United States

Voice atypicalities are a characteristic feature of schizophrenia, often associated with core negative symptoms. A recent meta-analysis identified atypicalities in pitch, speech rate, and pauses. However, heterogeneity across studies was large and replications almost nonexistent. Further, it is not clear whether vocal patterns are directly related to the mechanisms underlying the disorder and could therefore be found across languages, or not.
In this study we implemented a more rigorously cumulative scientific approach by collecting and analyzing a large cross-linguistic corpus of voice recordings. We critically employed meta-analytic priors to systematically assess the replicability of previous findings, and modeled between-participants variability and cross-linguistic differences.
We replicate previous meta-analytic findings across all languages for reduced pitch variability, while increased pause duration and lower speech rate results were replicated only in some languages. Most atypical voice patterns, thus, seem not to be distinctive of schizophrenia in general, but more specifically situated in linguistic/cultural differences.

CogSci 2021

Vocal patterns in schizophrenia: toward a cumulative approach

### 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!<br><br>

**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.

poster

A pilot study (n=68) investigated participants’ emotional valence after reading sentences that varied in self-relevance (referring to the participant/someone else), valence (positive/negative), and the gender of the person expressing the sentence (man/woman). Positive sentences induced more positive emotion than negative sentences. Self-relevance enhanced the emotional response, with positive self-relevant sentences rated more positively than positive other-relevant sentences, and negative self-relevant sentences rated more negatively than negative other-relevant sentences. Gender impacted these responses. First, the difference in emotional valence felt in response to positive versus negative sentences was larger for women than men – women responded more positively to positive sentences than men. Second, self-relevance enhanced the positive emotion in response to positive sentences more strongly when the sentence came from the opposite gender. These findings suggest that women may have stronger responses to emotional sentences and that processing biases may exist to prioritize positive self-relevant information from the opposite gender.

Are you talking about me? A pilot investigation of how gender modulates the effects of self-relevance and valence on emotional feelings

Delays between causes and effects are commonly found in cause-effect relationships in real life. However, previous studies have only investigated delays on the order of seconds. In the current study we tested whether people can learn a cause-effect relation with hour long delays. The delays between the cause and effect were either 0, 3, 9, or 21 hours, and the study lasted 16 days. Surprisingly, we found that participants were able to learn the causal relation about equally as well in all four conditions. These findings demonstrate a remarkable ability to accurately learn causal relations in a realistic timeframe that has never been tested before.

Causal Learning With Delays Up to 21 Hours

A good deal of recent research has examined children’s norm learning across a wide range of novel contexts. The typical interpretation of these findings is that children’s norm learning is driven by group-based biases. In this paper, we present an alternative interpretation and corresponding meta-analyses that cast the current body of evidence in a rather different light. First, we argue the extant literature uses an ill-suited standard for assessing bias. Rather than comparing children’s judgments to what is expected under random chance (a ‘random standard’), bias is better assessed by comparing children’s judgments to what is most probable, given their total evidence (an ‘evidential standard’). Next, we report a meta-analysis of the known findings to date (k = 40 effect sizes; N = 1,369 in total; ages 4- to 13-years-old) to compare children’s norm learning against an appropriate evidential standard. Meta-analytic estimates reveal that children’s norm learning is not restrictively biased toward narrow-scope inferences on account of group-based factors. Rather, the findings to date are consistent with children’s norm learning being rational (i.e., statistically appropriate, given their evidence) or even inclusively biased toward making the wide-scope inference that a novel norm applies to everyone in a population. We conclude with brief discussion of implications for current understanding and future research on norm acquisition.

Is children's norm learning rational? A meta-analysis

Is language unique in how it evokes conceptual content, and if so, why? In an influential study, Lupyan & Thompson-Schill (2012) report that labels (nouns like “dog”) have a stronger cuing effect in picture verification tasks than “equally informative
and predictive nonverbal cues”, like the sound of a barking dog. Here we sought to better understand the factors that lead to a label advantage. First, while we replicate the label advantage itself, our data do not support the assumption that labels and environmental
sounds are equally informative. Instead, we show that different cue types are associated with target images to different degrees, and that labels show the strongest association. Moreover, the degree of association is a better predictor of reaction times than cue type. Thus, we conclude that labels are not more effective at activating the same semantic content than non-verbal cues, but rather activate different semantic content.

Deconstructing the Label Advantage Effect

If speakers communicate efficiently, they should produce more linguistic material when comprehension difficulty increases. Comprehension difficulty can be impacted by the message itself (studied extensively) or by properties of the listener (studied less). Here we investigate the impact of listeners’ estimated proficiency on speakers’ productions, using referential choice as a case study. Compared to full noun phrases (fNPs, ‘the woman’), pronouns (‘she’) can convey similar content with less linguistic material: accordingly, speakers use more fNPs when the message is unpredictable (Tily & Piantadosi, 2009) and when listeners lack relevant information (Bard & Aylett, 2004). If referential choice is also impacted by listeners’ estimated proficiency, then speakers should use more fNPs when conversing with language learners. To test this, we compared participants' descriptions of the same picture book to learners and proficient speakers. Indeed, participants used more fNPs when their interlocutors were child- or adult-learners, illustrating efficient adaptation to listener type.

Efficient adaptation to listener proficiency: The case of referring expressions

Productive failure (PF) is a learning paradigm that reverses the standard order of instruction by asking students to solve problems prior to instruction. This paradigm has been shown to be effective for fostering student learning. To date, however, the role of student emotion in productive failure has not been investigated. In other paradigms, there is some evidence that failure elicits negative emotions and that these emotions can interfere with learning. This leads to a conundrum given productive failure’s positive effect on learning. To shed light on this, we report on results from a study (N = 48) in the productive failure paradigm. For the analysis, we used a mixed-methods approach to investigate the distribution of emotions in productive failure, how these changed across different instructional activities, and the relation between emotions and posttest performance.

Productive Failure and Student Emotions

The proliferation of the capability for producing and distributing deepfake videos threatens the integrity of systems of justice, democratic processes, and the general ability to critically assess evidence. This study sought to identify individual differences that predict one’s ability to detect these forgeries. It was hypothesized that measures of affect detection and political orientation would correlate with performance on a deepfake detection task. Within a sample (N = 173) of college undergraduates and participants from Amazon’s M-turk, affect detection ability was shown to correlate with deepfake detection ability, r(171) = .73, p < .001, and general orientation to the political left was shown to correlate with deepfake detection ability, r(171) = .42, p < .001. The results of this study serve to identify populations who are particularly susceptible to deception via deepfake video and to inform the development of interventions that may help defend against attempts to influence them.

Individual Differences in Deepfake Detection: Mindblindness and Political Orientation

Human-AI collaboration is an increasingly commonplace part of decision-making in real world applications. However, how humans behave when collaborating with AI is not well understood. We develop metacognitive bandits, a computational model of a human's advice-seeking behavior when working with an AI. The model describes a person's metacognitive process of deciding when to rely on their own judgment and when to solicit the advice of the AI. It also accounts for the difficulty of each trial in making the decision to solicit advice. We illustrate that the metacognitive bandit makes decisions similar to humans in a behavioral experiment. We also demonstrate that algorithm aversion, a widely reported bias, can be explained as the result of a quasi-optimal sequential decision-making process. Our model does not need to assume any prior biases towards AI to produce this behavior.

Explaining Algorithm Aversion with Metacognitive Bandits

Although the Traveling Salesman Problem (TSP) is an NP-hard problem, human schedulers can find strategies that are comparable if not better than existing algorithms. However, it is still unclear what heuristics they adopt in order to select an approximate solution (Schaefer, 2018). In solving a navigational problem in 3D, the directional heading is an essential factor because changes of direction require additional energy expenditure. In our study, we focus on the connection between local choices minimizing distance and local choices minimizing the turning angle. We then examine human solutions to the TSP in light of the tradeoff between local and global optimization of distance and turning angle. We conducted two experiments showing that while subjects were more likely to move to the nearest node when they plan the next step ahead, they also take the turning angle into consideration and overall adopt a strategy that combines local and global heuristics.

The Interplay Between Local and Global Strategies in Navigational Decisions

When humans make sense of the world, they do not understand it as a cascade of observations; rather, from a cascade of observations, humans assemble a holistic narrative, connecting their observations using prior knowledge and inference. The final product of observations connected with prior knowledge and inference may be modeled as a knowledge graph. The process of sensemaking described above is one we seek to emulate in the realm of image understanding through a computational system. Starting from observed objects and relationships in a sequence of images (from Visual Genome Scene Graphs), the system we are building consults a commonsense knowledge network (ConceptNet), over-generates a set of hypothesized narrative-based connections between observations, and evaluates and trims its hypotheses through Multi-Objective Optimization to create a consistent set. The resultant knowledge graph reflects the system’s consistent speculations, beyond the directly observable, of what is happening in, and across, the images.

Downloads

Next from CogSci 2021

Are you talking about me? A pilot investigation of how gender modulates the effects of self-relevance and valence on emotional feelings

Similar lecture

Category Learning is Shaped by the Multifaceted Development of Selective Attention

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES