28th International Congress of Psychology August 8 ... - U-netSURF

van der Linden, University of Twente, Enschede, The Netherlands
In this presentation we will review recent innovations in IRT. In particular, we will show how IRT
has developed from simple dichotomous models for item responses to models that measure
abilities on multiple dimensions, have parameters with a multiple-level structure, and take into
account the cognitive steps persons have to take to solve items. We will discuss the crucial role
recent developments in Bayesian statistics have played in making these models statistically
tractable and illustrate their use with the help of a few empirical examples.
5002.4 Item response models as Bayesian hierarchical model, K. Shigemasu, The University of
Tokyo, Tokyo, Japan
In this paper, various Item Response Models are treated as Hierarchical Models and the inferences
about the parameters and latent variables are done by the Bayesian approach. This approach has
been used for some time and proven to be very useful and productive. In this paper, we propose a
simpler and unified treatment of various models using normal random variables so that the
straightforward application of Gibbs Sampler should be possible. By doing so, we demonstrate
that the analysis of some complex models of the measurement models can be done easily as a kind
of the hierarchical model proposed here.
5002.5 Correspondence analysis of the person-misfit error responses in a Chinese vocabulary
comprehension test, Y.W. Cao 1 , H.C. Zhang 2 , 1 Shenzhen University, Shenzhen, China; 2 Beijing
Normal University, Beijing, China
A study of Chinese vocabulary comprehension test with 38 common used items were conducted,
the subjects of the study were 1255 first graders of different elementary schools. The error and
person mis-fitted results in children’s responses were identified by the person-fit index derived
from IRT, and those item distracters in responses and no answers were classified into eight types.
Then the correlations were investigated by correspondence analysis. As the result, some
characteristics of children’s learning of Chinese vocabularies were found and discussions made.
5003 INVITED SYMPOSIUM
Brain Mechanisms for Selective Attention
Convener and Chair: R. Desimone, USA
Co-convener: L. Chen, China
5003.1 Wholes, holes, and objects in selective attention, L. Chen, K. Zhou, S.N. Lu, S.Y. Hu,
Graduate School and Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
What is a perceptual object? The theory of early topological perception, which highlights that
topological properties constitute a formal description of fundamental perceptual organizations
(such as parsing visual scenes into potential objects, and other global, Gestalt-like operations), ties
a formal definition of object to invariant properties over topological transformation. The validity
of the topological definition of object was tested by using various paradigms in the study of visual
selective attention, including pre-cueing, multiple object tracking (MOT), and capture attention.
The results consistently demonstrate that topological constraints are vital to the forming of
perceptual objects.
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