How Language Can Help Categorization: a Neural Network Model of
Lexical Acquisition
Marco Mirolli1,2, Domenico Parisi1
1
2
Institute of Cognitive Sciences and Technologies, CNR, Viale Marx 15, 00137, Roma
Philosophy and Social Sciences Department, University of Siena, Via Roma 47, 53100, Siena
mirolli2@unisi.it
Understanding the relationship between language and thought is one of the most difficult and
important challanges facing cognitive science (Gentner and Goldin-Meadow 2003, Carruthers
and Boucher 1998). Language has not only a communicative (social) function, but also a
cognitive (individual) function (Vygotsky 1962, Jackendoff 1996, Clark 1998, Carruthers
2002). There are many ways in which the possession of language can improve the cognitive
capacity of the child. In the present work we discuss one of these ways: language as an aid to
categorization.
We describe a simple neural network model of lexical acquisition in the child and then we
compare the categorization abilities of neural networks with and without language, showing
that in fact language can improve categorization.
In our model lexical acquisition is divided in two successive learning phases, which are
meant to represent, approximatively, what happens during the first year of the child and
between 12 and 18 months, respectively.
During the first phase the neural network is divided into two separate sub-networks. The
first sub-network, which we call the ‘sensory-motor net’, learns to distinguish between two
different categories of perceptual (visual) stimuli (‘edible’ and ‘poisonous’ mushrooms) while
the second sub-network, which we call the ‘linguistic net’, learns to imitate the sounds which
are present in its environment. Both sub-networks learn their respective tasks using a backpropagation algorithm.
During the second phase, new connections between the hidden layers of the two subnetworks are established and the network learns to associate the perceptual properties of the
mushrooms with the linguistic signals which co-occur with them using a Hebbian learning
rule.
At the end of the second phase, the network is tested for both comprehension and
production of the linguistic signals, with very good results. We study the ‘internal
representations’ of mushrooms (that is, the activation patterns observed in the sensory-motor
hidden units) in different conditions. The results of this analysis show that language can
improve the categorization abilities of a neural network in three ways: (1) when the name of
the object is perceived together with the object’s perceptual properties; (2) when the network
‘talks to itself’ aloud (that is, if it names the perceived object and listens to the self-generated
linguistic stimulus); (3) when talking-to-oneself is internalized in a sort of ‘linguistic
thought’.
Bibliography
Carruthers 2002: “The cognitive functions of language”, Behavioral and Brain Sciences
Carruthers and Boucher 1998: Language and Thought: interdisciplinary themes, Cambridge
University Press
Clark 1998: "Magic Words: How Language Augments Human Computation", in Carruthers and
Boucher 1998
Gentner and Goldin-Meadow 2003: Language in Mind, MIT Press
Jackendoff 1996: “How Langauge Help Us Think”, Pragmatics and Cognition
Vygotsky 1962: Thought and Language, MIT Press