulary, and their ability to flexibly adapt their language across multiple tasks. Dr. Allen has drawn upon the findings from these studies to examine the impact of manipulating task instructions on task performance and to explore how educational technologies can be leveraged to facilitate learning. Laura has published approximately 80 peer reviewed publications, including 29 as first author, since 2013 – a short and impressively productive career to date!

Dr. Allen additionally received (as Co-PI) two four-year grants from the Institute of Education Sciences (IES) totaling approximately $1.4 million each. The purpose of these grants is to investigate how students process complex information in todays technology-driven society and to develop educational tools that provide students, teachers, and researchers with writing analytics and feedback.

2016: Dr. Rick Dale, UCLA

Dr. Rick Dale is an internationally recognized authority on experimental and computational analyses of language, human interaction, language evolution, cognitive dynamics, and big data. He uses computational modeling, analysis of naturalistic behavior, and human experimentation to investigate a range of linguistic behaviors related to conversation, thinking, sentence processing, word categorization, and deception.

Dr. Dale is particularly well known for inventing technologies to measure subtle supportive nonlinguistic gestures that people make during conversation (e.g., eye and arm movements) and for his sophisticated application of numeric methods including dynamical systems theory to make sense of how those gestures impact and influence peoples’ comprehension and interaction. He is also known for his efforts to step outside of a limited analysis of linguistic behavior to build a comprehensive analysis of how people use and understand language in naturalistic settings and without the imposition of too-artificial constraints.

2012: Dr. Michael Jones, Indiana University

Michael Jones’ research focuses on language learning, comprehension, and knowledge representation in humans and machines. Jones combines computational and experimental techniques to examine large-scale statistical structure of certain environments with the goal of understanding how this structure could be learned and represented with the mathematical capabilities of human learning and memory. Jones also studies associative and recognition memory, categorization, decision-making, and the role of attention in reading and perception. He is particularly interested in the temporal dynamics of learning in all these domains, and how to model the time course of knowledge acquisition. His secondary interests involve the application of these models to practical problems in text mining, intelligent search algorithms, and automated comprehension and scoring algorithms.

The National Science Foundation additionally awarded Jones a CAREER grant to investigate computational mechanisms for integrating linguistic and perceptual information in semantic representation. This project includes a very large scale “Semantic Pictionary” crowdsourcing project that includes several online games aimed at collecting massive amounts of perceptual data describing tens of thousands of words and explores mechanisms humans use to integrate the perceptual and linguistic information into a unified and embodied semantic representation.