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Gabriel A Radvansky - One of the best experts on this subject based on the ideXlab platform.
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Spatial and character Situation Model updating
Journal of Cognitive Psychology, 2014Co-Authors: Jacqueline M. Curiel, Gabriel A RadvanskyAbstract:During text comprehension, people create Situation Models to understand the described events. When a change in the Situation happens, Model updating can occur along multiple dimensions. Prior studies have looked at the separate effects of dimensional shifts finding an increase in processing time when a shift occurs. This reading time study used spatial and character shifts to assess the impact of updating on each other. Specifically, does a prior spatial shift affect character updating and vice versa? This follows recent work exploring the distinction between incremental and global updating. An experiment that manipulated spatial and character shifts as well as their order found influences of both shift types on comprehension, but they did not influence one another. A second experiment revealed that extensive prior knowledge of the spatial environment did not impact the pattern of results. Overall, these results are consistent with the idea that during the comprehension of continuous text event shifts are...
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Working memory, Situation Models, and synesthesia
The American journal of psychology, 2014Co-Authors: Gabriel A Radvansky, Bradley S. Gibson, M. Windy McnerneyAbstract:Research on language comprehension suggests a strong relationship between working memory span measures and language comprehension. However, there is also evidence that this relationship weakens at higher levels of comprehension, such as the Situation Model level. The current study explored this relationship by comparing 10 grapheme-color synesthetes who have additional color experiences when they read words that begin with different letters and 48 normal controls on a number of tests of complex working memory capacity and processing at the Situation Model level. On all tests of working memory capacity, the synesthetes outperformed the controls. Importantly, there was no carryover benefit for the synesthetes for processing at the Situation Model level. This reinforces the idea that although some aspects of language comprehension are related to working memory span scores, this applies less directly to Situation Model levels. This suggests that theories of working memory must take into account this limitation, and the working memory processes that are involved in Situation Model construction and processing must be derived.
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A Novel Study: A Situation Model Analysis of Reading Times
Discourse Processes, 2011Co-Authors: M. Windy Mcnerney, Kerri A. Goodwin, Gabriel A RadvanskyAbstract:One of the basic findings on Situation Models and language comprehension is that reading times are affected by the changing event structure in a text. However, many studies have traditionally used multiple, relatively short texts, in which there is little event consistency across the texts. It is unclear to what extent such changes will be observed when readers are given a longer, more coherent text, such as a novel. The current study discovered that while some of the characteristics of language comprehension found with multiple shorter texts also influence the reading of a novel, such as the introduction of new characters and causal breaks, there are several notable differences. In some cases, factors did not clearly influence comprehension, and a reverse influence was observed as with spatial and temporal shifts. Thus, more work is needed to develop Situation Model theory that can more readily account for complex, real-world comprehension.
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Spatial directions and Situation Model organization.
Memory & cognition, 2009Co-Authors: Gabriel A RadvanskyAbstract:Do spatial directions, such as “to the right,” influence the integration and segregation of information into Situation Models? According to a single-framework hypothesis, spatial location serves as an event framework, and spatial directions serve as relational information within that framework but do not establish separate sublocation frameworks. Alternatively, according to a fragmented-framework hypothesis, spatial directions lead the larger framework to be broken down such that each direction is treated as a separate sublocation, thereby producing retrieval interference. In three experiments, people memorized sentences about objects in locations. The results support the fragmented-framework hypothesis. Control conditions ruled out explanations based on the ease of memorization, retrieval demands, or sentence complexity.
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Aging and Situation Model processing
Psychonomic Bulletin & Review, 2007Co-Authors: Gabriel A Radvansky, Katinka DijkstraAbstract:Over the past several years, a number of studies have been done that assess processing at the level of the Situation Model in relation to issues of aging (Morrow, Leirer, & Altieri, 1992; Radvansky, Copeland, Berish, & Dijkstra, 2003; Radvansky, Copeland, & Zwaan, 2003; Stine-Morrow, Gagne, Morrow, & DeWall, 2004; Stine-Morrow, Morrow, & Leno, 2002). In contrast to age-related declines that have been demonstrated at surface form and textbase levels of processing, no such declines have been found in the creation and updating of Situation Models (Radvansky, 1999). This review focuses on the relevant factors in cognitive aging and Situation Model processing and places them within the larger frameworks of language processing, working memory capacity, and aging.
Mike Rinck - One of the best experts on this subject based on the ideXlab platform.
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Using Diagnostic Text Information to Constrain Situation Models
Discourse Processes, 2010Co-Authors: Stephan Dutke, Ulrich Von Hecker, Christiane Baadte, Andrea Hähnel, Mike RinckAbstract:During reading, the Model of the Situation described by the text is continuously accommodated to new text input. The hypothesis was tested that readers are particularly sensitive to diagnostic text information that can be used to constrain their existing Situation Model. In 3 experiments, adult participants read narratives about social Situations that were ambiguous in terms of whether they involved 2 or 3 social cliques. Diagnostic text information enabling the reader to constrain the Situation Model to 1 of the 2 potential versions required longer reading times than non-diagnostic information, and fundamentally affected the structure of the Situation Model. Recognizing the diagnostic value of critical text information did not require additional working memory resources, but updating the Situation Model according to the diagnostic information did. This evidence may suggest that some of the Situation Model updating is occurring offline. The results are discussed with regard to working memory resources required in updating Situation Models.
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Multidimensional Situation Models
2007Co-Authors: David J. Therriault, Mike RinckAbstract:The research landscape examining Situation Models in discourse has been transformed considerably in the last 20 years. Van Dijk and Kintsch (1983) originally included the concept of a Situation Model to address issues that were problematic for earlier versions of their theory (Kintsch & van Dijk, 1978). Specifically, van Dijk and Kintsch (1983) argued that Situation Models were necessary to explain issues of reference, coreference, coherence, perspective taking, translation, individual differences, memory, reordering effects, problem solving, updating knowledge, and learning. One might first notice the comprehensive nature of such a list. It is not surprising, then, that there is general agreement regarding the theoretical importance of Situation Models. What is surprising, however, as originally pointed out by Glenberg, Meyer, and Lindem (1987), is the lack of agreement regarding what constitutes a Situational Model and the types of information it might contain. For the purposes of this chapter, we use the term Situation Model to refer to a discourse representation that captures aspects of a micro-world created by the reader (Johnson-Laird, 1983; van Dijk & Kintsch, 1983). In this sense, a Situation Model can include propositional information, but also information beyond that given in the text proper. For example, Situation Models can contain information related to the gist of the text, a reader’s potential background knowledge, and inferences not explicitly stated in the text (Zwaan & Radvansky, 1998). One theory of Situation-Model construction (i.e., the event-indexing Model) suggests that readers comprehend information in the story world at an event level (Zwaan, Langston, & Graesser, 1995). In this sense, events are
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Emotional and Temporal Aspects of Situation Model Processing during Text Comprehension: An Event-Related fMRI Study
Journal of Cognitive Neuroscience, 2005Co-Authors: Evelyn C. Ferstl, Mike Rinck, Yves D. Von CramonAbstract:Language comprehension in everyday life requires the continuous integration of prior discourse context and general world knowledge with the current utterance or sentence. In the neurolinguistic literature, these so-called Situation Model building processes have been ascribed to the prefrontal cortex or to the right hemisphere. In this study, we use whole-head event-related fMRI to directly map the neural correlates of narrative comprehension in context. While being scanned using a spin-echo sequence, 20 participants listened to 32 short stories, half of which contained globally inconsistent information. The inconsistencies concerned either temporal or chronological information or the emotional status of the protagonist. Hearing an inconsistent word elicited activation in the right anterior temporal lobe. The comparison of different information aspects revealed activation in the left precuneus and a bilateral frontoparietal network for chronological information. Emotional information elicited activation in the ventromedial prefrontal cortex and the extended amygdaloid complex. In addition, the integration of inconsistent emotional information engaged the dorsal frontomedial cortex (Brodmann's area 8/9), whereas the integration of inconsistent temporal information required the lateral prefrontal cortex bilaterally. These results indicate that listening to stories can elicit activation reflecting content-specific processes. Furthermore, updating of the Situation Model is not a unitary process but it also depends on the particular requirements of the text. The right hemisphere contributes to language processing in context, but equally important are the left medial and bilateral prefrontal cortices.
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Distance Effects in Surface Structures and Situation Models
Scientific Studies of Reading, 1998Co-Authors: Mike Rinck, Gordon H. Bower, Karin WolfAbstract:We investigated the role of spatial distance in Situation Models, surface recency, and explicit mentioning of target items in the updating of Situation Models created from narratives. In 3 experiments, a distance effect on accessibility was observed: The accessibility of target items (objects and rooms) contained in the Situation Model decreased with increasing distance between the target and the reader's focus of attention. The first 2 experiments demonstrated that this distance effect was mainly spatial: Accessibility of targets depended on the number of rooms located between the target and the focus of attention, that is, the protagonist's location in the Situation Model. Recency in the surface structure of the narrative affected accessibility only when strong surface cues were available. Additionally, the high accessibility of objects located in the same room as the protagonist did not depend on the text explicitly stating the name of the room. The findings corroborate the importance of spatial distan...
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Spatial Situation Models and narrative understanding: Some generalizations and extensions
Discourse Processes, 1996Co-Authors: Mike Rinck, Pepper Williams, Gordon H. Bower, Eni S. BeckerAbstract:The generality of effects of Situation Models on narrative comprehension was investigated. In three experiments, a spatial gradient of accessibility in Situation Models was observed. The accessibility of objects contained in the Situation Model decreased with increasing spatial distance between the object and the focus of attention in the readers’ Situation Model. A variety of factors that might influence the spatial gradient effect were investigated: the way the relevant spatial information was acquired (studying a text vs. a layout), the spatial scenario (a research center vs. a day care center), the direction of spatial distance (backward vs. forward on the route), the language used (English vs. German), the manner in which the accessibility of objects was probed (object probe pairs vs. anaphoric sentences), the existence of prior knowledge about the objects (objects learned as part of the scenario vs. unknown objects), and the participants’ task (reading narratives vs. imagining their own movements). ...
Patrick Reignier - One of the best experts on this subject based on the ideXlab platform.
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Learning Situation Models in a smart home
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2009Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:This paper addresses the problem of learning Situation Models for providing context-aware services. Context for Modeling human behavior in a smart environment is represented by a Situation Model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a Situation Model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of Situations from multimodal data, supervised learning of Situation representations, and evolution of a predefined Situation Model with feedback. The Situation Model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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Learning Situation Models in a Smart Home
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2008Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:This article addresses the problem of learning Situation Models for providing context-aware services. Context for Modeling human behavior in a smart envi- ronment is represented by a Situation Model describing environment, users and their activities. A framework for acquiring and evolving different layers of a Situation Model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of Situations from multimodal data, supervised learning of Situation representations, and the evolution of a predefined Situation Model with feedback. The Situation Model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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Learning Situation Models for Providing Context-Aware Services
2007Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have services adapt behavior to individual preferences though feedback from users. This article describes a method for learning Situation Models to drive context-aware services. With this approach an initial simplified Situation Model is adapted to accommodate user preferences by a supervised learning algorithm using feedback from users. To bootstrap this process, the initial Situation Model is acquired by applying an automatic segmentation process to sample observation of human activities. This Model is subsequently adapted to different operating environments and human preferences through interaction with users, using a supervised learning algorithm.
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HCI (6) - Learning Situation Models for providing context-aware services
Universal Access in Human-Computer Interaction. Ambient Interaction, 2007Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have services adapt behavior to individual preferences though feedback from users. This article describes a method for learning Situation Models to drive context-aware services. With this approach an initial simplified Situation Model is adapted to accommodate user preferences by a supervised learning algorithm using feedback from users. To bootstrap this process, the initial Situation Model is acquired by applying an automatic segmentation process to sample observation of human activities. This Model is subsequently adapted to different operating environments and human preferences through interaction with users, using a supervised learning algorithm.
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Automatic Acquisition of Context Models and its Application to Video Surveillance
2006Co-Authors: Oliver Brdiczka, Patrick Reignier, Pong Chi Yuen, Sofia Zaidenberg, James L. CrowleyAbstract:This paper addresses the problem of automatically acquiring context Models from data. Context and human behavior are represented using a state Model, called Situation Model. This Model consists of different layers referring to entities, filters, roles, relations, Situation and Situation relationship. We propose a framework for the automatic acquisition of these different layers. In particular, this paper proposes a novel generic Situation acquisition algorithm. The algorithm is also successfully applied to a video surveillance task and is evaluated by the public CAVIAR video database. The results are encouraging
Oliver Brdiczka - One of the best experts on this subject based on the ideXlab platform.
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Learning Situation Models in a smart home
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2009Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:This paper addresses the problem of learning Situation Models for providing context-aware services. Context for Modeling human behavior in a smart environment is represented by a Situation Model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a Situation Model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of Situations from multimodal data, supervised learning of Situation representations, and evolution of a predefined Situation Model with feedback. The Situation Model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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Computers in the Human Interaction Loop - Situation Modeling Layer
Human–Computer Interaction Series, 2009Co-Authors: Jan Kleindienst, Oliver Brdiczka, Jan Cuřín, Nikolaos DimakisAbstract:CHIL services require observation of human activity. The observation of humans and their activities is provided by perceptual components. For most human activities, a potentially infinite number of entities could be detected, and an infinite number of possible relations exist for any set of entities. The appropriate entities and relations must be determined for a task or service to be provided. This is the role of the Situation Model. Situation Models allow focusing attention and computing resources to determine the information required for operation of CHIL services. In this chapter, we introduce concepts and abstractions of Situation Modeling schema used in the CHIL architecture.
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Learning Situation Models in a Smart Home
IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 2008Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:This article addresses the problem of learning Situation Models for providing context-aware services. Context for Modeling human behavior in a smart envi- ronment is represented by a Situation Model describing environment, users and their activities. A framework for acquiring and evolving different layers of a Situation Model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of Situations from multimodal data, supervised learning of Situation representations, and the evolution of a predefined Situation Model with feedback. The Situation Model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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Learning Situation Models for Providing Context-Aware Services
2007Co-Authors: Oliver Brdiczka, James L. Crowley, Patrick ReignierAbstract:In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have services adapt behavior to individual preferences though feedback from users. This article describes a method for learning Situation Models to drive context-aware services. With this approach an initial simplified Situation Model is adapted to accommodate user preferences by a supervised learning algorithm using feedback from users. To bootstrap this process, the initial Situation Model is acquired by applying an automatic segmentation process to sample observation of human activities. This Model is subsequently adapted to different operating environments and human preferences through interaction with users, using a supervised learning algorithm.
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Learning Situation Models for Providing Context-Aware Services
2007Co-Authors: Oliver BrdiczkaAbstract:This thesis addresses the problem of learning Situation Models for providing context-aware services in an intelligent environment. First, the notion of context for Modeling human behavior in an intelligent environment is motivated and introduced. Context is represented by a Situation Model describing environment, users and their activities. Two example implementations for the Situation Model are proposed. A framework for acquiring and evolving different layers of a Situation Model is then introduced. Several novel learning methods are part of this framework: role detection per entity, unsupervised extraction of Situations from multimodal data, supervised learning of Situation representations, and the evolution of a predefined Situation Model with feedback. The Situation Model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed framework has been implemented and evaluated for an intelligent home environment.
David E. Copeland - One of the best experts on this subject based on the ideXlab platform.
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Working memory span and Situation Model processing.
The American journal of psychology, 2004Co-Authors: Gabriel A Radvansky, David E. CopelandAbstract:This study looked at how comprehension and memory processing at the Situation Model level is related to traditional measures of working memory capacity, including the word span, reading span, operation span, and spatial span tests. Issues of particular interest were the ability to remember event descriptions, the detection and memory of functional relationships, the detection of inconsistencies, sensitivity to causal connectivity, and memory for surface form, textbase, and Situation-specific content. There was little evidence that traditional measures of working memory span were directly related to processing at the Situation Model level. However, working memory span was related to our few textbase-level tests.
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Aging and Situation Model Updating
Aging Neuropsychology and Cognition, 2003Co-Authors: Gabriel A Radvansky, David E. Copeland, Diane E. Berish, Katinka DijkstraAbstract:The aim of this study was to investigate age differences in narrative comprehension and memory, with a focus on the updating of Situation Models during comprehension. While there are large effects of aging on memory for propositional textbase information, there is very little evidence that older adults have difficulty at the Situation Model level. Because described events are often dynamic, a comprehender must consistently update their Situation Model to make it consistent with the new information. The current experiments investigated whether there are any age differences associated with the ability to update a Situation Model along the spatial and temporal dimensions. Although updating effects were observed, they were largely not influenced by age. The relation of these findings to an understanding of older adults’ language comprehension and memory performance is discussed.
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Working memory and Situation Model updating
Memory & Cognition, 2001Co-Authors: Gabriel A Radvansky, David E. CopelandAbstract:Situation Model updating requires managing the availability of information as a function of its relevance to the current Situation. This is thought to involve some aspect of working memory. The present study assesses the relation between updating ability and various measures of working memory span or capacity. In addition, a primitive general measure of Situation Model processing, a Situation Model identification test, and its relation to updating ability was also assessed. The present experiment used a version of a paradigm developed by Glenberg, Meyer, and Lindem (1987) to assess updating. Although updating was observed in both anaphoric reading time and recognition test accuracy measures, the reading time measure was relatively weak. Importantly, the updating effect on the recognition test was unrelated to working memory capacity. In contrast, updating was related to performance on the Situation Model identification task. Specifically, people who were good at Model processing were better able to keep associated objects available than were people who were less adept. There were no differences in the maintenance of dissociated objects. These results suggest that the relationship between Situation Model processing and working memory capacity is relatively weak.