As we navigate through the world, we often leave traces of our actions: a broken branch, a footprint in the mud, a dirty coffee mug at a desk. As observers, these traces enable us to make surprisingly complex social inferences about the actions that may have caused them: what the other person may have been doing, what their likely goals were, and more. But how might a conspicuous lack of evidence prompt this same reasoning? We hypothesize that children use intuitive physics to infer possible prior actions and their outcomes, even in the absence of evidence. In support of this proposal, we found that children readily reconstruct an agent’s actions after observing indirect evidence. Importantly, they are also able to use the difficulty of concealing such evidence to interpret its absence.

What happened here? Children integrate physical reasoning to infer actions from indirect evidence

The physical world is rich with social information that people readily detect and extract, such as inferring that someone was present when we encounter a stack of rocks in the woods. How do people recognize that a physical scene contains social information? Research in developmental psychology has argued that this capacity is supported by a sensitivity to violations of randomness. Here we present a computational model of this idea and test its explanatory power in a quantitative manner. Our model infers agency by estimating the likelihood that a scene would arise naturally, as determined by human intuitive physics instantiated as a physics engine. Our results suggest that people's ability to detect agency in a physical scene is sensitive not only to the superficial visual properties, but also to the underlying physical generative process. Our results highlight how people use intuitive physics to decide when to engage in nuanced social reasoning.

Detecting the involvement of agents through physical reasoning

To infer what others know, we must consider under what epistemic states their actions were both rational and probable. We test whether preschoolers can compare the probability of different actions (and outcomes) under different epistemic states—and use this to evaluate what others know. Specifically, four- to six-year-olds (n=90) were asked to help evaluate an agent’s knowledge state by asking the agent to complete either an “undiagnostic” task (where success was assured), or a “diagnostic task” (where the probability of random success was low). By age six, children understood that the “diagnostic” task would more likely reveal the agent’s knowledge state; four- and five-year-olds had no reliable preference, although children in all age groups understood that the “diagnostic” task was harder. These results suggest that, by the end of preschool, children understand how agents’ epistemic states and environment jointly determine success—considering whether agents’ actions imply knowledge, or just luck.

Children consider the probability of random success when evaluating knowledge

Human Theory of Mind enables us to attribute mental states like beliefs and desires based on how other people act. However, in many social interactions (particularly ones that lack observable action), people also directly think about other people's thinking. Here we present a computational framework, Bayesian inverse reasoning, for thinking about other people's thoughts. Our framework formalizes inferences about thinking by inferring a generative model of reasoning decisions and computational processes, structured around a principle of rational mental effort -- the idea that people expect other agents to allocate thinking rationally. We show that this model quantitatively predicts human judgements in a task where participants must infer the mental causes behind an agent's pauses as they navigate and solve a maze. Our results contribute to our understanding of the richness of the human ability to think about other minds, and to even think about thinking itself.

hinking about thinking through inverse reasoning

For a gesture to be successful, observers must recognize its communicative purpose. Are communicators sensitive to this problem and do they try to ease their observer’s inferential burden? We propose that people shape their gestures to help observers easily infer that their movements are meant to communicate. Using computational models of recursive goal inference, we show that this hypothesis predicts that gestures ought to reveal that the movement is inconsistent with the space of non-communicative goals in the environment. In two gesture-design experiments, we find that people spontaneously shape communicative movements in response to the distribution of potential instrumental goals, ensuring that the movement can be easily differentiated from instrumental action. Our results show that people are sensitive to the inferential demands that observers face. As a result, people actively work to help ensure that the goal of their communicative movement is understood.

I Know You Know I’m Signaling: Novel gestures are designed to guide observers’ inferences about communicative goals

Inferences about other people's knowledge and beliefs are central to social interaction. In many situations, however, it's not possible to be sure what other people know because their behavior is consistent with a range of potential epistemic states. Nonetheless, this behavior can give us coarse intuitions about how much someone might know, even if we cannot pinpoint the exact nature of this knowledge. We present a computational model of this kind of broad epistemic-state inference, centered on the expectation that agents maximize epistemic utilities. We evaluate our model in a graded inference task where people had to infer how much an agent knew based on the actions they chose. Critically, the agent's behavior was always under-determined, but nonetheless contained information about how much knowledge they possessed. Our model captures nuanced patterns in participant judgments, revealing a quantitative capacity to infer amorphous knowledge from minimal behavioral evidence.

I can tell you know a lot, although I'm not sure what: Modeling broad epistemic inference from minimal action

We propose a computational model of social preference judgments that accounts for the degree of an agents’ uncertainty about the preferences of others. Underlying this model is the principle that, in the face of social uncertainty, people interpret social agents’ behavior under an assumption of expected utility maximization. We evaluate our model in two experiments which each test a different kind of social preference reasoning: predicting social choices given information about social preferences, and inferring social preferences after observing social choices. The results support our model and highlight how uncertainty influences our social judgments.

Reasoning about social attitudes with uncertain beliefs

Theory of Mind enables us to attribute mental states to others. But we not only make inferences about mental states (like what someone believes or wants), but about mental processes (like if someone is distracted or whether they remember something). Here, we present a computational formalization of these kinds of inferences. We propose that inferences about mental processes are structured around a principle of rational mental effort: the expectation that other people allocate mental resources rationally so as to minimize thinking costs incurred while pursuing their goals. We develop this theory into a computational model in the context of the Rush Hour puzzle game. In two behavioral experiments testing different inferences about mental processing, we find that our model predicts participant judgments. This work advances our understanding of the richness of the human mind's ability to think about other minds, and even about thinking itself.

Thinking about Thinking as Rational Computation

Social biases can negatively impact our sense of belonging, achievement, and social relationships. However, it is unclear what inferential processes underlie how people detect biases. We propose that people infer social biases by positing prior beliefs to account for potential gaps between what someone observed (e.g., seeing you succeed on a challenging task) and how they responded (e.g., recommending you try something easier). We present a computational model formalizing this process, and validate it with two experiments. We find a strong quantitative fit between model predictions and participant judgments across a range of inferences, namely which prior bias the coach held, and even the player's emotional reactions to the coach's actions (i.e., how offended or flattered the player feels). This work bridges computational methods with social psychological research on social biases, by showing how mental state inferences contribute to our ability to rapidly detect biases.

Detecting social biases using mental state inference

People have a remarkable capacity to infer other people’s goals and intentions based on how they behave. Yet, humans are also motivated to ensure that others can infer their mental states easily and accurately. Past work has argued that people achieve this by introducing inefficiencies to their behavior, which reveal its underlying goal (e.g., exaggerating one’s movements so as to make their purpose obvious). We hypothesized that inefficiency is not a constitutive feature of signaling, and that people will often signal their goals and intentions solely through efficient action. We test this idea in a signal-design experiment where participants need to reach a goal while also making their behavior as inferable as possible. We find that people shape their behavior to increase inferability without jeopardizing efficiency. We then test how well these efficient signals reveal their goals using a computational model to simulate observer inferences. Our results show that humans not only exploit opportunities to reveal their goals without deviating from efficient action, but that these signals are shaped to allow observers to more quickly understand the intention underpinning the movement.

Guiding Inference: Signaling intentions using efficient action


**For a full list of posters in this session, click the See All link on the right side of the page.**

Poster Session 1 -  C : Cognitive Science

poster

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year! 

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).

CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Poster Session 1 -  D: Education, Development and Perspectives in Cognitive Science

Poster Session 2 -  H: Psychological Science

Poster Session 3 -  C : Cognitive Science

**Session Chair: **Agnieszka Konopka 
**Technical Session Chair: **Kimia Goderle 
**Session Description: ** The session is comprised of 15 minute talks followed by up to 5 minutes of live Q&A time for each presenter.

IP2: Communication

technical paper

**Session Chair: **Antonia Hamilton 
**Technical Session Chair: **Tiffany Doan 
**Session Description: ** The session is comprised of 15 minute talks followed by up to 5 minutes of live Q&A time for each presenter.

IP25: Social & judgements

**Session Chair: **Stephanie Denison 
**Technical Session Chair: **Tiffany Doan 
**Session Description: ** The session is comprised of 15 minute talks followed by up to 5 minutes of live Q&A time for each presenter.

IP30: Social cognition

This session has no scheduled virtual poster presentation time, but attendees will be able to comment and ask questions about your work/research asynchronously throughout the conference. 

**If you are a virtual poster presenter**, you can leave a message in your poster area chat space to advise times (and time zone) that you will be available either in the chat box, or by setting up your own online meeting room (Zoom, etc.). 
**If you are an attendee**, please check each poster's chat box and arrange time with presenter.

Virtual Posters

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. 
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).

Join us!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Julian Jara-Ettinger

10

16

1

Presentations

What happened here? Children integrate physical reasoning to infer actions from indirect evidence

Detecting the involvement of agents through physical reasoning

Children consider the probability of random success when evaluating knowledge

hinking about thinking through inverse reasoning

I Know You Know I’m Signaling: Novel gestures are designed to guide observers’ inferences about communicative goals

I can tell you know a lot, although I'm not sure what: Modeling broad epistemic inference from minimal action

Reasoning about social attitudes with uncertain beliefs

Thinking about Thinking as Rational Computation

Detecting social biases using mental state inference

Guiding Inference: Signaling intentions using efficient action

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