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One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of both
associative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models&#39; ability to generalize to additional experiments, including developmental data.

CogSci 2021

A large-scale comparison of cross-situational word learning models

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of both
associative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models' ability to generalize to additional experiments, including developmental data.

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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! 

**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.

A traditional view of understanding other’s mental states is that early in ontogeny infants start from a me-first position, and through themselves learn to understand others. Here we propose an opposite developmental trajectory, where infants might start out highly altercentric (Southgate, 2020) and through development increasingly rely on their own point of view. 

In a pre-registered cross-sectional study we present 1-6-year-old children with a task where altercentric modulation has been found with 14-month-old infants (Kampis & Kovács, 2020). There, infants tended to search longer in a box when another person believed an object to be present, than when she believed it was empty. We predicted this tendency to decrease with age. Preliminary results with n=191 children included based on their search in baseline trials, show a decrease in altercentric modulation with age (r=-.174, p= .016), which will be discussed together with the relationship between altercentrism and the development of self-concept.

Humans start out altercentric: the ontogenetic development of other-centered cognition

Reasoning by exclusion allows us to infer properties of unobserved objects from currently observed objects, formalized by P or Q, not P, therefore Q. Previous work suggested that, by age 3, children can use this kind of reasoning to infer the location of a hidden object after learning that another location is empty (e.g. Mody & Carey, 2016). In the current study, we asked whether children could use reasoning by exclusion to infer the identities of previously unobserved occluded objects in a task that required them to track the locations of multiple occluded objects. Forty-nine 4-7-year-olds viewed animated arrays of virtual “cards” depicting images which were then hidden by occluders. The occluders then swapped locations during the maintenance period. Children were asked to select which card was hidden in a probed location. During the encoding period, we manipulated whether children saw all the card faces (Face-up block) or all but one of the card faces (Exclusion block), for which children had to reason by exclusion to infer the target in half of the trials. We found that all children succeeded in the Face-up block, but only 6-year-olds succeed in the Exclusion block when they had to deploy logical reasoning to identify a previously-unseen hidden target. Our results suggest that children’s ability to reason by exclusion to infer the identity of a hidden target while tracking multiple objects and locations may undergo protracted development.

Children’s use of Reasoning by Exclusion to Track Identities of Occluded Objects

Selective attention allows adults to preferentially exploit input relevant to their goals. One critical role of selective attention is in adult category learning: adults can simplify the entities they encounter into groups of entities that they can treat as equivalent by focusing on category-relevant attributes, while filtering out category-irrelevant attributes. However, much category learning takes place during development, when selective attention substantially matures. We designed two experiments to disentangle the contributions of the focusing and filtering aspects of selective attention to category learning over development. Experiment 1 provided evidence that learning simple categories was accompanied by selective attention in both four year-old and five year-old children and adults. Experiment 2 further provided evidence that only focusing contributed to selective attention in four year-olds, whereas both focusing and filtering contributed to selective attention in five year-olds and adults. Thus, category learning recruits different aspects of selective attention with development.

Category Learning is Shaped by the Multifaceted Development of Selective Attention

Most of the recent success in machine learning has been achieved in supervised learning and predicated on the availability of large amounts of labelled training data. On the other hand, effectively using readily available unlabelled data has proven a much more difficult endeavour. In contrast to algorithms, infants spontaneously learn from the available sensory information without explicit instructions, supervision or feedback. Thus, infant learning can be viewed as a highly successful approach to unsupervised learning. In this work, we explore the parallels between infant learning and recent successes of unsupervised machine learning in the area of contrastive learning. We examine how the principles of infant learning and developmental cognitive neuroscience can inform and inspire the development of novel contrastive learning algorithms. We focus on the phenomenon of category learning and explore how these principles can be applied to better understand and improve contrastive methods.

Can algorithms learn from babies? Exploring how infant learning can inform and inspire unsupervised learning algorithms

From about six months of age onward, infants observing an action, such as a grasp, start to shift their gaze from the moving agent to the goal before the action is complete. A variety of factors that influence such goal-predictive gaze have been identified. However, the underlying cognitive processes are heavily debated. We propose that our minds structure sensorimotor dynamics into probabilistic, generative event-predictive models, and, choose actions with the objective to minimize predicted uncertainty. We implement this proposition by means of event-predictive learning and active inference. Trained on manual object-manipulations, the generation of goal-predictive gaze emerges: The model starts fixating the anticipated goal at the start of an observed event when a familiar agent (i.e., a hand) is involved. Meanwhile, the model keeps tracking unfamiliar agents (e.g., a claw) performing the same movement. We conclude that event-predictive learning combined with active inference may be critical for eliciting action-goal predictions.

I see where this is going: Modeling the development of infants' goal-predictive gaze

Goldwater and Gentner (2015) showed that the sensitivity for causal structures can be promoted with an intervention combining explication of causal models and guided structural alignment of situations from disparate fields with the same underlying causal model. We extended this intervention with inference questions and combined it with a subsequent complex problem-solving (CPS) task, in order to investigate whether enhanced sensitivity for causal structures results in better performance in CPS. This study (N = 108) compares the CPS performance indicators knowledge acquisition and knowledge application among three experimental groups (intervention, intervention extended with inference questions, control group) and reveals the following results: 1) The effectiveness of the intervention in increasing the sensitivity for causal structures was replicated. 2) Sensitivity for causal structures and CPS performance indicators were significantly positively correlated. 3) There is no direct effect of the intervention on CPS performance, but an indirect-only effect via enhanced sensitivity.

Promoting thinking in terms of causal structures: Impact on performance in solving complex problems

Humans are often faced with an exploration-versus-exploitation trade-off. A commonly used paradigm, multi-armed bandit, has shown humans to exhibit an "uncertainty bonus", which combines with estimated reward to drive exploration. However, previous studies often modeled belief updating using either a Bayesian model that assumed the reward contingency to remain stationary, or a reinforcement learning model. Separately, we previously showed that human learning in the bandit task is best captured by a dynamic-belief Bayesian model. We hypothesize that the estimated uncertainty bonus may depend on which learning model is employed. Here, we re-analyze a bandit dataset using all three learning models. We find that the dynamic-belief model captures human choice behavior best, while also uncovering a much larger uncertainty bonus than the other models. More broadly, our results also emphasize the importance of an appropriate learning model, as it is crucial for correctly characterizing the processes underlying human decision making.

Revisiting the Role of Uncertainty-Driven Exploration in a (Perceived) Non-Stationary World

Memories of most stimuli in the auditory and other domains are prone to the disruptive interference of intervening events, whereby memory performance continuously declines as the number of intervening events increases. However, melodies in a familiar musical idiom are robust to such interference. We propose that representations of musical structure emerging from syntactic processing may provide partially redundant information that accounts for this robust encoding in memory. The present study employs tonally ambiguous melodies which afford two different syntactic interpretations in the tonal idiom. Crucially, since the melodies are ambiguous, memory across two presentations of the same melody cannot bias whether the interpretation in a second listening will be the same as the first, unless a representation of the first syntactic interpretation is also encoded in memory in addition to sensory information. The melodies were presented in a Memory Task, based on a continuous recognition paradigm, as well as in a Structure Task, where participants reported their syntactic interpretation of each melody following a disambiguating cue. Our results replicate memory-for-melody's robustness to interference, and further establish a predictive relationship between memory performance in the Memory Task and the robustness of syntactic interpretations against the bias introduced by the disambiguating cue in the Structure Task. As a consequence, our results support that a representation based on a disambiguating syntactic parse provides an additional, partially redundant encoding that feeds into memory alongside sensory information. Furthermore, establishing a relationship between memory performance and the formation of structural representations supports the relevance of syntactic relationships towards the experience of music.

Musical syntactic structure improves memory for melody: evidence from the processing of ambiguous melodies

Children acquire conditionals late for reasons that are poorly understood. One possibility is because conditionals have multiple meanings. For instance, the statement “If he goes out without an umbrella, he will get wet” is logically true when he goes out without an umbrella and he gets wet (conjunction), when he goes out with an umbrella and does not get wet (bi-conditional) and when he goes out with an umbrella and (still) gets wet (conditional). Here, we employ a new paradigm to test these interpretations in young children. Eighty 3-6-year-olds were asked to match an if-then statement with one of two pictures: 1 depicting a scenario where the conditional is false vs. one of the 3 scenarios where the conditional is true. Results show that children had a conjunctive interpretation since age 3 but the development of the other two interpretations is protracted until after age 5, with great inter-subject variation.

Acquiring the meaning of conditionals

When an otherwise neutral stimulus signals an aversive event, we learn to use that information to avoid negative outcomes. Extended too broadly, however, fear learning can become maladaptive. In concept learning, people tend to restrict generalization to the narrowest category consistent with the learning set (Xu & Tenenbaum, 2007). We tested whether this pattern extends to fear conditioning. Shocks were associated with bananas (basic level group) or with fruit (superordinate group), but never with vegetables (both). In generalization, both groups were shown novel fruits and vegetables (no shocks). Surprisingly, while the basic level group’s Skin Conductance Response (SCR) reliably diminished from acquisition to generalization, the superordinate group’s SCR did not. Further, SCR to vegetables was greater in generalization for the superordinate group, suggesting that superordinate training encourages overgeneralizing threat beyond what concept learning models would predict. These novel findings have implications for both learning models and anxiety disorders (e.g., PTSD).

A large-scale comparison of cross-situational word learning models

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Humans start out altercentric: the ontogenetic development of other-centered cognition

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