The Experts below are selected from a list of 60561 Experts worldwide ranked by ideXlab platform
Asli Ozyurek - One of the best experts on this subject based on the ideXlab platform.
-
does space structure Spatial Language a comparison of Spatial expression across sign Languages
Language, 2015Co-Authors: Pamela M Perniss, Asli Ozyurek, I E P ZwitserloodAbstract:The Spatial affordances of the visual modality give rise to a high degree of similarity between sign Languages in the Spatial domain. This stands in contrast to the vast structural and semantic diversity in linguistic encoding of space found in spoken Languages. However, the possibility and nature of linguistic diversity in Spatial encoding in sign Languages has not been rigorously investigated by systematic crosslinguistic comparison. Here, we compare locative expression in two unrelated sign Languages, Turkish Sign Language ( Turk Isaret Dili , TID) and German Sign Language ( Deutsche Gebardensprache , DGS), focusing on the expression of figure-ground (e.g. cup on table) and figure-figure (e.g. cup next to cup) relationships in a discourse context. In addition to similarities, we report qualitative and quantitative differences between the sign Languages in the formal devices used (i.e. unimanual vs. bimanual; simultaneous vs. sequential) and in the degree of iconicity of the Spatial devices. Our results suggest that sign Languages may display more diversity in the Spatial domain than has been previously assumed, and in a way more comparable with the diversity found in spoken Languages. The study contributes to a more comprehensive understanding of how space gets encoded in Language.
-
Spatial Language facilitates Spatial cognition evidence from children who lack Language input
Cognition, 2013Co-Authors: Deidre Gentner, Asli Ozyurek, Ozge Gurcanli, Susan GoldinmeadowAbstract:Does Spatial Language influence how people think about space? To address this question, we observed children who did not know a conventional Language, and tested their performance on nonlinguistic Spatial tasks. We studied deaf children living in Istanbul whose hearing losses prevented them from acquiring speech and whose hearing parents had not exposed them to sign. Lacking a conventional Language, the children used gestures, called homesigns, to communicate. In Study 1, we asked whether homesigners used gesture to convey Spatial relations, and found that they did not. In Study 2, we tested a new group of homesigners on a Spatial Mapping Task, and found that they performed significantly worse than hearing Turkish children who were matched to the deaf children on another cognitive task. The absence of Spatial Language thus went hand-in-hand with poor performance on the nonlinguistic Spatial task, pointing to the importance of Spatial Language in thinking about space.
-
does space structure Spatial Language linguistic encoding of space in sign Languages
Cognitive Science, 2011Co-Authors: Pamela M Perniss, I E P Zwitserlood, Asli OzyurekAbstract:Does Space Structure Spatial Language? Linguistic Encoding of Space in Sign Languages Pamela Perniss (pamela.perniss@mpi.nl) Inge Zwitserlood (inge.zwitserlood@mpi.nl) Asli Ozyurek (asli.ozyurek@mpi.nl) Radboud University Nijmegen & Max Planck Institute for Psycholinguistics PO Box 310, 6500 AH Nijmegen, Netherlands space. The Spatial relationship between the signer’s hands represents the Spatial relationship between the referents, whereby the handshapes are iconic with certain features of the referents (e.g. the inverted cupped hand to represent the bulk of a house). In contrast, there is no resemblance, or iconicity, between the actual scene and the linguistic form of a spoken Language locative expression, as e.g. the English expression There is a bicycle next to the house. Abstract Spatial Language in signed Language is assumed to be shaped by affordances of the visual-Spatial modality – where the use of the hands and space allow the mapping of Spatial relationships in an iconic, analogue way – and thus to be similar across sign Languages. In this study, we test assumptions regarding the modality-driven similarity of Spatial Language by comparing locative expressions (e.g., cup is on the table) in two unrelated sign Languages, TID (Turk Isaret Dili, Turkish Sign Language) and DGS (Deutsche Gebardensprache, German Sign Language) in a communicative, discourse context. Our results show that each sign Language conventionalizes the structure of locative expressions in different ways, going beyond iconic and analogue representations, suggesting that the use of space to represent space does not uniformly and predictably drive Spatial Language in the visual-Spatial modality. These results are important for our understanding of how Language modality shapes the structure of Language. HOUSE loc here BICYCLE loc next-to-house Figure 1. Example of an ASL (American Sign Language) locative expression depicting the Spatial relationship of a bicycle next to a house (Emmorey, 2002). The expression contains the lexical signs for house (still 1) and bicycle (still 3), each followed by a locative predicate localizing the referent in space. Keywords: iconicity; Language modality; Spatial Language; locative expression; sign Language Introduction Despite the difference in modality of expression, signed (visual-Spatial) and spoken (vocal-aural) Languages similarly conform to principles of grammatical structure and linguistic form (Klima & Bellugi, 1979; Liddell, 1980; Padden, 1983; Stokoe, 1960; Supalla, 1986). However, in signed Language, the use of the hands as primary articulators within a visible Spatial medium for expression (i.e. the space around the body) has special consequences for the expression of visual-Spatial information (e.g. of referent size/shape, location, or motion). Spatial Language, such as locative expressions, is a primary domain in which modality affects the structure of representation. Locative expressions in both signed and spoken Language are characterized by linguistic encoding of entities and the Spatial relationship between them (cf. Talmy, 1985). However, sign Language locative expressions differ radically from those in spoken Language in affording a visual similarity (or iconicity) with the real-world scenes being represented. For example, a signed expression of the Spatial relationship between a house and a bicycle is clearly iconic of the scene itself. In the example from American Sign Language (ASL) in Figure 1, the signer depicts a bicycle as being located beside a house by placing her hands (her left hand representing the house in still 2; her right hand representing the bicycle in still 4) next to each other in sign In general, spoken Languages exhibit a wide range of cross- linguistic variation in the encoding of Spatial relationships in locative expressions, both in the devices used and in their morphosyntactic arrangement (Grinevald, 2006; Levinson & Wilkins, 2006). For example, spoken Language locative expressions exhibit the use of adpositions, like the Spatial prepositions used in English or the case-marking postpositions used in Turkish, or different types of locative or postural verbs (as in Ewe (Ghana) or Tzeltal (Mexico)). Such variation is not expected in signed Languages, however. Instead, signed Languages are assumed to be structurally homogenous in the expression of Spatial relationships. The affordances of the visual-Spatial modality for iconic, analogue Spatial representation are assumed to be the primary force in shaping Spatial expression, thus creating fundamental similarities in Spatial Language across different sign Languages (e.g. Aronoff, Meir, Padden & Sandler, 2003; Emmorey, 2002). A consequence of this assumption of similarity, rooted in the notion that signers will exploit the iconic affordances of the modality where possible, has been a dearth of empirical investigation in this domain. Where the encoding of Spatial relationships is mentioned in the literature, its iconic character is stated as fact, and conforms to the underlying assumption that Spatial relationships will be represented in an iconic, analogue way,
Karen Emmorey - One of the best experts on this subject based on the ideXlab platform.
-
the biology of linguistic expression impacts neural correlates for Spatial Language
Journal of Cognitive Neuroscience, 2013Co-Authors: Karen Emmorey, Stephen Mccullough, Sonya Mehta, Laura Boles L Ponto, Thomas J GrabowskiAbstract:Biological differences between signed and spoken Languages may be most evident in the expression of Spatial information. PET was used to investigate the neural substrates supporting the production of Spatial Language in American Sign Language as expressed by classifier constructions, in which handshape indicates object type and the location/motion of the hand iconically depicts the location/motion of a referent object. Deaf native signers performed a picture description task in which they overtly named objects or produced classifier constructions that varied in location, motion, or object type. In contrast to the expression of location and motion, the production of both lexical signs and object type classifier morphemes engaged left inferior frontal cortex and left inferior temporal cortex, supporting the hypothesis that unlike the location and motion components of a classifier construction, classifier handshapes are categorical morphemes that are retrieved via left hemisphere Language regions. In addition, lexical signs engaged the anterior temporal lobes to a greater extent than classifier constructions, which we suggest reflects increased semantic processing required to name individual objects compared with simply indicating the type of object. Both location and motion classifier constructions engaged bilateral superior parietal cortex, with some evidence that the expression of static locations differentially engaged the left intraparietal sulcus. We argue that bilateral parietal activation reflects the biological underpinnings of sign Language. To express Spatial information, signers must transform visual-Spatial representations into a body-centered reference frame and reach toward target locations within signing space.
-
evidence from an emerging sign Language reveals that Language supports Spatial cognition
Proceedings of the National Academy of Sciences of the United States of America, 2010Co-Authors: Jennie E Pyers, Anna Shusterman, Ann Senghas, Elizabeth S Spelke, Karen EmmoreyAbstract:Although Spatial Language and Spatial cognition covary over development and across Languages, determining the causal direction of this relationship presents a challenge. Here we show that mature human Spatial cognition depends on the acquisition of specific aspects of Spatial Language. We tested two cohorts of deaf signers who acquired an emerging sign Language in Nicaragua at the same age but during different time periods: the first cohort of signers acquired the Language in its infancy, and 10 y later the second cohort of signers acquired the Language in a more complex form. We found that the second-cohort signers, now in their 20s, used more consistent Spatial Language than the first-cohort signers, now in their 30s. Correspondingly, they outperformed the first cohort in Spatially guided searches, both when they were disoriented and when an array was rotated. Consistent linguistic marking of left–right relations correlated with search performance under disorientation, whereas consistent marking of ground information correlated with search in rotated arrays. Human Spatial cognition therefore is modulated by the acquisition of a rich Language.
-
perspectives on classifier constructions in sign Languages
2003Co-Authors: Karen EmmoreyAbstract:Contents: Preface. Part I: The Syntax and Morphology of Classifiers in Sign Languages. A. Schembri, Rethinking "Classifiers" in Signed Languages. B. Bergman, L. Wallin, Noun and Verbal Classifiers in Swedish Sign Language. M. Aronoff, I. Meir, C. Padden, W. Sandler, Classifier Constructions and Morphology in Two Sign Languages. A.Y. Aikhenvald, Commentary: Classifiers in Spoken and in Signed Languages: How to Know More. C. Grinevald, Classifier Systems in the Context of a Typology of Nominal Classification. Part II: Cross-Linguistic Variations in Classifier Constructions and Spatial Language. U. Zeshan, "Classificatory" Constructions in Indo-Pakistani Sign Language: Grammaticalization and Lexicalization Processes. G. Tang, Verbs of Motion and Location in Hong Kong Sign Language: Conflation and Lexicalization. L. Talmy, Commentary: The Representation of Spatial Structure in Spoken and Signed Language. Part III: Classifier Constructions and Gesture. S.K. Liddell, Sources of Meaning in ASL Classifier Predicates. K. Emmorey, M. Herzig, Categorical Versus Gradient Properties of Classifier Constructions in ASL. T. Supalla, Commentary: Revisiting Visual Analogy in ASL Classifier Predicates. S. Duncan, Gesture in Language: Issues for Sign Language Research. Part IV: The Acquisition of Classifier Constructions. D.I. Slobin, N. Hoiting, M. Kuntze, R. Lindert, A. Weinberg, J. Pyers, M. Anthony, Y. Biederman, H. Thumann, A Cognitive/Functional Perspective on the Acquisition of "Classifiers." G. Morgan, B. Woll, The Development of Reference Switching Encoded Through Body Classifiers in British Sign Language. E. Engberg-Pedersen, How Composite Is a Fall: Adults' and Children's Descriptions of Different Types of Falls in Danish Sign Language.
-
neural systems underlying Spatial Language in american sign Language
NeuroImage, 2002Co-Authors: Karen Emmorey, Stephen Mccullough, Laura Boles L Ponto, Thomas J Grabowski, Hanna Damasio, Richard D Hichwa, Ursula BellugiAbstract:A [15O]water PET experiment was conducted to investigate the neural regions engaged in processing constructions unique to signed Languages: classifier predicates in which the position of the hands in signing space schematically represents Spatial relations among objects. Ten deaf native signers viewed line drawings depicting a Spatial relation between two objects (e.g., a cup on a table) and were asked either to produce a classifier construction or an American Sign Language (ASL) preposition that described the Spatial relation or to name the figure object (colored red). Compared to naming objects, describing Spatial relationships with classifier constructions engaged the supramarginal gyrus (SMG) within both hemispheres. Compared to naming objects, naming Spatial relations with ASL prepositions engaged only the right SMG. Previous research indicates that retrieval of English prepositions engages both right and left SMG, but more inferiorly than for ASL classifier constructions. Compared to ASL prepositions, naming Spatial relations with classifier constructions engaged left inferior temporal (IT) cortex, a region activated when naming concrete objects in either ASL or English. Left IT may be engaged because the handshapes in classifier constructions encode information about object type (e.g., flat surface). Overall, the results suggest more right hemisphere involvement when expressing Spatial relations in ASL, perhaps because signing space is used to encode the Spatial relationship between objects.
-
neural systems underlying Spatial Language in american sign Language
NeuroImage, 2002Co-Authors: Karen Emmorey, Stephen Mccullough, Laura Boles L Ponto, Thomas J Grabowski, Hanna Damasio, Richard D Hichwa, Ursula BellugiAbstract:A [(15)O]water PET experiment was conducted to investigate the neural regions engaged in processing constructions unique to signed Languages: classifier predicates in which the position of the hands in signing space schematically represents Spatial relations among objects. Ten deaf native signers viewed line drawings depicting a Spatial relation between two objects (e.g., a cup on a table) and were asked either to produce a classifier construction or an American Sign Language (ASL) preposition that described the Spatial relation or to name the figure object (colored red). Compared to naming objects, describing Spatial relationships with classifier constructions engaged the supramarginal gyrus (SMG) within both hemispheres. Compared to naming objects, naming Spatial relations with ASL prepositions engaged only the right SMG. Previous research indicates that retrieval of English prepositions engages both right and left SMG, but more inferiorly than for ASL classifier constructions. Compared to ASL prepositions, naming Spatial relations with classifier constructions engaged left inferior temporal (IT) cortex, a region activated when naming concrete objects in either ASL or English. Left IT may be engaged because the handshapes in classifier constructions encode information about object type (e.g., flat surface). Overall, the results suggest more right hemisphere involvement when expressing Spatial relations in ASL, perhaps because signing space is used to encode the Spatial relationship between objects.
Pamela M Perniss - One of the best experts on this subject based on the ideXlab platform.
-
does space structure Spatial Language a comparison of Spatial expression across sign Languages
Language, 2015Co-Authors: Pamela M Perniss, Asli Ozyurek, I E P ZwitserloodAbstract:The Spatial affordances of the visual modality give rise to a high degree of similarity between sign Languages in the Spatial domain. This stands in contrast to the vast structural and semantic diversity in linguistic encoding of space found in spoken Languages. However, the possibility and nature of linguistic diversity in Spatial encoding in sign Languages has not been rigorously investigated by systematic crosslinguistic comparison. Here, we compare locative expression in two unrelated sign Languages, Turkish Sign Language ( Turk Isaret Dili , TID) and German Sign Language ( Deutsche Gebardensprache , DGS), focusing on the expression of figure-ground (e.g. cup on table) and figure-figure (e.g. cup next to cup) relationships in a discourse context. In addition to similarities, we report qualitative and quantitative differences between the sign Languages in the formal devices used (i.e. unimanual vs. bimanual; simultaneous vs. sequential) and in the degree of iconicity of the Spatial devices. Our results suggest that sign Languages may display more diversity in the Spatial domain than has been previously assumed, and in a way more comparable with the diversity found in spoken Languages. The study contributes to a more comprehensive understanding of how space gets encoded in Language.
-
does space structure Spatial Language linguistic encoding of space in sign Languages
Cognitive Science, 2011Co-Authors: Pamela M Perniss, I E P Zwitserlood, Asli OzyurekAbstract:Does Space Structure Spatial Language? Linguistic Encoding of Space in Sign Languages Pamela Perniss (pamela.perniss@mpi.nl) Inge Zwitserlood (inge.zwitserlood@mpi.nl) Asli Ozyurek (asli.ozyurek@mpi.nl) Radboud University Nijmegen & Max Planck Institute for Psycholinguistics PO Box 310, 6500 AH Nijmegen, Netherlands space. The Spatial relationship between the signer’s hands represents the Spatial relationship between the referents, whereby the handshapes are iconic with certain features of the referents (e.g. the inverted cupped hand to represent the bulk of a house). In contrast, there is no resemblance, or iconicity, between the actual scene and the linguistic form of a spoken Language locative expression, as e.g. the English expression There is a bicycle next to the house. Abstract Spatial Language in signed Language is assumed to be shaped by affordances of the visual-Spatial modality – where the use of the hands and space allow the mapping of Spatial relationships in an iconic, analogue way – and thus to be similar across sign Languages. In this study, we test assumptions regarding the modality-driven similarity of Spatial Language by comparing locative expressions (e.g., cup is on the table) in two unrelated sign Languages, TID (Turk Isaret Dili, Turkish Sign Language) and DGS (Deutsche Gebardensprache, German Sign Language) in a communicative, discourse context. Our results show that each sign Language conventionalizes the structure of locative expressions in different ways, going beyond iconic and analogue representations, suggesting that the use of space to represent space does not uniformly and predictably drive Spatial Language in the visual-Spatial modality. These results are important for our understanding of how Language modality shapes the structure of Language. HOUSE loc here BICYCLE loc next-to-house Figure 1. Example of an ASL (American Sign Language) locative expression depicting the Spatial relationship of a bicycle next to a house (Emmorey, 2002). The expression contains the lexical signs for house (still 1) and bicycle (still 3), each followed by a locative predicate localizing the referent in space. Keywords: iconicity; Language modality; Spatial Language; locative expression; sign Language Introduction Despite the difference in modality of expression, signed (visual-Spatial) and spoken (vocal-aural) Languages similarly conform to principles of grammatical structure and linguistic form (Klima & Bellugi, 1979; Liddell, 1980; Padden, 1983; Stokoe, 1960; Supalla, 1986). However, in signed Language, the use of the hands as primary articulators within a visible Spatial medium for expression (i.e. the space around the body) has special consequences for the expression of visual-Spatial information (e.g. of referent size/shape, location, or motion). Spatial Language, such as locative expressions, is a primary domain in which modality affects the structure of representation. Locative expressions in both signed and spoken Language are characterized by linguistic encoding of entities and the Spatial relationship between them (cf. Talmy, 1985). However, sign Language locative expressions differ radically from those in spoken Language in affording a visual similarity (or iconicity) with the real-world scenes being represented. For example, a signed expression of the Spatial relationship between a house and a bicycle is clearly iconic of the scene itself. In the example from American Sign Language (ASL) in Figure 1, the signer depicts a bicycle as being located beside a house by placing her hands (her left hand representing the house in still 2; her right hand representing the bicycle in still 4) next to each other in sign In general, spoken Languages exhibit a wide range of cross- linguistic variation in the encoding of Spatial relationships in locative expressions, both in the devices used and in their morphosyntactic arrangement (Grinevald, 2006; Levinson & Wilkins, 2006). For example, spoken Language locative expressions exhibit the use of adpositions, like the Spatial prepositions used in English or the case-marking postpositions used in Turkish, or different types of locative or postural verbs (as in Ewe (Ghana) or Tzeltal (Mexico)). Such variation is not expected in signed Languages, however. Instead, signed Languages are assumed to be structurally homogenous in the expression of Spatial relationships. The affordances of the visual-Spatial modality for iconic, analogue Spatial representation are assumed to be the primary force in shaping Spatial expression, thus creating fundamental similarities in Spatial Language across different sign Languages (e.g. Aronoff, Meir, Padden & Sandler, 2003; Emmorey, 2002). A consequence of this assumption of similarity, rooted in the notion that signers will exploit the iconic affordances of the modality where possible, has been a dearth of empirical investigation in this domain. Where the encoding of Spatial relationships is mentioned in the literature, its iconic character is stated as fact, and conforms to the underlying assumption that Spatial relationships will be represented in an iconic, analogue way,
-
space and iconicity in german sign Language dgs
2007Co-Authors: Pamela M PernissAbstract:This dissertation investigates the expression of Spatial relationships in German Sign Language (Deutsche Gebardensprache, DGS). The analysis focuses on linguistic expression in the Spatial domain in two types of discourse: static scene description (location) and event narratives (location and motion). Its primary theoretical objectives are to characterize the structure of locative descriptions in DGS; to explain the use of frames of reference and perspective in the expression of location and motion; to clarify the interrelationship between the systems of frames of reference, signing perspective, and classifier predicates; and to characterize the interplay between iconicity principles, on the one hand, and grammatical and discourse constraints, on the other hand, in the use of these Spatial devices. In more general terms, the dissertation provides a usage-based account of iconic mapping in the visual-Spatial modality. The use of space in sign Language expression is widely assumed to be guided by iconic principles, which are furthermore assumed to hold in the same way across sign Languages. Thus, there has been little expectation of variation between sign Languages in the Spatial domain in the use of Spatial devices. Consequently, perhaps, there has been little systematic investigation of linguistic expression in the Spatial domain in individual sign Languages, and less investigation of Spatial Language in extended signed discourse. This dissertation provides an investigation of Spatial expressions in DGS by investigating the impact of different constraints on iconicity in sign Language structure. The analyses have important implications for our understanding of the role of iconicity in the visual-Spatial modality, the possible Language-specific variation within the Spatial domain in the visual-Spatial modality, the structure of Spatial Language in both natural Language modalities, and the relationship between Spatial Language and cognition
Barbara Landau - One of the best experts on this subject based on the ideXlab platform.
-
containment and support core and complexity in Spatial Language learning
Cognitive Science, 2017Co-Authors: Barbara Landau, Kristen Johannes, Dimitrios Skordos, Anna PapafragouAbstract:Containment and support have traditionally been assumed to represent universal conceptual foundations for Spatial terms. This assumption can be challenged, however: English in and on are applied across a surprisingly broad range of exemplars, and comparable terms in other Languages show significant variation in their application. We propose that the broad domains of both containment and support have internal structure that reflects different subtypes, that this structure is reflected in basic Spatial term usage across Languages, and that it constrains children's Spatial term learning. Using a newly developed battery, we asked how adults and 4-year-old children speaking English or Greek distribute basic Spatial terms across subtypes of containment and support. We found that containment showed similar distributions of basic terms across subtypes among all groups while support showed such similarity only among adults, with striking differences between children learning English versus Greek. We conclude that the two domains differ considerably in the learning problems they present, and that learning in and on is remarkably complex. Together, our results point to the need for a more nuanced view of Spatial term learning.
-
update on what and where in Spatial Language a new division of labor for Spatial terms
Cognitive Science, 2017Co-Authors: Barbara LandauAbstract:In this article, I revisit Landau and Jackendoff's (1993) paper, “What and where in Spatial Language and Spatial cognition,” proposing a friendly amendment and reformulation. The original paper emphasized the distinct geometries that are engaged when objects are represented as members of object kinds (named by count nouns), versus when they are represented as figure and ground in Spatial expressions (i.e., play the role of arguments of Spatial prepositions). We provided empirical and theoretical arguments for the link between these distinct representations in Spatial Language and their accompanying nonlinguistic neural representations, emphasizing the “what” and “where” systems of the visual system. In the present paper, I propose a second division of labor between two classes of Spatial prepositions in English that appear to be quite distinct. One class includes prepositions such as in and on, whose core meanings engage force-dynamic, functional relationships between objects, with geometry only a marginal player. The second class includes prepositions such as above/below and right/left, whose core meanings engage geometry, with force-dynamic relationships a passing or irrelevant variable. The insight that objects’ force-dynamic relationships matter to Spatial terms’ uses is not new; but thinking of these terms as a distinct set within Spatial Language has theoretical and empirical consequences that are new. I propose three such consequences, rooted in the fact that geometric knowledge is highly constrained and early-emerging in life, while force-dynamic knowledge of objects and their interactions is relatively unconstrained and needs to be learned piecemeal over a lengthy timeline. First, the two classes will engage different learning problems, with different developmental trajectories for both first and second Language learners; second, the classes will naturally lead to different degrees of cross-linguistic variation; and third, they may be rooted in different neural representations.
-
Spatial Language and Spatial representation a cross linguistic comparison
Cognition, 2001Co-Authors: Edward Munnich, Barbara Landau, Barbara Anne DosherAbstract:We examined the relationship between Spatial Language and Spatial memory by comparing native English, Japanese, and Korean speakers' naming of Spatial locations and their Spatial memory for the same set of locations. We focused on two kinds of Spatial organization: axial structure of the reference object, and contact/support with respect to its surface. The results of two Language (naming) tasks showed similar organization across the three Language groups in terms of axial structure, but differences in organization in terms of contact/support. In contrast, the results of two memory tasks were the same across Language groups for both axial structure and contact/support. Moreover, the relationship between Spatial Language and Spatial memory in the two sets of tasks did not show a straightforward isomorphism between the two systems. We conclude that Spatial Language and Spatial memory engage the same kinds of Spatial properties, suggesting similarity in the foundations of the two systems. However, the two systems appear to be partially independent: the preservation of particular Spatial properties was not mandatory across Languages, nor across memory tasks, and cross-linguistic differences in Spatial Language did not lead to differences in the non-linguistic encoding of location. We speculate that the similarity in linguistic and non-linguistic representations of space may emerge as a functional consequence of negotiating the Spatial world.
-
what and where in Spatial Language and Spatial cognition
Behavioral and Brain Sciences, 1993Co-Authors: Barbara Landau, Ray JackendoffAbstract:Fundamental to Spatial knowledge in all species are the representations underlying object recognition, object search, and navigation through space. But what sets humans apart from other species is our ability to express Spatial experience through Language. This target article explores the Language of objects and places , asking what geometric properties are preserved in the representations underlying object nouns and Spatial prepositions in English. Evidence from these two aspects of Language suggests there are significant differences in the geometric richness with which objects and places are encoded. When an object is named (i.e., with count nouns), detailed geometric properties – principally the object's shape (axes, solid and hollow volumes, surfaces, and parts) – are represented. In contrast, when an object plays the role of either “figure” (located object) or “ground” (reference object) in a locational expression, only very coarse geometric object properties are represented, primarily the main axes. In addition, the Spatial functions encoded by Spatial prepositions tend to be nonmetric and relatively coarse, for example, “containment,” “contact,” “relative distance,” and “relative direction.” These properties are representative of other Languages as well. The striking differences in the way Language encodes objects versus places lead us to suggest two explanations: First, there is a tendency for Languages to level out geometric detail from both object and place representations. Second, a nonlinguistic disparity between the representations of “what” and “where” underlies how Language represents objects and places. The Language of objects and places converges with and enriches our understanding of corresponding Spatial representations.
Mariefrancine Moens - One of the best experts on this subject based on the ideXlab platform.
-
acquiring common sense Spatial knowledge through implicit Spatial templates
arXiv: Artificial Intelligence, 2017Co-Authors: Guillem Collell, Luc Van Gool, Mariefrancine MoensAbstract:Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of Spatial knowledge is often modeled with Spatial templates, i.e., regions of acceptability of two objects under an explicit Spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that restricts Spatial templates to explicit Spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit Spatial Language, i.e., those relationships (generally actions) for which the Spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting Spatial arrangements from implicit Spatial Language requires significant common sense Spatial understanding. Here, we introduce the task of predicting Spatial templates for two objects under a relationship, which can be seen as a Spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that Spatial locations are to a large extent predictable from implicit Spatial Language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g.,"man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate Spatial predictions, proving that the models acquire solid common sense Spatial knowledge allowing for such generalization.
-
structured learning for Spatial information extraction from biomedical text bacteria biotopes
BMC Bioinformatics, 2015Co-Authors: Dan Roth, Parisa Kordjamshidi, Mariefrancine MoensAbstract:We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural Language texts and putting this knowledge in databases for easy access by biologists. The task is challenging and the previous results are far below an acceptable level of performance, particularly for extraction of localization relationships. Therefore, we aim to design a new system for such extractions, using the framework of structured machine learning techniques. We design a new model for joint extraction of biomedical entities and the localization relationship. Our model is based on a Spatial role labeling (SpRL) model designed for Spatial understanding of unrestricted text. We extend SpRL to extract discourse level Spatial relations in the biomedical domain and apply it on the BioNLP-ST 2013, BB-shared task. We highlight the main differences between general Spatial Language understanding and Spatial information extraction from the scientific text which is the focus of this work. We exploit the text’s structure and discourse level global features. Our model and the designed features substantially improve on the previous systems, achieving an absolute improvement of approximately 57 percent over F1 measure of the best previous system for this task. Our experimental results indicate that a joint learning model over all entities and relationships in a document outperforms a model which extracts entities and relationships independently. Our global learning model significantly improves the state-of-the-art results on this task and has a high potential to be adopted in other natural Language processing (NLP) tasks in the biomedical domain.