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There have been recently multiple calls to investigate the rhythmic behaviors (RBs) of nonhuman animals, as a way to gain insight into the evolution of human rhythm cognition and musicality. Currently, the empirical data from non-human species is scarce. Most strikingly, we lack data from chimpanzees (Pan troglodytes), our closest genetic relatives. Here, we present an observational study conducted at three sites (N=41), in which we systematically documented RBs in chimpanzees, with a particular focus on functional contexts, sex differences and emotional correlates. We found that RBs were frequent in chimpanzees, occurred primarily in social contexts, and often had social consequences. RBs were not exclusively associated with high arousal or playfulness. RBs were more frequent in males than females, but sex did not affect their social efficacy. Our findings are consistent with social theories on the evolution of musicality, but also highlight a role for RBs in chimpanzee inter-sexual communication.

CogSci 2021

Rhythmic behaviors in chimpanzees: range, functional contexts, sex differences and emotional correlates

Welcome to the Cultural Evolution Society (CES) Conference 2024, which was a hybrid conference held at Durham University (UK). The conference was hosted in Durham by the Durham Cultural Evolution Research Centre (DCERC) with administrative assistance from the CES Transformation Fund team.

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Cultural Evolution Society Conference 2024

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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.

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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.

Adaptive decisions require that decision makers factor in the subjective values of different possible outcomes, and the probability of these outcomes occurring. Subjective values depend, among other things, on how far an outcome is away in time. This can be captured by assessing an individual’s delay discounting of different options. An individual’s risk preference also affects how attractive particular choice options appear to them. In humans, probability discounting and delay discounting are often related. People who show more risky behaviors also tend to be more impulsive and less patient. Based on such findings, single-process models of delay discounting and probability discounting have been suggested. In the current study, we tested if this relationship is equally present in chimpanzees, one of human’s closest extant evolutionary relatives. We presented 23 chimpanzees with a patience task and a risky-choice task. The patience task was designed to explicitly distinguish between delay preference and self-control (i.e., the ability to wait a given delay). Still, we found no strong correlations between risk and delay preferences. As this task has not been used with humans before, we implemented a computerized version and tested it in a sample of twenty adult participants. Initial results indicate that the task is well suited to capture patience, and it makes a promising candidate to be used in behavioral delay discounting experiments in humans.

Relationship between Delay Discounting and Risk Preference in Chimpanzees (Pan troglodytes) and Humans

Cooperation experiments have long been used to explore the cognition underlying animals' coordination towards a shared goal. While the ability to understand the need for a partner has been demonstrated in a number of species, far fewer studies have explored the behavioral strategies animals use to coordinate their behavior in such tasks. Here, we investigate the strategies two dolphin dyads used to coordinate their behavior during a cooperative button-pressing task that required precise behavioral synchronization. Both dyads were more likely to succeed if they used whistles prior to pressing their buttons, but the results showed that they adopted different strategies. Specifically, one dyad favored physical synchrony, waiting nearby for their partner and swimming together to approach the buttons. The other dyad was much more vocal, and more likely to swim independently before coordinating at the buttons. Only for this second dyad did increased whistling lead to more success. Our results suggest that bottlenose dolphins have the behavioral flexibility to employ either vocal signals or physical synchrony to coordinate their cooperative efforts.

In Sync or Vocal? How Bottlenose Dolphins Coordinate in a Cooperative Task

Attention can be biased by the previous learning and experience. We present an algorithmic-level model of this bias in visual attention that predicts quantitatively how bottom-up, top-down and selection history compete to control attention. In the model, the output of saliency maps as bottom-up guidance interacts with a history map that encodes learning effects and a top-down task control to prioritize visual features. We test the model on a reaction-time (RT) data set from the experiment presented by Feldmann-Wüstefeld et al. 2015. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms learning history effects on attention guidance.

A model of selection history in visual attention

Linguistic Typology of Motion Events in Visual Narratives

Cognition constrains and influences human cultural productions, among which are information technologies. Information technologies, because of and through their intensive use, can be expected to reflect human cognition particularly well. Cognitive approaches to information technologies have the potential of informing both cognitive science and historical disciplines. Beyond high ecological validity, we demonstrate the relevance of real-world data in testing and informing theories about how the mind works, through four different case studies and contexts: how we represent the world and space around us (Riggsby), how we represent more abstract -number- concepts (Chrisomalis), how we optimize written characters for our visual system (Miton), and coinage to minimize possible errors (Morin). Discussion and moderation will be assured by Valeria Giardino, a philosopher whose main research topic is reasoning with diagrams and the role of cognitive artifacts in improving thought.

The Deep History of Information Technologies: a Cognitive Perspective

Primate cognition research allows us to reconstruct the evolution of human cognition. However, temporal and contextual factors that induce variation in cognitive studies with great apes are poorly understood. Here we report on a longitudinal study where we repeatedly tested a comparatively large sample of great apes (N = 40) with the same set of cognitive measures. We investigated the stability of group-level results, the reliability of individual differences, and the relation between cognitive performance and individual-level characteristics. We found results to be relatively stable on a group level. Some, but not all, tasks showed acceptable levels of reliability. Cognitive performance across tasks was not systematically related to any particular individual-level predictor. This study highlights the importance of methodological considerations – especially when studying individual differences – on the route to building a more robust science of primate cognitive evolution.

A Longitudinal Study of Great Ape Cognition: Stability, Reliability and the Influence of Individual Characteristics

Conscious awareness to the events and stimuli around us is a central part of our everyday experience. Yet, are humans the only species that experiences conscious awareness? Since non-verbal species cannot report their internal states, philosophers and scientists have long debated whether the question of animal consciousness is empirically testable, and it still remains a topic of speculation (Dawkins, 2015; Gutfreund, 2017). In the large spectrum of views, some advocate that consciousness may require complex processes like language, a capacity that is unique to adult humans (Dennett, 1995) or a human-like theory of mind (Carruthers, 1998), which may extend to only a few selected species such as great apes (e.g., Krupenye, Kano, Hirata, Call, & Tomasello, 2016; but see Horschler, MacLean, & Santos, 2020). In contrast, others have used neuroanatomical similarities to argue that a number of species (including some birds and octopuses) are likely to be capable of generating conscious experience (see, for example, the Cambridge declaration on consciousness, 2012). Others argue that non-human animals are conscious on the basis of intelligent behaviors which, at least in humans, seem to coincide with conscious awareness as supporting evidence for animal consciousness. These include behaviors such as planning (Osvath & Osvath, 2008), or metacognition (Hampton, Engelberg, & Brady, 2020; Rosati & Santos, 2016), for review see Boly et al., 2013; Griffin & Speck, 2004. Yet, since many complex human behaviors and high-level functions can be performed outside of conscious awareness (i.e., Hassin, 2013), it is difficult to determine whether non-human animals that display intelligent behaviors are indeed conscious or not (Carruthers, 2018). Furthermore, given the ambiguity and difficulty in disentangling conscious from non-conscious processes in non-verbal species, many consider the question of animal consciousness as far from having been resolved (Dawkins, 2015; Gutfreund, 2017). For many, the gap in evidence needed to unambiguously infer animal consciousness is considered “as wide as ever” (Dawkins, 2012).

Unfolding Conscious Awareness from Non-Conscious Perception in Non-Human Animals

Jörg Cassens, Rebekah Wegener, Lorenz Habenicht, and Julian Blohm discuss the dialogic form of explanations. Explanations are a long established research topic in a wide variety of disciplines, ranging from philosophy (van Fraassen, 1980; Achinstein, 1983) over the cognitive sciences and psychology (Lalljee et al., 1983; Keil and Wilson, 2000; Lombrozo, 2006) to computer science in general and artificial intelligence in particular (Schank, 1986; Leake, 1992; Leake (1995); Sørmo et al., 2005). However, while there is compelling research supporting the value, structure and function of explanation, as Edwards et al. (2019) argue, “accounts of explanation typically define explanation (the product) rather than explaining (the process)”. By contrast, we aim at an understanding of explanation as a functional variety of language behaviour that treats explanations as being:



Explanation in Human Thinking

Normative learning theories dictate that we should preferentially attend to informative sources, but only up to the point that our limited learning systems can process their content. Humans, including infants, show this predicted strategic deployment of attention. Here we demonstrate that rhesus monkeys, much like humans, attend to events of moderate surprisingness over both more and less surprising events. They do this in the absence of any specific goal or contingent reward, indicating that the behavioral pattern is spontaneous. We suggest this U-shaped attentional preference represents an evolutionarily preserved strategy for guiding intelligent organisms toward material that is maximally useful for learning.


Macaques preferentially attend to intermediately surprising information

Individuals can learn differently, even in very simple tasks. In an association learning task, participants learned associations between correct keypresses and sets of categorically related images. Following a delay, participants were then tested on the associations in a surprise test. This task has been used to examine the relationship between reinforcement learning and working memory. However, the strategies that individuals use to explore the associations between keypresses and images as well as the opportunity to take advantage of a simple rule based on the structure of the task without having to retrieve the correct response may both impact test performance. Two experiments were conducted to test these hypotheses. Results showed that the performance difference from the end of the learning phase to the testing phase differed significantly between set size conditions in a way that is more consistent with strategic differences than an interaction between working memory and reinforcement learning.

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