Abstracts 2005 - The Psychonomic Society

Sunday Morning Papers 272–278
many malpractice suits in mammography are influenced, perhaps
wrongly, by this hindsight bias.
8:20–8:35 (272)
“As Soon As the Bat Met the Ball, I Knew It Was Gone”: Accuracy
and Hindsight Bias in Predicting Action Outcomes. ROB GRAY,
Arizona State University, SIAN L. BEILOCK, University of Chicago,&
THOMAS H. CARR, Michigan State University—Hindsight bias (the
“knew-it-all-along effect”) provides a measure of strength and resistance
to distortion of memory for predicted outcomes. A virtual-reality batting
task was used to compare novice and expert baseball players’
ability to predict their swing outcomes, as well as players’ susceptibility
to hindsight bias. During each swing, the simulation stopped
when bat met ball. Batters marked where on the field they thought the
ball would land. Correct feedback was then displayed, after which batters
attempted to re-mark the location they had indicated prior to feedback.
Expert batters made more accurate initial predictions and
showed less hindsight bias in their postfeedback marking. Furthermore,
prediction accuracy and hindsight bias were inversely related to
number of hits in the previous block of trials. These results suggest
that experts pay attention to different real-time performance information
than do novices and that experts’ attention changes dynamically
as a function of performance success or failure.
8:40–8:55 (273)
Paradoxical Optimism About What Might Have Been. AIDAN
FEENEY, IAN M. DAVISON, & VICTORIA M. TILLETT, Durham
University—When things go wrong, people often consider how events
might have turned out better. One view is that beliefs that things could
easily have been better amplify negative affect. In the first study described
here, we found that people expressed more confidence that a
preferred alternative outcome could have happened than did a group
of observers judging the same outcome. In a second study, we showed
that for recent and distant regrets, high self-esteem (HSE) participants
were more confident that things could have been as they would have
preferred than low self-esteem (LSE) participants. However, LSE participants
reported more intense regrets than did HSE participants. Degree
of confidence did not predict intensity of regret. Taken together,
these results suggest that people hold optimistic beliefs that things
could have been better and that rather than amplifying negative affect,
such beliefs make people feel better about themselves.
9:00–9:15 (274)
A Statistical–Ecological Account of the Effects of Sample Size on
Correlational and Causal Inference. RICHARD B. ANDERSON,
MICHAEL E. DOHERTY, & JEFF C. FRIEDRICH, Bowling Green
State University—Research on the distributional characteristics of
correlation coefficients, and of other measures of statistical association,
suggests that when the task is to detect the presence of a population
correlation, the environment can favor organisms that are limited
in their capacity to gather and process information. When the task
is to estimate the strength of a population correlation, however, such
organisms may be disadvantaged. In a behavioral study of correlation
detection, if the decision criterion was extremely liberal, detection
was more accurate when based on a small rather than a large sample;
if the criterion was moderate or conservative, accuracy was greater for
large than for small samples. A second study examined people’s ability
to estimate (rather than simply decide on the presence or absence
of) population correlations, and yielded preliminary evidence suggesting
that small samples may lead to exaggerated estimations of relationship
strength.
9:20–9:35 (275)
Comparison-Induced Anchoring Effects. JESSICA M. CHOPLIN &
MARK W. TAWNEY, DePaul University—We propose a comparisoninduced
distortion theory (Choplin & Hummel, 2002) account of anchoring
effects, wherein biases created by verbal comparisons mediate
the effects of anchors on estimation. This model, like previous
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models of anchoring effects, usually predicts biases toward anchor
values, but unlike previous models, it sometimes predicts biases away
from anchor values. Furthermore, unlike previous models, this model
predicts that the words used to express comparisons will influence estimation.
The predictions of this model were tested in two experiments.
In Experiment 1, participants were asked to compare the tobe-estimated
value to the anchor, and the words used to express the
comparison were manipulated before the participants estimated the
value. Experiment 2 featured the same task, but the range of acceptable
estimates was constrained in order to test the prediction that anchors
will sometimes bias estimation away from anchor values. The
results of these two experiments suggest that some anchoring effects
are comparison induced.
9:40–9:55 (276)
Understanding and Modeling Human Sequential Decision Making
Under Uncertainty Using Bayesian Statistics. BRIAN J. STANKIE-
WICZ, CHRIS GOODSON, & ANTHONY R. CASSANDRA, University
of Texas, Austin—The present study investigates human performance
in a sequential decision making with uncertainty task in which
the observer’s task is to localize a target using reconnaissance and “destroy”
the enemy using artillery. Each of these actions comes with a
cost, and the outcomes of these actions are probabilistic. Using a partially
observable Markov decision process (Bayesian model), we calculated
the optimal performance and compared this performance to
the humans’ performance. We found that subjects performed at about
60% efficiency in this task. Further studies revealed that one of the
primary limitations preventing human subjects from acting optimally
was an inability to accurately update the likelihood of the true state
of the system given the previous actions and observations. By providing
subjects with an external representation that updated the likelihoods,
subjects’ efficiency values increased to 80%–90%.
Associative Representations
Grand Ballroom West, Sunday Morning, 8:00–9:40
Chaired by Curt Burgess, University of California, Riverside
8:00–8:15 (277)
A Turing Test of a Generative Word Association Model. JON WILL-
ITS & CURT BURGESS, University of California, Riverside (read by
Curt Burgess)—The (free) association strength between words is used
as a basis for many theories of semantic memory and as an explanatory
construct for countless memory retrieval effects. Word associations represent
a set of relationships between words but provide little theoretical
insight into exactly how and why certain words are associated and others
are not. A model of word association productions is presented that
is based on the probability of two words co-occurring within language
and the contextual substitutability (or global co-occurrence similarity)
of the two words. The model was used to generate word associations
for 30 stimuli, and these associations were compared against those
produced by human subjects. Experts (cognitive psychologists) and
novices (undergraduates) were tested on their ability to distinguish the
human and model-generated associations, constituting a Turing test of
the model. This understanding of the word generation process provides
a theoretical understanding of this long-used technique.
8:20–8:35 (278)
Using Global Co-Occurrence to Predict Word Associations, Features,
and Semantic Priming. JON WILLITS & CURT BURGESS, University
of California, Riverside, & CATHERINE DECKER, Chaffey
College (read by Catherine Decker)—Featural similarity and word association
strength (both via production norms) can be used to model
most semantic memory effects. However, they provide little theoretical
insight into how representations based on these relationships come
about. We suggest that both are emergent by-products of statistical relationships
in the learning environment. Two simulations were performed,
modeling human-generated feature productions and word as-