Different contribution of the monkey prefrontal and premotor dorsal cortex in decision-making supported by inferential reasoning

Several studies reported similar neural modulations between brain areas of the frontal cortex, such as the dorsolateral prefrontal (DLPFC) and the premotor dorsal (PMd) cortex, in tasks requiring encoding of the abstract rules for selecting the proper action. Here, we compared the DLPFC and PMd neuronal activity of monkeys trained in choosing the highest ranking image of pair (target item), selected from an arbitrarily rank-ordered set (A>B>C>D>E>F) in the context of a transitive inference task. Once acquired by trial-and-error, the ordinal relationship between pairs of adjacent images (i.e. A>B; B>C; C>D; D>E; E>F), monkeys were tested in inferring the ordinal relation between items of the list not paired during learning. During inferential decisions, we observed that the choice accuracy increased and the reaction time decreased as the rank difference between the compared items enhanced. This result is in line with the hypothesis that after learning, the monkeys built an abstract mental representation of the ranked items, where rank comparisons correspond to the item position comparison on this representation. In both brain areas, we observed higher neuronal activity when the target item appeared in a specific location on the screen, with respect to the opposite position and that this difference was particularly enhanced at lower degrees of difficulty. By comparing the time evolution of the activity of the two areas, we revealed that the neural encoding of target item spatial position occurred earlier in DLPFC than in PMd, while in PMd the spatial encoding duration was longer.

Using the Weighted Kendall Distance to Analyze Rank Data in Psychology

Although the Kendall distance is a standard metric in computer science, it is less widely used in psychology. We demonstrate the usefulness of the Kendall distance for analyzing psychological data that take the form of ranks, lists, or orders of items. We focus on weighted extensions of the metric that allow for heterogeneity of item importance, item position, and item similarity, as well showing how the metric can accommodate missingness in the form of top-k lists. To demonstrate how the Kendall distance can help address research questions in psychology, we present four applications to previous data. These applications involve the recall of events on September 11, people's preference rankings for the months of the year, people's free recall of animal names in a clinical setting, and expert predictions involving American football outcomes.

A Bayesian Item Response Model for Examining Item Position Effects in Complex Survey Data

A multidimensional Bayesian item response model is proposed for modeling item position effects. The first dimension corresponds to the ability that is to be measured; the second dimension represents a factor that allows for individual differences in item position effects called persistence. This model allows for nonlinear item position effects on the item side as well as on the person side. Moreover, a flexible loading structure on the two dimensions is allowed. A fully Bayesian estimation procedure is proposed, and its performance is investigated by a simulation study. Further, the model is applied to empirical data collected in the Programme for International Student Assessment 2000 in the reading domain. The additional value of the model’s extended flexibility compared to more restrictive models is shown. The findings show that the linear hypothesis of change in performance during a test does not hold in general.

Does Speededness in Collecting Reasoning Data Lead to a Speed Factor?

The consequences of speeded testing for the structure and validity of a numerical reasoning scale (NRS) were investigated. Confirmatory factor models including an additional factor for representing working speed and models without such a representation were employed for investigating reasoning data collected in speeded paper-and-pencil testing and in only slightly speeded testing. For achieving a complete account of the data, the models also accounted for the item-position effect. The results revealed the factor representing working speed as essential for achieving a good fit in data originating from speeded testing. The reasoning factors based on data due to speeded and slightly speeded testing showed a high correlation among each other. The factor representing working speed was independent of the other factors derived from reasoning data but related to an external score representing processing speed.