United States

Seeking help can be a highly adaptive behavior. From an evolutionary perspective, strategic help-seeking can significantly improve an individual&#39;s fitness. However, it has not yet been investigated whether our closest living relatives –chimpanzees– seek help strategically. 

In Study 1, we investigated whether chimpanzees seek help selectively when they need it. Chimpanzees (N=19) sought help when it was necessary, but not if they could solve the problem themselves (Chi2(1)=30.821, p&lt;.001).

In Study 2, we investigated whether chimpanzees seek help strategically: Do they consider action-related costs of potential helpers as much as they consider their own? Chimpanzees (N=14) had a preference for a low-cost option when they had to obtain a reward on their own, but not when they sought help from others (Chi2(1)=7.989, p=.005). 

These findings imply that chimpanzees seek help when they need it, but they do not strategically consider other’s costs when deciding whom to seek help from.

CogSci 2021

Chimpanzees seek help, but not strategically

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Seeking help can be a highly adaptive behavior. From an evolutionary perspective, strategic help-seeking can significantly improve an individual's fitness. However, it has not yet been investigated whether our closest living relatives –chimpanzees– seek help strategically. 

In Study 1, we investigated whether chimpanzees seek help selectively when they need it. Chimpanzees (N=19) sought help when it was necessary, but not if they could solve the problem themselves (Chi2(1)=30.821, p<.001).

In Study 2, we investigated whether chimpanzees seek help strategically: Do they consider action-related costs of potential helpers as much as they consider their own? Chimpanzees (N=14) had a preference for a low-cost option when they had to obtain a reward on their own, but not when they sought help from others (Chi2(1)=7.989, p=.005). 

These findings imply that chimpanzees seek help when they need it, but they do not strategically consider other’s costs when deciding whom to seek help from.

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!<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.

Tetrapod vertebrates evolved acoustic calling to circumvent impediments in communication media. Motor and auditory neural underpinnings of such calling are hundreds of millions of years old. More complex vocalizations, known as display, however, have since convergently evolved in many lineages. I hypothesized that music-like calling might correspond to larger sizes of neural structures corresponding to visual-spatial and motor control capacities, perhaps due to processing overlap. I tested this theory on primates by comparing relative brain component sizes to several spectrographic indexes—song complexity, reappearance diversity [ARDI], and syllable count. Visual and spatial components had moderately positive associations with vocal complexity measures. Areas associated with emotion, arousal, and motivation as well as motor control (especially of head, neck, and eyes) had even stronger associations. Olfaction, however, had negative correlations with all indexes, suggesting an evolutionary trade-off between smell and other brain components during evolution towards signals of greater acoustic complexity.

Primates evolved spectrally complex calls in compensation for reduction in olfactory cognition

Children with developmental language disorder (DLD) regularly use the base form of verbs (e.g., dance) instead of inflected forms (e.g., danced). We propose an account of this behavior in which children with DLD have difficulty processing novel inflected verbs in their input. This leads the inflected form to face stronger competition from alternatives. Competition is resolved by the production of a more accessible alternative with high semantic overlap with the inflected form: in English, the bare form. We test our account computationally by training a nonparametric Bayesian model that infers the productivity of the inflectional suffix (-ed). We systematically vary the number of novel types of inflected verbs in the input to simulate the input as processed by children with and without DLD. Modeling results are consistent with our hypothesis, suggesting that children’s inconsistent use of inflectional morphemes could stem from inferences they make on the basis of impoverished data.

Making Heads or Tails of it: A Competition–Compensation Account of Morphological Deficits in Language Impairment

Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.

Are Convolutional Neural Networks or Transformers more like human vision?

The integration of behavioral phenomena into mechanistic models of cognitive function is a fundamental staple of cognitive science. Yet, researchers are beginning to accumulate increasing amounts of data without having the temporal or monetary resources to integrate these data into scientific theories. We seek to overcome these limitations by incorporating existing machine learning techniques into an open-source pipeline for the automated construction of quantitative models. This pipeline leverages the use of neural architecture search to automate the discovery of interpretable model architectures, and automatic differentiation to automate the fitting of model parameters to data. We evaluate the utility of these methods based on their ability to recover quantitative models of human information processing from synthetic data. We find that these methods are capable of recovering basic quantitative motifs from models of psychophysics, learning and decision making. We also highlight weaknesses of this framework and discuss future directions for their mitigation.

Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search

Vocal learning is considered a crucial component of human language. The ability of vocal learning is rare and among birds has been detected only in songbirds, hummingbirds. and parrots. Parrots are probably the most advanced vocal learners who learn new vocalisations throughout their lives and are known for their ability to imitate human speech. Thus parrots present an intriguing model to shed light on how human language evolved. However, only little is know about how widely vocal learning is distributed in Psitticaformes, an avian order comprising 399 species. In the past decade, surveying behaviour from online video repositories have become a promising research tool to investigate animal behaviour. In this study, we conducted a YouTube survey and provided an overview of the phylogenetic distribution of (allospecific) vocal learning in parrots to enhance our undertstanding of the evolution of language. We discuss why some parrot species are better imitators than others.

Phylogenitic map of vocal learning in parrots

Communication is highly overloaded. Despite this, even young children are good at leveraging context to understand ambiguous signals. We propose a computational account of overloaded signaling from a shared agency perspective which we call the Imagined We for Communication. Under this framework, communication is a way for cooperators to coordinate their perspectives, allowing them to act together to achieve shared goals. We assume agents are rational, utility maximizing cooperators, which puts constraints on how signals can be sent and interpreted. We implement this model in a set of simulations which demonstrate this model’s success under increasing ambiguity as well as increasing layers of reasoning. Our model is capable of improving performance with deeper recursive reasoning; however, it outperforms comparison baselines at even the shallowest level of reasoning, highlighting how shared knowledge and cooperative logic can do much of the heavy-lifting in language.

Modeling Communication to Coordinate Perspectives in Cooperation

Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance.

Scaffolded Self-explanation with Visual Representations Promotes Efficient Learning in Early Algebra

Theory-of-Mind inference is natural for humans but poses significant computational challenges. The core difficulty can be traced back to the exponential growth of paths to consider in planning given a mental state. In this paper we tackle this problem in a search-and-rescue task implemented in Minecraft. Our goal is to infer differences in knowledge from participants' continuous-time trajectory. By abstracting the spatio-temporal state space and the reward function together, we surface natural decision points, on which we compare the participants' behavior to myopic rational agents of varying knowledgeability. Collectively, the abstraction and rational agent analysis yield successful inference of participants' knowledge states and reveal distinct patterns of their exploratory behavior.

Inferring Knowledge from Behavior in Search-and-rescue Tasks

Human solutions to the Traveling Salesperson Problem (TSP) are surprisingly close to optimal and unexpectedly efficient. We posit that humans solve instances of the TSP by first clustering the points into smaller regions and then solving each cluster as a simpler TSP. Prior research has shown that participants cluster visual stimuli reliably. That is, their clustering and re-clustering of the same stimulus are similar, especially when the stimulus is relatively more clustered. In this study, participants solved the same TSP instances twice. On the second presentation, half of the instances were flipped about the horizontal and vertical axes. Participants solved the TSP reliably, with their two tours of the same instance sharing 77 percent of the same edges on average. In addition, within-participant reliability was higher for more clustered versus more dispersed instances. Our findings are consistent with the proposal that people use clustering strategies to solve the TSP.

The role of clustering in the efficient solution of small Traveling Salesperson Problems

Emotional capabilities of interactive virtual agents grow every year, while most of them lack in emotional intelligence. Their abilities to recognize and express emotions may be worthless, if the agent cannot decide how to behave adequately in a given social context. The central part of the problem, the logic of socially emotional behavior, remains an unsolved challenge. Here this challenge is addressed within a virtual reality paradigm of a poster presenter, designed on the basis of the emotional Biologically Inspired Cognitive Architecture (eBICA). The framework of eBICA combines the formalisms of semantic maps, moral schemas, mental states and narratives in order to achieve believable, socially acceptable interactive behavior of the presenter bot. The paradigm involves establishment and maintenance of a stable socially emotional contact with mutual empathy and trust. Results of evaluation of the prototype by participants at two international virtual conferences speak in favor of the selected approach.

Chimpanzees seek help, but not strategically

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Primates evolved spectrally complex calls in compensation for reduction in olfactory cognition

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