Netherlands

The recent surge in the use of AI-powered chatbots such as ChatGPT has led to new challenges in academia. These chatbots can enable student plagiarism and the submission of misleading content, undermining educational objectives. With plagiarism detectors unreliable in the face of this issue, educational institutions have been struggling to update their policies apace. This study assesses the effectiveness of sending warning messages - a common strategy used to discourage unethical use of ChatGPT - and investigates the use of the illusion of explanatory depth (IOED) paradigm as an alternative intervention. An international sample of students was asked to rate their understanding of, likelihood to use, and moral stance toward ChatGPT-generated text in assignments both before and after either reading a cautionary university message or explaining how ChatGPT works. Results showed that the explanation task did lead to the expected reduction in ratings of understanding, but despite this, neither moral acceptability nor likelihood to use decreased along with it. Similarly, reading the cautionary message neither resulted in a change in likelihood to use nor in moral acceptability, although it unexpectedly increased ratings of understanding. The results suggest that tackling students’ understanding of ChatGPT is insufficient when it comes to deterring its unethical use, and that future interventions might want to have students reflect on moral issues surrounding the use of AI-powered chatbots.

**Authors:**

Yomn Elsayed: Erasmus University Rotterdam; Steven Verheyen: Erasmus University Rotterdam

CogSci 2024

ChatGPT and the Illusion of Explanatory Depth

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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Studies on interactions between procedural and declarative learning have focused on largely on competition during encoding, consolidation, or use (retrieval). Less attention has been paid to interactions between the representations created by each system. In a behavioral study, we demonstrated that information from both declarative and procedural learning can contribute to response selection. Participants were instructed to use a completely diagnostic, verbalizable, shape-based rule to categorize exemplars and received feedback after each trial. However, the categories also differed probabilistically in their color distributions. Participants used both color (learned procedurally) and shape (learned declaratively) to categorize exemplars, making faster responses when both sources indicated the same category judgement, and slower when they conflicted. Debriefing confirmed that most participants were unaware of the color distributions (aware participants were analyzed separately). This result suggests that both the color (procedural) and shape (declarative) information contributed to response selection.

**Authors:**

Priya B. Kalra: University of Western Ontario; Laura Batterink: University of Western Ontario; John Paul Minda: University of Western Ontario

Procedural and Declarative Category Learning Simultaneously Contribute to Downstream Processes

Human moral judgments are both precise, with clear intuitions about right and wrong, and at the same time obscure, as they seem to result from principles whose logic often escapes us. The development of Artificial Intelligence (AI) applications requires an understanding of this subtle logic if we are to embed moral considerations in artificial systems. Reinforcement Learning (RL) algorithms have emerged as a valuable interactive tool for investigating moral behavior. However, being value-based algorithms, they face difficulty when it comes to explaining deontological, non-consequentialist moral judgments. Here, in a multi-agent learning scenario based on the Producer-Scrounger Game, we show that RL agents can converge towards apparently non-consequentialist outcomes, provided the algorithm accounts for the temporal value of actions. The implications of our findings extend to integrating morality into AI agents by elucidating the interplay between learning strategies, characteristics for accounting temporal values, and methods of considering the opponent’s payoff.

**Authors:**

Ali Shiravand: École Normale Supérieure - PSL; Jean-Baptiste André: École Normale Supérieure - PSL

Human-Like Moral Decisions by Reinforcement Learning Agents

Notifications convey important information, but they can also act as distractions, leading to resumption errors. Previous research has primarily focused on two types of notifications: pop-up notifications that appear quickly (1 second) and transparency reduction notifications that appear slowly (2 seconds). Pop-up notifications in an environment with perceptual feedback tend to result in the highest number of errors, while transparency reduction notifications may go unnoticed in an environment without feedback. To bridge this gap, the third variant of notification speed (1.5 seconds) was introduced in this study. The aim was to strike a balance between the noticeability of notification and minimizing the negative impact of attention redirection. Participants were instructed to perform the Modified Bourdon Test and close notifications. The findings revealed that the third variant, combining the features of pop-up and transparency reduction notifications, led to a decrease in resumption errors while still effectively capturing users' attention.

**Authors:**

Valeriya Sklemenova: HSE University; Anastasia Anufrieva: HSE University

Attentional sustainability of organizer users under fast and slow appearing notifications

The generation effect, demonstrating improved memory performance through self-generating information, was explored in this study. Participants engaged in semantic and perceptual generation tasks, where semantic tasks involved meaning-related associations, and perceptual tasks focused on surface characteristics. While previous studies separately examined these tasks, our project directly compared their impact. Experiment 1 revealed higher recognition performance for semantic generation over perceptual generation, with no significant difference in recognition across perceptual and semantic reading conditions. Experiment 2 incorporated judgments-of-learning (JOL) and no-JOL groups, demonstrating that participants accurately predicted and performed better on memory tasks involving the generation and semantic manipulations. Additionally, JOL-group participants outperformed the no-JOL group, suggesting that predicting one's memory performance enhances actual memory performance. Experiment 3 aimed to see the effects of the match between encoding and retrieval. Although the memory benefit for the JOL group diminished, it was still marginally significant, indicating the importance of the reactivity effect.

**Authors:**

Fatih Yavuz: Bilkent University; Miri Besken: Bilkent University

Investigation of The Generation Effect on Memory and Metamemory Through Semantic and Perceptual Cues

Despite the long-standing theoretical importance of the con- cept of illocutionary force in communication (Austin, 1975), quantitative measurement of it has remained elusive. The fol- lowing study seeks to measure the influence of illocutionary force on the degree to which subreddit community members maintain the concepts and ideas of previous community members’ comments when they reply to each other’s content. We leverage a information-theoretic framework implementing a measurement of linguistic convergence to capture how much of a previous comment can be recovered from its replies. To show the effect of illocutionary force, we then ask a large language model (LLM) to write a reply to the same previous comment as though it were a member of that subreddit community. Because LLMs inherently lack illocutionary intent but produce plausible utterances, they can function as a useful con- trol to test the contribution of illocutionary intent and the effect it may have on the language in human-generated comments. We find that LLMs indeed have statistically significant, lower entropy with prior comments than human replies to the same comments. While this says very little about LLMs on the basis of how they are trained, this difference offers a quantitative baseline to assess the effect of illocutionary force on the flow of information in online discourse.

**Authors:**

Zachary P Rosen: University of California, Los Angeles; Rick Dale: UCLA

LLMs Don’t “Do Things with Words” but Their Lack of Illocution Can Inform the Study of Human Discourse

The current study aimed to examine how the recognition of grey-scale photos of fearful or angry female bodies would be affected by three conditions: social situation (single person vs. facing vs. nonfacing dyad) the orientation of figures (upright vs. inverted); emotion complementarity (same vs. complementary). We hypothesized that the recognition of emotions would be the most accurate when either single or facing body pairs were presented, while the inversion would impair the perception of affective expressions. Facing bodies in fact had an advantage over nonfacing ones, same emotion condition also had higher accuracy than complementary, as well as the overall accuracy was higher for anger than fear, thus context was an important factor in differentiating between these two negative emotions. Inversion effect was not confirmed for emotions conveyed by bodies, therefore our results demonstrated that not only configural, but part-by-part analysis is also required for emotion recognition.
Keywords: body inversion effect, social interactions, bodily emotional expressions

**Authors:**

Gyopárka Barbara Lázár: Institute of Psychology; Beatrix Lábadi: Institute of Psychology University of Pécs

Inversion Effect of Emotional Bodies in Social Situations

The steady increase in time spent on smartphone applications and particularly on social networks, raises questions about the environmental and societal sustainability of such a phenomenon. Utility and enjoyment have a key role in such practices, but other factors such as passing time may also contribute. From May to November 2023, 5,028 people took part in a web survey aiming at producing durations prospectively using mobile applications like Facebook, Instagram, TikTok, Reading. The protocol introduces variables known to have an effect on time perception. On average, the produced durations were underestimated. This result is in line with the notion that tracking information and tracking time compete for the brain’s limited attentional resources and, hence, that attention plays a critical role in time estimation. Significant differences emerged between the applications tested. TikTok and Reading tasks appear the most underestimate but with opposite dynamics as the level of satisfaction and familiarity are lower for the first compared to the former. Among the variables studied to explain the difficulties in evaluating time spent, the importance of familiarity with the activity is undoubtedly something worth exploring in the context of the race between new algorithms and cognitive adaptability.

**Authors:**

Jean-Marc Josset: Orange Labs; Erwan Le Quentrec: Orange Innovation; Virginie van Wassenhove: CEA, INSERM

Our sweetest hours fly fastest… on smartphone

Electroencephalography (EEG) plays a pivotal role in the diagnosis of various neurological conditions, most notably major depressive disorder (MDD). However, deep learning-based methods currently employed for MDD detection tasks exhibit inadequate generalization capabilities, particularly across different EEG electrode channels, and demonstrate limited feature representation capacity. In this paper, we present a novel approach referred to as adaptive feature learning (AFL), which leverages kernel embedding to facilitate the learning of domain-invariant features across subjects within a reproducing kernel Hilbert space. This method aims to enhance the model's ability to generalize across multiple subjects' EEG signals. Furthermore, our research revealed that batch normalization (BN) layers within the existing MDD detection network frequently result in feature channel suppression, potentially compromising the representation power of the features. To address this issue, we propose channel activation (CA), which employs decorrelation to reactivate suppressed feature maps, thereby enhancing the model’s feature representation capability, particularly for subtle EEG changes. The effectiveness of the proposed methods is evaluated using the leave-one-subject-out protocol on MODMA and PRED+CT datasets, yielding detection accuracies of 90.56\% (MODMA) and 96.51\% (PRED+CT). Our experimental findings exhibit the superior performance of our method compared to state-of-the-art (SOTA) methods in terms of MDD recognition.

**Authors:**

Chenyang Xu: Tianjin University; Feiyi Fan: Institute of Computing Technology, Chinese Academy of Sciences; Jianfei Shen: Institute of Computing Technology, Chinese Academy of Sciences; Hanguang Wang: Tianjin University; Zhongyi Zhang: Tianjin University; Qinghao Meng: Tianjin University

An EEG-Based Depressive Detection Network with Adaptive Feature Learning and Channel Activation

In this paper, we argue that action is involved in the creation and representation of perceptual objects. We introduce leading philosophical theories regarding the structure of perceptual objects in modality-independent and multisensory settings. These accounts omit action as a causal factor that can facilitate feature binding and serve as a structural component of perceptual objects. We argue that action does play this causal role due to the connections between the brain’s motor system and perceptual processing as evidenced by neurophysiological and behavioral studies. These data include research on view-independent representations, peripersonal space, and event file coding. We conclude that to omit the influence of the motor system on the structure of perceptual objects is to have an incomplete account of object perception. Motor action is often required to drive the integration of sensory features into corresponding perceptual objects.

**Authors:**

Aleksandra Mroczko-Wasowicz: University of Warsaw; Spencer Ivy: University of Warsaw; Michał Bachanek: University of Warsaw; Aleksandra Cząstkiewicz: Maria Curie-Skłodowska University

Accounting for Action: Challenging the Traditional View of Multimodal Perceptual Objects

The theory of language prediction posits a competitive preactivation of semantic (meaning) and form (sound/grapheme) information, aiding in the selection of the most likely lexical candidate. Hypothetically, multiple semantic and form cohorts are preactivated before the actual lexical candidate is activated. This study explores this by examining young adults reading constrained sentences (discretely), with simultaneous electroencephalographic recording. Representational similarity analysis was conducted to assess word-specific, semantic-related, and form-related pair of sentences (focusing on the word preceding the expected word). To examine the temporality, cluster permutation and divergence point analyses were performed. The results indicated a semantic coactivation effect occurring before the phonological one and the recovery of the specific words. However, despite the phonological information being recovered before the word specific information, there were no significant differences in temporality. These findings indicate a semantic coactivation process for meaning selection during prediction, with form coactivation dependent on the expected word's selection.

**Authors:**

Armando Quetzalcóatl Angulo Chavira: UNAM; Alejandra Mitzi Castellón-Flores: Universidad Nacional Autónoma de México; Elia Haydee Carrasco-Ortíz: Universidad Autónoma de Queretaro; Natalia Arias-Trejo: Universidad Nacional Autónoma de Mexico, UNAM

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