Lee-Xieng Yang

A working memory test battery for MATLAB

We present a battery of four working memory tasks that are implemented using MATLAB and the free Psychophysics Toolbox. The package includes preprocessing scripts in R and SPSS to facilitate data analysis. The four tasks consist of a sentence-span task, an operation-span task, a spatial short-term memory test, and a memory updating task. These tasks were chosen in order to provide a heterogeneous set of measures of working memory capacity, thus reducing method variance and tapping into two content domains of working memory (verbal, including numerical, vs. spatial) and two of its functional aspects (storage in the context of processing and relational integration). The task battery was validated in three experiments conducted in two languages (English and Chinese), involving more than 350 participants. In all cases, the tasks were found to load on a single latent variable. In a further experiment, the latent working memory variable was found to correlate highly but not perfectly with performance on Raven’s matrices test of fluid intelligence. We suggest that the battery constitutes a versatile tool to assess working memory capacity with either English- or Chinese-speaking participants. The battery can be downloaded from www.cogsciwa.com (“Software” button).

Sequential Effects in Facial Expression Categorization

Facial expressions are highly dynamic signals that are rarely categorized as static, isolated displays. However, the role of sequential context in facial expression categorization is poorly understood. This study examines the fine temporal structure of expression-based categorization on a trial-to-trial basis as participants categorized a sequence of facial expressions. The results showed that the local sequential context provided by preceding facial expressions could bias the categorical judgments of current facial expressions. Two types of categorization biases were found: (a) Assimilation effects-current expressions were categorized as close to the category of the preceding expressions, and (b) contrast effects-current expressions were categorized as away from the category of the preceding expressions. The effects of such categorization biases were modulated by the relative distance between the preceding and current expressions, as well as by the different experimental contexts, possibly including the factors of face identity and the range effect. Thus, the present study suggests that facial expression categorization is not a static process. Rather, the temporal relation between the preceding and current expressions could inform categorization, revealing a more dynamic and adaptive aspect of facial expression processing.

Category variability effect in category learning with auditory stimuli

The category variability effect refers to that people tend to classify the midpoint item between two categories as the category more variable. This effect is regarded as evidence against the exemplar model, such as GCM (Generalized Context Model) and favoring the rule model, such as GRT (i.e., the decision bound model). Although this effect has been found in conceptual category learning, it is not often observed in perceptual category learning. To figure out why the category variability effect is seldom reported in the past studies, we propose two hypotheses. First, due to sequence effect, the midpoint item would be classified as different categories, when following different items. When we combine these inconsistent responses for the midpoint item, no category variability effect occurs. Second, instead of the combination of sequence effect in different categorization conditions, the combination of different categorization strategies conceals the category variability effect. One experiment is conducted with single tones of different frequencies as stimuli. The collected data reveal sequence effect. However, the modeling results with the MAC model and the decision bound model support that the existence of individual differences is the reason for why no category variability effect occurs. Three groups are identified by their categorization strategy. Group 1 is rule user, placing the category boundary close to the low-variability category, hence inducing category variability effect. Group 2 takes the MAC strategy and classifies the midpoint item as different categories, depending on its preceding item. Group 3 classifies the midpoint item as the low-variability category, which is consistent with the prediction of the decision bound model as well as GCM. Nonetheless, our conclusion is that category variability effect can be found in perceptual category learning, but might be concealed by the averaged data.