[ACT-R-users] Research Positions Available with AFRL's PALM Team

[Gluck, Kevin A Civ USAF AFMC 711 HPW/RHAC]

With apologies and respect to our valued colleagues of other
nationalities, only U.S. citizens and permanent legal residents of the
United States are eligible for these positions.

 

We have a variety of research positions available for talented
cognitive, computational, and computer scientists interested in working
with the U.S. Air Force Research Laboratory's Performance and Learning
Models (PALM) Team on basic and applied cognitive science research.
Full-time, paid positions range from undergraduate and graduate-level
internships and research assistantships, to post-doctoral research
appointments, to visiting faculty appointments.  Salaries are
commensurate with experience.  

 

The PALM research portfolio continues to expand and evolve.  We use a
combination of empirical human-subjects studies and formal, rigorous,
computational and mathematical modeling and simulation methods to
understand, replicate, and predict human performance and learning, and
to create new cognitive science-based technology options.  Currently
there are research efforts underway in all of the following areas (with
associated PIs):

 

Basic research:

  - large scale cognitive modeling (Scott Douglass)

  - representations and processes of spatial visualization (Glenn
Gunzelmann)

  - modeling the relationships between alertness and cognitive processes
(Glenn Gunzelmann)

  - persistent, generative, situated agents (Christopher Myers)

 

Applied research:

  - natural language comprehension and generation (Jerry Ball)

  - robust decision making in integrated human-machine systems (Kevin
Gluck)

  - model exploration and optimization using distributed and high
performance computing (Jack Harris)

  - mathematical models for performance prediction and prescription
(Tiffany Jastrzembski)

 

Brief elaborations of each area can be found below.  

 

Anyone interested in working with us on one or more of the research
efforts listed above is encouraged to contact the PI for that particular
research area as soon as possible.  Email addresses are
first.last<at>mesa.afmc.af.mil

 

Natural language comprehension and generation (Jerry Ball)

The focus of the natural language research is development of
computational cognitive models which are both functional and cognitively
plausible. There may be short-term costs associated with adoption of
cognitive constraints, but we expect, and have to some extent already
realized, longer-term benefits. We focus on communication via text
messaging, avoiding complex challenges of speech recognition, but make
no assumptions about the grammatical quality of messages and put no
arbitrary limits on their linguistic range. Our current project, the
Synthetic Teammate, is aimed at development of a cognitive agent capable
of functioning as the pilot of a simulated UAV. The cognitive agent
interacts with two teammates-a navigator, and a photographer-in order to
take pictures of ground targets over the course of a simulated 40 minute
reconnaissance mission. Lightweight agent versions of the navigator and
photographer currently support development, but the cognitive agent will
eventually interact with human teammates in an empirical study. 

 

Large scale cognitive modeling (Scott Douglass)

Explores how paradigms in software engineering called "meta-modeling"
and "model-integrated computing" can be used to produce domain-specific
modeling languages tailored to the specification and integration needs
of cognitive modelers.  These new formalisms will help cognitive
modelers increase the scale of their efforts by allowing them to specify
self-modifying models at high levels of abstraction.  These new
formalisms will share a foundation in general systems theory and will
therefore help their users: (a) compose and compare models; and (b)
integrate models into task environments and simulations that subscribe
to the same formal foundation.  This research reciprocates value back to
software engineering by demonstrating how specifications of cognitive
processes can be formally captured and exploited during the design of
human/machine systems.

 

Representations and processes of spatial visualization (Glenn
Gunzelmann)

Human spatial competence is applied ubiquitously as individuals encode
information about the location of objects in the world, plan routes and
navigate through the environment, reason about spatial relationships, or
make decisions in environments that are rich with spatial information.
Despite the criticality of spatial information processing in human
cognitive functioning, detailed mechanistic theories that can be used to
explain and predict behavior are lacking. Our research in this area is
targeted at producing a mechanistic, quantitative theory of human
spatial competence, focused on representing and processing visuospatial
knowledge. This research involves rigorous empirical data collection, to
understand human performance in this area and to support validation of
quantitative theoretical accounts instantiated as mathematical and
computational models.

 

Modeling the relationships between alertness and cognitive processes
(Glenn Gunzelmann)

Understanding the functioning of the human cognitive system is as
important as understanding the human physiological system in operational
environments. As an example, research on fatigue has uncovered
neurophysiological changes in the human brain resulting from sleep loss,
circadian desynchrony, or time on task. In addition, corresponding
deficits in human performance on a variety of tasks have been documented
in the empirical literature. What is unknown, however, are the
mechanisms through which physiological changes impact cognitive
performance. This line of research is aimed at understanding how
cognitive processing changes as a result of fatigue, bridging the gap
between mathematical models that capture the dynamics of overall change
in neurobehavior performance and in situ performance on particular
tasks.

 

Robust decision making in integrated human-machine systems (Kevin Gluck)

It is increasingly clear that the traditional boundaries between human
and machine are disappearing.  The future vision of integrated
human-machine decision systems is already upon us.  Hence, there is
escalating pressure on AFRL researchers to better understand the basic
science of mixed human - machine decision making, and make use of this
science to develop increasingly robust, automated knowledge-extraction
tools and intelligent machine-based decision aids that optimize, speed
up, and adaptively adjust inference, prediction, and decision processes.
This is a new-start research area in which we are interested in new
models and methods for assuring high quality decision processes and
outcomes, especially in complex and uncertain dynamic environments. 

 

Model exploration and optimization using distributed and high
performance computing (Jack Harris)

Computational complexity grows quickly with increases in the granularity
of models, the fidelity of the models' operating environment, and the
time scales across which these models are used in simulations.  We must
find ways to deal with the computational demands of large-scale basic
and applied cognitive modeling.  One approach is to acquire more
computational horsepower, such as through high performance computing
(HPC) clusters, volunteer computing, or cloud computing.  Another
approach is to reduce the size of the required computational space
through predictive analytics and parallelized exploration and
optimization algorithms.  Our view is that it is only through the
combined use of these approaches that we can meet our far-term
scientific and technological objectives, both as a research team and as
a broader research community.

 

Mathematical models for performance prediction and prescription (Tiffany
Jastrzembski)

Training people to stable levels of high performance in specialized
skills requires a great deal of investment in both time and capital, and
this is particularly true in highly complex domains like military
operations.  Given the length, complexity, resource limitations, and
cost of warfighter training, it is critical to ensure that the timing
and frequency of training events are tailored to the needs of the
learner to maximize learning and performance effectiveness.  This
research identifies the mathematical regularities of human learning and
forgetting as a function of the temporal distribution of training in
order to (1) validly, precisely, and quantitatively predict future
levels of learner performance, and to (2) prescribe more optimal
training schedules to enhance retention, achieve more effective
learning, and streamline training to the needs of the individual.

 

Persistent, generative, situated agents (Christopher Myers)

The typical approach to computational cognitive modeling is to isolate a
process of interest and capture enough detail within the model to
account for a set of data obtained from humans performing within a
particular task environment. The promise of this approach is that
veridical models of cognitive processes will eventually be integrated to
produce more complex processes. While this approach has proven
beneficial to isolating, studying, and understanding arguably distinct
cognitive processes, the resulting models are typically brittle,
engineered, short-lived and tailored to specific experimental psychology
paradigms. These characteristics are limitations to the development of
models which require persisting over long periods of time and generating
their own knowledge. This research is focused on identifying,
developing, and integrating process models of cognitive capacities to
enable persistent and generative models.

 

------------------------------------------------------- 

KEVIN GLUCK, PhD 

Senior Research Psychologist 

S&T Advisor, Cognitive Models and Agents Branch 

Air Force Research Laboratory 

6030 S. Kent St 

Mesa, AZ  85212-6061 

P: 480-988-6561 x-677; DSN 474-6677 

F: 480-988-2230; DSN 474-6688 

C: 480-229-4569 

  

"The true line is not between 'hard' natural science and 'soft' social
sciences, but between precise science limited to highly abstract and
simple phenomena in the laboratory and inexact science and technology
dealing with complex problems in the real world."

                       - Herb Simon, Models of My Life 

 

 

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