United States

In recent years, the brain and cognitive sciences have made great strides developing a mechanistic understanding of object recognition in mature brains. Despite this progress, fundamental questions remain about the origins and computational foundations of object recognition. What learning algorithms underlie object recognition in newborn brains? Since newborn animals learn largely through unsupervised learning, we explored whether unsupervised learning algorithms can be used to predict the view-invariant object recognition behavior of newborn chicks. Specifically, we used feature representations derived from unsupervised deep neural networks (DNNs) as inputs to cognitive models of categorization. We show that features derived from unsupervised DNNs make competitive predictions about chick behavior compared to supervised features. More generally, we argue that linking controlled-rearing studies to image-computable DNN models opens new experimental avenues for studying the origins and computational basis of object recognition in newborn animals.

CogSci 2021

Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

**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.

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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.

This is the first kinematic investigation of articulator motion in Austrian Sign Language, which connects kinesiology of sign production and linguistic markers of Aktionsart in the native language of the Deaf community in Austria. Our work used a 3D motion capture approach to sign language analysis to investigate the relationship between the semantics (event structure) of signed verbs, and kinematics of hand articulator movement. The data indicates that the underlying semantics of events in verb signs is reflected in sign duration and acceleration of the dominant hand during sign production. The finding that articulator dynamics (acceleration and deceleration of hand motion) characterizes the event structure in verb signs has significance for linguistic theory of visual communication, and understanding of the relationship between iconicity in sign language, and perceptual biases in meaning construction based on visual input.

Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

Visual and auditory channels have different affordances and this is mirrored in what information is available for linguistic encoding. The visual channel has high spatial acuity, whereas the auditory channel has better temporal acuity. These differences may lead to different conceptualizations of events and affect multimodal language production. Previous studies of motion events typically present visual input to elicit speech and gesture. The present study compared events presented as audio-only, visual-only, or multimodal (visual+audio) input and assessed speech and co-speech gesture for path and manner of motion in Turkish. Speakers with audio-only input mentioned path more and manner less in verbal descriptions, compared to speakers who had visual input. There was no difference in the type or frequency of gestures across conditions, and gestures were dominated by path-only gestures. This suggests that input modality influences speakers’ encoding of path and manner of motion events in speech, but not in co-speech gestures.

Sensory Modality of Input Influences the Encoding of Motion Events in Speech But Not Co-Speech Gestures

The claim that contextual knowledge exerts a top-down influence on sensory processing is supported by evidence for lexically-mediated compensation for coarticulation (LCfC) in spoken language processing. In this phenomenon, a lexically restored context phoneme (e.g., the final phoneme in Christma# or fooli#) influences perception of a subsequent target phoneme (e.g., a phoneme ambiguous between /t/ and /k/). A recent report shows that carefully vetted materials produce robust, replicable LCfC effects in younger adults (18-34 years old). Here, we asked whether we would observe LCfC in a sample of older adults (aged 60+). This is of interest because older adults must often contend with age-related declines in sensory processing, with previous research suggesting that older adults may compensate for age-related changes by relying more strongly on contextual knowledge. We observed robust LCfC effects in younger and older samples, with no significant difference in the effect size between age groups.

Lexically-Mediated Compensation for Coarticulation in Older Adults

After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., “higher likelihood of shock for bluer stimuli”). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both “similarity” and “relational” rules, and generalizes using a mixture of these strategies. Using a “mixture of experts” modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.

A Mixture of Experts in Associative Generalization

Many studies have demonstrated that testing students on to-be-learned materials can be an effective learning activity. However, past studies have also shown that some practice test formats are more effective than others. Open-ended recall or short answer practice tests may be effective because the questions prompt deeper processing as students must generate an answer. With closed-ended testing formats such as multiple-choice or true-false tests, there are concerns that they may prompt only superficial processing, and that any benefits will not extend to non-practiced information or over time. They also may not be effective for improving comprehension from text as measured by how-and-why questions. The present study explored the utility of practice tests with closed-ended questions to improve learning from text. Results showed closed-ended practice testing can lead to benefits even when the learning outcome was comprehension of text.

Can Closed-ended Practice Tests Promote Understanding from Text?

Processing differences between obligatory constituents like verbal complements and facultative ones like adjuncts are widely discussed in psycholinguistics. But the relative positional and semantic variability of adjuncts makes analyzing these differences a daunting challenge. Focusing on the intricate problem of the default position of manner adverbials in German, we present a re-analysis of a recent psycholinguistic study on their ordering preferences in which we explore the extent to which similarities between word embeddings can be used as stand-ins for shared semantic memory, representing the probability of seamless conceptual combination. In re-analyzing six experiments across different paradigms, the addition of the new predictors yields substantially better models which show that these factors considerably interact with established lexical and grammatical predictors.

Disentangling factors in the placement of manner adverbials in German: the effect of distributional similarity

Relational schema induction involves a series of learning tasks conforming to a common (group-like) structure. The paradigm contrasts associative versus relational aspects of learning for cognitive, developmental and comparative psychology. Yet, a theory accounting for the relationship between these forms of learning has not been fully developed. We use (mathematical) category theory methods to redress this situation: both forms of learning involve a (universal) construction that differs in terms of ``dimensionality'', i.e. one-dimensional (associative) versus two-dimensional (relational). Accordingly, the development of relational learning pertains to changes in the dimensionality of the underlying relational schemas induced.

A formal comparison/contrast of associative and relational learning: a case study of relational schema induction

Audio‐visual (AV) associative learning is central to many aspects of cognitive development and is key in reading acquisition. Most studies thus far have examined AV associative learning involving linguistic stimuli. Yet it is of importance to examine cross-modal learning free of familiarity confounds. We, therefore, designed an AV learning paradigm relying on novel, non-linguistic auditory and visual stimuli, which were both unfamiliar to participants. On top of AV learning, we collected performance in reading-related abilities, as well as in more domain-general skills, in a population of healthy Italian-speaking adults (N=57). By fitting trial-by-trial performance in our novel learning task, we demonstrate the expected variability in speed of learning (learning rate) across participants. We then show that speed of learning in our novel learning task is positively associated with working memory and replicate this result in a set of French-speaking participants (N=32), showing that it holds in another language.

A novel non-linguistic audio-visual learning paradigm to test the cognitive correlates of learning rate

Current models of cognitive control address selection among tasks in terms of a cost-benefit tradeoff. Importantly, they usually assume a fixed level of competence for each candidate task when estimating its value. However, performing a task can improve competence through learning, which should be factored into estimates of future value. Here, we consider an extension of the Expected Value of Control (EVC) theory that includes such estimates. We demonstrate that control allocation is a function of task learnability, and show the use of this model by generating novel predictions in cognitive effort discounting tasks. We argue that the value of learning in control allocation may account for the seemingly paradoxical finding that sometimes participants prefer more difficult (i.e., costly) tasks, and discuss how the model can be leveraged to further our understanding of human decision making and cognitive impairments.

The Value of Learning and Cognitive Control Allocation

Does knowing certain words help children learn other words? We hypothesized that knowledge of more general (more superordinate) words at time1 would lead to faster vocabulary growth as measured through vocabulary checklists administered at later timepoints. We find that this is indeed the case. Children who have similar vocabularies at time1 , but differ in their productive knowledge of more general words such as “animal,” “picture,” and “get” go on to have different rates of word learning. Knowledge of more general words is associated with faster vocabulary growth, particularly of words semantically related to the superordinate terms they are reported to produce. This positive relationship between knowledge of more general words and word learning remains even when controlling for measures of verbal and nonverbal intelligence.

Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

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Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

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Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

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Next from CogSci 2021

Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

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Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots

Stay up to date with the latest Underline news!

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