United States

How do people acquire information and make decisions in an inherently complex world? We use the game Entropy Mastermind to investigate the cognitive strategies people adopt under various task conditions in situations characterized by high combinatorial complexity. N = 42 participants completed a total of 271 games, varying in incentive structure: In the speed condition, participants were incentivised for solving games quickly; in the efficiency condition, incentives were given for solutions requiring few problem solving steps; in the mixed condition, both speed and efficiency were incentivised.
We found that participants adapted their problem solving strategies to the imposed constraints: In the speed condition simpler strategies were used, making feedback easy to interpret. Our results support the hypothesis that in complex environments people flexibly adjust their cognitive strategies to optimize the fit between situational requirements and their cognitive resources.

CogSci 2021

Fast or efficient? Strategy selection in the game Entropy Mastermind

**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,**
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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.

Perceptual training leads to improvements in a wide range of simple visual tasks. However, it is still unclear how effective it can be for more difficult visual tasks in real-world domains such as radiology. Is it possible to train people to the level of experts? If so, what method is best, and how much training is necessary? Over four training sessions, we trained medically naive participants to identify the degree of fatty liver tissue present in ultrasound images. We found that both COMPARISON and SINGLE-CASE perceptual training techniques resulted in significant post-training improvement, but that the SINGLE-CASE training was more effective. Whilst people showed rapid learning with less than one hour of training, they did not improve to the level of experts, and additional training sessions did not provide significant benefits beyond the initial session. This suggests that perceptual training could usefully augment, but not replace, the traditional rule-based training that medical students currently receive.

How effective is perceptual training? Evaluating two perceptual training methods on a difficult visual categorisation task

Many events that humans and other organisms experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporarily adjacent stimuli, others involve tracking the co-occurrences between temporarily distant stimuli (i.e., non-adjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here we report on four experiments that examined NAD learning from various types of visual stimuli. The results suggest that continuous movements aid the acquisition of NADs. We also found that human motion leads to more robust NAD learning compared to object motions, perhaps because of a richer representation. This richer representation could result in better memory and recall, and provide a stronger signal for NAD learning.

Dynamic Action Facilitates Learning of Non-Adjacent Dependencies in Visual Sequences

Misconceptions in science are ubiquitous and difficult to revise, but refutation texts are one effective tool for prompting conceptual change (Tippett, 2010). However, refutation texts are effective only to the extent that students are forming coherent representations. Self-explanation is a well-studied method for increasing coherence of readers’ mental representations (Allen, McNamara & McCrudden, 2015). The current study examines the unexplored question of whether these common interventions enhance each other’s positive learning outcomes. Two-hundred fifteen UCSD undergraduates were randomly assigned to read either a refutation or an expository text about the phases of the moon, and were prompted to either self-explain or think aloud while reading. Students then took a post-test assessing knowledge of moon phases and related misconceptions. We measured both accuracy and explanatory qualities, such as causality and circularity, in order to assess the relative efficacy of refutation and self-explanation and their combination.

Effects of Combining Refutation and Self-Explanation on Student Learning

Narratives can communicate moral character by describing one's past actions and motivations. When telling moral narratives, people might twist the truth to appear better than they are, while concealing this goal. We show that audiences evaluate a narrator’s moral character by inferring weights on three goals: providing accurate information, appearing morally good, and projecting an image of informativeness. Participants judged how narrators explained their choice during a “claim” task. Narrators could claim a raise at the risk of causing a co-worker to lose money. They could then lie or tell the truth and be direct or indirect about their motivations (e.g., “I’m not selfish” vs. “It was better for the co-worker”). Our results suggest that to be perceived as morally good, narrators must find a balance: trying too hard to appear generous is costly. We introduce a model for how narratives are constructed through recursive inference of audience perception.

Competing goals in the construction and perception of moral narratives

Most models of statistical learning do not consider the perceptual properties of the units that make up a sequence. Here, we manipulate the similarity of units in a modified version of classic statistical learning paradigm (Aslin et al., 1998). After a 3-minute familiarization stream, we asked the participants to rate their familiarity with words and part-words, with the addition of non-words (trisyllabic words without any previous exposure). We developed a simple recurrent neural network that used distributed representations for the syllables and produced similar results to the human data. We then explored how perceptual similarity impacted learning of different familiarization streams. Specifically, greater similarity relations among units were predicted to lead to poorer discrimination between words and part-words. Based on the model’s results, we created a new familiarization sequence of trisyllabic words and tested for impacts on perceptual similarity on human performance.

Perceptual similarity and learning from sequential statistics.

Chinese texts are renowned for the lack of physical spaces between words in a sentence. Reading these sentences requires a stage of word segmentation, the mechanism of which may involve visual statistical learning. In three experiments employing the RSVP task along with the Saffran et al. (1997) paradigm, we provided evidence that foreign learners of Chinese could capture the statistical information embedded in a string of characters and use that information to tell apart a “word” from a “nonword”. The statistical learning effect (.57) was comparable to that observed previously in an auditory task using the same stimuli. The results of the experiments also suggested that significant visual statistical learning required a conscious level of processing that directed the participants’ attention at the characters as well as an unconscious level, at which the distributional information across the characters can be continuously computed and accumulated.

Visual Statistical Learning in the Reading of Unspaced Chinese Sentences

Language emergence is characterized by a high degree of lexical variation. It has been suggested that the speed at which lexical conventionalization occurs depends partially on social structure. In large communities, individuals receive input from many sources, creating a pressure for lexical convergence. In small, insular communities, individuals can remember idiolects and share common ground with interlocuters, allowing these communities to retain a high degree of lexical variation. We look at lexical variation in Kata Kolok, a sign language which emerged six generations ago in a Balinese village, where women tend to have more tightly-knit social networks than men. We test if there are differing degrees of lexical uniformity between women and men by reanalyzing a picture description task in Kata Kolok. We find that women’s productions exhibit less lexical uniformity than men’s. One possible explanation of this finding is that women's more tightly-knit social networks allow for remembering idiolects, alleviating the pressure for lexical convergence, but social network data from the Kata Kolok community is needed to support this explanation.

Social structure and lexical uniformity: a case study of gender differences in the Kata Kolok community

The Fourth Industrial Revolution’s technological innovations are driving demand for high skill jobs in a global Knowledge Economy, precipitating Learning Scientists to advocate for developing Deeper Conceptual Understanding of STEM domains. In this study, Concreteness Fading is employed to develop Deeper Conceptual Understanding of Deductive Proof in geometry: enactive stage grounds justifications in spatial relationships through manipulating object-shapes; iconic stage grounds justifications in perceptual relationships through drawing images of object-shapes; symbolic stage grounds justifications in rule-based relationships through writing numbers in formulas about object-shapes. Think-aloud protocols were analyzed of post-secondary non-math majors (N = 8, male = 2, female = 6) randomly assigned to four conditions: enactive-iconic-symbolic (n = 2), enactive-symbolic (n = 2), iconic-symbolic (n = 2), symbolic (n = 2). Findings reveal reliance on affordances of spatial-based and perceptual-based proofs by participants after reaching a perceived impasse – demonstrating fascinating cognitive flexibility crucial for 21st century problem solving.

Affordances and Grounding Within Concreteness Fading When Learning Proof in STEM's Geometry

In addition to saliency and goal-based factors, a scene’s semantic content has been shown to guide attention in visual search tasks. Here, we ask if this rapidly available guidance signal can be leveraged to learn new attentional strategies. In a variant of the scene preview paradigm (Castelhano & Heaven, 2010), participants searched for targets embedded in real-world scenes with target locations linked to scene gist. We found that activating gist with scene previews significantly increased search efficiency over time in a manner consistent with formal theories of skill acquisition. We combine VGG16 and EBRW to provide a biologically inspired account of the gist preview advantage and its effects on learning in gist-guided attention. Preliminary model results suggest that, when preview information is useful, stimulus features may amplify the similarities and differences between exemplars.

Leveraging rapid scene perception in attentional learning

Certain colors are strongly associated with certain adjectives (e.g. red is hot, blue is cold). Some of these associations are grounded in visual experiences like seeing hot embers glow red. Surprisingly, many congenitally blind people show similar color associations, despite lacking all visual experience of color. Presumably, they learn these associations via language. Can we detect these associations in the statistics of language? And if so, what form do they take? We apply a projection method to word embeddings trained on corpora of spoken and written text to identify color-adjective associations as they are represented in language. We show that these projections are predictive of color-adjective ratings collected from blind and sighted people, and that the effect size depends on the training corpus. Finally, we examine how color-adjective associations might be represented in language by training word embeddings on corpora from which various sources of color-semantic information are removed.

Fast or efficient? Strategy selection in the game Entropy Mastermind

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How effective is perceptual training? Evaluating two perceptual training methods on a difficult visual categorisation task

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