Congratulations to James Pooley, 3rd year graduate student of Madlab, who successfully advanced on March 7, 2011. James’ research involves using Bayesian analysis to assess the stages of Alzheimer’s disease and related disorders (ADRD). One of his recent papers (published in the Journal of Mathematical Psychology) proposes that hierarchical Bayesian models can not only account for individual differences among ADRD patients, but can also efficiently explain the patients’ performances on memory recall tasks. Figure 1 shows that the observed serial position curves (indicated by black lines) are proportional to the model’s posterior predictions (indicated by areas of squares). Next, James is working on a model with more detailed processing assumptions to gain a better insight into human memory. So stay tuned.
We’ve just submitted a paper:
Zhang, S., & Lee, M.D. (submitted). Optimal experimental design for a class of bandit problems,
that applies a nice statistical framework, developed by Jay Myung and colleagues, to the problem of designing two-armed finite-horizon bandit problems.
We’ve just submitted a paper:
Lee, M.D., Zhang, S., & Shi, J. (submitted). The wisdom of the crowd playing the Price is Right.
looking at whether there it is possible to combine the four bids contestants make in the Price is Right, to give a more accurate estimate of the true value of the prize.
The nice finding is that using models of decision-making helps, because it lets you aggregate over what people know about the price, rather than what they say when they bid.
As the snippet below shows, sometimes people bid $1, not because they think that’s the right price, but because it’s a clever strategy to maximize their chance of winning. Also, apparently, people sometimes bid $420. As one of us originally said:
I think it’s rare to have a “lucky number” so big as 420. And I can’t think up a second reason why he was doing that!
Hemmer, P., Shi, J., & Steyvers, M. (submitted). The influence of prior knowledge on recall for height.
In this paper we explore how having general knowledge about heights of people, as well as specific knowledge about the height of men and women can influence recall for the height of a person.
We focus on naturalistic stimuli pertaining to the height of males and females because there is a prominent difference in mean height between genders. In addition, the stereotype of correlation between certain height ranges and gender is both accurate and universal and is drawn from real-world distributions and is consistent in nature.
Panel A shows how recall might be biased toward the overall mean population height. People at heights below the mean population height are overestimated and people at heights above the mean population height are underestimated. Read more »
- Lee, M.D., & Wetzels, R. (submitted). Individual differences in attention during category learning.
which we recently submitted to the Cognitive Science conference has an interesting result. It takes a standard condensation category learning task — in which it is usually assumed people pay attention to both stimulus dimensions, because they are both relevant to learning the category structure — and applies Nosofsky’s Generalized Context Model.
A standard analysis, assuming no individual differences, gives the standard divided attention result. But a re-analysis that allows for individual differences finds two groups of subjects, half of whom attend to one dimension, half of whom attend to the other, and basically none of whom divide their attention.
The posterior distributions over attention and generalization parameters for the two groups are shown in the top panels of the picture (click the thumbnail at the top of the post to get a good view), and their posterior predictives below that show their good fit to the qualitatively different category learning behavior of the two groups. Food for thought …