Concerns about moral contamination shape peoples attitudes towards the objects they encounter in daily life. For example,money seems less desirable when it comes from a robbery (Tasimi & Gelman, 2017). Drawing on the theory of dyadicmorality, we hypothesized that increasing an individuals sense of agency would reduce the salience of moral contagionand make people feel less vulnerable to moral contamination. Across two experiments, we adapted the study design ofTasimi and Gelman (2017), asking participants how much they desired a $1 (Experiment 1) or $100 (Experiment 2) billassociated with different negative moral histories. We modified the stimulus language so that participants were framed aseither the moral agent or patient for all scenarios. As predicted, participants in the agent language condition expressednearly the same level of desire regardless of the bills moral history, highlighting the role that feelings of agency play inmoral decision-making.

Behavior in classic framing tasks is unrelated to other cognitive bias measures, but little is known about the relation-ship among different types of framing effects. Across two experiments, participants in the US and India completed aclassic risky choice framing task, a metaphor framing task, and measures of cognitive style, linguistic proficiency, andmetaphor usage. We found no relationship between performance on the framing tasks for either sample, suggesting theytap into different underlying processes. Interestingly, language proficiency predicted risky choice framing behavior innative speakers and metaphor framing in non-native speakers. While there was a positive relationship between metaphorusage and metaphor framing for US participants, the sample from India showed a negative relationship, suggesting thatcurrent measures of metaphor usage may assess different behaviors for native versus non-native speakers. Overall, theresults suggest a heterogenous account of the mechanisms underlying framing effects even as they highlight the importantrole of language.

People frequently offload cognitive tasks onto the environment by,for example, digitally storing information they want to rememberlater. This frees up cognitive resources, leading to an increasedability to learn new information (the “Saving-Enhanced MemoryEffect”). We tested whether this effect would generalize beyondthe digital realm. On every trial, participants studied two printedlists of words before being tested on their memory for both lists.For half the trials, participants shredded the first list beforeattempting to learn the second one. For the remaining trials, theysaved the first word list in a folder before learning the second list.Results revealed a robust Saving-Enhanced Memory Effect, aspeople remembered more words on average from the second listwhen they had saved the initial word list. These findings suggestthat the effects of offloading memories onto the external world aresimilar for information stored in digital and physical formats.

Large Language Models (LLMs) have captured the world's attention for their surprisingly sophisticated linguistic abilities, but what they might reveal about human cognition remains unclear. Meanwhile, members of the public routinely share “prompt engineering” tips for eliciting “better” responses from LLMs such as OpenAI's ChatGPT. These efforts parallel research on linguistic framing, which shows that subtle linguistic cues shape people's attitudes and decision-making in a variety of contexts. In this study, we tested whether state-of-the-art LLMs would exhibit similar framing effects as human participants. We adapted a range of linguistic framing stimuli for use with LLMs based on a recently developed taxonomy of framing effects (e.g., lexical, figurative, and grammatical framing). Results revealed that some but not all framing effects replicated with LLMs. These findings have practical applications for interacting with AI systems and inform our understanding of the cognitive mechanisms that underlie the effects of framing.