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Responsive predicates are clause-embedding predicates like English &#39;know&#39; and &#39;guess&#39; that can take both declarative and interrogative clausal complements. The meanings of responsive predicates when they take a declarative complement and when they take an interrogative complement are hypothesized to be constrained in systematic ways across languages, suggesting that these constraints represent semantic universals. We report an artificial language learning experiment showing that one of these proposed constraints is indeed reflected in the inferences participants make while learning a novel responsive predicate. Our results add support to a growing body of evidence linking semantic universals to learning.

CogSci 2022

Learnability and constraints on the semantics of clause-embedding predicates

Responsive predicates are clause-embedding predicates like English 'know' and 'guess' that can take both declarative and interrogative clausal complements. The meanings of responsive predicates when they take a declarative complement and when they take an interrogative complement are hypothesized to be constrained in systematic ways across languages, suggesting that these constraints represent semantic universals. We report an artificial language learning experiment showing that one of these proposed constraints is indeed reflected in the inferences participants make while learning a novel responsive predicate. Our results add support to a growing body of evidence linking semantic universals to learning.

technical paper

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





We examined how rhythmic activities affect children’s perspective-taking in a referential communication task with 69 Chinese 5- to 6-year-old children. The child first played an instrument with a virtual partner in one of three coordination conditions: synchrony, asynchrony, and antiphase synchrony. Eye movements were then monitored with the partner giving instructions to identify a shape referent which included a pre-nominal scalar adjective (e.g., big cubic block). Participants with awareness of their partner’s perspective could, in principle, identify the intended referent before the shape was named when the target contrast (a small cubic block) was in shared ground whereas a competitor contrast was occluded for the partner. Children in the asynchrony and antiphase synchrony conditions, but not the synchrony condition, showed anticipatory looks to the target, suggesting that playing instruments asynchronously or in alternation facilitates perspective-taking, likely by training self-other discrimination and inhibitory control.

Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can differentiate between AI-based and human-written texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 human-written continuations (Study 1, N=120) or 9 original continuations (Study 2, N=302). Participants' task was to decide whether a continuation was written with an AI-tool or not, to indicate their confidence in each decision, and to assess the stylistic text quality. Results showed that participants generally had low accuracy for differentiating between text types but were overconfident in their decisions. Regarding the assessment of stylistic quality, AI-continuations were perceived as less well-written, inspiring, fascinating, interesting, and aesthetic than both human-written and original continuations.

The Pure Poet: How Good is the Subjective Credibility and Stylistic Quality of Literary Short Texts Written with an Artificial Intelligence Tool as Compared to Texts Written by Human Authors?

To enable natural and fluid human-robot interactions, robots need to not only be able to communicate with humans through natural language, but also do so in a way that complies with the norms of human interaction, such as politeness norms. Doing so is particularly challenging, however, in part due to the sensitivity of such norms to a host of different contextual and intentional factors. In this work, we explore computational models of context-sensitive human politeness norms, using explainable machine learning models to demonstrate the value of both speaker intention and task context in predicting adherence with indirect speech norms. We argue that this type of model, if integrated into a robot cognitive architecture, could be highly successful at enabling robots to predict when they themselves should similarly adhere to these norms.

Leveraging Intentional Factors and Task Context to Predict Linguistic Norm Adherence

To what extent can experience from language contribute to our conceptual knowledge? 
Computational explorations of this question have shed light on the ability of powerful neural language models (LMs)---informed solely through text input---to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction---a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrow, canary). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.

A Property Induction Framework for Neural Language Models

Explainable Artificial Intelligence (XAI) provides human understandable explanations into how AI systems make decisions in order to increase transparency. We explore how transparency levels in XAI influence perceptions of fairness, trust and understanding, as well as attitudes towards AI use. The transparency levels – no explanation, opaque, simple and detailed - were varied in two contexts - treatment prioritization and recidivism forecasting. In eight experimental groups, 573 participants judged these explanations. As predicted opaque explanations decreased trust and understanding, but surprisingly simple explanations that were more limited in the information they provided had stronger effects on trust and understanding than detailed explanations. Transparency levels did not have an impact on perceptions of fairness and attitudes towards AI, but context did, with the recidivism AI being perceived as less fair. The findings are discussed in relation to information overload and task subjectivity vs objectivity.

Explanations that backfire: Explainable artificial intelligence can cause information overload

As conversational agents become more human-like, people expect them to be engaging as well. However, developing agents that comprehend human desires and generate appropriate responses, continues to be a challenge. We, therefore, collected 2,300 human open-domain dialogs with self-labeled psychological variables such as empathy, connectedness, respect, and friendliness. We found that participants who talk coherently and disclose self-relevant information were engaging partners. Also, we found that various empathetic responses were critical for sincere interaction: agreement, perspective-taking, referring to someone as adorable, and asking questions. When comparing the most and least engaging dialogs, linguistic cues and length of sentences denoted different extents of perceived empathy and sincerity by the partner. Also, we found that a large language model, GPT-3, makes small talks in one shot, but it cannot generate many empathic expressions or sustain a lengthy conversation. We propose a new approach for enhancing conversational agents' social and engaging characteristics.

“Feels Like I’ve Known You Forever”: Empathy and Self-Awareness in Human Open-Domain Dialogs

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.

Modelling Competitive Human Action using Dynamical Motor Primitives for the Development of Human-Like Artificial Agents

Human communication comprises a complex and dynamic interplay of verbal and nonverbal communication channels. The investigation of these channels therefore represents a major methodological challenge.

Technical developments in interaction platforms using virtual characters provide tools for these investigations. Paradigms in which participants interact with algorithmically controlled agents have already enabled the investigation of individual nonverbal communication channels with the necessary experimental control. However, it is unclear how these results relate to human-human communication.

Here, we present a study with a new system for human-human interactions mediated by avatars. As a proof-of-concept, we tested the generalisability of gaze patterns during turn-taking in avatar-mediated conversations. Results show that given our Bayesian mixed effects model, priors and data, there is compelling evidence that gaze patterns are comparable to natural interactions. Exploratory analyses show that our system is suitable to shed light on variability in individual-specific gaze behaviour, which we plan to investigate further.

The relation between gaze and turn-taking in dyadic avatar-mediated conversations

Rumelhart Symposium

Humans are surprisingly adept at interpretingwhat is happening around them –theyspontaneously and rapidlysegmentand organizetheirdynamic experience into coherent event construals. Such event construals may offer a starting point for assembling a linguistic description of the event during speaking (Levelt, 1989). However, the precise format of event representations and their mapping to language have remained elusive, partly because research on how people mentally segment and perceive events (see Radvansky & Zacks, 2014 for a review) has largely proceeded separately from analyses of how events are encoded in language (see Truswell, 2019 for a review).

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Learnability and constraints on the semantics of clause-embedding predicates

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Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

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