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A Hybrid Neural Network and Virtual Reality System
for Spatial Language Processing
Guillermina C. Martinez1, Angelo Cangelosi1, Kenny R. Coventry2 1School of Computing and 2Department of Psychology Drake Circus, Plymouth PL4 8AA, UK guille@usitmail.com, acangelosi@plymouth.ac.uk, kcoventry@plymouth.ac.uk Abstract
shape) play an important role in the comprehension ofspatial prepositions.
This paper describes a neural network model for the study Traditionally, geometric constructs have been invoked to of spatial language. It deals with both geometric and underpin prepositions’ lexical entries (e.g., [10,11]). For functional variables, which have been shown to play an example, in the sentence, “The pear is in the bowl,” the important role in the comprehension of spatial prepositions. figure (the pear) is located in the region described by the The network is integrated with a virtual reality interface for prepositional phrase “in the bowl”, with the spatial relation the direct manipulation of geometric and functional factors. expressed by in corresponding to “contained interior to the The training uses experimental stimuli and data. Results Clearly, while geometry is important in the use and generalization errors. Cluster analyses of hidden activation show that stimuli primarily group according to extra- geometric variables need to be invoked in order to account for use and comprehension. For example the expression, theman is at the piano, implies that the man is playing thepiano, not just that he is in close proximity to it. There have 1 Introduction
been a number of empirical demonstrations showing thatextra-geometric factors play an important role in the use The aim of this work is to develop a hybrid neural network and comprehension of spatial prepositions. Functional (NN) and virtual reality (VR) system for the study of spatial language and cognition. It will also be tested as a prototype underlying the meaning of the spatial prepositions in, on natural language interface for virtual environments.
Functional relations have to do with how objects interact understanding of spatial terms such as over, above, under, with each other, and what the functions of objects are. For and below, has proven to be an important experimental field example, with in, Garrod and Sanford [7] and Coventry [3] for the investigation of cognition [3,13]. The use of an propose that the lexical entry is: in [functional containment expression involving a spatial preposition in English - in is appropriate if the ground is conceived of as fulfilling conveys to a hearer where one object (figure) is located in its containment function]. Whether or not in is appropriate relation to a reference object (ground). Understanding the depends on a number of factors which determine whether meaning of spatial prepositions is of particular importance the container is fulfilling its function. Empirical evidence in semantics as they are among the set of closed class terms for the importance of this functional analysis has been which are generally regarded as having the role of acting as forthcoming for topological prepositions.
organizing structure for further conceptual material [14].
It has also recently been shown that prepositions are Recently, both experimental research and computational influenced differentially by geometric and extra-geometric models have investigated the use of spatial prepositions, variables. Coventry, Prat-Sala and Richards [5] found that and their role in spatial cognition.
the comprehension of over and under was more influencedby function than above and below, while the comprehension 1.1 Psychological Literature on Spatial Language and
of above and below was better predicted by geometry than Function
over and under. In addition, effects of extra-geometric In the experimental psychological literature it has been shown that both geometric (e.g., the distance between two comprehension even when the prototypical geometric objects and their relative orientation) and extra-geometric variables (e.g., the function of an object and its size and appropriateness ratings of expressions such as the umbrella is over the man to describe a picture of a man holding an Sadler's [11] spatial templates for the prepositions over, umbrella were reduced when rain was depicted as falling on the man even when the umbrella was depicted directly The Regier model, even though it is able to reproduce many of the experimental and cross-linguistic data on theuse and learning of spatial terms, has the limitations of 1.2 Neural Network Models of Spatial Language
relying only on geometrical-based processing and only There is some computational work that has modeled the deals with abstract objects. The network uses different acquisition and use of spatial terms using neural networks geometrical indices, such as the center of mass between the two objects, their minimal distance, and the overlapping of approach. Harris [9] used neural networks to model the their shapes. Although the use of these geometric polysemy of the preposition over, that is the fact that the components does allow the system to deal with change over term over appears to have many different senses, such as time, no other information is extracted and used, such as "being above", "up", "across", etc. Harris's model used Recently, a new computational model for spatial propagation to learn to associate the correct meaning of language has been proposed by Regier & Carlson [13]. This over with different sentences. All input sentences contained does not use connectionist techniques. It is based both on the term over to relate the position of a figure object with attentional factors on the processing of geometrical features respect to a ground object. After learning the correct mapping of the meanings of over, the activity of some ofthe hidden units auto-organizes in a way that units become sensitive to certain features of the object set used in thetraining sentences. There are units whose activation The prototype of a hybrid NN and VR system has been distinguishes between objects which are or are not normally developed. The NN learns to use spatial prepositions in in contact with a surface, and other units that are sensitive response to input stimuli describing geometrical and to the size and shape of the objects.
functional relationships between two objects. The NN The model introduces the problem of polysemy and module is integrated with a VR interface, where a user can openness of the meaning of some spatial terms [9]. It shows directly manipulate geometric and extra-geometric factors.
the emergence of the role of object-knowledge effects for This system can be used as an experimental tool for spatial spatial language using auto-organization systems, such as language and for natural language interfacing in VR neural networks. However, this work lacks any reference to the role of geometrical features in the learning and use ofspatial prepositions. The encoding of input in only linguistic 2.1 Neural Network
terms does not allow any processing of geometrical The NN architecture consists of a multi-layer perceptron.
properties between objects. The neural network model is The input layer receives information about a visual scene subject to the problem of symbol grounding in cognitively depicting specific spatial configurations of objects. The output units activate the correct spatial preposition(s) Terry Regier [12] has proposed a computational model describing the scene. The network has four output units, for spatial prepositions using a method called "constrained respectively for the prepositions over, above, under and connectionism" [6]. The model is trained on the use of below. The activation of each unit corresponds to the level various spatial prepositions for static (e.g. over and above) of agreement for the use of a specific term. After training, and moving (e.g. through) objects, and makes explicit use the activation must correspond to the subjective ratings of the processing of geometrical information. The model collected in experimental studies. The hidden layer contains consists of a complex neural network in which the units' five units, a number sufficient for the network to learn the layers and connection patterns are structured according to training data. The number of input units varies according to neuropsychological and cognitive evidence; only a few the explicit/implicit encoding of some of the properties of units are based on unstructured parallel distributed processing. An image of two objects (ground and figure) is The training and testing task utilize the stimuli and data input to the lower layer of the network. Then the image from an experiment on the role of functional factors in the goes through several levels of geometrical processing. The rating of the spatial prepositions over/above/under/below output units, corresponding to spatial prepositions, are (experiment 2 in [5]). In this study, subjects used a 7-point activated according to the geometrical position of the figure Likert scale to rate the use of the four spatial prepositions object with respect to the central ground. Regier [12] tested this model for various cognitive and cross-linguistic spatial holding/wearing an object (e.g. umbrella, visor) to protect language phenomena. For example, the model proved himself from another object (e.g., rain, spray). In this suitable for reproducing the experimental data of Logan & experiment four independent variables were manipulated:ORIENTATION of the protecting object (3 levels: an umbrella Figure 1: Examples of experimental conditions in the second experiment of Coventry et al. [5]. The three scenes differ in the level of
variable FUNCTION. In the control condition (left) there is not rain, in the non-functional condition (center) the umbrella does not protect
the man from the rain, and in the functional condition (right) the umbrella is fulfilling its function of protection the man from the rain.
can be rotated at 90, 45, and 0 degrees) FUNCTIONfulfillment of protection from the rain (3 levels: yes, no, variable. Two units encode the levels of APPROPRIATENESS, function, e.g. umbrella or suitcase (2 levels: yes, no) and Network B: Localist Object Encoding OBJECT type (4 levels). This results in 72 experimental This network does not have an explicit representation of the scenes/conditions. An example of three scenes is presented object appropriateness, because eight localist units are used in Figure 1. The scenes differ in the level of the variable to represent all objects. There are also three localist units for ORIENTATION and three for FUNCTION. This architecture Three network architectures are used. They only differ in the number of input units and the way input scenes are Network C: Feature-based Object Encoding encoded. The five hidden units and the four output units are In this network the objects are encoded according to their the same in all networks (Figure 2).
geometrical and functional features. Each object isrepresented using eight feature-based units. Three unitsencode the dimension of the object in the three dimensions(x, y, z) and three encode the major shape components(hemispherical, conical, cuboid). Two units refer to thelexicalized function of the object (i.e. APPROPRIATENESS).
For example, the object umbrella is encoded as x=1, y=1,z=.67, appropriate=1, inappropriate=0.
There are three localist units for ORIENTATION and three forFUNCTION. This architecture has a total of 14 input units.
A standard error backpropagation algorithm was used, witha learning rate of .01, momentum of .9 and 10000 epochs.
Of the total of 72 scenes, 71 were used for each trainingepoch, and 1 for the generalization test. The training of eachnetwork type A/B/C was replicated ten times, by varying Figure 2: Neural network architecture
the initial random weights and the stimulus randomly takenout for the generalization test.
Network A: Localist experiment encoding The subjects’ mean ratings for the use of the four In this network, the number of input units exactly reflects prepositions were normalized in the range 0-1 and were the number and levels of the four experimental variables.
used as teaching input for the backpropagation training.
This architecture has a total of 12 localist input units. Weuse the term localist to indicate that for each variable only 2.2 Virtual Reality Environment
Three input units are used to encode the three levels of manipulation of 3D objects in the scene. For example, in ORIENTATION of the protecting object. Three localist units the umbrella scene there are three objects that the user can are used for the three levels of the FUNCTION independent manipulate: the man, the protecting object (e.g. umbrella or suitcase), and the rain. For the protecting objects, the user each preposition that were passed back to the VR interface can edit some of their features, such as the size and rotation.
The program starts by showing an almost full-screenwindow with eleven buttons and displays a man with his Table 1: Average training and generalization errors for the three
right hand up. This man is rotated 60 degrees around his Y- axis. The user can then display/hide an object and edit its features. Once all the attributes are ready, the user can click on the “NNAnswer” button to ask the NN module to provide the rating for the four prepositions (Figure 3).
3.2 Analysis of Internal Representations
To understand the way geometrical and extra-geometrical
factors are processed by the networks, a cluster analysis of
the hidden activation was performed. This informs us about
the major criteria used by the network to perform the spatial
language task. A greater distance between clusters indicates
which variables are used first to process (i.e. separate)
stimuli and experimental conditions.
For each of the three network architectures, we chose the five out of the ten replications with the best learningperformance. The connection weights of the fifteen selectednetworks after epoch 10000 were used to calculate thehidden activation. The activation values of the five hiddenunits for each of the 72 input scenes were saved and used toperform a cluster analysis. Subsequently, we studied the Figure 3: Interface of the VR system. The user can choose the
cluster diagrams to identify the order in which some protecting object to display and edit its features. After the NNprocesses the scene, the ratings for the four spatial prepositions functional and/or geometrical factors are used to separate are shown in the bottom right corner of the interface.
clusters of experimental scenes. Although there wasvariability between the five cluster analyses of eacharchitecture, it was possible to identify some common This VR module was developed in Java using Borland’s clustering strategies for each condition.
Builder Java3D library. Through the Java3D API is possibleto create simple virtual reality worlds. The Java program also controlled the communication with the NN module With the experiment encoding architecture there are three diagrams that share the use of common and consistentclustering criteria. In these networks, clusters are created 3 Results
ORIENTATION variable. The first divisions group inputscenes according to the degree of rotation (0, 45, 90) of the 3.1 Training and generalization
protecting object. The second consistent clustering criterion The training task was relatively easy to learn for a groups scenes according to the type of objects falling on the multiplayer perceptron, mainly due to the limited set of man (e.g. rain or spray). In the fourth diagram, the early training data (71 training stimuli). The final error for all clustering criteria are a mix of the FUNCTION fulfillment and different architectures resulted in an average SSE 0.05. The the ORIENTATION variables. The fifth diagram does not networks were also able to generalize well to the stimulus have an identifiable clustering criterion.
taken out from the training set. The average generalization error for all architectures was 0.04. Table 1 reports the In the five diagrams for the architecture with localist object detailed average errors for each architecture. The results are encoding, the early divisions into clusters are determined by similar in the three conditions, with a tendency for the the variables ORIENTATION and by that of the falling object.
feature-based object encoding network to reach lower There is not clear and consistent prioritization of these two The whole VR and NN system was also successfully tested. After manipulating the properties of objects in the VR interface, the network produced the correct rating for The condition with feature-based encoding of objects hasfour diagrams that share the same clustering criteria. The affect the use and comprehension of spatial terms [2].
APPROPRIATENESS of objects for the protection function.
Secondly, the clusters are then subdivided according to the geometrical properties (e.g. through feature-based input unit type of falling objects. Thirdly, scenes group into clusters of network C) and its subsequent effect on the network that have similar dimensions or shape components. Figure 4 processing strategies seem to more adequately reflect the shows a cluster diagram for this condition. A major phenomena observed in experimental subjects. This better difference between this condition and the other two is that match between the network and experimental data favors up to the last level of clustering the appropriate and the use of such a type of architecture for the further inappropriate objects are always kept separate. In networks development of a computational model of spatial language A and B only at the level of the final clusters the two objects are separated. Finally, one cluster out of five uses anunclear and inconsistent grouping strategy.
4 Conclusion
This hybrid NN and VR system allowed us to model the effects of functional and geometrical factors on the provides a prototype NLP interface for interactive VR Further research is being conducted in order to develop a psychologically plausible neural network model for theprocessing of spatial language. The current prototype modelshows the importance of explicitly encoding and inputtingthe extra-geometrical features of objects, as well as theirgeometrical properties. However, the use of a pre-defined set of functional features and its distributed and explicit encoding in the input units is not yet satisfactory. Acomputational model of spatial language and cognitionshould be able to derive, on-demand, and use the right set of properties that are salient to the scene and its context. Thisis the direction that we are following in our on-goingresearch.
References
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[13] Regier, T. & Carlson, L.A. (in press). Grounding spatial [10] Herskovits, A. (1986). Language and spatial cognition. investigation. Journal of Experimental Psychology: General. [11] Logan, G.D. & Sadler, D.D. (1996). A computational analysis [14] Talmy, L. (1983). How language structures space. In H. Pick of the apprehension of spatial relations. In P. Bloom, M. A.
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