S1 (FriAM 1-65) - The Psychonomic Society

Saturday Noon Posters 4061–4068
(4061)
Reasoning Over Isomorphic and Nonisomorphic Logic Structures.
JOHN BEST, Eastern Illinois University—A variety of dual-process
theories claim that reasoning is usually accomplished by a heuristic
system that may at times be overridden by a deductively competent analytic
system. One alternative states that all reasoning is accomplished
by one system whose representations are strengthened with experience.
In this study, subjects solved 16 conditional reasoning problems
and then 3 “logic table” problems that either were isomorphs of each
other, or did not share an underlying logic structure. Crossed with this
condition, half of the participants were given instructions and an answer
grid that emphasized the role of the analytic system. Contrary to
the predictions of dual-process but consistent with single-process alternatives,
the subjects in each condition solved the third logic table
problem much more quickly than they did the first (mean reduction =
38%), with no corresponding fall-off in deductive accuracy for any of
the four groups.
(4062)
Cross-Cultural Cognitive Differences in Analogical Reasoning: A
Computational Account. ROBERT G. MORRISON, Northwestern
University, LEONIDAS A. A. DOUMAS, Indiana University, &
LINDSEY E. RICHLAND, University of California, Irvine—We previously
reported that when children can identify the critical relations
in a scene analogy problem, development of their ability to reason
analogically interacts with both relational complexity and featural distraction
(Richland, Morrison, & Holyoak, 2006). Here, we report that
unlike 3- to 4-year-old American children, Hong Kong children are sensitive
to featural distraction, but not to relational complexity. Differences
in the linguistic structure of Chinese and English task versions
suggest that this result is driven by variations in children’s knowledge
representations. Performance differences are eliminated when Hong
Kong children perform under a working memory dual task. We present
computer simulations in LISA (Hummel & Holyoak, 1997,
2003) explaining these results as trade-offs between inhibition in
working memory and the sophistication of relational knowledge representations.
Specifically, we show that changes in inhibition can best
explain the overall developmental progression; however, differences
in knowledge representation best account for cultural differences.
(4063)
Can Domain Knowledge Improve Causal Reasoning? MICHELLE R.
ELLEFSON & CHRISTIAN D. SCHUNN, University of Pittsburgh—
Grasping scientific phenomena includes more than the mere memorization
of scientific facts. It requires the creation of appropriate models
of the relations among important aspects of scientific phenomena.
Many of these relations are causal. The present study explored students’
ability to reason about simple and complex causes, both within
and outside of the scientific domain that they were studying. The participants
were urban high school students in biology, chemistry, and
physics courses (n = 99). Overall, the results suggest that reasoning
about interactions among two or more causal variables is more difficult
than reasoning about the main effect of one causal variable. Students
did not achieve higher accuracy for reasoning questions in the
domain that they were studying. Therefore, because the complexity of
the questions influenced performance more than domain knowledge
did, the results suggest that domain knowledge is neither necessary
nor sufficient for complex reasoning in introductory science courses.
(4064)
Thinking Dispositions, Metacognition, and Syllogistic Reasoning.
JAMIE A. PROWSE TURNER, ERIN L. BEATTY, & VALERIE A.
THOMPSON, University of Saskatchewan—We studied the relationship
between three measures of thinking dispositions and (1) metacognitive
ability and (2) reasoning performance on a syllogistic reasoning
task. The measures of thinking dispositions included the general
decision-making style instrument (GDMS; Scott & Bruce, 1995), the
114
actively open-minded thinking scale (AOT; Stanovich & West, in
press), and the cognitive reflection test (CRT; Frederick, 2005). We
measured metacognitive accuracy in a number of ways, including asking
for item-by-item estimates of performance and several posttask estimates
of performance. Regression equations established that different
sets of variables predicted accuracy on the reasoning task, the
item-by-item estimates, and the posttask estimates of performance.
These findings are consistent with the hypothesis that item-by-item
estimates and posttask estimates tap different processes (Stankov,
2000). We plan a follow-up study that includes measures of cognitive
ability and performance on belief-biased materials.
(4066)
Premise Versus Conclusion Believability in Categorical Reasoning.
SHARON LEE ARMSTRONG, La Salle University—Previous studies
have shown that neutral content produces superior performance over
emotional or abstract content in evaluating conditional arguments and
categorical syllogisms. Furthermore, consistent with theories of motivated
reasoning, it is often suggested that it is prior beliefs about
conclusions that drive validity judgments. However, my previous research
found that it was the believability of the first premise, rather
than the believability of the conclusion, that played the primary role
in predicting correct validity judgments in conditional reasoning
tasks. To determine whether this finding is peculiar to conditional reasoning,
the present study investigated the role of the believability of
different argument statements in the evaluation of categorical syllogisms.
Comparisons of the patterns of validity judgments across the
two types of logic problems are discussed with regard to current theories
of reasoning.
(4067)
The Acquisition of Causal Variables. NOAH D. GOODMAN &
JOSHUA B. TENENBAUM, MIT—Standard theories of causal learning
identify causal relations between preexisting variables, such as the
nodes in a Bayesian network. But how are these variables acquired?
How does the continuous flux of perception become symbolically
coded in variables that support appropriate causal relations? We argue
that whereas some variables are innate, others must be learned, in
ways that depend on both bottom-up perceptual coherence and the
functional role that variables play in the learner’s causal model of the
world. We present a Bayesian framework for explaining how causal
variables and relations can be learned simultaneously, in the form of
a grounded causal model: a system of event concepts connected by
causal relations and explicitly grounded in perception. We also present
empirical studies showing that participants can learn grounded
causal models from dynamic perceptual evidence. Several factors
modulate learning difficulty as predicted by the Bayesian model, including
perceptual distinctiveness and causal contrast.
(4068)
Similarity Falls Out of a Model of Analogy. ERIC G. TAYLOR &
JOHN E. HUMMEL, University of Illinois, Urbana-Champaign (sponsored
by John E. Hummel)—The concept of “similarity” plays a central
role in cognition. Existing models of similarity capture either “featural”
or “structural” aspects of similarity, but typically not both. We
attempted to capture both by fitting similarity judgment data with the
LISA model of relational reasoning (Hummel & Holyoak, 1997, 2003).
LISA simulates both structural effects on similarity, such as violations
of the metric axioms and the dominance of alignable over nonalignable
differences in similarity judgments, and the relation between structural
and featural aspects as embodied by the combined effects of MIPs and
MOPs (featural matches “in” or “out of place”; Goldstone, 1994) on
similarity judgments. LISA’s ability to capture both featural and structural
aspects of similarity derives from its knowledge representations,
which are simultaneously symbolic, by virtue of dynamically binding
relational roles to their arguments, and semantically rich, by virtue of
their distributed representations of those roles and arguments.