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Inferences about other people&#39;s knowledge and beliefs are central to social interaction. In many situations, however, it&#39;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&#39;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.

CogSci 2021

I can tell you know a lot, although I&#39;m not sure what: Modeling broad epistemic inference from minimal action

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**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!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**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/).


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.

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

technical paper

The pattern of implicatures of modified numeral `more than n' depends on the roundness of n. Cummins, Sauerland, and Solt (2012) present experimental evidence for the relation between roundness and implicature patterns, and propose a pragmatic account of the phenomenon. More recently, Hesse and Benz (2020) present more extensive evidence showing that implicatures also depend on the magnitude of n and propose a novel explanation based on the Approximate Number System (Dehaene 1999). Despite the wealth of experimental data, no formal account has yet been proposed to characterize the full posterior distribution over numbers of a listener after hearing `more than n'. We develop one such account within the Rational Speech Act framework, quantitatively reconstructing the pragmatic reasoning of a rational listener. We show that our pragmatic account correctly predicts various features of the experimental data.

The Shape of Modified Numerals

The Uniform Information Density hypothesis (UID) predicts that lexical choice between long and short word forms depends on the predictability of the referent in context, and recent studies have shown such an effect of predictability on lexical choice during online production. We here set out to test whether the UID predictions hold up in a related setting, but different language (German) and different phenomenon, namely the choice between compounds (e.g. Badewanne / bathtub) or their base forms (Wanne / tub). Our study is consistent with the UID: we find that participants choose the shorter base form more often in predictive contexts, showing an active tendency to be information-theoretically efficient.

A bathtub by any other name: the reduction of German compounds in predictive contexts

Do people cater their lies to their own beliefs or others' beliefs? One dominant individual-based account considers lying to be an internal tradeoff between self-interest, norms, and morals. However, recent audience-based accounts suggests that lying behavior can be better explained within a communicative framework, wherein speakers consider others' beliefs to design plausible lies---highlighting the role of theory-of-mind in strategic lying. We tease apart these accounts by examining human lying behavior in a novel asymmetric, dyadic lying game in which speakers' beliefs differ from those they ascribe to their audience. We compare participants' average reported lie (controlling for the truth) across conditions that manipulated the player's and the audience's beliefs. We find that people spontaneously tune their lies to beliefs unique to their audience, more than to their own beliefs. These results support the audience-based account of lying: estimates of how listeners will respond determine how people decide to lie.

Lies are crafted to the audience

Speaking in a foreign accent has often been thought to carry many disadvantages. Here we probe the social evaluation of foreign-accented vs. native speakers using spoken utterances that either obey or violate the pragmatic principle of Informativeness. We show that listeners form different impressions of native and non-native speakers with identical pragmatic behavior: specifically, in contexts where violations of Informativeness can be detrimental to or misleading for the listener, people rated underinformative speakers more negatively on trustworthiness and interpersonal appeal compared to informative speakers, but this tendency was mitigated in some cases for speakers with foreign accents. Furthermore, this mitigating effect was strongest for less proficient non-native speakers who were presumably not fully responsible for their linguistic choices. Contrary to previous studies, we also find no consistent global bias against non-native speakers. Thus the fact that non-native speakers have imperfect control of the linguistic signal affects pragmatic inferences and social evaluation in ways that can lead to surprising social benefits.

Listeners evaluate native and non-native speakers differently (but not in the way you think)

Counterfactual theories of causal judgment propose that people infer causality between events by comparing an actual outcome with what would have happened in a relevant alternative situation. If the candidate cause is “difference-making”, people infer causality. This framework has not been applied to people’s judgments about unprecedented societal events (e.g., global pandemics), in which people have limited causal knowledge (e.g., about effective policies). In these contexts, it is less clear how people reason counterfactually. This study examined this issue. Participants judged whether a mandatory evacuation reduced population bite rates during a novel insect infestation. People tended to rely on prior causal knowledge, unless data from close alternatives (i.e., structurally similar counterfactuals) provided counterevidence. There were also notable individual differences, such that some people privileged prior knowledge regardless of the available counterevidence or privileged far alternatives (i.e., structurally distinct counterfactuals), which may have implications for understanding public disagreement about policy issues.

How People Make Causal Judgments about Unprecedented Societal Events

The need to update our estimates of probabilities (e.g., the accuracy of a test) given new information is commonplace. Ideally, a new instance (e.g., a correct report) would just be added to the tally, but we are often uncertain whether a new instance has occurred. We present an experiment where participants receive conflicting reports from two early-warning cancer tests, where one has higher historical accuracy (HA). We present a model showing that while uncertain which test is correct, estimates of the accuracy of both tests should be reduced. However, among our participants, we find two dominant approaches: (1) participants increase the more HA test, reducing the other; (2) participants make no change to either. Based on mixed methods we argue that both approaches represent two sides of a ‘binary’ decision i.e., (1) update as if we have complete certainty which test is correct and (2) update as if we have no information.

Categorical Belief Updating Under Uncertainty

Language is inherently flexible: people continually generalize over observed data to produce creative linguistic expressions. This process is constrained by a wide range of factors, whose interaction is not fully understood. We present a novel study of the creative use of verb constructions ``in the wild'', in a very large social media corpus. Our first experiment confirms on this large-scale data the important interaction of category variability and item similarity within creative extensions in actual language use. Our second experiment confirms the novel hypothesis that low-frequency exemplars may play a role in generalization by signaling the area of semantic space where creative coinages occur.

Coin it up: Generalization of creative constructions in the wild

One of the more surprising findings in visual cognition is the apparent sparsity of our scene percepts. Yet, scene perception also enables planning and navigation, which require a detailed, structured analysis of the scene geometry, including exit locations and the obstacles along the way. Here, we hypothesize that computation of navigational affordances (e.g., paths to an exit) is a “default” task in the mind, and that task induces selective analysis of the scene geometry most relevant to computing these affordances. In an indoor scene setting, we show that observers more readily detect changes if these changes impact shortest paths to visible exits. We show that behavioral detection rates are explained by a new model of attention that makes heterogeneous-precision inferences about the scene geometry, relative to how its different regions impact navigational affordance computation. This work provides a formal window into the contents of our scene percepts.

Automatic computation of navigational affordances explains selective processing of geometry in scene perception: behavioral and computational evidence

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.

Perception of soft materials relies on physics-based object representations:Behavioral and computational evidence

Physics and mind are two major causes of motion. In a leash-chasing display, a disc (“sheep") is being chased by another disc (“wolf"), which is physically constrained by a leash attached to a third disc (“master"). A number of interesting motions can emerge from this simple system, such as a wolf being dragged away from its target. Therefore, it is important for vision to jointly infer a physics-mind combination that can best explain the motions. Here we reported two discoveries from studying this display to support this theory. First, an intuitive physical system like a leash can greatly lessen the detrimental effects of spatial deviation and the diminishing objecthood on perceived chasing, strengthening its robustness. Second, a mutual dependency exists between physics and mind, where disrupting one will inevitably result in an impaired perception on the other. These results collectively support a joint perception of physics and mind.

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

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The Shape of Modified Numerals

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

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Next from CogSci 2021

The Shape of Modified Numerals

Similar lecture

In Touch with Causation: Understanding the Impact of Kinesthetic Haptics on Causality

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads