Abstracts 2005 - The Psychonomic Society
Abstracts 2005 - The Psychonomic Society
Abstracts 2005 - The Psychonomic Society
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Thursday Evening Posters 1008–1013<br />
stimuli. Participants performed object-based and perspective-based<br />
judgments for rooms and bodies in different combinations of reference<br />
frame congruence, defined by the position of the person and the<br />
computer monitor. Rather than showing one dominant reference<br />
frame, results revealed that upright coincided with any two congruent<br />
reference frames, suggesting flexibility in reference frame use for spatial<br />
problem solving.<br />
(1008)<br />
Perspective and Instruction Effects on Mentally Representing a<br />
Virtual Environment. HOLLY A. TAYLOR, Tufts University, &<br />
FRANCESCA PAZZAGLIA, University of Padua (sponsored by<br />
Holly A. Taylor)—How do instructions to focus on particular aspects<br />
of an environment and learning from different spatial perspectives affect<br />
one’s cognitive map? Participants learned an urban virtual environment<br />
from either a survey or a route perspective and were instructed<br />
to focus either on landmarks or on intersections. <strong>The</strong> route<br />
group learned the environment by watching a virtual person walking<br />
through it, whereas the survey group learned by watching a dot moving<br />
through a map. While learning, participants were stopped at critical<br />
points, and their attention was focused on either landmarks or intersections.<br />
After learning, all participants performed several spatial<br />
tasks: navigation, map drawing, and pointing. Individual differences<br />
in the cognitive style of spatial representation were recorded. Results<br />
showed that spatial perspective, instructions, and individual differences<br />
in spatial representations interacted to affect performance.<br />
<strong>The</strong>se results will be discussed in the context of spatial mental models<br />
and their influences.<br />
(1009)<br />
Encoding Direction During the Processing of Proximity Terms.<br />
AARON L. ASHLEY & LAURA A. CARLSON, University of Notre<br />
Dame—A target’s location may be described by spatially relating it to<br />
a reference object. Different types of spatial terms emphasize different<br />
aspects of this spatial relation, with projective terms (e.g.,<br />
“above”) explicitly conveying direction (but not distance), and proximity<br />
terms (e.g., “near”) explicitly conveying distance (but not direction).<br />
It has been suggested that only aspects of the relation that are<br />
explicitly conveyed by the spatial term are encoded when interpreting<br />
a spatial description. However, recent research has demonstrated that<br />
distance information is encoded during the processing of projective<br />
spatial relations, because such information is important for finding<br />
the target. In the present research, we demonstrate that direction information<br />
is similarly encoded during the processing of proximity<br />
terms that convey a close distance (“near,” “approach”), but not for<br />
those conveying a far distance (“far,” “avoid”), in support of the idea<br />
that multiple aspects of the spatial relation are encoded when they assist<br />
locating the target.<br />
(1010)<br />
<strong>The</strong> Effect of Recipient Perspective on Direction-Giving Processes.<br />
ALYCIA M. HUND & KIMBERLY M. HOPKINS, Illinois State University—Getting<br />
to unfamiliar destinations often involves relying on<br />
directions from others. <strong>The</strong>se directions contain several cues, including<br />
landmarks, streets, distances, and turns. <strong>The</strong> goal of this project<br />
was to understand the cues people use when giving directions for navigation.<br />
In particular, does the information provided depend on<br />
whether a route or survey perspective is employed? Sixty-four participants<br />
provided directions to help a fictitious recipient get from starting<br />
locations to destinations in a fictitious model town. On half of the<br />
trials, the recipient was driving in the town (a route perspective). On<br />
the remaining trials, the recipient was looking at a map of the town (a<br />
survey perspective). As predicted, people included significantly more<br />
landmarks and left/right descriptions when addressing a recipient driving<br />
in the town. In contrast, they used significantly more cardinal descriptors<br />
when addressing a recipient looking at a map. <strong>The</strong>se findings<br />
suggest that perspective affects direction-giving processes.<br />
53<br />
• COGNITIVE SKILL ACQUISITION •<br />
(1011)<br />
<strong>The</strong> Separation of Words and Rules: Implicit Learning of Abstract<br />
Rules for Word Order. ANDREA P. FRANCIS, Michigan<br />
State University, GWEN L. SCHMIDT & BENJAMIN A. CLEGG,<br />
Colorado State University—Artificial grammar learning studies have<br />
implied that people can learn grammars implicitly. Two studies using<br />
word strings, rather than traditional letter strings, examined the incidental<br />
learning of three-word orders. English speakers practiced unfamiliar<br />
strings ordered as either “verb noun noun” or “noun noun<br />
verb.” Despite possible prior associations between the words and the<br />
“noun verb noun” order, self-timed reading speed decreased following<br />
exposure to the unfamiliar rule. This pattern generalized beyond<br />
the specific instances encountered during practice, suggesting that<br />
learning of the structure was abstract. A second experiment found<br />
learning when nouns were replaced with pseudowords, showing that<br />
learning was possible in the absence of preexisting meaning and<br />
meaningful relationships between items. <strong>The</strong>se findings suggest that<br />
word orders can be learned implicitly and that words and orders can<br />
be dissociated during learning. <strong>The</strong>se results extend artificial grammar<br />
learning to more ‘language-like’ materials and are consistent with<br />
accounts emerging from structural priming research.<br />
(1012)<br />
Implicit Learning of Artificial Grammars: Under What Conditions?<br />
ESTHER VAN DEN BOS & FENNA H. POLETIEK, Leiden<br />
University—Numerous artificial grammar learning (AGL) experiments<br />
have shown that memorizing grammatical letter strings enables<br />
participants to subsequently discriminate between grammatical and<br />
ungrammatical strings at least as well as does looking for underlying<br />
rules. <strong>The</strong> present study examined the circumstances triggering implicit<br />
learning. We suggest that implicit learning occurs during memorizing,<br />
because structure knowledge facilitates this task. In general,<br />
we propose that implicit learning occurs whenever structure knowledge<br />
contributes to fulfilling a person’s current goal. This goal directedness<br />
hypothesis was tested in an AGL study. Adults and children<br />
performed an induction task to which knowledge of the grammar<br />
could be more or less functional. Both groups showed the same pattern<br />
of performance on a subsequent grammaticality judgment test:<br />
Functional conditions (identifying semantic referents, memorizing)<br />
outperformed nonfunctional conditions (identifying different semantic<br />
referents, rating likeability, computing values associated with sentences).<br />
<strong>The</strong>se results suggest that implicit learning is goal directed,<br />
occurring whenever structure knowledge facilitates one’s current task.<br />
(1013)<br />
Movement Matters: Enhancing Artificial Grammar Performance<br />
With Animation. BILL J. SALLAS, ROBERT C. MATHEWS, &<br />
SEAN M. LANE, Louisiana State University, & RON SUN, Rensselaer<br />
Polytechnic Institute—When learning abstract material, one approach<br />
involves exposure to many examples of the corpus (memorybased),<br />
while a second approach involves learning the underlying<br />
structure (model-based). Research (Domangue et al., 2004) using an<br />
artificial grammar task has found that memory-based processing leads<br />
to fast but relatively inaccurate performance and model-based processing<br />
leads to slow but accurate performance at test. Attempts to integrate<br />
memory- and model-based training to facilitate fast and accurate<br />
performance were unsuccessful. <strong>The</strong> present experiment utilized<br />
a computer-animated training task, whereas previous research used a<br />
pen-and-paper task. Training with an animated representation, or diagram,<br />
of the grammatical rules led to fast and accurate performance<br />
at test. Animation without this explicit representation led to fast but<br />
inaccurate performance. Our results suggest that it is possible to integrate<br />
memory- and model-based processing to enhance performance.<br />
In addition, our results bear on the current debate on the utility of animation<br />
for learning.