United States

While naturalistic daylong audio recordings of children’s auditory environments have the potential to reveal key insights about the input children receive and inform our theories of language development, it also presents various methodological hurdles. In the present work, we used three fully transcribed daylong audio recordings to investigate the challenge of manually extrapolating aggregate statistics and quantify the kinds of sampling choices daylong researchers can make. Our findings highlight sampling choices that maximize sampling from the full distribution of the day and potential tradeoffs between human effort and obtaining accuracy.


CogSci 2023

Optimizing Random Sampling of Daylong Audio

technical paper

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Using evidential expressions to indicate one’s source of information for an utterance tends to convey uncertainty on the speaker’s part. Previous accounts of this uncertainty inference attribute it to either extralinguistic reasoning about evidence directness, or to pragmatic reasoning about alternative utterances. Here we present a novel hybrid account, and introduce a set of utterances which allows us to tease apart the three accounts’ predictions. We test these predictions in two studies by manipulating the directness of evidence indicated by an evidential expression. Exp. 1 shows that listeners infer more uncertainty with extreme values of directness. Exp. 2 shows that speakers are more likely to indicate evidence in contexts where the evidence is unreliable. We argue that these findings support an account which involves both extralinguistic and pragmatic reasoning, and develop a formal implementation of such an account within the Rational Speech Act framework.


Evidential uncertainty involves both pragmatic and extralinguistic reasoning: a computational account

Projection inferences are inferences about speaker commitment to a content embedded under an entailment-canceling operator, such as polar interrogatives with clause-embedding predicates (e.g., Does John know that Julian dances salsa?) Inferred speaker belief regarding whether Julian dances salsa is modulated by multiple factors, including the predicate used, interlocutors' prior beliefs about the content, and the content's at-issueness. We propose a first step towards a unified probabilistic model of projection inferences about content embedded under clause-embedding predicates, couched in the RSA framework. In a behavioral study, we investigate the beliefs about contents embedded under the predicates ``think'' and ``know'' that listeners ascribe to the speaker and a potential attitude holder (John). The empirical data were used to inform the model. The resulting model predictions mirror some, but not all, of the qualitative empirical patterns. This is the first step toward a systematic analysis of projection inferences using probabilistic pragmatic models.


Towards a computational account of projection inferences in clause-embedding predicates

When we reason about what could have been, some possibilities seem plausible, and others far-fetched. According to a recent theory, counterfactual possibilities are plausible if they can be generated by making local, probabilistic adjustments to the causes of what actually happened. We provide evidence that people think about counterfactuals in this way even when they have to infer the causes of what happened. We told participants about the diet of a fictional animal, and then asked them simple counterfactual questions. For example, given that the animal has eaten 1 berry today, how much food could it plausibly have eaten instead? When the amount of food eaten by the animal licensed an inference about a causally upstream variable, participants inferred the state of this variable and used it to guide their counterfactual plausibility judgments. 
More generally, the distribution over counterfactual values derived from participants' judgments was remarkably similar to the distribution predicted by the model.


Causal inference shapes counterfactual plausibility

There is increasing pressure on social media companies to reduce the spread of misinformation on their platforms. However, they would prefer not to be the arbiters of truth as the truth can be subjective or otherwise hard to determine. Instead, they would prefer that social media users themselves show better discernment when deciding which information to share. Here we show that allowing people to share only those social media posts that they have indicated are true significantly improves sharing discernment, as measured by the difference in the probability of sharing true information versus the probability of sharing false information. Because it doesn’t require social media companies to be the arbiters of truth, this self-censorship intervention can be employed in situations where social media companies suspect that individuals are propagating misinformation but are not sufficiently confident in their suspicions to directly censor the individuals involved. As such, self-censorship can usefully supplement externally imposed (i.e. traditional) censorship in reducing the propagation of false information on social media platforms.


Self-Censorship Appears to be an Effective Way of Reducing the Spread of Misinformation on Social Media

Pre record assistants have become fixtures in everyday settings, but most research focuses on their development rather than their use following deployment. To facilitate study of their use in office settings, we introduce OfficeDial, a multimodal dataset containing audio recordings, transcriptions, eye tracking data, and screen recordings from conversations between humans and Pre record assistants in office environments. Conversations are paired with physical and behavioral measures of cognitive load. We study the associations between verbal behavior and noise level and reveal key relationships between verbal redundancy, disfluency, and noise level. We make our new dataset available to interested researchers to inspire further exploration.


Linguistic Cognitive Load Analysis on Dialogues with an Intelligent Pre record Assistant

Collaboration can make collective judgments more accurate than individual judgments, but it also comes with costs in time, effort, and social cohesion. But how do we estimate these costs? In two experiments, we introduce children and adults to two teams in which the teammates disagree about the optimal solution to a novel problem, and ask which team would need more time to reach a consensus decision. We find that all ages expect slower decisions from teams with more people or factions, and expect the number of factions to matter more than the number of people. But only adults expect decisions initially endorsed by a stronger faction to be faster than those endorsed by a weaker faction. Results are discussed in context of children’s reasoning about dominance, and models of time-rational collective decision-making.


Herding cats: children’s intuitive theories of persuasion predict slower collective decisions in larger and more diverse groups, but disregard factional power

Not too long ago most headlines talked about our fear of AI. Today, AI is ubiquitous, and the conversation has moved on from whether we should use AI to how we can trust the AI systems that we use in our daily lives. In this talk I look at some key technical ingredients that help us build confidence and trust in using intelligent technology. I argue that intuitiveness, interaction, explainability and inclusion of human domain knowledge are essential in building this trust. I present some of the techniques and methods we are building for making AI systems that think and interact with humans in more intuitive and personalised ways, enabling humans to better understand the solutions produced by machines, and enabling machines to incorporate human domain knowledge in their reasoning and learning processes.

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Optimizing Random Sampling of Daylong Audio

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Evidential uncertainty involves both pragmatic and extralinguistic reasoning: a computational account