Cognitive efficiency modes in old age: performance on sequential and coordinative verbal and visuospatial tasks

In an experiment using a large set of verbal and spatial tasks requiring low or high degrees of executive control, 3 distinct age-related effects were found. The smallest effect (no slowing) was tied to lexical tasks with low executive involvement, the largest deficit (age-related slowing factor of 2.2) was tied to visuospatial tasks with high executive involvement, an intermediate level of deficit (slowing factor of 1.7) was found for visuospatial tasks with low executive load and verbal tasks with high executive load. These age-related dissociations were incompatible with any "common cause" formulation. The mechanism responsible for the dissociation between verbal and visual tasks, and between low and high executive load remains to be determined. The latter may reflect capacity limits.     

Using segmented linear regression models with unknown change points to analyze strategy shifts in cognitive tasks

Some years ago, Beem (1993, 1995) described a program for fitting two regression lines with an unknown change point (Segcurve). He suggested that such models are useful for the analysis of a variety of phenomena and gave an example of an application to the study of strategy shifts in a mental rotation task. This technique has also proven to be very fruitful for investigating strategy use and strategy shifts in other cognitive tasks. Recently, Beem (1999) developed SegcurvN, which fitsn regression lines with (n - 1) unknown change points. In the present article we present this new technique and demonstrate the usefulness of a three-phase segmented linear regression model for the identification of strategies and strategy shifts in cognitive tasks by applying it to data from a numerosity judgment experiment. The advantages and shortcomings of this technique are evaluated.     

Coherence-driven resolution of referential ambiguity: A computational model

We present a computational model that provides a unified account of inference, coherence, and disambiguation. It simulates how the build-up of coherence in text leads to the knowledge-based resolution of referential ambiguity. Possible interpretations of an ambiguity are represented by centers of gravity in a high-dimensional space. The unresolved ambiguity forms a vector in the same space. This vector is attracted by the centers of gravity, while also being affected by context information and world knowledge. When the vector reaches one of the centers of gravity, the ambiguity is resolved to the corresponding interpretation. The model accounts for reading time and error rate data from experiments on ambiguous pronoun resolution and explains the effects of context informativeness, anaphor type, and processing depth. It shows how implicit causality can have an early effect during reading. A novel prediction is that ambiguities can remain unresolved if there is insufficient disambiguating information.