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
Writing for Coh-Metrix: A systematic approach to revising texts to foster gist inferences
University of Manitoba
A computational cognitively-inspired technology for clinical diagnosis
University of Mannheim
Mousetrap: An integrated, open-source mouse-tracking package
University of Koblenz-Landau, Max Planck Institute for Research on Collective Goods, University of Mannheim
A flexible, cross-platform, open framework for interactive experiments
Arizona State University
Does agency matter? Path analysis within a game-based system
University of Memphis
A comparative study on measures of text formality
University of California - Merced
Linguistic alignment in debate
Word2Word: A visualization tool for high-dimensional semantic data
University of Memphis
Analyzing Directed Data by using MPT Models of Source Monitoring
Using automated semantic measures to test the assumptions of memory models: Do random representations reflect the organization of semantic memory?
More data trumps smarter algorithms: Training computational models of semantics on very large corpora
University of Minnesota
TREND: A tool for rapid online research literature analysis and quantification
University of Central Florida
Train-to-code: An adaptive expert system for training systematic observation and coding skills
University of Alberta
Toward a more psychologically relevant high-dimensional model of lexical semantics
The Air Force Research Laboratory's Human Effectiveness Directorate
Computational cognitive modeling of adaptive choice behavior in a dynamic decision paradigm
Tracking attention with the focus-window technique: The information filter must be calibrated
Uzilla: A new tool for web usability testing
State University of New York
Multi-dimensional data visualization
George Mason University
ACT-PRO action protocol analyzer: A tool for analyzing discrete action protocols
University of California - Riverside
A computational approach to modeling population differences
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
University of Memphis
Abstract noun classification: using a neural network to match word context and word meaning
New York University
Visualization of multivariate data: Human factors considerations
Analysis of periodic data using walsh functions
Yale University and Northwestern University
A flexible programming language for generating stimulus lists for cognitive psychology experiments
University of Kansas
Simple, applied text parsing
University of Pittsburgh
Winford A Gordon
University of North Carolina
Mark Alan Johnson
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.
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.