The Impact of Familiarity on Naming Variation: A Study on Object Naming in M andarin C hinese (2023)

Abstract: Different speakers often produce different names for the same object or entity (e.g., “woman” vs. “tourist” for a female tourist). The reasons behind variation in naming are not well understood. We create a Language and Vision dataset for Mandarin Chinese that provides an average of 20 names for 1319 naturalistic images, and investigate how familiarity with a given kind of object relates to the degree of naming variation it triggers across subjects. We propose that familiarity influences naming variation in two competing ways: increasing familiarity can either expand vocabulary, leading to higher variation, or promote convergence on conventional names, thereby reducing variation. We find evidence for both factors being at play. Our study illustrates how computational resources can be used to address research questions in Cognitive Science.

## introduction when talking about objects in everyday experiences, people need to engage in the cognitive process of searching their lexicon to identify the most appropriate name to refer to them. this process involves intricate cognitive mechanisms that enable us to connect the properties of the object with the corresponding entries in our lexicon. often, different individuals use different names to refer to the same object, reflecting the inherent variability in how we categorize and label our surroundings @xcite ; for instance, the woman in figure 1a can be called "woman", "tourist", or "person", among other choices. the reasons behind this variability are still not well understood. most previous research on naming has been done in western languages (mostly english); and, in cognitive science, mostly with highly idealized stimuli, such as drawings of prototypical objects for a given category. @xcite introduced manynames, a dataset with realistic stimuli which provides an average of 31 english names for 25k objects in naturalistic images such as those in figure 1 . in this study, we present manynames zh, foot_0 a new dataset for object naming that provides mandarin chinese names for a subset of the many-names data (1319 images, average 20 names per image). figure 1 shows three example images with their corresponding names in manynames zh. we use this language and vision resource to address an open research question in cognitive science, namely, the role of object familiarity on naming variation. familiarity is defined in psycholinguistic research as the level of prior exposure or knowledge that individuals have about specific stimuli, such as words and objects @xcite . we explore two seemingly opposite hypotheses, which respectively focus on two different aspects of naming variation: convergence on a conventional name, and size of the available vocabulary. hypothesis 1 (h1) posits that higher familiarity results in lower variation. this is based on the assumption that people tend to converge on a conventional name for familiar objects. conversely, less familiar kinds of objects afford different conceptualizations, potentially increasing naming variation. for instance, most people are arguably more familiar with dogs than with bears, and indeed in figure 1b chinese subjects mostly converge on the majority name "狗" ("dog"), while they use a wider range of words to refer to the polar bear in figure 1c . h1 has received support in some, but not all studies in cognitive science (see section 2). hypothesis 2 (h2) instead suggests that higher familiarity is associated with increased naming variation. h2 is based on the idea that we need a larger vocabulary to refer to kinds of objects that we talk a lot about, to encode finer-grained distinctions in an efficient way @xcite . for instance, @xcite note that people elicit more 女人 (12), 女士 (2), 人 (2), 大人 (1), 女 (1), 游客 (1) woman ( 12 ), lady (2), person (2), adult (1), female (1), tourist (1) familiarity: 4.2 / h: 1.8 / n: 6 (a) 狗 (21), 狗狗 (1),罗威勒狗 (1) dog (21), puppy (1), rottweiler (1) familiarity: 4.1 / h: 0.5 / n: 3 (b) 北极熊 (8), 熊 (7), 动物 (2), 狗 (1), 海马 (1), 杂技 (1) polar bear (8), bear (7), animal (2), dog (1), seahorse (1), acrobatics (1) familiarity: 2.5 / h: 2 / n: 6 (c) figure 1 : examples of images and their corresponding names in manynames zh. numbers in parentheses are counts across subjects. familiarity is estimated by weighted average of lexical frequency (see section 4); h, or entropy, measures naming variation (see section 4); n is the number of distinct names. we find evidence for both hypotheses in our analysis of the manynames zh data, and suggest how to reconcile the two. ## background object naming in psycholinguistics and cognitive science. naming an object involves the selection of a specific term to refer to it @xcite . in our daily life, it's common for objects to simultaneously fit into several categories; for instance, a given baby can belong to multiple overlapping categories like person, female, baby, and girl, among others. the names associated to these categories (e.g. "human", "person", etc.) are then all valid alternative names for this baby @xcite , resulting in variation. by far the most examined dimension of variation has been the taxonomic one, starting with seminal work by rosch and colleagues @xcite . this line of work divides categories into three levels: superordinate (e.g., animal), basic (e.g., dog), and subordinate (e.g., rottweiler). rosch and subsequent work showed that, in general, people prefer names corresponding to the basic level, which is hypothesized to represent a good balance between the specificity and distinctiveness of the categories @xcite . however, another very prominent source of variation is so-called cross-classification @xcite , whereby objects belong to different categories that are not hierarchically organized but merely overlap (for instance, woman and tourist). in cognitive science, picture naming is the most widely used experimental paradigm for aspects related to naming @xcite @xcite @xcite . participants are presented with a visual stimulus and asked to produce the first name that comes to mind. the resulting datasets are called picture-naming norms, or naming norms for short. an important point for our purposes is the fact that, typically, due to the research goals of most of this research, the stimuli are prototypical pictures that represent categories, rather than the varied kinds of instances that one encounters in real life. therefore, subjects reach a very high agreement in this task in terms of lexical choices @xcite . this is also true for the few naming norms that exist for mandarin chinese @xcite @xcite . manynames @xcite draws inspiration from this paradigm but uses real-world images that show objects in their natural contexts, which elicits much more variation. previous work has shown that properties related to lexical access (word frequency, age of acquisition) affect the production probability of names @xcite @xcite : all else being equal, more frequent words and words acquired earlier are preferred. although less studied, research also shows that the properties of the pictured objects influence people's naming choices; objects that are less typical for the category denoted by the most produced name trigger higher variation @xcite . people's naming choices are more varied for objects that are less typical for a frequent name. we focus on a different factor, namely familiarity (see below for more information). ## results fixed effect estimates are shown in when holding other factors constant; and the converse for animal_plants. this supports h2: for domains that we are highly familiar with, we develop a larger vocabulary, and more lexical choices result in higher variation. furthermore, when examining the relationship between naming variation and familiarity across domains, we observe that clothing is the only domain in which a higher familiarity of an object tends to increase, rather than decrease, naming variation. ## discussion our results suggest that, in general, higher familiarity predicts lower naming variation (hypothesis 1) when mandarin chinese speakers name visually presented objects. this indicates that people tend to converge on a common name for kinds of objects they're more familiar with. for instance, in the animals_plants domain, people exhibit relatively low naming variation when referring to dogs (see figure 1b , where "dog" was produced by 21 out of 23 subjects). we hypothesize that this can be attributed to the prevalence of dogs as pets in our daily lives. instead, we are less familiar with e.g. bears; in figure 1c , people use "北极熊" ("polar bear") and "熊" ("bear") in almost equal proportion, and they also use the more general term "动物"("animal"). note that some people do not correctly identify the kind of animal, naming it instead "狗" ("dog") or "海马" ("seahorse"). 11 however, an intriguing contradiction to this finding emerges when we consider the effect of different domains on naming variation. although humans are arguably more familiar with people than with animals (conjecture supported by the data in we thus find evidence for both hypotheses, which however play at different levels of granularity. at the level of a specific object, higher familiarity with that object's category implies lower variation because people converge on the same label for the object. at the level of the domain or supra-category, instead, higher familiarity implies higher variation because of the richer vocabulary available for speakers. ## conclusion in this paper, we have introduced manynames zh, a new language and vision dataset designed for the task of object naming in mandarin chinese. the new dataset is the result of crowdsourcing names in mandarin chinese, based on the images from the english manynames dataset, with pre-and postprocessing steps. manynames zh consists of a carefully curated subset of 1319 images, each accompanied by an average 20 names provided by different human annotators. it allows the community to expand the empirical basis of findings on naming, by including a major language from a typologically different family than english. with the availability of manynames subsets in three languages, english, catalan @xcite , and mandarin chinese, researchers can also conduct cross-linguistic studies and comparative analyses on object naming. with this new dataset, we have explored the relationship between object familiarity and the degree of naming variation. we observe two opposite factors at play. on the one hand, when familiarity with objects in a given supra-category or domain increases (such as with the people domain), vo-cabulary size correspondingly increases, too. this affords higher naming variation because it gives speakers more options to choose from. on the other hand, within a given category, more familiar sub-categories will afford conventionalization of the label used to talk about it, which elicits lower naming variation. this helps explain conflicting results found in psycholinguistic studies on naming, which found the effect of domain on vocabulary size @xcite ; a negative correlation between familiarity and variation variation @xcite ; and no relation between the two factors @xcite , respectively. our analysis is based on a snapshot of mandarin chinese in which the vocabulary is frozen and we only observe the use. however, the patterns observed result from the dynamic evolution of vocabulary over time. our results suggest that the need to frequently talk about a given kind of object triggers the development of a richer vocabulary that accounts for relevant distinctions within that broad class; and that higher communication about a specific kind of object triggers the convergence on a single label. future work should test this hypothesis empirically.