Human factors in GIScience laboratory at the Pennsylvania State University

The human factors in GIScience Laboratory (Human Factors Lab) of The Pennsylvania State University’s Department of Geography is located in University Park, PA (USA). University Park and bordering State College, PA are found in the heart of PA between the cities of New York City, NY, Philadelphia, PA, and Pittsburgh, PA. The laboratory is directed by Dr. Alexander Klippel and is part of the GeoVISTA Center. The Human Factors Lab contributes to Penn State Geography’s strong tradition as a leader in research on map perception, spatial cognition, and behavior in spatial environments. This report focuses upon basic research topics in spatial cognition, including: (1) perceptual and cognitive factors in map symbolization and design, (2) the creation of cognitively ergonomic route directions for next generation location based services (LBS), (3) You-Are-Here maps and the creation of a sense of place through map-like representations, (4) the conceptualization and representation of dynamic phenomena (i.e., geographic movement pattern), and (5) the relationship between linguistic and non-linguistic conceptualization.     

Computer-based instruction and the practical aspects of memory

This article examines the relationship between basic memory research and computer-based instruction (CBI). The results of basic memory research have helped determine the current directions of computer-assisted instruction (CAI) research and development. These directions have included examining the utility of such learning systems for facilitating memory outcomes and developing CBI systems based on cognitive instructional principles. Examples of such systems include the emerging generation of hypermedia and artificial tutoring systems. Inconclusive results, however, have been found regarding CBI's effects on memroy phenomena. The CBI literature has thus not established the utility of any memory theory for naturally occurring phenomena. Several plausible reasons for this “null finding” are discussed.     

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.     

路径知识习得的认知老化效应

年龄与路径知识习得的关系是空间与认知老化两大领域研究的重要议题。老年人在面对不同的路径学习环境时呈现出不同的认知老化表现。以往与年龄相关的路径知识习得能力变化的研究, 主要支持了认知老化衰退理论。然而近来发现随着年龄增长, 老年人保留了一种空间认知补偿能力。由此, 在对前人文献进行回顾和反思的基础上, 整合路径知识习得的认知老化表现及机制以探究缓解空间认知老化可能的内部因素和外部有效措施。     

The basis of shared intentions in human and robot cognition

There is a fundamental difference between robots that are equipped with sensory, motor and cognitive capabilities, vs. simulations or non-embodied cognitive systems. Via their perceptual and motor capabilities, these robotic systems can interact with humans in an increasingly more “natural” way, physically interacting with shared objects in cooperative action settings. Indeed, such cognitive robotic systems provide a unique opportunity to developmental psychologists for implementing their theories and testing their hypotheses on systems that are becoming increasingly “at home” in the sensory--motor and social worlds, where such hypotheses are relevant. The current research is the result of interaction between research in computational neuroscience and robotics on the one hand, and developmental psychology on the other. One of the key findings in the developmental psychology context is that with respect to other primates, humans appear to have a unique ability and motivation to share goals and intentions with others. This ability is expressed in cooperative behavior very early in life, and appears to be the basis for subsequent development of social cognition. Here we attempt to identify a set of core functional elements of cooperative behavior and the corresponding shared intentional representations. We then begin to specify how these capabilities can be implemented in a robotic system, the Cooperator, and tested in human–robot interaction experiments. Based on the results of these experiments we discuss the mutual benefit for both fields of the interaction between robotics and developmental psychology.     

Computational modeling/cognitive robotics complements functional modeling/experimental psychology

This position paper explores the possible contributions to the science of psychology from insights obtained by building and experimenting with cognitive robots. First, the functional modeling characteristic of experimental psychology is discussed. Second, the computational modeling required for cognitive robotics is described, and possible experiments with them are illustrated. Next, we argue that cognitive developmental robots, robots that “live” through a development phase where they learn about their environments in several different modes, can provide additional benefits to the science of psychology. Finally, the reciprocal interactions between computational modeling/cognitive robotics and functional modeling/experimental psychology are explored. We conclude that each can contribute significantly to the other.     

Socioeconomic Status and Coronary Heart Disease Risk: The Role of Social Cognitive Factors

The aim of this study is to examine existing research on social cognitive factors that may, in part, mediate the relationship between socioeconomic status (SES) and coronary heart disease (CHD). We focus on how social status is 'carried' in the mental systems of individuals, and how these systems differentially affect CHD risk and associated behaviors. To this end, literatures documenting the association of various social cognitive factors (e.g., social comparison, perceived discrimination, and self-efficacy) with cardiovascular disease are reviewed as are literatures regarding the relationship of these factors to SES. Possible mechanisms through which social cognitions may affect health are addressed. In addition, directions for future research are discussed, and a model identifying the possible associations between social cognitive factors, SES, and coronary disease is provided.     

Animats and what they can tell us

Animats-autonomous robots or simulations of animals-and the animat approach represent the most recent attempt to comprehend the capacity of animals for autonomous generation of adaptive, intelligent behavior in complex, changing environments. Motivated by perceived limitations in classical artificial intelligence (AI), the animat approach promulgates an alternative, bottom-up route to understanding intelligent behavior. Important tenets include: (1) that adaptive behavior is best understood by focusing on the interaction between a behaving individual and its environment, hence the interest in `embodied' physical robots `situated' in natural environments; (2) that specific abilities, `behaviors', are more natural units of analysis and design than general, information-processing functions and world models; and (3) that high-level behaviors will emerge as systems composed of simple behavioral competences become more complex. Thus, animat research often begins with low-level sensorimotor abilities and then moves up towards higher, cognitive functions. Both in analysis and in design, the animat approach borrows heavily from ethology, psychology, neurobiology and evolutionary biology, as well as from connectionism. For AI and robotics researchers, understanding the mechanisms behind adaptive behavior is secondary to creating them, but natural scientists can hope for tools and concepts to aid understanding of biological systems.