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Technology to predict responses is a key element in human-to-human messaging that has increasingly been utilized to enable AI-mediated communication. When response suggestions from AI are incorporated into human messaging, it will have an impact on the flow and content of the dialogue. In this paper, we investigated the effect of AI response suggestion sentences used for chat messaging on dialogue flow from two aspects: dialogue act and initiative. Usage rates of response suggestions for different dialogue acts were measured with BLEU scores, and we found that the response suggestions contributed to the establishment of a proper dialogue flow, such as an answer in response to a question. The results of our case study indicated that users who take the initiative in the dialogue tend to utilize response suggestions less frequently. We also found that some written responses were based on the suggested sentence structure but conveyed different messages.

CogSci 2023

The Effect of Response Suggestion on Dialog Flow: Analysis Based on Dialogue Act and Initiative

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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)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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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.

This study used XAI, which shows its purposes and attention as explanations of its process, and investigated how these explanations affect human trust in and use of AI. In this study, we generated heat maps indicating AI attention, conducted Experiment 1 to confirm the validity of the interpretability of the heat maps, and conducted Experiment 2 to investigate the effects of the purpose and heat maps in terms of reliance (depending on AI) and compliance (accepting answers of AI). The results of structural equation modeling (SEM) analyses showed that (1) displaying the purpose of AI positively and negatively influenced trust depending on the types of AI usage, reliance or compliance, and task difficulty, (2) just displaying the heat maps negatively influenced trust in a more difficult task, and (3) the heat maps positively influenced trust according to their interpretability in a more difficult task.

Modeling Reliance on XAI Indicating Its Purpose and Attention

To inform Explainable AI (XAI) design for updating users’ beliefs about AI based on their mental models, we examined the similarities and differences between humans and AI in object detection in driving scenarios. In humans, individuals differed in adopting focused or explorative attention strategies, with better performance associated with the focused strategy. AI (Yolo-v5s) had higher similarity in attended features to the focused than the explorative strategy in humans, and achieved human-expert-level performance in vehicle detection even in difficult cases such as occlusion and degradation. In contrast, it performed much poorer than humans in detecting humans with low attended feature similarity due to humans’ attention bias for stimuli with evolutionary significance. Also, higher similarity to humans’ attended features was associated with better AI performance, suggesting that human attention may be used for guiding AI design. These findings have significant implications for both AI and XAI designs.

Humans vs. AI in Detecting Vehicles and Humans in Driving Scenarios

As more of society uses virtual environments from collaborative learning environments to the now ubiquitous Zoom online meeting platform, there is an opportunity for artificial agents (AAs) to foster and support human collaboration. However, little appears to be known about whether people would actually want to collaborate with such an AA and what impact if any it has on cognitive processes underlying human collaboration. To begin to answer these questions, we devised a Wizard of Oz (WoZ) study where teams of 3 participants were tasked with having to locate and corral targets into a containment area in a virtual desert environment with the Wizard (confederate) playing the role of the Operator who could see the location of participants, targets and the containment area, and could communicate this information to participants. Although agent responsiveness had limited impact on performance and workload, as expected the responsiveness of the agent did influence participant's perceptions of whether the agent understand them and provided timely responses.

How do People Perceive Collaborative Conversational Agents?

Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planners from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner and a family of well-known planners, including online planners, optimal planners and satisficing planners. Each planner is evaluated based on its ability to predict the actions and planning times of participants in a new behavioral experiment. Our results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and human planning times. This finding is consistent with the view that humans differ from standard AI planners by integrating a mechanism for evidence accumulation.

Comparing AI planning algorithms with humans on the Tower of London task

While saliency-based explainable AI (XAI) methods have been well developed for image classification models, they fall short in comparison with human explanations. Here we examined human explanation strategies for image classification and their relationship with explanation quality to inform better XAI designs. We found that individuals differed in attention strategies during explanation: Participants adopting more explorative strategies used more visual information in their explanations, whereas those adopting more focused strategies included more conceptual information. In addition, visual explanations were rated higher for effectiveness in teaching learners without prior category knowledge, whereas conceptual explanations were more diagnostic for observers with prior knowledge to infer the class label. Thus, individuals differ in the use of visual and conceptual information to explain image classification, which facilitate different aspects of explanation quality and suit learners with different experiences. These findings have important implications for adaptive use of visual and conceptual information in XAI development.

Individual differences in explanation strategies for image classification and implications for explainable AI

Since Darwin (1871), researchers have proposed that
musicality evolved in a reproductive context in which males
produce music to signal their mate quality. The extent to
which evidence supports this contention, however, remains
unclear. Related traits in many non-human animals are
sexually differentiated, and while some sex differences in
human auditory perception have been documented, the
pattern of results is murky. Here, we study melodic
discrimination, mistuning perception, and beat alignment
perception in 360,009 men and 194,291 women from 208
countries. We find that, in contrast to other non-music human
traits, and in contrast to non-human traits, there was no
overall advantage for either gender, and the observed gender
differences were minuscule (Cohen’s d: 0.009 - 0.11) and of
inconsistent direction. These results do not provide
compelling support for human music perception being a
sexually dimorphic trait, and therefore it is unlikely to have
been shaped by sexual selection.

Human music perception ability is not a sexually dimorphic trait

For cognition to evolve, environmental cues must be sufficiently reliable to allow an animal to regularly make correct decisions. For example, the sound of rustling must covary with prey for attempting to detect prey to be possible. However, the reliability of information is not solely a property of the environment, but also results from the sophistication of an animal’s sensory systems. Here, we extend signal detection theory to examine how selection acts on sensory systems, alongside an animal’s policy for using information. Our model finds cognition can still be favored if sensory systems are poor, by compensating with greater bias to avoid costly errors. Yet, investing in reliable information and bias are not always alternatives, and can coevolve in order to be doubly protected from important errors. Sophisticated sensory systems evolve when the environment is complex, metabolic costs are low, and both false alarms and missed detections are moderately costly.

The joint evolution of sensory systems and decision policy allows cognition

With the advancement of technology, audiovisual (AV) products (i.e., films, TV shows, podcasts...) are now disseminated online and thus easily accessible by internet users. This paper takes advantage of eye-tracking technology to investigate how audiences receive and process different translated swearwords in films, which is still underrepresented and an uncharted area in audiovisual translation (AVT) studies. An eye-tracking experiment was carried out to expose 150 participants to four film clips and allocating them into one of the five subtitling groups: four experimental groups with four different translation strategies of swearwords ranging from low to high profanity, and one control group with only original English captions. Questionnaires were supplemented to gauge their immersion, satisfaction, enjoyment, comprehension and sense of offensiveness. This doctoral project is intended to add more human-related element in and expand the knowledge boundary of AVT field triangulated with cognitive science, audience reception in film study, psycholinguistics in general.

An eye-tracking study on the audience reception and processing of English-Chinese swearwords in films

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.

Loaded Language and Conspiracy Theories on Reddit and Parler

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.

Next from CogSci 2023

Modeling Reliance on XAI Indicating Its Purpose and Attention

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