Ad-hoc Meaning Substitution Causes Shape and Material Biases : Computational Explanation for Emergence of Word Learning Biases

Ad-hoc Meaning Substitution Causes Shape and Material Biases: Computational Explanation for Emergence of Word Learning Biases Kosuke Kurosaki (kurosaki@complex.eng.hokudai.ac.jp) Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, JAPAN Takashi Omori (omori@lab.tamagawa.ac.jp) Intelligent Robot Research Center, Tamagawa University Research Institute, Tokyo, JAPAN Abstract We illustrated a computational mechanism of shape bias and material bias with “ad-hoc meaning substitution (AMS)” hypothesis and veriﬁed it by computer simu- lations. AMS represents that when given a novel word and a instance object/substance, children substitute a known noun meaning nearest to the instance and the instance itself as an ad-hoc template of the novel noun meaning. The substitution enables fast mapping and subsequent vocabulary spurt. To describe internal pro- cess of AMS, we introduced “word distributional pro- totype (WDP)” as an explicit representation of word meaning with an inductive learning function. Simula- tion 1 revealed that when neural networks with WDP and AMS were given biased vocabularies reﬂecting those of young children, it demonstrated shape, material, and overgeneralized shape biases which means wrong shape bias over material bias. This result suggests that the triad of word meaning induction, AMS, and early biased vocabulary is essential for emergence of the biases. Sim- ulation 2 introduced a notion of maturity that denote a degree of learning convergence for each word meaning, and then networks showed neither shape nor material bias during early small vocabulary. This result indicates that the age of bias emerges is decided by the matu- rity. These results suggest that phenomena concerning shape and material biases are explicable with the simple ad-hoc learning mechanism instead of meta learning or built-in language-speciﬁc ones. (Dickinson, 1988; Soja et al., 1991). Those biases are investigated by novel noun generalization task (Samuel- son, 2002; Samuelson & Smith, 1999). In the task, ex- perimenter prepares solid and nonsolid sets. Each set consists of three stimuli: a novel target stimulus, shape- match stimuli that have the same shape as the target stimulus, and material-match stimuli that have the same material . First, an experimenter assigns a novel noun to the target stimulus in front of a subject. Next, the ex- perimenter presents a corresponding shape-match stim- ulus and a material-match stimulus together and asks the subject to select one that can be called by the same noun as the target stimulus. When either is selected sig- niﬁcantly more often by some subjects, we conclude that subjects have a bias to generalize novel nouns based on similarity in shape or material. Shape bias is a behavior that when a novel solid tar- get stumulus is named with a novel noun, people tend to extend the noun to shape-match stimulus (Landau, Smith, & Jones, 1988). Material bias is a behavior that when a novel nonsolid target stimulus is named with a novel noun, they tend to generalize the noun to the material-match stimulus (Dickinson, 1988; Soja et al., 1991). We can summarize some experimental ﬁndings as below: (1.1.1a) shape bias doesn’t appear to solid stimuli when subjects have small vocabulary (Samuelson & Smith, 1999; Smith, 1995); and (1.1.1b) it does after middle vocabulary (Landau, Smith, & Jones, 1988; Soja et al., 1991; Dickinson, 1988; Imai & Gentner, 1997); (1.1.2a) material bias doesn’t appear during small vo- cabulary (Samuelson & Smith, 1999); or (1.1.2b) “over- generalized shape bias,” which means novel noun named to nonsolid target stimulus is extended also to shape- match stimuli, happens to appear during small vocabu- lary (Samuelson, 2002); and (1.1.2c) material bias ap- pears after suﬃciently large vocabulary (Soja et al., 1991; Dickinson, 1988). From these ﬁndings, we argue that the biases result from simple learning (“learned bias account [LBA]”) be- cause: (1.2a) The fact that shape and material biases appear after children have acquired certain number of words indicates that they emerge as a consequence of vocabulary learning; (1.2b) emergence of the overgen- eralized shape bias suggests that both biases consist of common mechanism instead of separate modules and it causes the overgeneralization; (1.2c) both biases arise in almost identical situations except for the solidity of tar- Introduction When we encounter a novel word such as Gavagai and guess its meaning, too many logically possible meanings exist (Quine, 1960). Nevertheless, children can estimate words meanings very fast (Carey & Bartlett, 1978). Such fast mapping can’t be explained by existing machine learning algorithms based on trial and error. The ability appears after child’s productive noun vocabulary exceeds about 50 words, and the vocabulary starts to grow quite rapidly(vocabulary spurt). To explain these phenomena, developmental psychol- ogists have suggested word learning biases. When ap- plying a novel name to an object, the biases make chil- dren focus on particular features instead of other possible features and estimate words’ meaning accurately (e.g. Landau, Smith, & Jones, 1988; Soja, Carey, & Spelke, 1991). Problem is that these biases are just phenomeno- logical explanations that can’t explain why they exist or how they are processed in human brain. In this paper, we try to illustrate a computational mechanism of shape bias (Landau, Smith, & Jones, 1988) and material bias