Differences between Spatial and Visual Mental Representations

This article investigates the relationship between visual mental representations and spatial mental representations in human visuo-spatial processing. By comparing two common theories of visuo-spatial processing – mental model theory and the theory of mental imagery – we identified two open questions: (1) which representations are modality-specific, and (2) what is the role of the two representations in reasoning. Two experiments examining eye movements and preferences for under-specified problems were conducted to investigate these questions. We found that significant spontaneous eye movements along the processed spatial relations occurred only when a visual mental representation is employed, but not with a spatial mental representation. Furthermore, the preferences for the answers of the under-specified problems differed between the two mental representations. The results challenge assumptions made by mental model theory and the theory of mental imagery

A semantic and language-based representation of an environmental scene

The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide themodeling input of our approach. This structure-based model identifies the spatial, relational, and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation

Areas of Same Cardinal Direction

Cardinal directions, such as North, East, South, and West, are the foundation for qualitative spatial reasoning, a common field of GIS, Artificial Intelligence, and cognitive science. Such cardinal directions capture the relative spatial direction relation between a reference object and a target object, therefore, they are important search criteria in spatial databases. The projection-based model for such direction relations has been well investigated for point-like objects, yielding a relation algebra with strong inference power. The Direction Relation Matrix defines the simple region-to-region direction relations by approximating the reference object to a minimum bounding rectangle. Models that capture the direction between extended objects fall short when the two objects are close to each other. For instance, the forty-eight contiguous states of the US are colloquially considered to be South of Canada, yet they include regions that are to the North of some parts of Canada. This research considers the cardinal direction as a field that is distributed through space and may take on varying values depending on the location within a reference object. Therefore, the fundamental unit of space, the point, is used as a reference to form a point-based cardinal direction model. The model applies to capture the direction relation between point-to-region and region-to-region configurations. As such, the reference object is portioned into areas of same cardinal direction with respect to the target object. This thesis demonstrates there is a set of 106 cardinal point-to-region relations, which can be normalized by considering mirroring and 90° rotations, to a subset of 22 relations. The differentiating factor of the model is that a set of base relations defines the direction relation anywhere in the field, and the conceptual neighborhood graph of the base relations offers the opportunity to exploit the strong inference of point-based direction reasoning for simple regions of arbitrary shape. Considers the tiles and pockets of same cardinal direction, while a coarse model provides a union of all possible qualitative direction values between a reference region and a target region

USING STUDENT CHARACTERISTICS, STUDENT SPATIAL-CONTENT KNOWLEDGE, AND TEACHER SPATIAL-CONTENT KNOWLEDGE TO PREDICT STUDENT SPATIAL-CONTENT KNOWLEDGE OF LUNAR PHASES

Student demographic characteristics of gender and race/ethnicity, students’ spatial-content knowledge as measured by pre-instructional performance on the Lunar Phases Concept Inventory (LPCI) (Lindell & Olsen), and post-instructional performance on the Purdue Visualization of Rotations Test (PSVT) (Bodner & Guay, 1997), and teachers’ spatial-content knowledge as measured by the two assessments were considered to predict students’ overall understanding of lunar phases as measured by post-instructional results on the LPCI. A mixed modeling approach was used in a hierarchal manner to evaluate the student learning outcomes. Results showed that student gender was not a significant predictor of post-instructional student performance, but students who identified as “Other” for their race/ethnicity were shown to be a significant predictor. When considering pre-instructional spatial-content knowledge based on Wilhelm’s four spatial-mathematical domains, Periodic patterns and Spatial Projection were found to be significant predictors, but the domains of Geometric & Spatial Visualization and Cardinal Directions were not found to be significant predictors. Students’ post-instructional performance on the PSVT was found to be a significant predictor with student learning outcomes. Repeated Measures ANOVA tests showed that students’ gain scores on the LPCI were significantly different for six of the seven teachers under consideration

The impact of culture and recipient perspective on direction giving in the service of wayfinding

We examined how culture and recipient perspective affect direction giving during wayfinding. Participants from the United States and the Netherlands provided directions from starting locations to destinations for fictional recipients driving through a town (route perspective) or looking at a map of the town (survey perspective). US participants provided street names more frequently than did Dutch participants, whereas Dutch participants provided landmarks more frequently than did US participants. Moreover, US participants provided more cardinal descriptors when addressing listeners adopting a survey perspective relative to a route perspective but more landmarks and left-right descriptors when addressing listeners adopting a route perspective relative to a survey perspective. Participants from the Netherlands evinced a similar pattern with the important distinction that they mostly ignored cardinal terms, unless explicitly primed to do so and in a survey condition. In addition, this very low usage of cardinal terms seemed to be replaced by using more landmark descriptions. This study revealed remarkable flexibility in people's spatial descriptions but also stressed major differences in the use of spatial terms between US and Dutch participants

Towards Modeling Conceptual Dependency Primitives with Image Schema Logic

Conceptual Dependency (CD) primitives and Image Schemas (IS) share a common goal of grounding symbols of natural language in a representation that allows for automated semantic interpretation. Both seek to establish a connection between high-level conceptualizations in natural language and abstract cognitive building blocks. Some previous approaches have established a CD-IS correspondence. In this paper, we build on this correspondence in order to apply a logic designed for image schemas to selected CD primitives with the goal of formally taking account of the CD inventory. The logic draws from Region Connection Calculus (RCC-8), Qualitative Trajectory Calculus (QTC), Cardinal Directions and Linear Temporal Logic (LTL). One of the primary premises of CD is a minimalist approach to its inventory of primitives, that is, it seeks to express natural language contents in an abstract manner with as few primitives as possible. In a formal analysis of physical primitives of CD we found a potential reduction since some primitives can be expressed as special cases of others

Interpreting Spatial Language in Image Captions

The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively

Generalizability of Predictive Performance Optimizer Predictions across Learning Task Type

The purpose of my study is to understand the relationship of learning and forgetting rates estimated by a cognitive model at the level of the individual and overall task performance across similar learning tasks. Cognitive computational models are formal representations of theories that enable better understanding and prediction of dynamic human behavior in complex environments (Adner, Polos, Ryall, & Sorenson, 2009). The Predictive Performance Optimizer (PPO) is a cognitive model and training aid based in learning theory that tracks quantitative performance data and also makes predictions for future performance. It does so by estimating learning and decay rates for specific tasks and trainees. In this study, I used three learning tasks to assess individual performance and the model\u27s potential to generalize parameters and retention interval predictions at the level of the individual and across similar-type tasks. The similar-type tasks were memory recall tasks and the different-type task was a spatial learning task. I hypothesized that the raw performance scores, PPO optimized parameter estimates, and PPO predictions for each individual would be similar for two learning tasks within the same type and different for the different type learning task. Fifty-eight participants completed four training sessions, each consisting of the three tasks. I used the PPO to assess performance on task, knowledge acquisition, learning, forgetting, and retention over time. Additionally, I tested PPO generalizability by assessing fit when PPO optimized parameters for one task were applied to another. Results showed similarities in performance, PPO optimization trends, and predicted performance trends across similar task types, and differences for the different type task. As hypothesized, the results for PPO parameter generalizability and overall performance predictions were less distinct. Outcomes of this study suggest potential differences in learning and retention based on task-type designation and potential generalizability of PPO by accounting for these differences. This decreases the requirements for individual performance data on a specific task to determine training optimization scheduling