United States

Previous research found that loaded language (i.e., words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others) is used with significantly greater frequency by users of an online anti-vaccination forum compared to users of a vaccination-neutral forum. Our present research expands on these findings by examining the presence of loaded language on forums dedicated to discussing a broader range of conspiratorial topics. We compare loaded language usage on the subreddit r/conspiracy with usage on r/science and r/wallstreetbets. Results corroborate the previous literature. We additionally examined the presence of loaded language in a dataset from the alt-tech social networking platform Parler on January 6, 2021, when the U.S. Capitol was attacked because of conspiracy theories regarding the 2020 Presidential Election. Results show that loaded language, whose usage is linked to the cognitive motivations underlying belief in conspiracy theories, is a linguistic marker of conspiracy theorizing.

CogSci 2023

Loaded Language and Conspiracy Theories on Reddit and Parler

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

The legal system is concerned with interpreting and applying existing laws to real world circumstances. In the U.S., the final arbiters of these laws are the nine justices of the U.S. Supreme Court. The cases presented before the court generally involve disputes between two parties, the petitioner and the respondent. However, in some cases third parties provide additional information to the court as amici curiae (‘friends of the court’). The present study uses corpus statistics and moral foundation theory to explore and compare the types of arguments brought up by the different parties during oral arguments over the past 6 decades, with a focus on amicus curiae arguments and how they affect the results of the case. Overall, the results show that both amici and respondents rely on moral arguments more frequently than petitioners do.

Supreme Court Oral Arguments by Amicus Curiae: A comparative analysis

Drawing on qualitative discourse analysis, this study conducted semi-structured interviews with seventeen sexual minorities, to investigate how the categorization mechanism generates institutional, interpersonal, and internalized stigmas and stressors against Chinese sexual minority youth, and how the categorization mechanism is represented in their contextualized lived experience. Heterosexual matrix and homonormativity hidden in Chinese traditions and ideologies are identified under the framework of queer linguistics. This study reveals that Chinese sexual minority youth are otherized as problematic out-groups on the textual level. They are negatively constructed as hypersexualized, pathologized, and dehumanized. On the micro-linguistic level, deixis, evaluations, and emotive words are used to classify in-groups and out-groups, show intimate and remote social distances, and express positive and negative attitudes. Results uncover that the categorization mechanism produces asymmetric relationships and power dynamics between heterosexual and non-heterosexual people, and between homonormative and non-homonormative sexual minorities.

The categorization mechanism in queer linguistics: A study on discourses of Chinese LGB youth

Across cultures, people use natural and supernatural explanations to explain adverse life events, such as illness or death. Yet little is known about the psychological implications of this type of causal reasoning. Here, we ask, does explanatory coexistence help or hinder coping with significant misfortune? We examined this question through structured interviews with a diverse sample of 147 Los Angelinos who had suffered from severe illness or bereavement during the COVID-19 pandemic. As predicted, mean scores on the Posttraumatic Growth Inventory were significantly higher for participants who employed coexisting explanations for their misfortune than those who employed singular explanations (natural, supernatural) alone. These findings provide evidence of an association between coexistence reasoning about misfortune and positive post-event processing.

Coexistence Reasoning about Misfortune During COVID-19 is Associated with Positive Psychological Well-Being

How responsible someone is for an outcome depends on both the causal role of their actions, and what those actions reveal about their moral character. Prior work has successfully modeled people's causal attributions and mental state inferences using planning algorithms assumed to approximate people's intuitive theory of mind about others' behavior. In this paper, we develop a unified computational framework for responsibility judgments in which the same generative planner can model both of these processes. We test our framework on a variety of animated social scenarios in two experiments. Experiment 1 features simple cases of helping and hindering. Experiment 2 features more complex interactions that require recursive reasoning, including cases where one agent affects another by merely signaling their intentions without physically acting on the world. Our model accurately captures participants' counterfactual simulations and intention inferences. Together, these two factors explain responsibility judgments.

A computational framework for responsibility judgments from counterfactual simulations and intention inferences

Causal pluralism proposes that humans can reason about causes and effects in terms of both dependency and process relations, depending on the scenario. Empirical support for this view is provided by responses to double prevention scenarios in which an actor attempts to bring about an outcome, a preventer attempts to prevent the outcome, and a double preventer intervenes to stop the preventer’s prevention attempt. In contrast to the predictions of the causal pluralism account, two pre-registered experiments (Ns = 400 and 450) indicate (a) that intentional actions are judged to be significantly more causative of an outcome than unintentional actions for both the actor and the double preventer and (b) that reasoners interpret the double preventer’s link to the outcome in terms of a process relation. These results underscore the need to revisit fundamental questions regarding how reasoners form and reason over representations of causal scenarios featuring intentional actions.

Revisiting Causal Pluralism: Intention, Process, and Dependency in Cases of Double Prevention

A causal illusion occurs when people perceive a causal relationship between two events that are not contingent on each other. How this illusion varies when people reason diagnostically (i.e., in an effect-to-cause direction) is not well known. Thus, this experiment explored how diagnostic reasoning affects causal illusions. Participants learnt about a illusory cause-effect relationship in which the probability of the cause and the probability of the effect were orthogonally manipulated to be either high or low. Participants learnt either predictively (i.e., cause-to-effect) or diagnostically, and at test had to make two causal judgements that encouraged either predictive (cause-to-effect) or diagnostic (effect-to-cause) reasoning. Diagnostic reasoning at test increased the strength of the cause density bias and decreased the strength of the effect density bias. It also decreased causal ratings, but only after predictive learning. These findings highlight the promise of diagnostic reasoning as a tool to debias causal decision making.

Question Framing Modulates the Cause Density and Effect Density Biases in Causal Illusions

Many methods exist to learn causal models from data, as causal relationships form the basis for successful actions. These methods are frequently evaluated based on the completeness of the models they can infer. Yet, there is a gap between the highly complete and potentially complex models algorithms can learn and the types of information people can use successfully to make decisions. To address this we conduct two experiments to understand how the size and features of causal models influence how well they can be used for decision-making. In Experiment 1 we systematically vary model size for a series of topics, finding that there is a negative and linear relationship between causal model size and decision accuracy. In Experiment 2 we examine how model structure influences decisions, varying whether the models include feedback loops, again finding that smaller models lead to better choices, and that feedback loops are also beneficial.

Quantifying the Utility of Causal Models for Decision-Making

Infants must balance interacting with a caregiver and navigating the environment, driven by their curiosity and attachment. This decision involves trading off proximity seeking with the caregiver and novelty seeking in the surroundings. Our hypothesis is that different attachment styles may arise as adaptive reward-seeking strategies based on different frequencies and scales of rewards from both caregiver and environment, including negative rewards due to distressing encounters. We implement a computational model using a reinforcement learning framework to model infants’ attachment and exploratory behaviors as a bandit problem. We study how infants’ exploratory behaviors emerge as they learn from their caregiver and how these learned behaviors transfer to a new caregiver. Our work expands upon existing computational models of attachment and models how social factors can influence curiosity-seeking as infants learn to explore versus exploit.

Reinforcement learning models of tradeoffs between infant attachment and curiosity

Categorical biases in working memory (WM) can impact our recollection of visual stimuli. These are often biased towards or away from category prototypes. Color naming data can be used to predict biases in behavioral and neural data, suggesting sensory WM is affected by the use of verbal labels.

We investigated a possible link between categorical biases and verbal labels for orientation and spatial WM. Forty subjects performed standard orientation and spatial WM tasks, and were later prompted to freely label the stimuli. 

Accuracy is significantly higher when recalling cardinal locations and orientations compared to non-cardinal ones. This contrast is also evidenced by subject-unique neural activity patterns. The behavioral error pattern resembled verbal labeling strategies: Terms for cardinal stimuli were used sparsely and precisely (e.g. vertical, horizontal) while terms describing non-cardinal stimuli were used liberally (e.g. diagonal). Cardinal biases in WM stimuli might result from the use of categorical, verbal representations.

Does visual working memory rely on categorical and verbal neural representations?

High-quality parent language may support preschoolers’ language development by shaping underlying neurodevelopment. Indeed, more interactive language relates to higher frontal brain activity (Romeo et al., 2018). However, it remains to be explored whether other features of parent language may also be related to brain activity. This study examined whether parental linguistic sophistication is related to children’s spontaneous high-frequency neural activity, as indexed by EEG gamma power. Transcripts of parent-child dyads (N=36, children M=4.1 years old) completing a puzzle task were analyzed for mean length of utterance (measure of grammatical complexity) and number of different words (measure of vocabulary sophistication). We found that higher parental mean length of utterance was related to higher regional EEG gamma power (F(2.61,80.78)=2.92, p=.046), suggesting that more grammatically complex parent language is linked with higher child neural maturity. Future studies should explore the directionality of this finding and further measures of parent language quality.

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Supreme Court Oral Arguments by Amicus Curiae: A comparative analysis

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