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

measure? A. Miyake, University of Colorado at Boulder, Boulder, CO, USA
One influential line of research on working memory (WM) focuses on individual differences in
so-called WM span tasks. Unlike traditional, storage-oriented span tasks (e.g., digit span), WM
span tasks (e.g., reading span) require simultaneous processing and storage of information and can
predict people's performance on complex cognitive tasks (e.g., reading comprehension, abstract
reasoning) quite well. What do WM span tasks really measure? Where do their predictive powers
come from? In this presentation, I will address these two central yet highly controversial questions
regarding WM span tasks and the nature of individual differences in WM and outline my current
answers to them.
2005.3 Mathematical and computational modeling of phonological working memory, G. Hitch,
University of York, Heslington, UK
Conceptual models have played an important part in developing our understanding of working
memory, but necessarily have their limitations. For example, if one wants to account for the
detailed operation of phonological working memory and its role in activities such as vocabulary
learning, there is a clear need to go beyond the simple concept of a phonological loop. Progress in
mathematical modeling and computational simulation of phonological working memory will be
described. The use of such detailed models to guide research will be illustrated by tests of their
ability to predict performance in tasks such as learning a novel phonological sequence.
2005.4 The mental workspace of visual and spatial working memory, R.H. Logie, Kings
College, Aberdeen, UK
This paper will present experimental evidence from healthy adults and individuals with disorders
of visuospatial cognition to argue that visuo-spatial working memory: (a) comprises a domain
specific cognitive resource (b) can be further subdivided into a “visual cache” for relatively static
visual arrays and an “inner scribe” for dynamic sequences of movements or pathways (c) deals
with the product of activated traces from the store of knowledge and past experiences and (d) is
not an interface or “waiting area” between perception and long term memory, but functions as a
mental workspace in which meaningful material is held and manipulated.
2006 INVITED SYMPOSIUM
Causal models in reasoning and learning, Part I
Convener and Chair: M.R. Waldmann, Germany
2006.1 Causal learning in children, A. Gopnik, University of California, Berkeley, CA, USA
We propose that children employ specialized cognitive systems that allow them to recover an
accurate “causal map” of the world: an abstract, coherent, learned representation of the causal
relations among events. This kind of knowledge can be perspicuously understood in terms of the
formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and
inference may involve computations similar to those for learning causal Bayes nets and for
predicting with them. Experimental results suggest that 2- to 4-year-old children construct new
causal maps, discriminate between intervention and covariance information, and infer unobserved
common causes, and that their learning is consistent with the Bayes net formalism.
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