S1 (FriAM 1-65) - The Psychonomic Society

Thursday Evening Posters 1117–1120
(where color is a cue to identity; e.g., fire engine or banana). The main
findings were that transformation had no influence on repetition priming
(i.e., priming was equivalent for transformed and untransformed
objects), and that priming differed according to whether objects were
correctly or incorrectly colored and whether the object was represented
as a picture or object name. For correctly colored objects priming
was observed only for pictures, whereas for incorrectly colored
objects priming was observed for both pictures and object names. We
discuss these findings in light of recent accounts of priming in binary
decision tasks.
(1117)
Attention and Search Difficulty in Contextual Cuing: Implications
for the Explicit–Implicit Memory Distinction. HEATHER H.
MCINTYRE, Georgia Institute of Technology (sponsored by Daniel H.
Spieler)—The role of attention in implicit learning was recently clarified
in the contextual cuing paradigm. Specifically, Chun and Jiang
(2001) showed that individuals become increasingly faster at locating
a target within a search array when the spatial configuration of the target
and subset of distractors sharing features with the target (i.e.,
color) are kept constant across multiple blocks, with no advantage to
keeping the irrelevant distractors constant. Furthermore, the magnitude
of contextual cuing is purported to be directly proportionate to
the difficulty of the search as a result of an increase in perceptual load
and a more precise focus of attention on relevant search items. However,
data from 80 participants suggest that the extent to which contextual
cuing emerges in increasingly difficult search tasks is a function
of executive attention and working memory span. Implications
for the explicit–implicit distinction in memory systems are discussed.
(1118)
Recognition and Categorization After Exposure to Equally and Unequally
Distributed Training Stimuli, in AGL. FENNA H. POLETIEK
& LARA WARMELINK, Leiden University, & NICK CHATER, University
College London—Poletiek and Chater (2006) suggest that frequency
distribution of a training sample of exemplars affects learning
in artificial grammar learning. Exemplars with a high probability to
be produced by the grammar—i.e., highly frequent in a random output—presumably
are more typical for the structure than low probability
exemplars. Inequalities in the distribution provide cues to the
learner about differences between exemplars with regard to their prototypicality
for the grammar. Oppositely, however, recollecting as
many items as possible from a training sample should benefit from
presenting each item equally often, as predicted by the power law of
practice. This interaction between task goal and distributional char-
68
acteristics of exemplars of a grammar was tested experimentally. We
explore first the implications of this effect for understanding the influence
of distributional aspects of the input sample on grammar induction
and memory processes. Additionally, the relation between
recognition and classification processes is discussed.
(1119)
Associative Processes in Probabilistic Sequence Learning. JENNIFER
P. PROVYN & MARC W. HOWARD, Syracuse University (sponsored
by Marc W. Howard)—Temporally defined associations in
episodic recall tasks exhibit a contiguity effect in both the forward
and backward directions. Many researchers believe that episodic and
implicit memory depend on different brain systems. We systematically
traced out the functional form of temporally defined associations
in a serial reaction time task. Stimuli were sampled probabilistically
from a ring. On 70% of the trials, the stimulus presented was
one step forward in the ring, corresponding to lag+1. Remote lags
were uniformly sampled on 30% of the trials. We examined RT as a
function of lag. Surprisingly, we observed graded contiguity effects
in both forward and backward directions, as well as an associative
asymmetry favoring forward associations. The striking similarity between
the forms of temporally defined associations in explicit and
implicit tasks suggests either that episodic recall and probabilistic sequence
learning share an overlapping associative mechanism or that
similar computational principles underlie temporally defined associations
across domains.
(1120)
Statistical Learning Set: Emerging Biases in the Learning of an
Artificial Grammar. RICK DALE, University of Memphis, &
CHRISTOPHER M. CONWAY, Indiana University—Over a half-century
ago, Harlow’s experiments on “learning to learn” in macaques inspired
awe, generated a rich experimental literature, then moved into
relative obscurity. Harlow’s original notions of learning set are closely
related to recent investigations of learning biases in human statistical
learning (e.g., Lany & Gomez, 2004; Thiessen & Saffran, 2007), but
the link between these two research areas has not been previously explored
in detail. In a series of experiments, we demonstrate that successive
learning experiences by humans in an implicit statistical learning
paradigm—requiring participants to map sequences of nonsense
syllables to an artificial visual referent system—dramatically affect
their subsequent acquisition of novel patterns in the same learning environment.
We relate these findings to Harlow’s original conceptions
of learning set, and extend them to emergent learning biases in language
acquisition.