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Cultural attractor landscapes describe the time-evolution of cultural variants over transmission events. When variants sit at a local minimum of a stable attractor landscape, there will be no cumulative error over transmissions, laying the foundation for cumulative culture. But because cultural attractors are emergent products of dynamic populations of cognitive landscapes, which are in turn emergent products of individual experience within a culture, stable cultural attractor landscapes cannot be taken for granted. Yet, little is known about how cultural attractors form or stabilize. We present an agent-based model of cultural attractor dynamics, which adapts a cognitive model of unsupervised learning of phoneme categories in individual learners to a multi-agent, sociocultural setting wherein individual learners provide the training input to each other. We find that constraints at the level of cognition, development, and demographic structure determine the tendency for populations to self-organize into and dynamically stablilize a cultural attractor landscape.

CogSci 2021

The Emergence of Cultural Attractors: An Agent-Based Model of Collective Perceptual Alignment

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,**
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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/).


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.

When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding between blame or forgiveness. However, Bayesian models of theory of mind have generally not accounted for these mistakes, instead modeling agents as mostly optimal in achieving their goals. As a result, they are unable to explain phenomena like locking oneself out of one's house, or losing a game of chess. Here, we extend the Bayesian Theory of Mind framework to model boundedly rational agents who may have mistaken goals, plans, and actions. We formalize this by modeling agents as probabilistic programs, where goals may be confused with semantically similar states, plans may be misguided due to resource-bounded planning, and actions may be unintended due to execution errors. We present experiments eliciting human goal inferences in two domains: (i) a gridworld puzzle with gems locked behind doors, and (ii) a block-stacking domain. Our model better explains human inferences than alternatives, while generalizing across domains. These findings indicate the importance of modeling others as bounded agents, in order to account for the full richness of human intuitive psychology.

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

Children are often drawn to novelty, but these preferences may depend on their goals. In two experiments (N = 302), we show that children have differing preferences for novelty when seeking information compared to when they are asked to prioritize other goals. In Experiment 1, 4-7-year-olds wanted to have typical items (e.g., a four-legged chair) and learn about atypical items (e.g., a ten-legged chair). In Experiment 2, 4-6- year-olds wanted to learn about foreign characters, but liked foreign and local characters equally. We propose that children prefer to learn about novel instances for the promise of new information, which is evident in at least two domains (artifacts and people). However, this preference diminishes when children are asked about who they like, and it reverses to a familiarity preference when choosing between artifacts to acquire. In sum, our findings suggest that children’s preferences for novelty versus familiarity are sensitive to different goals.

Children's Novelty Preferences Depend on Information-Seeking Goals

Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for this success. However, it is unclear what causal roles are played by this reward-sharing mechanism. In order to systematically examine the effects of sharing rewards in animal coordinated hunting, we conduct a suite of modeling experiments using a state-of-the-art multi-agent reinforcement learning algorithm. The models are trained and evaluated with a task that simulates real-world collective hunting. We manipulate four evolutionarily important variables: reward distribution, hunting party size, free-rider problems, and hunting difficulty. Our results indicate that individually rewarded predators outperform predators that share rewards, especially when the hunting is difficult, the group size is large, and the action cost is high. Moreover, predators with shared rewards suffer from the free-rider problem. We conclude that sharing reward is neither necessary nor sufficient for modeling animal coordinated hunting through reinforcement learning.

Sharing is not Needed: Modeling Animal Coordinated Hunting with Reinforcement Learning

It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human reading time, which is an approximation of human sentence comprehension difficulty. Nevertheless, parsers usually take as input sentences that have already been processed or even manually annotated. We propose to study a more realistic scenario, where the various processing levels (tokenization, PoS and morphology tagging, lemmatization, syntactic parsing and sentence segmentation) are predicted incrementally from raw text. To this end, we propose a versatile modeling framework, we call the Reading Machine, that performs all such linguistic tasks and allows to incorporate cognitive constrains such as incrementality. 
We illustrate the behavior of this setting through a case study where we test the hypothesis that the complexity metrics computed at different processing levels predicts human reading difficulty, and that when cognitive constraints are applied to the machine (e.g., incrementality), it yields better predictions.

On the Role of Low-level Linguistic Levels for Reading Time Prediction

Selective attention involves attending to task-relevant information and inhibiting task-irrelevant information. While spatial priming is known to efficiently shape selective attention, the nature of language-mediated effects on selective attention is not well-understood, particularly in young toddlers. We com- pare the impact of language-mediated and spatially-mediated attention in an eye-tracking paradigm in which two objects are presented in one of four possible locations and one of the objects is highlighted. The impact of labelling on attention orienting during a prime phase was tested in a subsequent probe phase, where either the identity, location or both were manipulated, and compared to the impact of spatial priming. To elucidate the role of development on these effects, the study was conducted with 18- and 26-month-old toddlers. The results revealed that both language-mediated and spatially-mediated priming lead to attention orienting during the probe phase: at- tended information during the prime phase facilitates attention during the probe phase while ignored features are inhibited. However, in contrast to spatially-mediated attention during the prime phase, language-mediated attention can override these inhibitory effects. The impact of language on overcoming inhibitory effects is particularly noteworthy in the older age group.

Language- and spatially-mediated attention in toddlers

We live in an age where robots are increasingly present in the social and moral world. Here, we explore how children and adults think about the mental lives and moral standing of robots. In Experiment 1 (N = 116), we found that children granted humans and robots with more mental life and vulnerability to harm than an anthropomorphized control (i.e., a toy bear). In Experiment 2 (N = 157), we found that, relative to children, adults ascribed less mental life and vulnerability to harm to robots. In Experiment 3 (N = 152), we modified our experiment to be within-subjects and measured beliefs concerning moral standing. Though younger children again appeared willing to assign mental capacities — particularly those related to experience (e.g., being capable of experiencing hunger) — to robots, older children and adults did so to a lesser degree. This diminished attribution of mental life tracked with diminished ratings of robot moral standing. This informs ongoing debates concerning emerging attitudes about artificial life.

Developmental changes in perceived moral standing of robots

When people are unsure of the intended meaning of a word, they often ask for clarification. One way of doing so-often assumed in models of communication-is to point at a potential target: "Do you mean [points at the rabbit]?'' However, what if the target is unavailable? Then the only recourse is language itself, which seems equivalent to pulling oneself up from a swamp by one's hair. 
We created two computational models of communication, one able to point and one not. The latter incorporates inference to resolve the meaning of non-pointing signals. Simulations show agents in both models reach perceived understanding equally quickly. While this means agents think they are successfully communicating, non-pointing agents understand each other only at chance level.
This shows that state-of-the-art computational explanations have difficulty explaining how people solve the puzzle of underdetermination, and that doing so will require a fundamental leap forward.

Computational challenges in explaining communication: How deep the rabbit hole goes

Discrete emotions are known to elicit changes in decision-making. Previous research has found that affect biases response times and the perception of evidence for choices, among other key factors of decision-making. However, little is known how affect influences the specific cognitive mechanisms that underlie decision-making. We investigated these mechanisms by fitting a hierarchical reinforcement-learning decision diffusion model to participant choice data. Following the collection of baseline decision-making data, participants took part in a writing exercise to generate neutral or discrete emotions. Following the writing exercise, participants made additional decisions. We found that exposure to discrete emotions modulates decision-making through several mechanisms including rates of learning and evidence accumulation, separation of decision thresholds, and sensitivity to noise. Furthermore, we found that exposure to each of the four discrete emotions modulated decision-making differently. These findings integrate learning and decision process models to expand on previous research and elucidate processes of affective decision-making.

Incidental discrete emotions influence processes of evidence accumulation in reinforcement-learning

Do nonhuman animals (hereafter “animals”) possess empathy and if so to which degree? Can we develop a conceptual framework that allows us to characterize similarities and differences between implementations of empathy in humans and animals? We aim to answer these questions in two steps. First, we develop a new conceptual framework by distinguishing different levels of empathy starting with paradigmatic cases of human empathy developing in human ontogeny. Second, we describe in detail which of these levels of empathy can be found in other species based on animal studies. This approach allows a detailed characterization of the relation of empathy in humans and other animals.

A conceptual framework for empathy and its application to investigate nonhuman animals

Social interactions serve as the primary training ground for many of a child’s positive cognitive and developmental abilities. Parent responsiveness has been identified as one key mechanism through which children gain more mature skills, but the question of how children elicit responses from their parents remains to be fully investigated. In this study, we utilized head-mounted eye trackers to track moment-by-moment shifts in gaze, manual action, and speech during parent-child toy play. This allowed us to identify the moments preceding a parent response and the type and timing of the parent’s response relative to the child’s behaviors. We found that child events of attention and action – where they were both touching and looking at a toy – were more successful in eliciting parent responses overall and in eliciting multimodal parent responses than events of just child look or child touch. The parent’s latency to respond to their child differed by event type and duration, suggesting that child behaviors influence parent responses. Implications and future directions are discussed.

The Emergence of Cultural Attractors: An Agent-Based Model of Collective Perceptual Alignment

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Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

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The Emergence of Cultural Attractors: An Agent-Based Model of Collective Perceptual Alignment

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Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

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Cognitive Machinery and Behaviours

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PRESENTATIONS

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