The Emergence of the Shape Bias Results from Communicative Efficiency (2021)

Abstract: By the age of two, children tend to assume that new word categories are based on objects’ shape, rather than their color or texture; this assumption is called the shape bias. They are thought to learn this bias by observing that their caregiver’s language is biased towards shape based categories. This presents a chicken and egg problem: if the shape bias must be present in the language in order for children to learn it, how did it arise in language in the first place? In this paper, we propose that communicative efficiency explains both how the shape bias emerged and why it persists across generations. We model this process with neural emergent language agents that learn to communicate about raw pixelated images. First, we show that the shape bias emerges as a result of efficient communication strategies employed by agents. Second, we show that pressure brought on by communicative need is also necessary for it to persist across generations; simply having a shape bias in an agent’s input language is insufficient. These results suggest that, over and above the operation of other learning strategies, the shape bias in human learners may emerge and be sustained by communicative pressures.

## introduction learning new words involves inferring what the referents are in addition to learning a mapping from these inferred referents to linguistic conventions. this is a difficult problem, famously illustrated by @xcite problem of referential inscrutability. yet, human children manage just fine. a common explanation is that they must be using additional sources of information to learn new words. one such source is inductive word learning biases, such as the shape bias, which appears in children around two years of age (heibeck and @xcite . this bias can be observed when children and adults are presented with a novel object and word. they will often assume that this word refers to a class categorized by the shape of the novel object. thus, when they are presented with more exemplars they will tend to classify those that share the same shape in the same lexical category, rather than those which share the same color or texture, (figure 1 ). in this paper, we endeavor to answer two questions: how did the shape bias emerge in our lexical categorization systems? and why does it persist across generations, languages, and cultures? using neural emergent communication agents as our model of linguistic convention formation, we show how efficient communication strategies can explain both the emergence and persistence of this bias. languages have been shown to follow principles of efficient communication for naming systems in a variety of semantic domains, including kinship terms @xcite and color terms @xcite . a given naming system tends to strike a perfect balance between its overall informativity and simplicity as a function of speakers' need to distinguish referents in that system @xcite . the effects of efficient communication strategies in one semantic domain on other related domains have yet to be considered. in this paper, we demonstrate how efficient communication strategies in one semantic domain -shape -can affect the overall lexicon and lead to categorical biases, like the shape bias. ## a model of lexical category learning in order to model the problem of language learning we first need a task, agent architecture, and learning procedure that allow us to simultaneously learn visual and linguistic representations. then, we can ask how visual and lexical categorization systems biased towards shape can both (1) arise in a language, and furthermore, (2) persist across generations of language learners. ## measuring efficiency in neural agents in order to determine if agents are efficient communicators and whether the shape bias follows the communicative efficiency trade-off, we first define the metrics we will use to estimate communicative need, information loss, and complexity. there are some differences between our approach from previous metrics that should be noted @xcite . first, we do not assume that communication between a speaker and a listener is always successful; we are in a language learning context where learners make mistakes. for this reason, our informativity measure is based on communicative success rate, rather than the expected cost of needing to refer to an explicit shape. second, we do not assume that the naming systems these agents learn have perfect coverage. thus, we cannot define an explicit generative model for lexical categories and count the minimum number of rules necessary to describe a semantic system as a complexity measure, but instead we must rely on estimates of mutual information (mi) between agents' linguistic representations and the available shape classes in our dataset. mi between linguistic representations (words) and semantic classes (meaning) has in fact previously been used to estimate complexity of color naming systems @xcite . communicative need is defined as the proportion of 'shape games' played during training. a shape game is a game where the distractors and the target only differ in terms of shape (but share the same color, texture, and size), so it is necessary for the speaker's message to encode information about shape distinction to successfully communicate the target to the listener (the same applies to color games, where the target and distractor objects only differ in terms is color). agents trained with a higher proportion of shape games have a higher communicative need to distinguish shape classes. information loss is defined as the proportion of test games where the listener fails to identify the correct target given the speaker's message, or (1accuracy). for example, higher accuracy on shape games means smaller information loss about shape in agents' lexical categories. complexity is defined as the mi between class labels (the 10 shape and 8 color classes in our dataset) and agents' language embeddings. in order to obtain the distributions over embeddings and class labels, we take the embedding of the first character of a message generated by speakers for all 11,000 test images, as well as the shape/color label for those same images. we chose to use only the first embedding as it is a good baseline for agents' linguistic representations 10 . given that each embedding contains multiple features (the embedding size is 64), we sum the mi estimates between each feature and the labels to get the estimated mi between the whole embeddings and labels. in this case, we use the method from ross (2014) for estimating mi between a continuous and a discrete distribution. higher mi indicates more similarity between linguistic representations and a given meaning space. thus, for example, higher mi between agents' embeddings and test image shape labels indicates a more complex set of shape distinctions in agents' lexical categories. ## experiment 1: the emergence of a shape bias our first research question asks how a bias towards shape can emerge simultaneously in both linguis-tic and visual representations without conditioning learning on an existing language. we hypothesise that the shape bias emerges due to communicative need and that agents use efficient communication systems when referring to complex referents. ## discussion we have shown that a shape bias can emerge and persist across generations of emergent communication agents if there is a high enough communicative need for shape. in this final section, we address what our results may teach us about the shape bias in human learners and neural networks. previous work considered the lexicon one semantic domain at a time, but our simulations demonstrate that communicative need affects the structure of the lexicon as a whole. experiment 1 showed us how the need for distinction in one semantic domain may inadvertently affect another domain and lead to categorization biases, such as the shape bias. human learners may also experience this need for discriminating shapes in communicative contexts, which could help them learn this bias. experiment 2 showed that without external pressures we expect that, as language evolves, learners will eventually fall back to using their innate inductive biases to form lexical categories. there is evidence that the shape bias in humans is not innate, but learnt, as it starts around two years old and grows stronger over time @xcite . these results suggest that the shape bias in human learners may be sustained by communicative need for shape in addition to the learning mechanisms which ensure its transmission from a caregivers' lexical categorizations to children's. there are several reasons we might expect people to have a high communicative need for shape in the real world. most importantly, there are functional reasons that motivate the use of shape distinction. in the case of artifacts, shape is often related to an affordance (e.g. 'hammer' refers to a class of objects which share the same shape, and that shape is what determines its use). we may often be trying to refer to objects' functions during communication. the shape bias is in fact strongest in contexts where the referents are assumed to be artifacts rather than natural kinds, often even disappearing in the latter context @xcite @xcite @xcite . we would predict this to be the case, since we do not expect a shape bias in contexts with low communicative need for shape. other types of neural networks have been shown to develop a shape bias (1) by increasing the size of models or (2) by augmenting their input data @xcite @xcite @xcite . note that neither of these approaches can account for the bias being contextually modulated the way it is in humans. when it comes to making models larger (1), we show that it is possible for relatively small models to learn a shape bias, and furthermore, we do not find that the size of agent models affected our experiment 1 results (see appendix g). as for using augmented input data (2), this is done by increasing the number of distinct shapes versus other features in a dataset such that objects differ more in terms of shape than other features, for example by having fewer colors, like using black and white images. the reason these data augmentations increase shape bias is in fact because they increase the need to distinguish shapes during training instances when each training image is seen an even number of times. we show that these artificial data augmentations are not necessary if you increase the model's need to distinguish shape through how it interacts with the data during training (see appendix f). thus, considering the shape distinction need probability during learning rather than using augmented data may help us develop models with a stronger shape bias while maintaining representative datasets. in future work, we hope to address the context specificity of the shape bias and consider whether neural agents can learn to have a shape bias in some referential spaces but not others as a function of communicative need across different contexts. comprehension module: an lstm with input embedding size and output hidden size; an mlp consisting of a linear transformation from hidden size to hidden mlp size, a non-linear relu transformation, a linear transformation from hidden mlp size to hidden size. we consider the final state after message length states. message classifier: this is not part of the agents, but the classifier we used to evaluate complexity of different agent languages. it is composed of a single linear layer of input size message length and of output size number of shape(10)/color(8) categories. simple cnn vision module: this module is not contained in our regular agents but used in our experiment in appendix c where we compare our vision setup to this simple cnn setup. this module can replace the perception and representation modules defined above. it is composed of: a conv2d layer with 3 input channels, 20 output channels, a kernel size 5 and stride of 1; a relu non linear transformation; a max pool 2d layer with kernel size 2 and stride of 2; another conv2d layer with 20 input channels, 50 output, kernel of 5 and stride of 1; a relu non linear transformation; a max pool 2d layer with kernel size 2 and stride of 2; a batchnorm2d layer; a two layered mlp with dropout at 0.7 that reduces the output to hidden size. optimizer: we used an adam optimizer across all models during training (kingma and ba, 2014).