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Representing a location in space requires two things: an anchor point, and a code (or coordinate system) to define other locations relative to that anchor point. Recent work has shed light on the latter, providing evidence that the default ‘format’ of visuospatial representation may be polar coordinates (i.e., angle/distance relations). Yet the former remains a topic of debate. For example, a classic distinction in the realm of spatial navigation research pits representation relative to landmarks against representation relative to boundaries. Here, we exploit the polar format of spatial representations to propose a new method for assessing the locus of spatial representation. Specifically, we show that from simple localization errors we can infer the anchor point from which observers localized a target point. We highlight a few basic demonstrations of this method and discuss possible applications for further research on spatial representation.

CogSci 2021

‘Decoding’ the locus of spatial representation from simple localization errors

**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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### 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! 

**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.

When given unrestricted time to process an image, people are faster and more accurate at making categorical decisions about a depicted object (e.g., Labrador) if it is close in sensorimotor and linguistic distributional experience to its target category concept (e.g., dog). In this preregistered study, we examined whether sensorimotor and linguistic distributional information affect object categorisation differently as a function of time available for perceptual processing. Using an ultrarapid categorisation paradigm with backwards masking, we systematically varied onset timing (SOA) of a post-stimulus mask (17-133ms) following a briefly displayed (17ms) object. Preliminary results suggest that linguistic distributional distance between concept and category (e.g., Labrador → dog), but not sensorimotor distance, affects categorisation accuracy and RT even in rapid categorisation, and that these effects do not vary systematically by SOA. These findings support the role of a linguistic shortcut (i.e., using linguistic distributional instead of sensorimotor information) in rapid object categorisation.

Linguistic distributional information about object labels affects ultrarapid object categorization

Remembering names is generally considered a difficult task. The face-name mnemonic strategy enhances encoding of face-name associations using a name transformation, a prominent facial feature, and interactive imagery. Name recall involves mentally retracing these steps. However, the components of the face-name mnemonic strategy are relatively unexplored. The present study tested the effectiveness of variants of the face-name mnemonic strategy in comparison to an uninstructed group for learning face-name associations. Experiment 1 demonstrated a significant advantage of the name-transformation mnemonic strategy compared to the uninstructed approach. Experiment 2 further examines the name-transformation mnemonic strategy with young adults. This study demonstrates the effectiveness of a variation of the face-name mnemonic strategy with implications for memory rehabilitation interventions.

The effectiveness of face-name mnemonics on name recall

Many researchers have demonstrated the change of body representation, including the rubber hand illusion and pinocchio illusion. However, they focused on the change of a single body part. The human body has a symmetric structure; therefore, the modification of body representation can be facilitated by the corresponding movement of symmetric parts. In our four experiments, participants moved their hands in different manners with distorted vision and were measured whether their behavior changed throughout the task. The four tasks differed in whether participant's hands were moving simultaneously or separately and whether they moved their hands to the same point or a different point. A behavioral change occurred in all experiments. When the participants moved their hands to the same point simultaneously the greatest behavioral change was facilitated. Neither moving both hands simultaneously or moving to the same position facilitated the change.

Change of Body Representation in Symmetric Body Parts

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

Competence Assessment by Stimulus Matching: An Application of GOMS to Assess Chunks in Memory

Spacing out information promotes retention more than massing information – a robust finding in psychological science. Research on the spacing effect has primarily manipulated aspects of the learning schedule (e.g., item repetitions, retention interval). Limited work has considered how familiarity with the to-be-learned information impacts the spacing effect. The current study addressed this gap by examining how word frequency/familiarity affects retention on a massed or spaced schedule. One-hundred adults were presented with 24 high familiarity (e.g., apple), low familiarity (e.g., vestige), or nonsense (e.g., blicket) words on a massed and spaced schedule. Retrieval was tested after a 5-minute delay, revealing a significant spacing effect regardless of word familiarity. Furthermore, overall performance was significantly greater for highly familiar items. These results suggest that spacing is effective regardless of general word familiarity. Future studies will assess how learners’ prior knowledge of test items – as measured by a vocabulary test – impacts the spacing effect.

Does the spacing effect depend on prior knowledge? Evaluating the role of word familiarity in learning from spaced vs. massed schedules

Studies have shown that video game players exhibit superior performance to non-video game players on a number of cognitive tasks. Methods to compare the groups have
mainly involved using measures of central tendency such as the mean without examining intra-individual differences in performance. In the present study, top-ranking Tetris players and novice Tetris players completed a reaction time cognitive task. Results show that the top-ranking players exhibit faster reaction times compared to novice players. Beyond the mean RT, we used the ex-Gaussian modeling technique and found differences in variability and attention between the two groups. Future studies can use modeling techniques such as the exGaussian distribution to analyze the whole distribution at an
individual level beyond measures of central tendency and further examine the behavioral differences between video game players and non-video game players.

Need for speed: Applying ex-Gaussian modeling techniques to examine intra-individual reaction time variability in expert Tetris players

When learning to bind visual symbols to sounds, to what extent do beginning readers track seemingly irrelevant information such as a symbol’s position within a visual display? In this study, we used adult typical readers’ own webcams to track their eye movements during a paired associate learning task that arbitrarily bound unfamiliar characters with monosyllabic pseudowords. Overall, participants’ error rate in recognition (Phase 1) decreased as a function of exposure, but was not modulated by the episodic memory-based effect of ‘looking-at-nothing’. Moreover, participants’ lowest error rate in both recognition and recall (Phases 1 and 2) was associated with item consistency across multiple exposures, in terms of spatial and contextual properties (i.e., stimulus’ screen location and co-occurrences with specific distractor items during encoding). Taken together, our findings suggest that normally developing readers extract statistical regularities in the input during visual-phonological associative learning, leading to rapid acquisition of these pre-orthographic representations.

Episodic Memory Cues in Acquisition of Novel Visual-Phonological Associations: a Webcam-Based Eye-Tracking Study

In collaborative tasks, humans can make better joint decisions by aggregating individual information in proportion to their communicated confidence (Bahrami et al., 2010). However, if people blindly rely on their partner’s confidence expressions, they could easily reach suboptimal solutions when their collaborator's confidence judgments are not calibrated to their performance, but for instance exhibit an overconfidence bias. Given that calibrated advisers are rated as more credible (Sah et al., 2013), we propose that prior experience with a collaborator will lead to a recalibration of their confidence judgements before incorporating their advice. In an online experiment, participants first viewed two other fictitious participants, one calibrated and one biased, perform a categorization task. Following this, participants completed a similar task by taking advice from just one of the two previously observed advisers on a given trial. We tested whether participants chose the adviser who had the trial-by-trial highest expressed or recalibrated confidence.

Do humans recalibrate the confidence of advisers?

We perceive and categorize the world constrained by the restrictions of our sensory and neural systems. Additionally, naming modulates how we categorize the things we see. Here we explore a third influence on visual categorization, we study how the perceived statistical regularities of shapes and colors modulate our experience in categorizing objects. We conducted our analyses in artificial systems. We used computer vision to process pictures of real objects, and artificial neural networks to categorize them. We found that the statistical regularities of different object sets produced either shape or color biases, depending on the nature of the set. Our statistical-based categorization approach presents complementary mechanisms of categorization biases, relevant for a more comprehensive understanding of the linguistic shape bias, the color bias of food, and it let us hypothesize why categorization may vary across populations.

The Statistical Properties of Color and Shape of Objects in Visual Categorization

In this experiment, we investigated the role of dimensional distinctiveness on the generalization of novel names for unfamiliar objects. In a comparison design, we manipulated the sequence of trials difficulty, starting either with more difficult trials or with easier trials. To achieve this, we manipulated the dimensional distinctiveness of the first comparison trials and of the, later, transfer trials. Results showed that high-distinctiveness (easy) stimuli increased children’s later performance in the low-distinctiveness (difficult) condition whereas low-distinctiveness early training led to no later improvement in easier trials. Last, a correct answer for the first trial in the first learning part predicted the level of performance in the second learning part. We interpret these findings in terms of differential costs of comparison for varying levels of distinctiveness and level of abstraction from one condition to another.

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Linguistic distributional information about object labels affects ultrarapid object categorization

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