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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&#39; prior beliefs about the content, and the content&#39;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&#39;&#39; and ``know&#39;&#39; 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.


CogSci 2023

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

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.


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>
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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.

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

When you encounter a new claim, how do you decide whether to trust it or to seek more evidence first? In two studies, we show that children use environmental cues about informational reliability to rationally adapt their information seeking. In Study 1, children exposed to an environment containing some misinformation (as opposed to all true information) sampled more evidence before verifying a test claim in a novel domain. In Study 2, children showed a graded sensitivity to environmental reliability: information seeking increased linearly with the proportion of false statements heard during exposure. Additionally, these statements were presented as online search results, which demonstrated that children make sophisticated inferences about their informational environments, beyond speaker-specific cues, to adjust their skepticism toward new information. These results further emphasize the importance of considering the environmental context of learning when developing interventions to promote healthy skepticism and lifelong learning.


Children flexibly adapt their evidentiary standards to their informational environment

How can we quantify the degree to which a piece of evidence affects a person's belief? Philosophers investigating theories of Bayesian Confirmation have identified a plurality of potential measures, each with their own virtues and shortcomings. Psychologists meanwhile have largely neglected this question, which has limited their ability to understand differential belief updating, cases where certain individuals or groups respond to the same evidence in different ways. In this study, we examine how competing Bayesian confirmation measures track commonsense notions of evidential strength. We demonstrate how these measures can be computed from participants' belief reports, and identify cases where the measures come apart in their characterization of participants' belief updating. In so doing, this project seeks to build a connections between investigations of psychological belief updating processes and formal epistemic theories of confirmation.


Bayesian confirmation and commonsense notions of evidential strength

In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.


Bridging Declarative, Procedural, and Conditional Knowledge Gap Using Deep Reinforcement Learning

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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Causal inference shapes counterfactual plausibility

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Causal inference shapes counterfactual plausibility

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