Agentive linguistic framing affects responsibility assignments toward AIs and their creators

Tech companies often use agentive language to describe their AIs (e.g., The Google Blog claims that, “Gemini can understand, explain and generate high-quality code,”). Psycholinguistic research has shown that violating animacy hierarchies by putting a nonhuman in this agentive subject position (i.e., grammatical metaphor) influences readers to perceive it as a causal agent. However, it is not yet known how this affects readers’ responsibility assignments toward AIs or the companies that make them. Furthermore, it is not known whether this effect relies on psychological anthropomorphism, or a more limited set of linguistic causal schemas. We investigated these questions by having participants read a short vignette in which “Dr. AI” gave dangerous health advice in one of two framing conditions (AI as Agent vs. AI as Instrument). Participants then rated how responsible the AI, the company, and the patients were for the outcome, and their own AI experience. We predicted that participants would assign more responsibility to the AI in the Agent condition, and that lower AI experience participants would assign higher responsibility to the AI because they would be more likely to anthropomorphize it. The results confirmed these predictions; we found an interaction between linguistic framing condition and AI experience such that lower AI experience participants assigned higher responsibility to the AI in the Agent condition than in the Instrument condition (z = 2.13, p = 0.032) while higher AI experience participants did not. Our findings suggest that the effects of agentive linguistic framing toward non-humans are decreased by domain experience because it decreases anthropomorphism

A Grounded Mental Model of Physical Systems: A Modular Connectionist Architecture

Some basic characteristics of subjects' use of mental models of physical systems are discussed. Many representations for physical knowledge suggested so far, including qualitative-reasoning-based models, do not account for these experimental findings. This paper presents a connectionist architecture which suggests an explanation of these experimental results. Two simulation experiments are described which demonstrate how mental models of physical systems may evolve and why grounding symbols used by a mental model to a quantitative representation is necessary

Creating the Sound of Sarcasm

The study of spoken language requires controlling multiple aspects of the speech signal. Here we report a procedure to create a sarcastic version of sincerely spoken audio stimuli by changing aspects of prosody relevant to sarcasm (pitch, pace, and loudness) while controlling all other acoustic differences. Two rating experiments validated the efficacy of this procedure for spoken conversations (“Maybe they are more delicate than you realized”) and descriptions (“Angie thanked John for doing such a great job”; emphasis indicates manipulation). </jats:p

A Bayesian approach to establishing coreference in second language discourse: Evidence from implicit causality and consequentiality verbs

This study investigated Chinese-speaking English learners’ use of implicit causality and consequentiality biases in establishing coreference under a Bayesian view of reference interpretation, which distinguishes between context-based priors about which entity will be re-mentioned and new evidence provided by the referential expression form. In two sentence-completion experiments, participants wrote continuations to sentence fragments with either implicit causality (Experiment 1) or consequentiality (Experiment 2) biases that ended either with or without a pronoun. In both experiments, L2 speakers showed native-like re-mention biases following no-pronoun fragments, indicating native-like predictions about the next-mentioned referent. Following pronoun fragments in NP2-biasing contexts, L2 speakers produced more NP1 continuations than native speakers. We show that this difference lies in different beliefs about pronoun use in the two populations. Specifically, L2 speakers showed a stronger association between pronouns and NP1 referents than native speakers following NP2-biasing verbs.</jats:p

Repeated names, overt pronouns, and null pronouns in Spanish

In two self-paced, sentence-by-sentence reading experiments we examined the difference in the processing of Spanish discourses with repeated names, overt pronouns, and null pronouns in emphatic and non-emphatic contexts. In Experiment 1, repeated names and overt pronouns caused a processing delay when they referred to salient antecedents in non-emphatic contexts. In Experiment 2, both processing delays were eliminated when an emphatic cleft-structure was used. The processing delay caused by overt pronouns referring to salient antecedents in non-emphatic contexts in Spanish contrasts with previous findings in Chinese, where null and overt pronouns elicited similar reading times. We explain both our Spanish findings and the Chinese findings in a unified framework based on the notion of balance between processing cost and discourse function in line with the Informational Load Hypothesis