Abstracts 2005 - The Psychonomic Society

Friday Noon Posters 2106–2111
& KEITH J. HOLYOAK, UCLA—Relational thinking is a powerful and
uniquely human ability, yet it is fragile, because of its heavy dependence
on working memory (WM) resources. Categories defined by relations,
although essential to such cognitive tasks as analogical reasoning,
problem solving, and language comprehension, may thus be
particularly susceptible to interference by concurrent tasks. This
study explored the effects of a concurrent WM task on relational category
acquisition and postlearning goodness-of-exemplar judgments.
A dual task during category learning resulted in slightly impaired relational
responding, relative to a nondual-task control, but preserved
the basic pattern of postlearning judgments. By contrast, a dual task
during the judgment phase completely disrupted relational responding.
These results suggest that the learning and encoding of relational categories
are relatively robust to WM interference, whereas postlearning
category use that requires the integration of relations is highly sensitive
to such disruption.
(2106)
Ideals and Typicality in Relational Categories: A Double Dissociation.
ANIKET KITTUR, KEITH J. HOLYOAK, & JOHN E. HUM-
MEL, UCLA—Feature typicality is one of the most robust phenomena
observed in studies of category learning. However, human
knowledge includes categories that are highly relational in nature. Although
some studies have indicated that such relational categories also
rely on typicality, others suggest that they may instead be based on
ideals. This study examines the influence of typicality and ideals in
relationally defined categories. The results show that participants base
goodness-of-exemplar judgments on relational ideals, with no effect
of feature-based typicality. In a recognition task, however, judgments
were based solely on the typicality of the exemplars, with no influence
of relations. Together, these data support a dual-process view of categorization,
in which distinct relational and feature-based systems acquire
information in parallel but compete for response control. The
winning system appears to inhibit the loser, even when the winner
does not provide a sufficient basis for a decision and the loser does.
(2107)
Learning Relational Systems of Concepts. CHARLES KEMP, Massachusetts
Institute of Technology, THOMAS L. GRIFFITHS, Brown
University, & JOSHUA B. TENENBAUM, Massachusetts Institute of
Technology—We present a computational framework for learning abstract
relational knowledge, with the aim of explaining how people acquire
intuitive theories of physical, biological, or social systems. The
approach is based on a probabilistic model for relational data with latent
classes. The model simultaneously determines the kinds of entities
that exist in a domain, the number of these latent classes, and the
relations between classes that are possible or likely. This model goes
beyond previous psychological models of category learning that define
categories in terms of their associated attributes, rather than the
relationships those categories participate in with other categories. We
show how this domain-general framework can be applied to model
several specific unsupervised learning tasks, including learning kinship
systems, learning causal theories, and learning to jointly cluster
animal species and their features.
(2108)
Within- Versus Between-Category Knowledge Effects in Observational
Learning. JOHN P. CLAPPER, California State University, San
Bernardino—This research investigates the role of prior knowledge in
observational (unsupervised) category learning. Knowledge is often
assumed to facilitate category learning by increasing the relatedness
or cohesion among features within individual categories. In these experiments,
pairs of categories related to prior knowledge (familiar
themes) were learned better than neutral categories when shown together
in a randomly mixed sequence, but not when shown separately
in a blocked sequence. Thus, thematic knowledge was helpful only
when learners had to explicitly distinguish between separate categories—that
is, in the mixed condition. In addition, pairs of categories
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related to the same theme were learned no better than pairs of neutral
categories; significant knowledge effects were observed only when
the categories were related to different (contrasting) themes. Overall,
thematic relevance appeared to facilitate learning in these experiments
mainly by increasing between-category discriminability, rather than
within-category cohesiveness.
(2109)
Does Feature Overlap and Feature Redundancy Influence the
Level of Abstraction of the First Categories Learned? JADE GI-
RARD & SERGE LAROCHELLE, Université de Montréal—In two
very similar sets of three experiments, participants had to learn a twolevel
hierarchy of artificial categories. In the first experiment of each
set, the categories of both levels were defined by a single necessary
and sufficient attribute. The second and third experiments investigated
whether the two constraints identified by Gosselin and Shyns
(2001) as determining categorization speed—namely, feature overlap
and feature redundancy—also influence the level of abstraction of the
first categories learned. Each of these factors favored either the more
general or the more specific category level. The results were similar
in all experiments of both sets. Learning to which category the stimuli
belonged proceeded equally rapidly at both levels. However, participants
learned more rapidly to reject membership in distant globallevel
categories than in neighboring specific-level categories. These
results suggest that feature overlap and feature redundancy do not determine
the first level of abstraction.
(2110)
Attention Allocation Strategies Early in Category Learning. AARON
B. HOFFMAN & BOB REHDER, New York University—A categorylearning
study using eyetracking was conducted to assess subjects’ attention
allocation strategies early in learning. Rehder and Hoffman (in
press) tested three-dimensional stimuli and found that learners usually
attended all dimensions at the start of learning. Because subsequent
performance implicated both similarity-based and rule-based
learning mechanisms, we concluded that people maximize their information
intake in order to maximize the number of learning mechanisms
that can be brought to bear on a learning problem. In contrast,
the present study tested five-dimensional stimuli and found that the
typical learner attended a subset of the dimensions early in learning.
We interpret this result as indicating that learners restrict their attention
as a result of cognitive capacity limits. In addition, the study investigates
(1) changes in performance as a function of rapid shifts in
attention and (2) the relationship between selective attention assessed
with theoretical model weights and that assessed with eye movements.
(2111)
Memory Systems That Underlie Exemplar Representation: Evidence
for Multiple Systems in Category Learning. MICHAEL A.
ERICKSON & JESSE S. BRENEMAN, University of California,
Riverside—There is substantial debate regarding the memory systems
underlying category learning. Many theorists agree that category
learning utilizes multiple memory systems. For example, Erickson
and Kruschke (1998, 2002) provided evidence that rule-consistent
stimuli are classified using rule-based representation, whereas exceptions
are classified using exemplar representation. Still, the substrates
of these different representational systems remain to be identified.
We present three category-learning experiments that use a
rule-and-exception category structure and dual-task procedures to
provide evidence for stimulus-dependent representation and identify
which memory systems are responsible for the classification of ruleconsistent
and exception training items. Using a response location
switch procedure, Experiment 1 provides evidence that exemplar-based,
but not rule-based, representation relies on procedural memory. Using
a delayed mental rotation task, Experiment 2 provides evidence that
exemplar representation relies on perceptual memory systems. Using
a memory-scanning task, Experiment 3 provides evidence that rule,
but not exemplar, representation relies on verbal memory systems.