S1 (FriAM 1-65) - The Psychonomic Society

Papers 66–71 Friday Afternoon
SYMPOSIUM: Reuniting Motivation and Cognition:
Motivational Factors in Learning and Performance
Regency ABC, Friday Afternoon, 1:30–3:50
Chaired by W. Todd Maddox and Arthur B. Markman
University of Texas, Austin
1:30–1:45 (66)
Reuniting Motivation and Cognition: Motivational Factors in
Learning and Performance. W. TODD MADDOX & ARTHUR B.
MARKMAN, University of Texas, Austin—Psychology typically
makes a conceptual distinction between motivation (processes that
drive an individual to act) and cognition (processes by which information
is processed). Despite the separation of these factors within
psychology, there are good reasons to unify research on motivation
and cognition. Because motivation drives action, there is no cognition
in the absence of motivational influences. Furthermore, cognitive neuroscience
and clinical neuropsychology suggest that the brain areas
responsible for motivational influences are not anatomically or functionally
separable from those responsible for information processing.
The goal of this symposium is to present research that reunites research
on motivation and cognition. Talks will address foundational
issues about the structure of the motivational–cognition interface.
This work explores the motivation–learning interface as well as the effects
of motivation on expert performance (e.g., choking under pressure).
The symposium concludes with a discussion that links the topics
together and points out directions for future research.
1:50–2:05 (67)
Using Classification to Understand the Motivation–Cognition Interface.
ARTHUR B. MARKMAN & W. TODD MADDOX, University
of Texas, Austin—Our research explores the cognitive consequences
of motivational incentives in the form of potential gains or losses that
are contingent on overall task performance. The influence of incentives
depends on whether local task feedback provides rewards or punishments.
Using perceptual classification tasks, we demonstrate that
gain incentives lead to more flexible use of explicit rules when the participants
gain points in their feedback than when they lose points. Loss
incentives lead to more flexible performance when participants lose
points than when they gain points. This fit between global and local
rewards is beneficial for performance for tasks that call for flexible
rule use, but not for tasks that require implicit integration of information
from multiple dimensions in a manner that is not easily verbalized.
This work has implications for our understanding of stereotype
threat, the cognitive neuroscience of learning and performance,
and the cognitive deficits that arise with mental disorders.
2:10–2:25 (68)
Structural and Dynamic Elements in Means–Ends Relations: Multifinality
Quest and the Range of Means to a Focal Goal. ARIE W.
KRUGLANSKI & CATALINA KOPETZ, University of Maryland,
College Park—This presentation introduces the concept of multifinality
quest for means that while serving the current explicit (or focal)
goal serve also other cognitively active objectives. The simultaneous
presence of several goals is usually thought to introduce goal-conflict,
implying the need to exercise goal choice. Such conflict may be
avoided via “multifinal” means affording joint pursuit of the conflicting
goals. Multifinal means typically constitute a subset of all the
means to a focal goal one could consider. Accordingly, the activation
of additional goals should narrow the set of acceptable means to a
focal objective. Moreover, the quest for “multifinal” means should
constrain the set of acceptable activities to ones that benefit the entire
set of active goals. Our experiments demonstrate this phenomenon
and identify its two moderators. One moderator concerns the feasibility
of finding multifinal means given the nature of the activated
goals (their relatedness). The second moderator concerns the individuals’
commitment to the focal explicit goal, that tends to “crowd out”
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the alternative goals. Both moderators liberate the means to the focal
goal from constraints imposed by the alternative goals, hence increasing
the set size of means generated to the focal goal.
2:30–2:45 (69)
Individual Differences in Motivation and Their Effects on Cognitive
Performance. ALAN PICKERING, University of London—A longestablished
tradition in biologically based theories of personality is to
propose that individuals differ in the functioning of basic motivational
systems. In particular, individuals are thought to vary in the reactivity
of the system dealing with appetitive motivation and approach behavior,
while there is argued to be independent variation in another
system dealing with aversive motivation and avoidance behavior. A
control system (dealing with motivational conflicts) has also been proposed.
Differing motivational contexts will engage these systems differentially
and will thus alter the effects of personality on behavior.
We show here also that neurocomputational models of learning under
appetitive motivational contexts are very sensitive to interindividual
differences in key parameter settings that might plausibly reflect biological
variation underlying aspects of personality. We therefore argue
that, when exploring motivational effects on cognition, one would improve
understanding and increase statistical power if one considered
personality variables. We illustrate these ideas further with behavioral
findings from cognitive paradigms.
2:50–3:05 (70)
Motivation, Emotion, and Attention: A Dynamic Approach. ZHENG
WANG, Ohio State University, & JEROME R. BUSEMEYER, Indiana
University—Real time data were collected to measure the emotion, attention,
and the channel choices that participants made while watching
television. The hedonic valence and arousal levels of television
content were manipulated. Continuous self-report of emotion, physiological
responses (heart rate to measure attention, skin conductance
to measure arousal, and facial EMG to measure hedonic valence), and
channel-changing behavior were measured. The data were analyzed
and interpreted using a state space model, where emotional television
information was dynamic input that affected the latent motivational
states, which in turn were reflected by the observational measures associated
with them. Dynamics of the motivational states is described
by a transition equation, and relationships between the latent motivational
states and observational variables (heart rate, skin conductance
level, zygomatic activity, corrugator activity, and self-reported
arousal, negativity, and positivity) were identified. These motivational
variables then provide the inputs that drive a diffusion model of
channel-changing behavior.
3:10–3:25 (71)
Performance Under Pressure: Insights Into Skill Failure and Success.
SIAN L. BEILOCK, University of Chicago, & MARCI S. DECARO,
Miami University—We explored how individual differences in working
memory (WM) and consequential testing situations impact math
problem-solving strategies and performance. Individuals performed
multistep subtraction and division problems under low- or high-pressure
conditions and reported their problem-solving strategies (Experiment 1).
Under low pressure, the higher their WM, the better their math performance
and the more likely they were to use computationally demanding
algorithms (vs. simpler shortcuts) to solve the problems.
Under pressure, higher WMs switched to simpler (and less efficacious)
problem-solving strategies and their performance suffered. Experiment
2 turned the tables, using a math task for which a simpler
strategy was optimal. Now, under low pressure, the lower their WMs,
the better their performance. And, under pressure, higher WMs’ performance
increased by employing the simpler strategies used by lower
WMs. WM availability influences how individuals approach math
problems, with the nature of the task performed and the performance
environment dictating skill success or failure.