A Neural Theory of Attentive Visual Search: Interactions of Boundary, Surface, Spatial, and Object Representations

Visual search data are given a unified quantitative explanation by a model of how spatial maps in the parietal cortex and object recognition categories in the inferotemporal cortex deploy attentional resources as they reciprocally interact with visual representations in the prestriate cortex. The model visual representations arc organized into multiple boundary and surface representations. Visual search in the model is initiated by organizing multiple items that lie within a given boundary or surface representation into a candidate search grouping. These items arc compared with object recognition categories to test for matches or mismatches. Mismatches can trigger deeper searches and recursive selection of new groupings until a target object io identified. This search model is algorithmically specified to quantitatively simulate search data using a single set of parameters, as well as to qualitatively explain a still larger data base, including data of Aks and Enns (1992), Bravo and Blake (1990), Chellazzi, Miller, Duncan, and Desimone (1993), Egeth, Viri, and Garbart (1984), Cohen and Ivry (1991), Enno and Rensink (1990), He and Nakayarna (1992), Humphreys, Quinlan, and Riddoch (1989), Mordkoff, Yantis, and Egeth (1990), Nakayama and Silverman (1986), Treisman and Gelade (1980), Treisman and Sato (1990), Wolfe, Cave, and Franzel (1989), and Wolfe and Friedman-Hill (1992). The model hereby provides an alternative to recent variations on the Feature Integration and Guided Search models, and grounds the analysis of visual search in neural models of preattentive vision, attentive object learning and categorization, and attentive spatial localization and orientation.Air Force Office of Scientific Research (F49620-92-J-0499, 90-0175, F49620-92-J-0334); Advanced Research Projects Agency (AFOSR 90-0083, ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100); Northeast Consortium for Engineering Education (NCEE/A303/21-93 Task 0021); British Petroleum (89-A-1204); National Science Foundation (NSF IRI-90-00530

Diagrammatic Attention Management and the Effect of Conceptual Model Structure on Cardinality Validation

Diagrams are frequently used to document various components of information systems, from the procedures established for user-system interaction, to the structure of the database at the system’s core. Past research has revealed that diagrams are not always used as effectively as their creators intend. This study proposes a theory of diagrammatic attention management to contribute to the exploration of diagram effectiveness. Based upon diagrammatic attention management, this study demonstrates that the type of diagram most commonly used to represent conceptual models is less effective than three other alternatives for validating the models’ cardinalities. Most conceptual models are documented using entity-relationship diagrams that include a full transaction cycle or module on a single page, i.e., an aggregate diagrammatic format. Participants in this study using three alternative representations (disaggregate diagrammatic, aggregate sentential, and disaggregate sentential) outperformed users of the aggregate diagrammatic format for cardinality validation. Results suggest that to facilitate effective use of aggregate diagrams, users need a mechanism by which to direct their attention while using the diagrams. If such an attention direction mechanism is not inherent in a diagram, it may need to be applied as an external tool, or the diagram may need to be disaggregated to facilitate use

Business Process Models for Risk Analysis: Expert View

The recent financial turbulences raise questions on how risk analysis is conducted. Regulatory requirements and professional standards have been introduced in the last decade in order to obtain a more reliable internal control on financial reporting process with a new emphasis on business processes. However, there are no standards yet available on how business processes should be captured for facilitating risk analysis in audit assignments. Representations of business processes have been investigated in the field of business process modeling. There exists a broad spectrum of notations and formalisms with relative strengths and weaknesses. Many of the popular notations build on a graph-based representation where activities of a process are connected with directed arcs defining the control flow. Such notations have been widely adopted for redesigning business processes. But also text-based formats have been defined. Corresponding process specifications define the activities of a process as lists with additional free text information. This raises the question whether the tools and methods for analyzing business process risks in auditing practice is appropriate for its objective. This paper reveals the benefits of adopting business process models for auditors toward understanding a companies business processes and the issues need to be considered for further development. The analysis also shows that practitioners use process models rather for risk elicitation and less in risk assessment

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

Visual Attention Overload: Representation Effects on Cardinality Error Identification

Attention overload occurs when people are presented with so many different stimuli that they are unable to adequately direct their cognitive processing to all of the inputs. Visual attention overload is conceptually similar and occurs when people are given visual stimuli in a format that prevents them from effectively processing all of the stimuli. The current study examines whether visual attention overload results in differential performance with conceptual model representations for a task requiring identification of errors in relationship cardinalities. This study suggests that visual attention management is an important part of cognitive fit. Specifically, a representation that inhibits processing of information because of visual attention overload is not expected to have cognitive fit with a task that requires repeated use and scanning of the same objects that were previously inhibited. This study allows us to move beyond the “spatial representations should be used for spatial tasks” approach to instead attempt to identify the types of tasks that require repeated use of the representations and therefore are likely not to have cognitive fit with a diagram representation if the diagram is sufficiently complex

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

Spreadsheet Error Correction Using an Activity Framework and a Cognitive Fit Perspective

Errors in a spreadsheet constitute a serious reason for concern among organizations as well as academics. There are ongoing efforts toward finding ways to reduce errors, designing and developing visualization tools to support error correction activities being one of them. In this paper, we propose a framework for classifying activities associated with spreadsheet error correction. The purpose of this framework is to help in understanding the activities that are important for correcting different types of spreadsheet errors and how different visualization tools can help in error correction by effectively supporting these activities. An experiment is designed to test the effectiveness of a visualization tool that supports one of the most important activities from the framework – chaining activity. Two groups of subjects, with and without the visualization tool, are required to correct two types of errors. Our hypotheses are derived based on the notion of cognitive fit between problem representation and task, and the results of the experiment support most of the hypotheses. Thus, this study demonstrates the usefulness of the activity-based framework for spreadsheet error correction, and also provides guidelines for designing and developing tools for spreadsheet audit. It also provides empirical evidence to the cognitive fit theory by showing that performance is significantly better when visual support tools result in a match between problem representation and the task in hand, as in the case of correcting link errors with the tool used in this study. Theoretical and practical implications of the findings are discussed

EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain