Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Modeling the Effects of Emotion on Cognition
Marc Spraragen
Information Sciences Institute
University of Southern California
4676 Admiralty Way, Suite 1001 Office 674
Marina del Rey, CA 90292
sprarage@usc.edu
impact from this work will be particularly important as applied to intelligent gaming environments and characters, as
well as to social and behavioral modeling (Zyda et al. 2010).
My thesis will demonstrate that seemingly disparate experimental results of the effects of emotion on human cognition can be integrated smoothly into a single computational
model based on the theories of emotion and cognition outlined above.
Abstract
Understanding the interaction between emotion and cognitive processes is important for developing architectures for
general intelligence, and vital for the fields of human social and behavioral modeling, game intelligence, and humancomputer interaction. However, relatively little work in AI
has been done on emotion in intelligent architectures, particularly on the effect of emotions on cognitive processes such
as inference, planning and learning, despite research showing
that emotion is a crucial and often beneficial factor in human
decision-making. My work will provide a new emotionalcognitive architecture, focusing on a small set of theories,
mechanisms and algorithms for the modeling of a wide array of emotional effects on human cognitive processes. The
work and its results will be evaluated against current computational models of cognition and emotion, and validated by
results from human cognitive science, neuroscience, and psychology.
Research Plan
Our group has designed an emotional cognitive architecture,
EmoCog (Lin et al. 2011), which will form the basis for my
work. My research goal is to design, implement, evaluate,
validate, and report on the architecture and mechanisms that
realistically model the effects of emotion on the EmoCog
cognitive processes and their combination and interplay. My
thesis will encompass the modeling of emotional effects on
cognitive processes, while another concurrent thesis in our
group will model the generation of emotions in EmoCog.
This combined work will complete a full, realistic computational model of emotional-cognitive integration.
Plan subgoals: 1) Design and implement mechanisms
to model emotional effects on the following cognitive processes in EmoCog: recall and inference. 2) Design and implement mechanisms to emotional effects on the following
cognitive processes in EmoCog which use recall and inference: search, decision, appraisal, planning, and learning. 3)
Evaluate EmoCog emotional-cognitive phenomena expression in terms of scope, depth, and clarity with respect to
other current models of emotional effects on cognition. 4)
Validate EmoCog emotional-cognitive phenomena expression against experimental data from human studies of emotion and cognition. 5) Report in thesis form on the above
subgoals. 6) Integrate with the other EmoCog work in our
group: emotion generation processes. This will begin with
the establishment of a feedback loop between cognition and
emotion. For instance, success or failure of a plan step may
alter emotional state, and this state may in turn affect the
formation of future plan cases. 7) Apply EmoCog to the
creation of emotion-enabled game engines and virtual training environments, providing more realistic and valuable interactions with virtual humans than are currently available.
8) Decouple emotional effects mechanisms from EmoCog
Introduction
The tightly linked interaction between human emotion and
cognition has been studied from ancient times, emotion at
first being cast as simply a distraction from logical thought.
In recent years, however, research has shown that emotional
cues also act as a heuristic in decision-making tasks (Damasio 1994), which proves particularly helpful when the need
for a decision is imminent (Finucane et al. 2000). This
research, among other studies, has caused an increase in
the computational modeling of emotion and cognition, but
typically to model a single result, phenomenon, or theory.
This has led to incomplete, competing models which leave
aside the question of a complete integration of emotion and
cognition. I believe that combining the following complementary theories from cognitive science, neuroscience, and
psychology can inform a more integrated modeling: emotion as a signal to refocus cognitive attention (Simon 1967),
and emotion establishing memory-based biases and heuristics (Bower 1981; Damasio 1994). My hypothesis is that
incorporating these theories into any well-defined cognitive
architecture will allow the expression of a wide and openended set of results from human emotion studies. I feel the
c 2011, Association for the Advancement of Artificial
Copyright Intelligence (www.aaai.org). All rights reserved.
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and provide them as a toolkit for use in other emotionalcognitive architectures.
fects modeled in EmoCog. For instance, planning in a positive/negative mood causes plan steps to seem less or more
costly, respectively (Phillips, Smith, and Gilhooly 2002),
which was modeled simply by weighting the algorithm’s
heuristic function. This leads to, in a positive mood, acceptance of plans which prove more costly than predicted,
and conversely to more careful planning in a negative mood.
Progress
The basic design of EmoCog (Lin et al. 2011) is complete,
and incorporates relevant architecture and mechanisms for
my work. The most important structure is the memory network, featuring multiple node types, e.g. ”physical object”
or ”plan step,” and multiple associative and semantic link
types, such as ”causality.” The network is acted upon by interlocking cognitive process modules, for instance an inference engine and automatic planner. These processes are invoked by a meta-management module which incorporates an
attention focus on a portion of the network. The cognitive
processes are directly influenced by the emotion-based attributes (e.g., ”arousal,” ”valence”) of the memory network’s
individual nodes and links, giving rise to the phenomena that
I am modeling.
We have begun the emotional effects design using a mood
setting as representative of emotional state, defining mood as
an aggregate of emotion-based attributes across the memory
network. Mood-congruent recall (Bower 1981), and both
the impairment and enhancement of planning under different
moods (Mayer 1986; Phillips, Smith, and Gilhooly 2002),
can be implemented in EmoCog using this approach. I intend to branch out from mood to model the effects of different and often concurrent emotions (e.g., fear, joy, anger) on
cognitive processes.
Current implementation of the EmoCog architecture and
process has yielded a prototype study of mood-based effects
on an automatic planner. I was able to reproduce the result
that positive mood impairs planning, while negative mood
may enhance planning. The computational aspect of this
study uses a weighted A* algorithm, which is analogous in
several details to cognitive science findings in terms of ef-
References
Bower, G. 1981. Mood and Memory. American psychologist.
Damasio, A. 1994. Descartes’ Error: Emotion, Reason, and
the Human Brain. Putnam Adult.
Finucane, M. L.; Alhakami, A.; Slovic, P.; and Johnson,
S. M. 2000. The affect heuristic in judgments of risks and
benefits. Journal of Behavioral Decision Making 13(1):1–
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Lin, J.; Spraragen, M.; Blythe, J.; and Zyda, M. 2011.
EmoCog : Computational Integration of Emotion and Cognitive Architecture. In Proceedings of the Twenty-Fourth
FLAIRS Conference.
Mayer, J. D. 1986. How Mood Influences Cognition. Chichester, West Sussex: Ellis Horwood Limited. 290–314.
Phillips, L. H.; Smith, L.; and Gilhooly, K. J. 2002. The effects of adult aging and induced positive and negative mood
on planning. Emotion 2(3):263–272.
Simon, H. 1967. Motivational and Emotional Controls of
Cognition. Psychological Review.
Zyda, M.; Spraragen, M.; Ranganathan, B.; Arnason, B.;
and Landwehr, P. 2010. Designing a Massively Multiplayer
Online Game / Research Testbed Featuring AI-Driven NPC
Communities. In Artificial Intelligence in Digital Entertainment.
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