Efficient Color Naming Systems Emerge from Cultural Evolution via Iterated Learning and Communication

Core Concepts

Cultural evolution via iterated learning and communication can explain the emergence of efficient color naming systems.

Abstract

The content discusses how efficient color naming systems can emerge through a process of cultural evolution that combines iterated learning and communication. Key points: Semantic systems, such as color naming, are argued to reflect pressure for efficiency, balancing simplicity and informativeness. The authors explore a model of cultural evolution that combines iterated learning (where a convention is learned by one generation and passed to the next) and communication (where agents communicate and update their knowledge). They show that this model, implemented using neural networks, converges to color naming systems that are efficient in an information-theoretic sense (the Information Bottleneck principle) and similar to human color naming systems. The authors compare this to other proposals, such as iterated learning alone, communication alone, or the greater learnability of convex categories. They find that the combination of iterated learning and communication provides a more plausible explanation for the emergence of efficient and human-like color naming systems. The authors also show that there exist many efficient color naming systems that are dissimilar to human systems, highlighting the importance of the cultural evolutionary process in shaping semantic systems. The results suggest that iterated learning plus communication, as formalized in the neural iterated learning (NIL) algorithm, provides a better account of the cultural evolution of efficient and human-like semantic systems than either iterated learning or communication alone.

Stats

"We considered a natural class of artificial color naming systems (see e.g. Abbott et al., 2016; Zaslavsky et al., 2022). In this class, each named category w is modeled as a spherical Gaussian-shaped kernel with mean (prototype) xw in 3-dimensional CIELAB color space." "We generated artificial color category systems with K = 3...10 categories each, by first sampling η randomly from a uniform distribution over the interval [0.001,0.005] for each system and then sampling the prototype xw of each category w randomly, without replacement, from a uniform distribution over the cells of the color naming grid shown in the top left panel of Figure 1."

Quotes

"We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems." "We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient."

Key Insights Distilled From

Cultural evolution via iterated learning and communication explains efficient color naming systems

by Emil Carlsso... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2305.10154.pdf

Deeper Inquiries

How would the results change if the communication process involved more realistic pragmatic inference and contextual information, rather than the simplified signaling game used here?

Incorporating more realistic pragmatic inference and contextual information into the communication process would likely lead to more nuanced and sophisticated semantic systems. By considering contextual cues and pragmatic aspects, the agents would be able to adapt their communication strategies based on the specific situation, leading to more flexible and adaptive language systems. This could result in semantic categories that are not only efficient and informative but also more contextually appropriate and tailored to specific communicative needs. The inclusion of pragmatic inference would also enable the agents to infer implicit meanings, resolve ambiguities, and account for speaker intentions, making the communication more natural and human-like.

To what extent do the findings generalize beyond the domain of color naming to other semantic domains?

While the study focused on color naming systems, the findings are likely to generalize to other semantic domains as well. The principles of iterated learning and communication are fundamental to the cultural evolution of language and semantic systems across various domains. The balance between simplicity and informativeness, the role of cultural transmission in shaping semantic categories, and the impact of communication strategies on semantic evolution are universal concepts that apply to a wide range of linguistic and cognitive phenomena. Therefore, the insights gained from studying color naming systems are likely to be applicable to other semantic domains such as kinship terms, spatial relations, and numeral systems.

What are the specific features of the iterated learning and communication processes that are critical for producing efficient and human-like semantic systems, versus those that could be replaced by alternative mechanisms?

The critical features of the iterated learning and communication processes for producing efficient and human-like semantic systems include: Iterated Learning: Iterated learning allows for the accumulation and transmission of cultural knowledge across generations, leading to the gradual refinement and optimization of semantic systems. This process favors simplicity and regularity, making the systems easier to learn and use over time. Communication: Communication enables agents to exchange information and negotiate shared meanings, leading to the emergence of shared conventions and efficient communication strategies. The interactive nature of communication allows for the alignment of mental representations and the establishment of common ground, essential for effective communication. Balancing Simplicity and Informativeness: The interplay between simplicity and informativeness ensures that semantic systems are both efficient in conveying information and cognitively manageable. This balance is crucial for the evolution of human-like semantic categories that are both expressive and easy to learn. While these features are essential for the cultural evolution of semantic systems, alternative mechanisms could potentially replace certain aspects of the process. For instance, different learning algorithms or communication strategies could be explored to understand their impact on semantic evolution. Additionally, incorporating additional cognitive constraints or environmental factors into the model could provide further insights into the dynamics of semantic change and adaptation.