Castellan Prize

SCiP’s primary annual award is the The John Castellan Student Paper Award for the most outstanding student paper. Student papers on the application of computational or computerized methods to any area of psychology (theoretical, experimental, applied) are welcome. Eligibility is open to work done by a student currently enrolled in undergraduate or graduate courses, or work done as part of a course, thesis, or other student research by a person who graduated within the past year. The student must be the primary author and the presenter of the paper to be considered. The award is presented at the conference.

History of Winners




Paper Title


John Hollander

University of Memphis

The spatial role of verbs in embodied language processing


Molly Apsel

Indiana University - Bloomington

forager: A Python Framework for Modeling Mental Search


No Award


Moein Razavi

Texas A&M University

Multimodal-Multisensory Experiments: Design and Implementation


Henry Dong

University of California, Berkeley


Mitchell Dandignac

Miami University

Writing for Coh-Metrix: A systematic approach to revising texts to foster gist inferences


Matt Cook

University of Manitoba

A computational cognitively-inspired technology for clinical diagnosis


Pascal Kieslich

University of Mannheim

Mousetrap: An integrated, open-source mouse-tracking package


Felix Henninger

University of Koblenz-Landau, Max Planck Institute for Research on Collective Goods, University of Mannheim

A flexible, cross-platform, open framework for interactive experiments


Erica Snow

Arizona State University

Does agency matter? Path analysis within a game-based system


Haiying Li

University of Memphis

A comparative study on measures of text formality


Alexandra Paxton

University of California - Merced

Linguistic alignment in debate


Brent Kievit-Kylar

Indiana University

Word2Word: A visualization tool for high-dimensional semantic data


Jun Xie

University of Memphis

Analyzing Directed Data by using MPT Models of Source Monitoring


Brendan Johns

Indiana University

Using automated semantic measures to test the assumptions of memory models: Do random representations reflect the organization of semantic memory?


Gabriel Recchia

Indiana University

More data trumps smarter algorithms: Training computational models of semantics on very large corpora


Richard Landers

University of Minnesota

TREND: A tool for rapid online research literature analysis and quantification


Jessica Ray

University of Central Florida

Train-to-code: An adaptive expert system for training systematic observation and coding skills


Cyrus Shaoul

University of Alberta

Toward a more psychologically relevant high-dimensional model of lexical semantics


Christopher Myers

The Air Force Research Laboratory's Human Effectiveness Directorate

Computational cognitive modeling of adaptive choice behavior in a dynamic decision paradigm


Michael Jones

Queen's University

Tracking attention with the focus-window technique: The information filter must be calibrated


Andrew Edmonds

Clemson University

Uzilla: A new tool for web usability testing


Matthew Pastizzo

State University of New York

Multi-dimensional data visualization


Wai-Tat Fu

George Mason University

ACT-PRO action protocol analyzer: A tool for analyzing discrete action protocols


Patrick Conley

University of California - Riverside

A computational approach to modeling population differences


Ricard Downing

University of Missouri

The missouri developmental disability resource center: A web site responding to a critical need for information of parents with a child with a disability


Katja Wiemer-Hastings

University of Memphis

Abstract noun classification: using a neural network to match word context and word meaning


Ed Colet

New York University

Visualization of multivariate data: Human factors considerations


Hilary Broadbent

Brown University

Analysis of periodic data using walsh functions


Steven Greene

Yale University and Northwestern University

A flexible programming language for generating stimulus lists for cognitive psychology experiments


Michael Granaas

University of Kansas

Simple, applied text parsing


Timothy Post

University of Pittsburgh


Winford A Gordon

University of North Carolina


Mark Alan Johnson

Washington University


Timothy Post

Syracuse University

Software control of reaction time studies

FABBS Early Career Impact Award


The Federation of Associations in Behavioral & Brain Sciences (FABBS) represents a coalition of scientific societies with a common goal of advancing our understanding of mind, brain and behavior. SCiP has been a member society of FABBS for several years. FABBS educates federal representatives and Congress on sciences of the mind, advocates for legislation and policy that enhance scientific training and research, and more. Because we are a FABBS society, members of SCiP get free access to Policy Insights from the Behavioral and Brain Sciences (PIBBS).

FABBS organizes the Early Career Impact Award, and each of its member societies, every few years, chooses a recipient whose research has already made a big impact at an early stage of professional development.

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.