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Symposium 4: cognitive neuroscience of categorization
Brain and Cognition 53 (2003) 106–107 www.elsevier.com/locate/b&c
Symposia Abstracts Symposium 4: Cognitive neuroscience of categorization Organizer: Stevan Harnad, Universite´ du Que´bec a` Montre´al Neural network models of category learning and language, Angelo Cangelosi, University of Plymouth, UK Category learning is one of the fundamental abilities of cognitive organisms. For instance, categorical perception allows us to ‘‘sort’’ things in the world (objects, events, states, properties, and actions) into distinct, discrete categories with minimum or no overlap. This constitutes the core of our knowledge of the world about things that are in it, and what to do with them. Category learning and categorical perception have been hypothesized to constitute the groundwork of cognition, as in the case of the acquisition and evolution of language (Cangelosi & Harnad, 2002; Harnad, 1987). Categorical perception effects have been studied with both innate and learnt categories in human subjects, animals and artificial neural networks. Computational modelling through neural networks also permits the investigation of the functional role of categorical perception in category learning and language acquisition. In a series a models of category learning it has been shown that neural networks ‘‘warp’’ the similarity space of categories. Before category learning, the members of different categories are represented (in the hidden nodes) in a highly convoluted way. During the training, the structure of internal similarity space changes. The perceived differences between members of the same category are reduced and compressed, and the differences between members of different categories expand (Harnad, Hanson, & Lubin, 1991; Nakisa & Plunkett, 1998; Tijsseling & Harnad, 1997). These phenomena are similar to those observed in human subjects (Andrews, Livingston, & Harnad, 1998; Goldstone, 1994; Pevtzow & Harnad, 1997). For example, neural networks’ internal representations can be easily compared with psychological data using multidimensional scaling methods (Livingston & Andrews, 1995). Some neural network models study both category learning and language acquisition. Two different ways of acquiring categories can be simulated: (a) ‘‘sensorimotor toil’’ when new categories are acquired through real-time interaction and trial-and-error experience in sorting them, and (a) ‘‘symbolic theft’’ when new categories are acquired by using combinations of linguistic symbols describing them. In models that use both category learning methods, warping effects are enhanced in categories acquired through symbolic theft (Cangelosi, Greco, & Harnad, 2000). The ‘‘warping’’ of similarity space that occurs when categories are acquired by sensorimotor toil is transferred and further warped when categories are: acquired through language. In addition, symbolic theft learning provides an evolutionary advantage that might have contributed to the evolution of language in humans (Cangelosi & Harnad, 2002). In evolutionary neural network models of syntax acquisition (Cangelosi & Parisi, 2002), enhanced categorical perception effects have been found in syntactic categories such as nouns and verbs. When the network must respond to the same object in different contexts with different actions (verbs), the similarity space of verbs is optimized with respect to that of nouns (Cangelosi & Parisi, 2001). It is also hypothesized that verbs have a more beneficial effect on behavior than nouns because the latter tend to covary 0278-2626/$ - see front matter Ó 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0278-2626(03)00085-X
with the network’s sensorimotor tasks (actions/verbs), while nouns tend to covary with the objects of reality that may be responded to with different actions in different occasions. To understand better the relations between category learning, language processing and sensorimotor knowledge, the method of synthetic brain imaging (Arbib, Billard, Iacoboni, & Oztop, 2000; Horwitz, Tagamets, & McIntosh, 1999) has been applied to these artificial neural networks. Early analyses on data obtained from different experimental conditions (e.g., manipulations of the network architecture) show that the representations of perceptual categories and syntactic classes are sensitive to the internal organization of the network and the level of integration of linguistic information with sensorimotor knowledge (Cangelosi & Parisi, unpublished data). Moreover, these models show functional organizations that reflect those observed in human experiments (Martin et al., 1995; Perani et al., 1999) and that can be used to test hypotheses on the evolution of syntax. These neural network modeling studies support a series of general hypothesis on the role and interaction between category learning and language acquisition. First, categorical perception induced by language can be seen as an instance of the Whorfian Hypothesis (Whorf, 1964). Our language influences the way the world looks to us. Second, the enhancement of dissimilarities in the category similarity space due to language acquisition (symbolic theft) and its beneficial effects in the emergence of language highlight some of the evolutionary and adaptive advantages of language. Finally, the use of evolutionary models of category and language learning produces some functional and architectural equivalence between cognitive computational models and real organisms. For instance, the application of synthetic brain imaging methods to such models allows us to understand the interaction between categorical, linguistic and sensorimotor knowledge in the evolution of language capabilities in organisms.
References Andrews, J., Livingston, K., & Harnad, S. (1998). Categorical perception effects induced by category learning. Journal of Experimental Psychology: Human Learning and Cognition, 24(3), 732–753. Arbib, M. A., Billard, A., Iacoboni, M., & Oztop, E. (2000). Synthetic brain imaging: Grasping, mirror neurons and imitation. Neural Networks, 13, 975–997. Cangelosi, A., Greco, A., & Harnad, S. (2000). From robotic toil to symbolic theft: Grounding transfer from entry-level to higher-level categories. Connection Science, 12, 143–162. Cangelosi, A., & Harnad, S. (2002). The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories. Evolution of Communication, 4(1). Cangelosi, A., Parisi, D. (2001). How nouns and verbs differentially affect the behavior of artificial organisms. In J. D. Moore, & K. Stenning (Eds.), Proceedings of the 23rd Annual Conference of the Cognitive Science Society (pp. 170–175). London: Erlbaum. Cangelosi, A., & Parisi, D. (Eds.) (2002). Simulating the evolution of language. London: Springer. Goldstone, R. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123, 178–200.
Symposia Abstracts / Brain and Cognition 53 (2003) 106–107 Harnad, S. (Ed.) (1987). Categorical perception: The groundwork of cognition. New York: Cambridge University Press. Harnad, S., Hanson, S. J., & Lubin, J. (1991). Categorical perception and the evolution of supervised learning in neural nets. In D. W. Powers, & L. Reeker (Eds.), Working papers of the AAAI spring symposium on machine learning of natural language and ontology (pp. 65–74). Horwitz, B., Tagamets, M. A., & McIntosh, A. R. (1999). Neural modeling, functional brain imaging, and cognition, Trends Cognitive Science, 3, 91–98. Livingston, K. R., & Andrews, J. K. (1995). On the interaction of prior knowledge and stimulus structure in category learning. Quarterly Journal of Experimental Psychology Section A—Human Experimental Psychology, 48, 208–236. Nakisa, R. C., & Plunkett, K. (1998). Evolution of a rapidly learned representation for speech. Language and Cognitive Processes, 13, 105–127.
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Perani, D., Cappa, S. F., Schnur, T., Tettamanti, M., Collina, S., Rosa, M. M., & Fazio, F. (1999). The neural correlates of verb and noun processing: A PET study. Brain, 122, 2337–2344. Pevtzow, R., & Harnad, S. (1997). Warping similarity space in category learning by human subjects: The role of task difficulty. In M. Ramscar, U. Hahn, E. Cambouropolos, & H. Pain (Eds.), Proceedings of SimCat 1997: Interdisciplinary workshop on similarity and categorization (pp. 189-195). Department of Artificial Intelligence, Edinburgh University. Tijsseling, A., & Harnad, S. (1997). Warping similarity space in category learning by backprop nets. In M. Ramscar, U. Hahn, E. Cambouropolos, & H. Pain (Eds.), Proceedings of SimCat 1997: Interdisciplinary workshop on similarity and categorization (pp. 263-269). Department of Artificial Intelligence, Edinburgh University. Whorf, B. L. (1964). Language, thought and reality. Cambridge, MA: MIT Press.
Symposia Abstracts Symposium 4: Cognitive neuroscience of categorization Organizer: Stevan Harnad, Universite´ du Que´bec a` Montre´al Neural network models of category learning and language, Angelo Cangelosi, University of Plymouth, UK Category learning is one of the fundamental abilities of cognitive organisms. For instance, categorical perception allows us to ‘‘sort’’ things in the world (objects, events, states, properties, and actions) into distinct, discrete categories with minimum or no overlap. This constitutes the core of our knowledge of the world about things that are in it, and what to do with them. Category learning and categorical perception have been hypothesized to constitute the groundwork of cognition, as in the case of the acquisition and evolution of language (Cangelosi & Harnad, 2002; Harnad, 1987). Categorical perception effects have been studied with both innate and learnt categories in human subjects, animals and artificial neural networks. Computational modelling through neural networks also permits the investigation of the functional role of categorical perception in category learning and language acquisition. In a series a models of category learning it has been shown that neural networks ‘‘warp’’ the similarity space of categories. Before category learning, the members of different categories are represented (in the hidden nodes) in a highly convoluted way. During the training, the structure of internal similarity space changes. The perceived differences between members of the same category are reduced and compressed, and the differences between members of different categories expand (Harnad, Hanson, & Lubin, 1991; Nakisa & Plunkett, 1998; Tijsseling & Harnad, 1997). These phenomena are similar to those observed in human subjects (Andrews, Livingston, & Harnad, 1998; Goldstone, 1994; Pevtzow & Harnad, 1997). For example, neural networks’ internal representations can be easily compared with psychological data using multidimensional scaling methods (Livingston & Andrews, 1995). Some neural network models study both category learning and language acquisition. Two different ways of acquiring categories can be simulated: (a) ‘‘sensorimotor toil’’ when new categories are acquired through real-time interaction and trial-and-error experience in sorting them, and (a) ‘‘symbolic theft’’ when new categories are acquired by using combinations of linguistic symbols describing them. In models that use both category learning methods, warping effects are enhanced in categories acquired through symbolic theft (Cangelosi, Greco, & Harnad, 2000). The ‘‘warping’’ of similarity space that occurs when categories are acquired by sensorimotor toil is transferred and further warped when categories are: acquired through language. In addition, symbolic theft learning provides an evolutionary advantage that might have contributed to the evolution of language in humans (Cangelosi & Harnad, 2002). In evolutionary neural network models of syntax acquisition (Cangelosi & Parisi, 2002), enhanced categorical perception effects have been found in syntactic categories such as nouns and verbs. When the network must respond to the same object in different contexts with different actions (verbs), the similarity space of verbs is optimized with respect to that of nouns (Cangelosi & Parisi, 2001). It is also hypothesized that verbs have a more beneficial effect on behavior than nouns because the latter tend to covary 0278-2626/$ - see front matter Ó 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0278-2626(03)00085-X
with the network’s sensorimotor tasks (actions/verbs), while nouns tend to covary with the objects of reality that may be responded to with different actions in different occasions. To understand better the relations between category learning, language processing and sensorimotor knowledge, the method of synthetic brain imaging (Arbib, Billard, Iacoboni, & Oztop, 2000; Horwitz, Tagamets, & McIntosh, 1999) has been applied to these artificial neural networks. Early analyses on data obtained from different experimental conditions (e.g., manipulations of the network architecture) show that the representations of perceptual categories and syntactic classes are sensitive to the internal organization of the network and the level of integration of linguistic information with sensorimotor knowledge (Cangelosi & Parisi, unpublished data). Moreover, these models show functional organizations that reflect those observed in human experiments (Martin et al., 1995; Perani et al., 1999) and that can be used to test hypotheses on the evolution of syntax. These neural network modeling studies support a series of general hypothesis on the role and interaction between category learning and language acquisition. First, categorical perception induced by language can be seen as an instance of the Whorfian Hypothesis (Whorf, 1964). Our language influences the way the world looks to us. Second, the enhancement of dissimilarities in the category similarity space due to language acquisition (symbolic theft) and its beneficial effects in the emergence of language highlight some of the evolutionary and adaptive advantages of language. Finally, the use of evolutionary models of category and language learning produces some functional and architectural equivalence between cognitive computational models and real organisms. For instance, the application of synthetic brain imaging methods to such models allows us to understand the interaction between categorical, linguistic and sensorimotor knowledge in the evolution of language capabilities in organisms.
References Andrews, J., Livingston, K., & Harnad, S. (1998). Categorical perception effects induced by category learning. Journal of Experimental Psychology: Human Learning and Cognition, 24(3), 732–753. Arbib, M. A., Billard, A., Iacoboni, M., & Oztop, E. (2000). Synthetic brain imaging: Grasping, mirror neurons and imitation. Neural Networks, 13, 975–997. Cangelosi, A., Greco, A., & Harnad, S. (2000). From robotic toil to symbolic theft: Grounding transfer from entry-level to higher-level categories. Connection Science, 12, 143–162. Cangelosi, A., & Harnad, S. (2002). The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories. Evolution of Communication, 4(1). Cangelosi, A., Parisi, D. (2001). How nouns and verbs differentially affect the behavior of artificial organisms. In J. D. Moore, & K. Stenning (Eds.), Proceedings of the 23rd Annual Conference of the Cognitive Science Society (pp. 170–175). London: Erlbaum. Cangelosi, A., & Parisi, D. (Eds.) (2002). Simulating the evolution of language. London: Springer. Goldstone, R. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123, 178–200.
Symposia Abstracts / Brain and Cognition 53 (2003) 106–107 Harnad, S. (Ed.) (1987). Categorical perception: The groundwork of cognition. New York: Cambridge University Press. Harnad, S., Hanson, S. J., & Lubin, J. (1991). Categorical perception and the evolution of supervised learning in neural nets. In D. W. Powers, & L. Reeker (Eds.), Working papers of the AAAI spring symposium on machine learning of natural language and ontology (pp. 65–74). Horwitz, B., Tagamets, M. A., & McIntosh, A. R. (1999). Neural modeling, functional brain imaging, and cognition, Trends Cognitive Science, 3, 91–98. Livingston, K. R., & Andrews, J. K. (1995). On the interaction of prior knowledge and stimulus structure in category learning. Quarterly Journal of Experimental Psychology Section A—Human Experimental Psychology, 48, 208–236. Nakisa, R. C., & Plunkett, K. (1998). Evolution of a rapidly learned representation for speech. Language and Cognitive Processes, 13, 105–127.
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Perani, D., Cappa, S. F., Schnur, T., Tettamanti, M., Collina, S., Rosa, M. M., & Fazio, F. (1999). The neural correlates of verb and noun processing: A PET study. Brain, 122, 2337–2344. Pevtzow, R., & Harnad, S. (1997). Warping similarity space in category learning by human subjects: The role of task difficulty. In M. Ramscar, U. Hahn, E. Cambouropolos, & H. Pain (Eds.), Proceedings of SimCat 1997: Interdisciplinary workshop on similarity and categorization (pp. 189-195). Department of Artificial Intelligence, Edinburgh University. Tijsseling, A., & Harnad, S. (1997). Warping similarity space in category learning by backprop nets. In M. Ramscar, U. Hahn, E. Cambouropolos, & H. Pain (Eds.), Proceedings of SimCat 1997: Interdisciplinary workshop on similarity and categorization (pp. 263-269). Department of Artificial Intelligence, Edinburgh University. Whorf, B. L. (1964). Language, thought and reality. Cambridge, MA: MIT Press.