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While many formal frameworks distinguish between desires and intentions, and considerable empirical work shows that people interpret them differently, no studies examine how people reason about them. We extend Harner and Khemlani’s (2020) model-based theory of relations describing desire. The theory holds that people represent desires, as in, e.g., Pav wants to visit Angkor Wat, by pairing a factual representation of the negation of the desire (e.g., that Pav is not currently visiting Angkor Wat) with a future possibility where the desire is realized. We propose that intentions, which people express using verbs like plan, are represented as future actions that agents seek to perform. A particular individual’s plans must be consistent with one another, whereas desires can conflict with these plans. We show how the model theory distinguishes desires and intentions, namely that models can be coherent even when a desire and a plan are inconsistent with each other. The distinctions make predictions about how reasoners should assess the consistency of statements concerning desires and intentions, and we report on two experiments that validate them.

CogSci 2021

Desires can conflict with intentions; plans cannot

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

Underline hosted the two main days of AGI 2020 on Wednesday June 24th and Thursday June 25th. Both days were **live-streamed** and you can read the full program bellow. All presentations are available for a **asynchronous viewing**. All presenters are grouped into panels. You can also hear guest talks by **Ben Goertzel** (SingularityNET), **Valery Tarasov** (Semenov Institute of Chemical Physics), **Tatiana Shavrina** (Sberbank) and **Joscha Bach** (MIT Media Lab). 

AGI-20

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

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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**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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.

A fundamental characteristic of human language is its combinatorial nature, which facilitates the communication of infinite meanings (i.e., words) built from finite sounds (i.e., phonemes). Investigating the selective drivers of this combinatorial feature represents a major challenge in the field of language evolution and has prospered into a rich interdisciplinary field. Here, we discuss the emergence of combinatorial structures in (human and non-human) communication systems from an information and coding theoretical perspective. We describe how “noise” (i.e., factors constraining communication processes) can hamper and distort signal transmission and perception, and how such noise-induced impairments can be circumvented through a combinatorial coding scheme that adds redundancy to a signalling system, in turn increasing its robustness and enhancing signal detection and discrimination. In doing so we argue that basic combinatoriality has emerged due to universal constraints imposed on signalling systems, and that human language-like productive combinatoriality builds upon this phenomenon.

An information and coding theoretical approach to combinatorial communication

Word learning models are typically evaluated as the problem of observing words together with sets of atomic objects and learn-ing an alignment between them. We use ADAM, a Python software platform for modeling grounded language acquisition, to evaluate a particular word learning model, Pursuit (Stevens, Gleitman, Trueswell, & Yang, 2017),under more realistic learning conditions (see e.g. Gleitman and Trueswell (2020) for review). In particular, we manipulate the degree of referential ambiguity and the salience of attentional cues available to the learner, and we present extensions to Pursuit which address the challenges of non-atomic meanings and exploiting attentional cues.

Grounding Word Learning Across Situations

Iconicity refers to instances in which the form of language resembles its meaning (Perniss et al., 2010). The most prominent example in spoken language is onomatopoeia (e.g., woosh, which sounds like a gust of wind). Here we tested whether iconicity makes language more vivid by depicting the sensorimotor experiences being referred to. In Experiment 1, 44 participants read ten short passages (five iconic, five non-iconic), and then rated their vividness on several scales. These passages differed on two key words, which were either iconic (e.g., screech) or non-iconic (e.g., yell). We found no evidence that iconic language was more vivid. In Experiment 2, 199 participants each rated one longer passage that was either iconic or non-iconic (differing on eight key words). We found only marginal evidence of iconic language being more vivid, on subscales related to felt vividness. These results suggest that iconicity does not make written language more vivid.

Is Iconic Language More Vivid?

As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words. Morphological learning is made challenging by the fact that even fully productive rules have exceptions, as in the well-known case of English past tense verbs, which features the -ed rule against the irregular verbs. The Tolerance Principle is a recent proposal that provides a precise threshold of exceptions that a productive rule can withstand. Its empirical application so far, however, requires the researcher to fully specify rules defined over a set of words. We propose a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary. When the search for broader productivity fails, the model recursively subdivides the vocabulary and continues the search for productivity over narrower rules. Trained on psychologically realistic data from child-directed input, our model displays developmental patterns observed in child morphology acquisition, including the notoriously complex case of German noun pluralization. It also produces responses to nonce words that, despite receiving only a fraction of the training data, are more similar to those of human subjects than current neural network models' responses are.

The Greedy and Recursive Search for Morphological Productivity

Production of referring expressions (the dog, it, Snoopy) is a complex process regulated by a combination of linguistic and cognitive constraints. In this paper, we explore the impact of world knowledge on the types of references speakers produce, focusing on predictability of event progressions. We argue that speakers are more likely to use a full noun phrase rather than drop the subject or use a pronoun when they describe an event progression they find surprising. In order to avoid the influence of distributional properties of event descriptions, we created an artificial world and trained subjects to recognize typical collision-event progressions within. Speakers then described novel scenes, which either conformed to their expectations or violated them, in a free production experiment. The results reveal that unpredictable event progressions lead to a more frequent production of full noun phrases, in contrast to reduced linguistic expressions (pronouns and null subjects). We conclude that speakers choose more informative descriptions to talk about surprising events.

Speakers Use More Informative Referring Expressions to Describe Surprising Events

In this paper we use pupillometry, a non-invasive, naturalistic method of measuring attention and cognitive load, to measure the effect of stress clash (Chinése shíp) and its metrical repair (Chínese shíp) during auditory sentence processing. We addressed two main research questions. The first question explores whether phonologically-disfavored metrical structures induce processing costs indexed by changes in pupil size. The second investigates whether the application of an optional process of stress retraction called the Rhythm Rule (Liberman & Prince, 1977) ameliorates or compounds any general penalty for stress clash. We find that unrepaired stress clash leads to greater pupil diameter relative to non-clashing sequences, indicating increased attention and cognitive load. We also find that repaired sequences lead to a decrease in overall pupil diameter, indicating facilitation.

The online advantage of repairing metrical structure: Stress shift in pupillometry

When do people use knowledge about the world in order to comprehend language? We asked whether pronoun resolution decisions are influenced by knowledge about physical plausibility. Results showed that referents which are more physically plausible in described events were more likely to be selected as antecedents of ambiguous pronouns, implying that resolution decisions were driven by physical inference. An alternative explanation is that these decisions were driven instead by distributional word knowledge. We tested this by including predictions of a statistical language model (BERT) and found that physical plausibility explained variance on top of the statistical language model predictions. This indicates that at least part of people's pronoun resolution judgments comes from knowledge about the world and not the word. This result constrains psycholinguistic models of comprehension—world knowledge must influence propositional interpretation—and raises the broader question of how non-linguistic physical inference processes are incorporated during comprehension.

The Role of Physical Inference in Pronoun Resolution

During lexical access, listeners turn unfolding phonetic input into words. We tested how participants interpret words that aren't in their lexicon, either due to their coarticulation cues or because they label a novel object. In a 2-picture Visual World study, 57 adults saw a familiar object and an unfamiliar object, while hearing sentences directing their gaze to the target in 3 conditions: with a familiar word (“crib”), a novel word (“crig”), or a familiar word with coarticulation cueing a novel word (“cri(g)b”). When coarticulation cues matched the novel word (“cri(g)b”), participants looked more at the unfamiliar object than when the cues matched the familiar word, suggesting lexical competition can include a novel word under appropriate circumstances. When hearing a novel word (e.g. “crig”), participants showed two patterns: Roughly half looked more at the unfamiliar object, as expected, while the rest surprisingly looked more at the familiar object. We discuss the interaction of mutual exclusivity, phonetic similarity, and coarticulation cues in driving lexical access.

Listeners can use coarticulation cues to predict an upcoming novel word

Research suggests that people minimize the amount of effort used to generate natural language descriptions of visual scenes. In the case of visual scenes with multiple groups, recent work has found that people tend to generate quantitative descriptions that mention the number and cardinality of groups (e.g., ``two groups of three limes''), but omit the total quantity (e.g., ``six limes''). This finding suggests that people groupitize, that is, they more quickly determine the number of grouped items by rapid enumeration of subgroups, rather than slower item-by-item counting. %This hypothesis predicts that during description, people exert less effort to encode and report only the number and cardinality of groups. 
A recent proposal predicts that during description, people exert less effort by encoding and reporting only information that is readily available to perception.
In previously studied description tasks, people may have omitted the total quantity from their descriptions because of considerations of brevity and informativity. In this paper, we describe a study designed to test how individuals balance effort, brevity, and informativity when evaluating quantified descriptions. The experiment was designed to elicit more fine-grained preferences for descriptions using direct comparisons between two competing descriptive forms. The results suggest that perceptual effort plays a central role in how people describe grouped collections of items.

Preferences in the quantified description of visual groups

Metaphor is an important part of everyday thought and language. Although we are often not aware of metaphor in everyday speech, on occasion, a particularly creative or novel use of metaphor will make us pay attention. It has been hypothesized that one of the driving cognitive factors behind the use of novel metaphor is a need to describe a new reality (as opposed to a preexisting reality) that would otherwise be difficult to convey using conventionalized metaphor. To this extent, novel metaphor use in everyday language may be more associated with episodic memory demands in contrast to conventional metaphor that is associated with semantic memory. To test this idea we analyzed novel metaphor use in the Hippocorpus --- a corpus of more than 5000 recalled and imagined stories about memorable life events in the first person perspective. In this dataset, recalled events have been shown to rely on episodic memory to a greater extent than descriptions of imagined events (i.e., narrating an event as if it happened to you but not describing an event that actually happened to you), which largely draw on semantic memory. We hypothesized that novel metaphor use during event narration should be modulated by the extent to which language users are able to draw on primary experience to describe events. We found that novel metaphor counts in recalled events were significantly higher than imagined events. Importantly, we found that factors that influence the extent to which language users are able to draw on primary experience during event narration (i.e., openness to experience, similarity to one's own experience, and how memorable or important an event was) modulated novel metaphor use in different ways in imagined compared to recalled events. The work paves the way for using large scale corpora to analyze underlying cognitive processes that modulate metaphorical language use.

Desires can conflict with intentions; plans cannot

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An information and coding theoretical approach to combinatorial communication

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