Shane T Mueller, Ph.D.


Background

Picture of Dr. Mueller Shane T. Mueller, Ph.D.

I am a Senior Research Scientist at Klein Associates Division of ARA, Inc. Before that, I was a postdoctoral researcher Prof. Richard Shiffrin in the Memory and Perception Lab at Indiana University's Department of Psychology. I obtained my doctoral degree in the Cognition and Perception program of the University of Michigan's Psychology Department. While at Michigan, I worked in the Brain, Cognition, and Action Laboratory under the direction of David E. Meyer. I graduated summa cum laud from Drew University with degrees in Mathematics and Psychology.

Research Interests

I study the human cognitive, perceptual, and memory systems using empirical, computational, mathematical, and statistical techniques. My primary research interest is in developing models of how human memory systems represent knowledge, and how people use that knowledge to accomplish tasks. This ranges from low-level representations of the perceptual systems to high-level decisions made on the basis of expert knowledge.

  • Human Memory Representations, Limitations, and Strategies:

    • The Bayesian Recognitional Decision Model (BRDM), a computational model combining the Recognition-primed decision (RPD) Model and REM-II. BRDM is used to account for how experts make decisions based on cues in the environment and their episodic memory and semantic knowledge.
    • Development of Contextual Semantic representations, using the REM-II model of human memory. This bayesian model of human memory accounts for how experiences form meaningful memories through semantic knowledge, and how semantic knowledge form through the accrual of meaningul episodic memories.
    • Phonological representations of information in verbal short-term memory. Our model of phonological similarity, PSIMETRICA, has accounted for violations of the word length effect in immediate serial recall.
    • Visual letterforms and word-forms, an important visual representation of verbal information. This is
    • My dissertation investigated recall strategies in immediate serial recall using the EPIC Computational Architecture. I'm interested in determining the strategic and architectural factors that influence performance in short-term memory tasks.

  • Measurement and Metrics:

    • I am the primary developer of PEBL (the Psychology Experiment Building Language), and the PEBL test battery, which distributes a suite of (currently 21) free commonly-used psychological tests.
    • I developed the Cognitive Decathlon for the DARPA's BICA (Biologically-Inspired Cognitive Architecture) program. BICA's goals were to develop artificially-intelligent agents based on principles of neuroscience; the Cognitive Decathlon was a form of the Turing test which was intended to measure the embodied intelligence of these agents.
    • I am currently conducting research to investigate how the physical insults produced by protective gear (as worn by our military, firefighters, police, astronauts, farmers, etc.) impact critical high-level cognitive functioning.
    • In conjunction with research of memory phenomena, I've developed techniques to measure phonological similarity (PSIMETRICA), articulatory duration, and (based on REM-II), a computational system called Contextual Semantic Analysis (CSA) that can measure semantic similarity of words.
    • I have helped developed A, a non-parametric measure of signal detection sensitivity that replaces A-prime, and a novel ROC analysis called the DS-ROC function that can help measure the influence of perceptual processes in signal detection tasks.

  • Other Research Interests:

    • At ARA, I have recently been developing a technique we call Cultural Mixture Modeling to assess consensus and cultural differences among survey respondents.
    • I've contributed to some work on mimicry of degree distributions in social and other networks.
    • I've studied how the maxim that football quarterbacks need to be highly decisive is somewhat inaccurate: quarterbacks gain the most yardage on plays where they are patient and do not fear indecision.

Recent Papers, Posters and Slides from Talks

Decision Making:

  • Mueller, S. T. (2009). A Bayesian Recognitional Decision Model. To appear in Journal of Cognitive Engineering and Decision Making.
  • Rodriguez, J., McClelland, G., Grome, B., Crandall, B., & Mueller, S. T. (2008). Modeling human factors involved in chemical/biological warning and reporting. Chemical Biological Defense (CBD) Physical Science and Technology Conference, New Orleans, LA, November 2008. Winner, Best Research: Information Systems Technology
  • Veinott, E. & Mueller, S. T. (2008). Indecision in the pocket: An analysis of the relative success of fast and slow quarterback passing decisions. Northern California Symposium on Statistics and Operations Research in Sports, Menlo Park, CA, Oct., 2008.

Semantic Representation in Episodic Memory:

Measurement and Metrics

Signal Detection Theory

Visual Representations of words and letters

Phonological Similarity and Verbal Working Memory

Detailed Research Information

Detailed information about some of my past and current research is found here. Complete listing is found in my Curriculum Vitae, available in pdf format

Phonological Similarity

In order to measure phonological similarity which affects performance in immediate serial recall, I developed software tools and techniques called PSIMETRICA: Phonological SIMilarity METRIC Analysis, (published in the 2003 article Theoretical implications of articulatory duration, phonological similarity, and phonological complexity on verbal working memory.) This paper describes techniques and methods for measuring phonological similarity and the articulatory duration of words, two important factors in predicting immediate serial recall accuracy. The article has been called "The most careful and sophisticated analysis of the roles of spoken duration and phonological similarity in verbal STM" (Baddeley, 2003). The lisp-code for the PSIMETRICA software is available below, and slides from a talk on PSIMETRICA I have given are available as well.

Alphabetic Letter Similarity

This project investigates the visual representation of letter stimuli, and collects a number of data sets which have reported the visual similarity of the English alphabet, using a number of different methods. These are available in the Letter Similarity Data Set Archive, which are also summarized in a manuscript that is under revision.

Tree Distance Measures

One way to represent the similarity structure of a set of stimuli is using a hierarchical clustering tree. To help evaluate such trees, I have created a set an R (or S-Plus) implementation of the tree distance metrics described by Boorman & Olivier [Boorman, S. A., and Olivier, D. C. (1973) Metrics on Finite Trees. Journal of Mathematical Psychology, 10, 26-59.] The C-distance metrics replicate those found in the above paper; the D-metrics do not--this may be an error in the program or in the original paper. R software is available here.

Decay processes in Verbal Short-term Memory

A widely-held notion about decay in short-term memory conjectures that information disappears about two seconds after it is presented. Yet this conjecture lacks face validity and wide empirical support. These inconsistencies are resolved in an under-review paper entitled "A Note on the two-second decay conjecture in Verbal Working Memory".

Strategies in Immediate serial recall

My dissertation concerned the use of different strategies in immediate serial recall. Results showed that the recency effect, whose magnitude differs greatly across experiments, is modulated by the strategic goals of the research participants. The research on strategy involves both experimental research and computational modeling using the EPIC cognitive architecture. An under-revision manuscript based on this and subsequent work is available here

Area-Based measures in Signal Detection Theory.

The traditional interpretation of A' (pronounced A Prime), a common area-based measure of sensitivity in signal detection, is often wrong. Here is an Excel worksheet that compute A, the correct measure for the average of minimum and maximum permissible ROC curves that can pass through a specific point. An article showing this, published in psychometrika, is available here.

Software

I have created a number of software tools to assist in creating, designing and running experiments. They are described below with instructions on how to acquire them.

PSIMETRICA: Phonological SImilarity METRIC Analysis

This software has been under development and refinement since 1998, and is a series of lisp routines that allows words to be represented according to their phonological content, and then compared and evaluated according to numerous dimensions of phonological similarity. This software is described in the paper above. The software includes definitions and measures on many of the classic data sets used to demonstrate the phonological similarity effect in short-term memory.

Nonword and Word Evaluation and Creation Software

This software, available for Linux and other unix platforms, allows for the creation and evaluation of nonwords according to the conditional probabilities of letters in the written text. Included are utilities to extract these conditional probabilities from text, routines to generate new nonwords, a list of the words from the CMU machine-readable dictionary, pre-computed conditional probability databases based on the dictionary and the Kucera/Francis corpus, and routines to evaluate these nonwords (or actual words) based on these conditional probabilities to determine their 'wordleness', or regularity according to English letter combination probabilities. Additionally, routines are included that allow Levenshtein (edit) distance to be computed between words, as well as an experimental 'Partial' Levenshtein distance. These are used by a final set of utilities that determine the neighborhood distribution of a word or nonword: the distribution of Levenshtein distances between a word and all other words in the dictionary. For those who are unable to run the program, it contains 1000 sample non-words of each length 4 through 10, their wordliness scores and their neighborhood distributions. Additionally, it contains the same values for all the words in the CMU dictionary.

Similarity-Based List Generation and Stimulus Selection

This software contains the seven-dimension similarity matrices for the words of the Toronto Noun Pool. These include five measures of phonological similarity developed using PSIMETRICA: Onset, nucleus, coda, initial phoneme, and stress similarity. Additionally, a measure of semantic similarity based on LSA is included, as well as a measure of graphemic similarity (edit or Levenshtein distance). Together, these dimensions can be used to select from the noun pool subsets of a specified size that are either similar or different on the different dimensions. For example, varying onset similarity while holding nucleus similarity constant. Included are a bunch of pre-selected lists, along with their measures on the relevant dimensions. Many of these lists have the virtue of being similar without being 'obvious'; they may not rhyme or share many phonemes, but they are still similar in subtle and useful ways.

Contact

Email:

smueller at ara dot com
smueller at obereed dot net
Personal Information