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Co-creating meaning in conversation is challenging. Success is often determined by people’s abilities to coordinate their language in strategic ways to signal problems and to align mental representations. Here we explore one set of grounding mechanisms, known as interactive linguistic alignment, that makes use of the ways people re-use, i.e., “align to,” the lexical, semantic, and syntactic forms of others’ utterances. In particular, the focus is on the temporal development of multi-level linguistic alignment and examine how its expression is related to communicative outcomes within a unique collaborative problem-solving paradigm. The primary task, situated within an educational video game, requires creative thinking between three people where the paths for possible solutions are highly variable. We find that over time interactions are marked by decreasing lexical and syntactic alignment, with a trade-off of increasing semantic alignment. However, a greater semantic alignment does not necessarily translate into better team performance. Overall, these findings provide greater clarity on the role of grounding mechanisms in complex and dynamic collaborative problem-solving tasks.

CogSci 2021

Multi-Level Linguistic Alignment in a Virtual Collaborative Problem-Solving Task

### 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!<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.

technical paper

Many real-world tasks require agents to coordinate their behavior to achieve shared goals. Successful collaboration requires not only adopting the same communicative conventions, but also grounding these conventions in the same task-appropriate conceptual abstractions. We investigate how humans use natural language to collaboratively solve physical assembly problems more effectively over time. Human participants were paired up in an online environment to reconstruct scenes containing two block towers. One participant could see the target towers, and sent assembly instructions for the other participant to reconstruct. Participants provided increasingly concise instructions across repeated attempts on each pair of towers, using more abstract referring expressions that captured each scene's hierarchical structure. To explain these findings, we extend recent probabilistic models of ad hoc convention formation with an explicit perceptual learning mechanism. These results shed light on the inductive biases that enable intelligent agents to coordinate upon shared procedural abstractions.

Learning to communicate about shared procedural abstractions

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.

Interaction Flexibility in Artificial Agents Teaming with Humans

Using the visual-word paradigm, the present study investigated the effects of word onset and offset masking on the time course of non-native spoken-word recognition in the presence of background noise. In two experiments, Dutch non-native listeners heard English target words, preceded by carrier sentences that were noise-free (Experiment 1) or contained intermittent noise (Experiment 2). Target words were either onset- or offset-masked or not masked at all. Analyses showed that onset masking delayed target word recognition more than offset masking did. These results suggest that – in line with contemporary models of spoken-word recognition – non-native listeners strongly rely on word onset information when hearing words in noise.

The Effects of Onset and Offset Masking on the Time Course of Non-Native Spoken-Word Recognition in Noise

In the face of strong evidence that a coin landed heads, can someone simply choose to believe it landed tails? Knowing that a large earthquake could result in personal tragedy, can someone simply choose to desire that it occur? We propose that in the face of strong reasons to adopt a given belief or desire, people are perceived to lack control: they cannot simply believe or desire otherwise. We test this “reason-based constraint” account of mental state change, and find that people reliably judge that evidence constrains belief formation, and utility constrains desire formation, in others. These results were not explained by a heuristic that simply treats irrational mental states as impossible to adopt intentionally. Rather, constraint results from the perceived influence of reasons on reasoning: people judge others as free to adopt irrational attitudes through actions that eliminate their awareness of strong reasons. These findings fill an important gap in our understanding of folk psychological reasoning, with implications for attributions of autonomy and moral responsibility.

Reason-Based Constraint in Theory of Mind

People can produce drawings of specific entities (e.g., Garfield), as well as general categories (e.g., ``cat''). What explains this ability to produce such varied drawings of even highly familiar object concepts? We hypothesized that drawing objects at different levels of abstraction depends on both sensory information and representational goals, such that drawings intended to portray a recently seen object preserve more detail than those intended to represent a category. Participants drew objects cued either with a photo or a category label. For each cue type, half the participants aimed to draw a specific exemplar; the other half aimed to draw the category. We found that label-cued category drawings were the most recognizable at the basic level, whereas photo-cued exemplar drawings were the least recognizable. Together, these findings highlight the importance of task context for explaining how people use drawings to communicate visual concepts in different ways.

Visual communication of object concepts at different levels of abstraction

A new phenomenon is the spread and acceptance of “fake news” on an individual user level, facilitated by social media such as Twitter. So far, state of the art socio–psychological theories and cognitive models focus on explaining how the accuracy
of fake news is judged on average, with little consideration of the individual. This paper takes it to a new level: A breadth of core models are comparatively assessed on their
predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. We used two previously collected large data sets with a total of 3309 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person. A hybrid model that combines those models outperformed the prediction of all individual models pointing to an adaptive tool-box.

When Does an Individual Accept Misinformation?

Finding and exploiting loopholes is a familiar facet of fable, law, and everyday life. But cognitive, computational, and empirical work on this behavior remains scarce. Engaging with loopholes requires a nuanced understanding of goals, social ambiguity, and value alignment. We trace loophole behavior to early childhood, and we propose that exploiting loopholes results from a conflict in actors' goals combined with a pressure to cooperate. A survey of 260 parents reporting on 425 children reveals that loophole behavior is prevalent, frequent, and diverse in daily parent-child interactions, emerging around ages five to six and tapering off from around ages nine to ten into adolescence. A further experiment shows that adults consider loophole behavior in children as less costly than non-compliance, and children increasingly differentiate loophole behavior from non-compliance from ages four to ten. We discuss limitations of the current work together with a proposal for a formal framework for loophole behavior.

Loopholes, a Window into Value Alignment and the Learning of Meaning

Planning, the process of evaluating the future consequences of actions, is typically formalized as search over a decision tree. This procedure increases expected rewards but is computationally expensive. Past attempts to understand how people mitigate the costs of planning have been guided by heuristics or the accumulation of prior experience, both of which are intractable in novel, high-complexity tasks. In this work, we propose a normative framework for optimizing the depth of tree search. Specifically, we model a metacognitive process via Bayesian inference to compute optimal planning depth. We show that our model makes sensible predictions over a range of parameters without relying on retrospection and that integrating past experiences into our model produces results that are consistent with the transition from goal-directed to habitual behavior over time and the uncertainty associated with prospective and retrospective estimates. Finally, we derive an online variant of our model that replicates these results.

Planning to plan: a Bayesian model for optimizing the depth of decision tree search

A large body of work has demonstrated the utility
of the Bayesian framework for capturing inference
in both specialist and everyday contexts. However,
the central tool of the framework, conditionalization
via Bayes’ rule, does not apply directly to a
common type of learning: the acquisition of conditional
information. How should an agent change
her beliefs on learning that “If A, then C”? This
issue, which is central to both reasoning and argumentation,
has recently prompted considerable research
interest. In this paper, we critique a prominent
proposal and provide a new, alternative, answer.

How to Revise Beliefs from Conditionals: A New Proposal

Legal documents, in the form of terms of service agreements and other private contracts, are now an increasingly prevalent part of everyday life. While legal documents have long been acknowledged to be difficult to understand without training, it remains an open question whether the ever-increasing exposure to contracts might have mitigated this difficulty. Moreover, insofar as this difficulty has persisted, there remains no systematic analysis of which linguistic structures contribute most heavily to the processing difficulty of legal texts, nor whether this difficulty is heightened for those with less language experience. Here, we investigate these issues, and in a well-powered experiment find evidence that (a) both recall and comprehension of legal propositions in a contract are hindered by use of a legal register relative to plain-English translations; (b) certain linguistic structures, such as center-embedding, hinder recall to a greater degree than others, such as passive voice; and (c) language experience influences comprehension of legal propositions. Surprisingly, language experience did not influence recall, nor was there an interaction between legal register and language experience on recall or comprehension. These findings suggest that legal language poses heightened difficulties for those with less language experience--who tend to be of lower socioeconomic status and with diminished access to the justice system--and that eliminating complex features of legalese would benefit those of all reading levels.

Multi-Level Linguistic Alignment in a Virtual Collaborative Problem-Solving Task

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Learning to communicate about shared procedural abstractions

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Multi-Level Linguistic Alignment in a Virtual Collaborative Problem-Solving Task

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Next from CogSci 2021

Learning to communicate about shared procedural abstractions

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Arc Length as a Geometric Constraint for Psychological Spaces

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Downloads