Eric G. Freedman

The Cambridge Handbook of Visuospatial Thinking: The Comprehension of Quantitative Information in Graphical Displays

This chapter reviews research on individual differences in spatial cognition from a somewhat historical perspective. It commences with a review of the factor analysis literature, which dominated early research in spatial abilities. Then, the chapter considers research on the analysis of spatial abilities from the perspective of cognitive psychology. Individual differences in large-scale or environmental spatial abilities such as wayfinding and navigation are examined. Finally, it considers some of the functions of spatial ability in occupational and academic performance. The research reviewed in this chapter provides strong evidence that spatial ability is differentiated from general intelligence. It shows that spatial ability is not a single, undifferentiated construct, but composed of several separate abilities, such as spatial visualization, flexibility of closure, spatial memory, and perceptual speed. Recent research has also begun to analyze complex tasks involved in these professions in terms of their demand on spatial skills.

Toward a Model of Knowledge-Based Graph Comprehension

Research on graph comprehension has been concerned with relatively low-level information extraction. However, laboratory studies often produce conflicting findings because real-world graph interpretation requires going beyond the data presentation to make inferences and solve problems. Furthermore, in real-world settings, graphical information is presented in the context of relevant prior knowledge. According to our model, knowledge-based graph comprehension involves an interaction of top-down and bottom up processes. Several types of knowledge are brought to bear on graphs: domain knowledge, graphical skills, and explanatory skills. During the initial processing, people chunk the visual features in the graphs. Nevertheless, prior knowledge guides the processing of visual features. We outline the key assumptions of this model and show how this model explains the extant data and generates testable predictions.

A computational approach to control in complex cognition.

Cognitive deficits associated with dorsolateral prefrontal cortex (DLPFC) damage are often most apparent in higher cognitive tasks that involve problem solving and managing multiple goals. However, computational models of prefrontal deficits on such tasks are difficult to construct. Problem solving is most naturally modeled with symbolic systems (e.g. production systems), but the effects of lesions are most naturally modeled with subsymbolic systems (neural networks). We show that when we adopt a simple and plausible model of neural computation, there is a natural and explicit mapping from symbolic, goal-driven cognition onto neural computation. We exploit this mapping to construct a neural network model that is capable of solving complex problems in the Tower of London task. The model leads to a specific hypothesis about the role of DLPFC in such tasks, namely, that DLPFC represents internally generated subgoals that modulate competition among posterior representations. When intact, the model accurately simulates the behavior of college students even on the most difficult problems. Furthermore, when the subgoal component is lesioned, it accurately simulates the behavior of prefrontal patients, including the fact that their deficits are most apparent on the most difficult tasks and that they have special difficulty with tasks that require inhibiting a prepotent response.

Visuospatial Cognition in Electronic Learning.

Static, animated, and interactive visualizations are frequently used in electronic learning environments. In this article, we provide a brief review of research on visuospatial cognition relevant to designing e-learning tools that use these displays. In the first section, we discuss the possible cognitive benefits of visualizations consider used in e-learning environments. In the second section consider cognitive constraints on the use of visualizations and design guidelines intended to reduce impact of these cognitive constraints. Finally, we consider how individual differences interact with learning from visualizations and how the use of visualizations might be altered for students of different abilities.

The Role of Diatonicism in the Abstraction and Representation of Contour and Interval Information

Previous research on melody recognition indicates that listeners can recognize contour information when melodies are retained for brief intervals and can recognize interval information of melodies held in longterm memory. However, past research has failed to control for the diatonicism and familiarity of the melodies. In three experiments, the relative contributions of contour and interval information during the abstraction of novel diatonic and nondiatonic sequences are examined. Listeners recognize the melodic contours of melodies held over an extended retention interval. Additionally, listeners use the diatonic context to recognize both the contour and interval information. In nondiatonic contexts, listeners rely predominantly on the contour information. In addition, musically experienced listeners can recognize both the contour and interval information, whereas musically inexperienced listeners rely predominantly on the contour information. Recognition of melodic contour remained relatively accurate during a 24-hr retention interval. Thus, the results indicate that the diatonic scale mediates the abstraction of interval information. Listeners seem to acquire a musical schema for diatonic melodies.

Universal computation by networks of model cortical columns

We present a model cortical column consisting of recurrently connected, continuous-time sigmoid activation units that provides a building block for neural models of complex cognition. Recent progress with a hybrid neural/symbolic cognitive model of problem-solving [T. A. Polk et. al., 2002] prompted us to investigate the adequacy of these columns for the construction of purely neural cognitive models. Here we examine the computational power of networks of columns and show that every Turing machine maps in a straightforward fashion onto such a network. Furthermore, several hierarchical structures composed of columns that are critical in this mapping promise to provide biologically plausible models of timing circuits, gating mechanisms, activation-based short-term memory, and simple if-then rules that will likely be necessary in neural models of higher cognition.