S1 (FriAM 1-65) - The Psychonomic Society

Posters 2041–2047 Friday Noon
portantly, this facilitation could only have occurred if people noticed
the shared label, an example of category discovery via “similarity detection”
as opposed to contrast or “difference detection.” This ability
might compensate for the limitations of a purely failure-driven
approach.
(2041)
Motivational Focus Interacts With Reward Structure in Category
Learning. RUBY T. NADLER, JOHN PAUL MINDA, & LILY PEI-
SHIUAN LIN, University of Western Ontario—Recent research suggests
that categories are learned by multiple brain systems, and that
situations that allow for greater cognitive flexibility will lead to better
performance on rule-described categories that depend on an explicit
system. We examined the effects of motivational focus (e.g.,
promotion or prevention) and task reward structure (e.g., gain or loss)
on participants’ ability to learn suboptimal or optimal rules. Two conditions
featured a motivational focus that matched the reward structure
of the task (promotion/gain and prevention/loss). Two conditions
featured a motivation structure that did not match the reward structure
of the task (promotion/loss and prevention/gain). We found that
performance was generally better in the matching conditions than in
the mismatching conditions and that participants in the matching conditions
were more likely to learn complex, optimal rules. These results
suggest that the matching conditions allow for greater cognitive flexibility
and support a multiple-systems account of category learning.
(2042)
The Incidental Learning of Within-Category Attribute Correlations
in a One-Attribute Rule Visual Search Classification Paradigm.
GUY L. LACROIX, Carleton University, GYSLAIN GIGUÈRE, University
of Quebec, Montreal, GLEN HOWELL, Carleton University,
& SERGE LAROCHELLE, University of Montreal—Giguère, Lacroix,
and Larochelle (2007) showed that participants could incidentally learn
within-category attribute correlations in a one-attribute rule classification
task. Nevertheless, learning was limited. In this study, participants
categorized stimuli in a one-attribute rule visual search classification
paradigm. We hypothesized that the search component would
increase learning. Two experiments were conducted. Both involved a
classification training phase of 640 trials. The stimuli were six-shape
displays that included a rule attribute and five diagnostic attributes.
In Experiment 1, the rule attribute (and up to two other attributes)
were removed at transfer. The results showed that several attributes
(color, texture, and size) of varying diagnosticity were used to correctly
classify the stimuli. In Experiment 2, attribute values were
changed at transfer. Slower RTs were obtained when attribute values
from conflicting categories were used. These experiments provide evidence
that within-category attribute correlations can be learned in a
classification task without inference learning instructions.
(2043)
New Category Representations Are Biased by Implicit Perceptions.
MICHAEL E. ROBERTS & ROBERT L. GOLDSTONE, Indiana
University—Our study examines the effects of implicit perceptual representations
when a participant first learns a category. Participants sequentially
viewed pictures of two category members for familiar categories
(e.g., dog, cow, airplane, etc.) and novel categories (artificial
stimuli), and the second, primed member was followed by either a relevant
or irrelevant story. After training on all categories in a set, participants
completed a delayed matching test that showed a category
label and then a picture of a category member or a member from another
category. Participants responded significantly faster and more
accurately for trials involving familiar categories, and we attribute this
enhancement to well-learned category representations. However, for
the novel category set, participants responded significantly faster to
primed members that had been followed by relevant stories. This result
suggests that participants perceptually invoked the primed member
representation while reading the story, and that these automatic invocations
biased the new category representation.
75
(2044)
Contribution of the Declarative Memory System to Probabilistic
Category Learning. JULIE SPICER, Columbia University, MURRAY
GROSSMAN, University of Pennsylvania, & EDWARD E. SMITH,
Columbia University—The multiple memory systems approach posits
that different forms of knowledge are acquired and stored in qualitatively
different ways. Evidence has suggested that probabilistic category
learning, as assessed with the weather prediction (WP) task, can
be supported by two systems: a system that acquires nondeclarative
habit knowledge and a system that acquires declarative flexible knowledge.
One focus of recent research has been to characterize the mechanisms
through which each system contributes to behavior as information
is acquired in parallel. In the present study, we have further
characterized the contribution of the declarative system to WP learning.
We show that individual differences in working memory relate
positively to categorization accuracy and that patients with Alzheimer’s
Disease do not reach an above-chance level of categorization accuracy,
supporting a strong role for declarative learning in the WP task. Implications
for the role of nondeclarative learning are also discussed.
(2045)
Category Learning, Binding, and the Medial Temporal Lobe: Evidence
From Early Alzheimer’s Patients. JARED X. VAN SNELLEN-
BERG & JANET METCALFE, Columbia University, MURRAY
GROSSMAN, University of Pennsylvania, & EDWARD E. SMITH,
Columbia University (sponsored by Edward E. Smith)—Despite considerable
impairments in explicit memory, patients with medial temporal
lobe (MTL) lesions and patients with Alzheimer’s disease (AD)
have been shown to have intact learning in a number of implicit learning
paradigms, including category learning. Some recent evidence,
however, suggests that “explicitness” per se may not determine the integrity
of learning in individuals with a compromised MTL. One alternative
is that a “binding” process, in which distinct elements of a
stimulus or event become associated in memory, is critically mediated
by MTL. In a test of this hypothesis, we showed that AD patients are
at chance performance, and significantly worse than control participants,
on an implicit two-category learning task that requires binding
for successful performance.
(2046)
Modeling Process Differences in Implicit and Explicit Category
Learning: A Symbolic-Connectionist Approach. LEONIDAS A. A.
DOUMAS, Indiana University, & ROBERT G. MORRISON, Northwestern
University—Much experimental evidence suggests both implicit
(i.e., feature-based) and explicit (i.e., rule-based) mechanisms
for learning categories, and these systems may compete and/or interact
in different circumstances. However, most computational attempts
to capture these processes typically rely on very different representational
assumptions. For instance, models of implicit category learning
are typically based on connectionist architectures consisting of
networks of distributed units. In contrast, models of explicit category
learning typically employ some type of symbolic architecture allowing
for the flexibility characteristic of categories learned via rules. We
present a model of category learning, DORA, that uses different learning
mechanisms on a single distributed representation, allowing us to
account for both implicit and explicit learning from a visual category
learning task (Maddox, Ashby, & Bohil, 2003). We also successfully
simulate differential working memory effects on implicit and explicit
versions of this task as reported by Zeithamova and Maddox (2006).
• ANIMAL COGNITION •
(2047)
Rats’ Memory for Event Duration: Effect of Postsample Cues to
Remember and Forget. ANGELO SANTI, NEIL MCMILLAN, &
PATRICK VAN ROOYEN, Wilfrid Laurier University—Rats were
trained to discriminate 2 sec versus 8 sec of magazine light illumination
by responding to either a stationary lever or a moving lever. Dur-