This panel discusses how digital anthropologists can use data science to help us better understand people and communities in the “Phygital” world, and why this growing field is a critical “Job of the Future” across the public and private sector. This panel discussion–featuring experts from digital anthropology, data science, journalism, social media and tech –will delve deeply into how this field will play a role in AI, combat online extremism and violence and support a more equal and ethical digital future. Non-anthropologists and anthropologists alike are warmly welcome!

**Presenters**

* Cristian Huepe, Co-director, Social Listening Lab (SoL-UC), Universidad Católica de Chile.
* Himanshu Panday, Co-founder, Dignity in Difference
* Matt Artz, Founder of Azimuth Labs
* Christine Burke, VP Cultural Insights, TelmarHelixa

**Moderator**

Katie Hillier, Chief Digital Anthropologist, Liiv Center

The Job of the Future: Digital Anthropology Meets Data Science

The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

The study of language variation examines how language varies between and within different groups of speakers, shedding light on how we use language to construct identities and how social contexts affect language use. A common method is to identify instances of a certain linguistic feature - say, the zero copula construction - in a corpus, and analyze the feature’s distribution across speakers, topics, and other variables, to either gain a qualitative understanding of the feature’s function or systematically measure variation. In this paper, we explore the challenging task of automatic morphosyntactic feature detection in low-resource English varieties. We present a human-in-the-loop approach to generate and filter effective contrast sets via corpus-guided edits. We show that our approach improves feature detection for both Indian English and African American English, demonstrate how it can assist linguistic research, and release our fine-tuned models for use by other researchers.

Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties

Deep nets are great, and they would be even greater if they could collaborate with each other through a flexible communication protocol. Since many networks come equipped with language model interfaces--that is, they can process and produce tokens in English (or other languages)--it makes sense to look into natural language as a universal neural network communication protocol. The questions I would like to discuss in my talk, then, are the following:<br>
* What are the challenges we face in developing a universal language interface among deep networks?
* What do we currently know about the way in which deep nets are using language to interact in machine-to-machine communication scenarios?
* Which characteristics of human linguistic communication do we expect or wish machine-to-machine communication to possess? Should it be essentially the same as human-to-human (and human-to-machine) communication?

Machine-to-machine communication: Do we need it? What should it be like? Is it "language"?

Empirical studies have demonstrated that when comprehenders are faced with informationally redundant utterances, they may make pragmatic inferences to accommodate the informationally redundant utterance (Kravtchenko & Demberg, 2015. Previous work has also shown that the strength of these inferences depends on prominence of the redundant utterance – if it is stressed prosodically, marked with an exclamation mark, or introduced with a discourse marker such as “Oh yeah”, atypicality inferences are stronger (Kravtchenko & Demberg, 2015; 2022; Ryzhova & Demberg, 2020).
The goal of the present paper is to demonstrate how both the atypicality inference and the effect of prominence can be modelled using the rational speech act (RSA) framework. We show that atypicality inferences can be captured by introducing joint reasoning about the habituality of events, following Degen, Tessler, and Goodman (2015); Goodman and Frank (2016). However, we find that joint reasoning models principally cannot account for the effect of differences in utterance prominence. This is because prominence markers do not contribute to the truth-conditional meaning. We then proceed to demonstrate that leveraging a noisy channel model, which has previously been used to model low-level acoustic perception (Bergen & Goodman, 2015), can successfully account for the empirically observed patterns of utterance prominence.

Modeling atypicality inferences in pragmatic reasoning

Generalizations are a fundamental linguistic tool for efficiently passing along information. To interpret the intended strength of a generalization, listeners rely on prior knowledge. Experienced and inexperienced listeners may interpret the same generalization differently, potentially leading to miscommunication. Speakers could mitigate such miscommunication by avoiding generalizations that inexperienced listeners are likely to misinterpret. However, experienced speakers may struggle to understand the perspective of an inexperienced listener. The present study examined whether experienced speakers adjust their use of generalizations based on the expertise of their intended audience. Results showed that any such adjustments are minimal and insufficient to avoid miscommunication as operationally defined. Future research may clarify the practical impact of such miscommunication by examining how generalizations are used in relation to speakers’ and listeners’ goals.

Adjusting the Use of Generalizations Based on Audience Expertise

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.

Pragmatic Reasoning in Structured Signaling Games

Young children tend to prioritize objects over layouts in their drawings, often juxtaposing “floating” objects in the picture plane instead of grounding those objects in drawn representations of the extended layout. In the present study, we explore whether implicitly directing children’s attention to elements of the extended layout through a drawing’s communicative goal—to indicate the location of a hidden target to someone else—might lead children to draw more layout information. By comparing children’s drawings to a different group of children’s verbal descriptions, moreover, we explore how communicative medium affects children’s inclusion of layout and object information. If attention modulates children’s symbolic communication about layouts and objects, then children should both draw and talk about layouts and objects when they are relevant to the communicative task. If there are challenges or advantages specific to either medium, then children might treat layouts and objects differently when drawing versus describing them. We find evidence for both of these possibilities: Attention affects what children include in symbolic communication, like drawings and language, but children are more concise in their inclusion of relevant layout or object information in language versus drawings.

Young children’s drawings and descriptions of layouts and objects

This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

Modelling Dual-Processes in a Connectionist Network

Rap lyrics are a popular and understudied domain of human culture. In particular, improvised rap lyrics provide a unique window into the cognitive and linguistic constraints of creative language production. Although the vast majority of rap lyrics are written (premeditated), improvising lyrics has long been a core element of hip-hop culture. Very few efforts have investigated the neural underpinnings of improvised rap, and none so far have focused on the language output itself. This project compares phonemic, rhyme, and semantic features of written and improvised rap lyrics from 7 expert rappers in order to uncover related phonological structures. Here, I demonstrate that the phonemes of these two modes of production seem to be drawn from different distributions. In addition, across various metrics, improvised lyrics from these experts display smaller phonological structures and less variation than written lyrics from those same artists, all while consistently exhibiting large rhyming patterns (3+ syllable).

What’s Different About Improvised Rap?

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

We examined the signatures of music improvisation following a targeted manipulation that involves a mirroring task. In 18 pairs participants completed a solo music improvisation performance using a percussion instrument (cajon), a mirroring task with a partner, and joint music improvisation. Across pairs, we manipulated three different types of mirroring to examine its effects on coordination during joint music improvisation (Hierarchical mirroring, partner A leading and partner B following; Turn-Taking mirroring, partner A leading the first half and partner B leading the latter half; and Egalitarian mirroring, partners co-creating spontaneous movement together). From audio recordings, we extracted signatures of interpersonal coordination from the acoustic performances using cross-correlation. There were only marginally significant differences in interpersonal coordination during improvisation, with pairs in the Turn-Taking condition exhibiting lower levels of cross-correlation than in the Egalitarian condition. This work provides some initial theoretical insights about downstream effects of interpersonal dynamics on music improvisation.

Underline digital video library

The Job of the Future: Digital Anthropology Meets Data Science

The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties

Machine-to-machine communication: Do we need it? What should it be like? Is it "language"?

Modeling atypicality inferences in pragmatic reasoning

Adjusting the Use of Generalizations Based on Audience Expertise

Pragmatic Reasoning in Structured Signaling Games

Young children’s drawings and descriptions of layouts and objects

Modelling Dual-Processes in a Connectionist Network

What’s Different About Improvised Rap?

Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

Improving Improv: Effects of Interpersonal Coordination on Music Improvisation

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