Canada

Research in event cognition has focused on how people perceive
and remember events under experimental conditions. This
research study aims to explore the temporal duration of
self-reported events from daily life (Sreekumar, et al., 2018;
Zhuang, et al., 2012). The small amount of prior work that
exists suggests that daily event durations have a Gaussian
distribution and that people have prior beliefs that reflect this
reality (Griffiths &amp; Tenenbaum, 2006). Forty-eight participants
provided activity duration data as they went about their
everyday lives for 14 days. Descriptive analyses and activity
duration modeling (mixture models of gaussian, gamma, normal
and exponential distributions) were used to characterize event
durations within activity types. Results show that most of the
events present an exponential pattern of durations, while others
show a bimodal pattern. Although some preplanned events have
a characteristic time, many daily events have a substantial
exponential component.

CogSci 2022

How long are real-life events?

Research in event cognition has focused on how people perceive
and remember events under experimental conditions. This
research study aims to explore the temporal duration of
self-reported events from daily life (Sreekumar, et al., 2018;
Zhuang, et al., 2012). The small amount of prior work that
exists suggests that daily event durations have a Gaussian
distribution and that people have prior beliefs that reflect this
reality (Griffiths & Tenenbaum, 2006). Forty-eight participants
provided activity duration data as they went about their
everyday lives for 14 days. Descriptive analyses and activity
duration modeling (mixture models of gaussian, gamma, normal
and exponential distributions) were used to characterize event
durations within activity types. Results show that most of the
events present an exponential pattern of durations, while others
show a bimodal pattern. Although some preplanned events have
a characteristic time, many daily events have a substantial
exponential component.

poster

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Mental imagery is theorized to play a key role in mood and mood disorders due to the emotional impact of visualizations and biases in the processing of negative versus positive imagery. Although differences in emotional imagery have been linked to mental health outcomes, it is unclear if individuals experiencing emotional distress differ in their baseline ability to generate mental images (i.e., ‘imagery ability’). Recent research has highlighted linkages between imagery ability and facets of trait mindfulness, such as the tendency to observe and describe inner thoughts. Thus, we suspected that individual differences in trait mindfulness may help explain inconsistent findings regarding the relationship between imagery ability and emotional distress. A path analysis revealed that trait mindfulness significantly and fully mediated the relationship between imagery vividness and depression, indicating that mindfulness is a critical aspect of imagery phenomenology, as well as emphasizing the importance of mindfulness to mental health.

The relationship between individual differences in mental imagery vividness and emotional distress

Transformer-based language representation models, such as GPT-3 and DeBERTa, have yielded state-of-the-art results on several benchmarks in Natural Language Processing (NLP), including question answering, story generation and information extraction. However, little is currently understood about their ability to make (approximately rational) decisions, even when simple bets with clear net-positive, zero-risk choices are presented. We design a suite of multi-option decision-making experiments in the same vein as other classic studies (Kahneman & Tversky, 1979). Applying over 20 structural variants of these ‘benchmarks’ (to test for generalization and robustness) to three established language representation models, we use detailed experiments to show that the models lack fundamental decision-making ability, even after several controls. Techniques such as selective fine-tuning can help improve performance partially, but more fundamental NLP research may be needed before such models can be robustly and generally applied in real-world problem domains where decision making is essential.

Evaluating Language Representation Models on Approximately Rational Decision Making Problems

Compositionality has been argued to a necessary component of interpreting language, yet there appear to be many linguistic phenomena that do not overtly exhibit semantic compositional behavior. One of the challenges involves the phenomena of contextual modulations referred to collectively as semantic coercion or logical metonymy. In this paper, we present a computational model that provides the “compositional flexibility” in the interpretation of a verb with its arguments, for such coercive contexts in English. Specifically, we argue that such constructions typically have surface structural correlates in the form of dense paraphrases, and that these forms can be used to model the masked content in the coerced compositional context. We present preliminary results using a transformer architecture on a masked completion task. Our results show that modeling logical metonymy is a challenging task but can be substantially improved by fine-tuning through dense paraphrasing.

Interpreting Logical Metonymy through Dense Paraphrasing

Limits and risks of GPT-3 applications
Recent GPT-3 applications have fed new optimism in AI research. To explore limits and risks, we investigated how far we can get in developing a so-called digital replica of Daniel Dennett, generating text outputs from a large language model trained on Dennett’s philosophical writing. In consultation with Dennett himself, we compare several fine-tuning strategies and evaluate outputs. Analyzing the failures and the successes will allow us to address technical and ethical issues such as:
1. How accurate can such models be with current technology?
2. To what extent (if at all) might it be acceptable to present a model’s outputs as representing an author’s view?
3. Should copyright holders have control over such models?
4. How one can address risks of making such models public (including possible over-reliance on their accuracy)?
5. How good are experts and non-experts in distinguishing humans from replicas?

Limits and risks of GPT-3 applications

The impressive recent performance of large language models such as GPT-3 has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3 to a classic problem in human inductive reasoning known as property induction. Our results suggest that while GPT-3 can qualitatively mimic human performance for some inductive phenomena (especially those that depend primarily on similarity relationships), it reasons in a qualitatively distinct way on phenomena that require more theoretical understanding. We propose that this emerges due to the reasoning abilities of GPT-3 rather than its underlying representations, and suggest that increasing its scale is unlikely to change this pattern.

Human-like property induction is a challenge for large language models

In an agent-based simulation, we investigate the implications of social interaction and cognitive diversity on creative processes of divergent thinking. Agents performed a verbal association task individually and jointly in pairs. We created pairs of varying cognitive diversity by manipulating properties of the vector spaces defining their semantic memory. We find that cognitive diversity positively stimulates the flexibility of agents’ collective cognitive search, giving rise to higher fluency (more solutions) and originality (more ‘rare’ solutions). While cognitively similar agents tend to exploit local semantic neighborhoods, diversity promotes more explorative search, with longer distances traveled in semantic space. This helps diverse pairs reach more distant areas of semantic space and escape cognitive fixation. However, our model also suggests that too high levels of diversity can have detrimental effects, as overly exploratory behaviors make pairs leave solution saturated areas prematurely and increase the risk of reaching semantic “dead ends”.

Cognitive diversity promotes collective creativity: an agent-based simulation

Pursuing the mechanisms and methods to improve the creative potential in team activities becomes significant in modern society. One candidate of the creative method is LEGO Serious Play (LSP), where intuitive modelling with LEGO blocks, corresponding to Play in LSP, is considered effective in teams’ creative problem-solving. To understand the mechanism for Play to influence creativity in interpersonal interactions, we compared Play (intuitive modelling) and Non-Play modelling with LEGO blocks. Our experiments consisted of two phases, individual and collaborative modelling, and two groups conducted the former phase, intuitive modelling before thinking (Play) and thinking before modelling (Non-Play). We found that Play significantly promoted utterances about abstract concepts during the collaborative phase, while utterances about concrete models showed no difference between the two groups. This finding suggests that the interplay between mentioning abstract concepts and handling concrete objects may be a mechanism of Play as intuitive modelling to improve team creativity.

Play as intuitive modelling for fostering creativity in interacion

What factors induce empathy for virtual agents? To explore this question, we examine the relationship between humans and agents in the inverted cyberball task where a participant chose to help an ostracised agent. This study especially controls experience in the preparation task from self-involvement as the base of empathy. In the one condition, participants have induced the same experience as the ostracised agent, while in the other condition, participants observed ostracising relations between two agents. As a result, self-involvement in the preparation task does not influence the helping behavior of the ostracised agent. The factor relating to the helping behavior is whether the participants noticed the relation. This notice was also associated with empathy trait. From these results, we can conclude that empathy can acquire others' perspectives even when they have no experience with the pain of ostracism.

Effect of self-experience on empathic helping to virtual agents

Inferring others' preferences is one of the central cognitive tasks of social life. The inferred preferences are used to predict the behavior of others in novel situations; therefore, a certain level of abstraction is required. We considered the inference of preferences as a classification problem on an abstract latent feature space. Participants (n=96) were asked to show 89 images of automobiles to either an agent that had been programmed with a specific person's preference and which showed facial expressions according to that preference or an agent that did not show facial expressions. Next, participants were asked to predict the agent's likes and dislikes for novel 23 automobiles. The results showed that participants could predict the agent's likes and dislikes for the unseen automobiles with 74% accuracy, indicating that they learned the decision boundary of agents' preferences on the latent feature space using expressions of emotion as labels.

People learn other's preferences on a latent feature space using emotion expressions as labels

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind.

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