United States

The last few years have witnessed amazing breakthroughs in machine intelligence, using systems that rely on neural networks as advocated in Parallel Distributed Processing (Rumelhart, McClelland et al, 1986). Yet in this talk, I will argue, in agreement with Lake et al. (2017) and others, that we still have a long way to go before any machine has truly captured human like cognitive and learning abilities. Unlike Lake et al., I will argue that we should seek the reasons for many of the amazing achievements of human intelligence not in built in biases or special purpose start-up software, but in a fuller appreciation of the roles of culture and experience. I will argue for a central role for culturally constructed formal systems as powerful tools that extend human abilities beyond what can be achieved without these resources. I will also argue for a central role of language-based instruction and explanation.

CogSci 2021

Are people still smarter than machines? If so, why?

The purpose of the International Conference on Computer-Human Interaction Research and Applications (CHIRA) is to bring together professionals, academics and students who are interested in the advancement of research and practical applications of interaction design & human-computer interaction. Five tracks cover different aspects of Computer-Human Interaction, including Interaction Design, Human Factors, Entertainment, Cognition, Perception, User-Friendly Software and Systems, Pervasive Technologies and Interactive Devices.

CONFERENCE AREAS<br>
1 . Human Factors and Information Systems<br>
2 . Interactive Devices<br>
3 . Interaction Design<br>
4 . Adaptive and Intelligent Systems

CHIRA 2021

The purpose of the International Conference on Computer-Human Interaction Research and Applications (CHIRA) is to bring together professionals, academics and students who are interested in the advancement of research and practical applications of interaction design & human-computer interaction.

technical paper

### 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.

The block design test (BDT), in which a person has to recreate a visual design using colored blocks, is notable among cognitive assessments because it makes so much of a person's problem-solving strategy ``visible'' through their ongoing manual actions. While, for decades, numerous pockets of research on the BDT have identified certain behavioral variables as being important for certain cognitive or neurological hypotheses, there is no unifying framework for bringing together this spread of variables and hypotheses. In this paper, we identify 25 independent and dependent variables that have been examined as part of published BDT studies across many areas of cognitive science and present a sample of the research on each one. We also suggest variables of interest for future BDT research, especially as made possible with the advent of advanced recording technologies like wearable eye trackers.

Measuring More to Learn More From the Block Design Test: A Literature Review

Recurrent neural network (RNN) language models that are trained on large text corpora have shown a remarkable ability to capture properties of human syntactic processing (Linzen & Baroni, 2021). For example, the fact that these models display human-like structural priming effects (Prasad, Van Schijndel, & Linzen, 2019; van Schijndel & Linzen, 2018) suggests that they develop implicit syntactic representations that may not be unlike those of the human language system. A rarely explored question is whether RNNs are also able to simulate aspects of human multilingual sentence processing (Frank, 2021) even though training RNNs on two or more languages simultaneously is technically unproblematic.

Cross-language structural priming in recurrent neural network language models

Three leading analogical reasoning paradigms: scene analogies and pictorial A:B::C:D analogies (both semantically-rich) and geometric analogies (semantically-lean) were solved by 251 participants. Pictorial analogies included four types of lures among the response options (perceptual, categorical, semantic, relational). Moreover, distractors related to both B and C were introduced. Additionally, a fluid intelligence test was applied to examine the relationship between the paradigms and general reasoning ability. Results indicated that: (a) objects semantically related to C yielded the strongest distraction in four-term analogies, categorical and relational distractors yielded moderate effects, perceptual distraction was negligible; (b) distractors related to B were relatively easy to ignore, suggesting that C is the primary object of reference during the response selection; (c) whilst the three tasks correlated significantly without control for fluid intelligence, only the semantically-rich tasks did after fluid intelligence was accounted for, suggesting a certain common mechanism, independent of fluid intelligence, underlying the two.

Distraction in Semantic Analogies and Their Relationship with Abstract Reasoning

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov vio- lations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a com- putational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models peo- ple as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning ex- tend across a range of settings, and do so in line with the pre- dictions of a model that describes the process by which causal judgments are drawn.

Testing a Process Model of Causal Reasoning With Inhibitory Causal Links

One of the major debates in the field of word production is whether lexical selection is competitive or not. For nearly half a century, semantic interference effects in picture naming latencies have been claimed as evidence for competitive (relative threshold) models of lexical selection, while semantic facilitation effects have been claimed as evidence for non-competitive (simple threshold) models instead. In this paper, we use a computational modeling approach to compare the consequences of competitive and noncompetitive selection algorithms for blocked cyclic picture naming latencies, combined with two approaches to representing taxonomic and thematic semantic features. We show that although our simple model can capture both semantic interference and facilitation, the presence or absence of competition in the selection mechanism is unrelated to the polarity of these semantic effects. These results question the validity of prior assumptions and offer new perspectives on the origins of interference and facilitation in language production.

Behavioral interference or facilitation does not distinguish between competitive and noncompetitive accounts of lexical selection in word production

Tools are peculiar parts of our environment and tool manufacture remains one of the most prodigious achievements of humankind over the last million years. Chimpanzees, along with other non-primate species, also use and sometimes manufacture tools. In my research, I have investigated the cognitive, ecological, social and emotional factors influencing tool use in wild and captive apes, with a focus on Ugandan chimpanzees. In parallel, I have researched cognitive aspects of the evolution of emotional and intentional communication by studying primate, particularly great ape, vocalizations. Finally, in more recent years, I have investigated the same topics in children, to investigate the possible homologies with humans and our shared ancestry and specificities. My goal is to understand the evolutionary pressures that launched humans on the particular evolutionary pathway that have allowed them to become the ultimate culture-bearers. I am also interested in how other species, in turn, see the world. The research program I develop integrates these interests in a comparative, ecological, cognitive and socio-emotional approach to cultural knowledge in great apes and humans.

How does the Chimpanzee Mind Represent its Cultures?

The social brain hypothesis (Humphrey, 1976) poses that the intricacies of social life may have been a significant selection pressure for the evolution of mind. This evolution may act on competition between group members and strategies to outcompete others (Machiavellian intelligence hypothesis: Byrne & Whiten, 1988), or on cooperative tendencies between group-members that provide benefits that cannot be reached by a single individual (Vygotskian intelligence hypothesis: Moll & Tomasello, 2007). The latter hypothesis, however, creates an evolutionary conundrum, as cooperation is prone to free-riders, and with defection being an evolutionary stable system, the occurrence and complexity of cooperation in humans and other animals remains a puzzle. Several theoretical advances have been made to explain the evolution of cooperation nonetheless, with kin-selection (Hamilton, 1964) and reciprocal altruism (Trivers, 1971) being the most prominent ones. However, the proximate mechanisms that foster the strategies proposed in the Vygotskian intelligence hypothesis and the required cognition in nonhuman animals, remain a hotly debated topic (see Massen et al., 2019).

Studying the Evolution of Cooperation and Prosociality in Birds

Humans have the astonishing capacity to quickly adapt to varying environmental demands and reach complex goals in the absence of extrinsic rewards. Part of what underlies this capacity is the ability to flexibly reuse and recombine previous experiences, and to plan future courses of action in a psychological space that is shaped by these experiences. Decades of research have suggested that humans use hierarchical representations for efficient planning and flexibility, but the origin of these representations has remained elusive.
This study investigates how 73 participants learned hierarchical representations through experience, in a task in which they had to perform complex action sequences to obtain rewards. Complex action sequences were composed of simpler action sequences, which were not rewarded, but whose execution led to changes in the environment. 
After participants learned action sequences, they completed a transfer phase. Unbeknownst to them, we manipulated either complex or simple sequences by exchanging individual elements, requiring them to relearn. 
Relearning progressed slower when simple (rather than complex) sequences were changed, in accordance with a hierarchical representations in which lower levels are quickly consolidated, potentially stabilizing exploration, while higher levels remain malleable, with benefits for flexible recombination.

How the Mind Creates Structure: Hierarchical Learning of Action Sequences

Many students fail to develop adequate understanding of mathematical equivalence in early grades, which impacts later algebra learning. Work from McNeil and colleagues proposes that this failure is partly due to the format of traditional instruction and practice with highly similar problems, which encourages students to develop ineffective representations of problem types (McNeil, 2014, McNeil & Alibali, 2005). In the current study, we explore students’ learning trajectories in two matched equivalence interventions. We show that, relative to an active control, the principle-based treatment intervention gives rise to a greater number of successful learners, a designation that, in turn, leads to improved performance on distal transfer assessments. We further demonstrate a predictive relationship between students’ engagement with the intervention, via workbook completion, and likelihood of becoming a successful learner. Our findings have implications for early detection of learning and subsequent scaffolding for low-performing students.

Predicting Learning and Knowledge Transfer in Two Early Mathematical Equivalence Interventions

Understanding cause and effect relationships gives power to produce desired effects and avoid negative outcomes. Despite the power of causal explanations, people often lack full understanding of how causes relate to or produce their effects. In two experiments, we explored how people search for information to enrich their causal explanations of real-world phenomena when given the chance. Participants completed an information search task that provided a causal relationship where they could seek out mechanistic information at different steps between the cause and the effect. We measured where people searched in the causal chain of events that made the explanation. We found that when allowed to search freely (Experiment 1) or when instructed that they must search for information (Experiment 2) participants consistently sought out information closest to the root cause in the explanation. We discuss implications for how to improve the teaching of new explanations to maximize the informational desires of the learner.

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Measuring More to Learn More From the Block Design Test: A Literature Review

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Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges

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Next from CogSci 2021

Measuring More to Learn More From the Block Design Test: A Literature Review

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Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

RESOURCES

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