United States

A key debate in language learning centers on how people successfully learn the extension of a novel word, despite inherent ambiguity in the input. Across two studies, we tested whether learners reduce ambiguity about a word’s extension by actively sampling the environment. Adult participants were first presented with ambiguous learning situations in which novel words were presented with a set of exemplars that were drawn from a subordinate-level category (e.g., Dalmatians), a basic-level category (e.g., dogs), or a superordinate-level category (e.g., animals). Learners then had the opportunity to sample the label of additional exemplars. Participants systematically adapted their sampling choices as a function of training. Moreover, participants varied in their sampling strategies, pursuing both confirmatory strategies (selecting exemplars similar to the training set) and constraining strategies (selecting exemplars that constrain the word’s extension). Overall, these findings show that learners spontaneously pursue sampling strategies that support generalizing novel word meanings.

CogSci 2023

Active information-seeking in support of learning extensions of novel words

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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. 
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).

Join us!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/tzgk/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

People make subjective judgments about the healthiness of food. These judgments are highly influenced by personal beliefs, associations, and attitudes, as well as group-level cultural constructs. Consequently, people rely upon different latent representations of food when judging what is healthy and deciding what to consume. In this paper, we model shared representations of food healthiness by aligning learned word representations to the consensus among a group of respondents. To do so, we extend Cultural Consensus Theory to include latent constructs structured as fine-tuned word representations. We then apply the model to a dataset of people’s judgments of food healthiness. We show that our method creates a robust mapping between learned word representations and culturally constructed representations that guide consumer behavior.

Predicting judgments of food healthiness with deep latent-construct cultural consensus theory

An influential theory of human fairness has suggested that norms of equity evolved as community sizes grew, markets and institutions stabilized and world religions came about. The theory assumes that humans predominantly interacted with kin in the evolutionary past, lived in genetically related groups and did not have formal norms of cooperation. In this article I present anthropological evidence to the contrary. Contemporary hunter-gatherer societies from around the globe live in nomadic camps with fluid membership and low genetic relatedness; cooperate extensively based on principles of need, equity and reciprocity; have high prevalence of food sharing, cooperative hunting, alloparenting; and demonstrate formal norms of redistribution for essential goods. This directly contradicts the claim that fairness is a recently evolved disposition for market-integrated societies.

Markets, Religion, Community Size and the Evolution of Fairness? Not really

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

The Contribution of Human Cognition and Decision-Making to Understanding the Dynamics of Chemical Manufacturing Process Tasks

Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.


Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation

The public is united in neither understanding global warming’s (GW’s) causes nor its urgency. This experiment assesses a novel text (informally called “Carbon DiLoopy”) intended to spawn science-normative changes in people’s GW beliefs by explaining rising CO2’s negative cognitive effects––without mentioning GW or climate change. Therefore, it represents an indirect way to increase GW concern. Two control/replication texts explained (a) the carbon cycle and (b) human-caused GW’s scientific consensus. Each text, containing roughly 400-words, was assessed regarding its impacts in changing GW beliefs and attitudes. The carbon-cycle control text yielded expected null results. The scientific-consensus text caused gains in general concern about rising atmospheric CO2, and––replicating past studies––in GW acceptance and concern. The novel diLoopy text induced gains in all three measured concern/acceptance dimensions: (1) CO2’s effects on cognition, (2) rising CO2 in general, and (3) GW. Finally, we found no evidence for backfire or polarization effects.

**List of Authors:**

Alex Kihiczak: University of California, Berkeley; Michael Ranney: University of California, Berkeley

CO2 as “Carbon DiLoopy:" Boosting People’s Global Warming Acceptance and Concern by Explaining CO2’s Cognitive Effects

It is a well-known observation that people tend to dislike risky situations that could potentially lead to a loss, a phenomenon that is called loss aversion. This is often explained using valuation bias, i.e., the subjective value of losses is larger than the subjective value of gains of equal magnitude. However, recent studies using the drift-diffusion model have shown that a pre-valuation bias towards rejection is also a primary determinant of loss-averse behavior. It has large contributions to model fits, predicts a key relationship between rejection rates and response times, and explains the most individual heterogeneity in the rejection rates of participants. We analyzed data from three previously published experiments using the drift- diffusion model and found that these findings generalize to them. However, we found that valuation bias plays the most important role in predicting how likely a person is to accept a given gamble. Our findings also showed that a person's loss aversion parameter, lambda, which captures their propensity to avoid losses is closely related to valuation bias. These results combined highlight the importance of valuation bias in understanding people's choice patterns. Finally, using the leaky, competing, accumulator model, we show that valuation bias may be linked to people paying more attention to losses as compared to gains. This finding provides hints about the cognitive underpinnings of valuation bias.

Using Computational Models to Understand the Role and Nature of Valuation Bias in Mixed Gambles

Complex decision making (DM) requires coordination of elementary information processes subserved by a distributed network of brain areas. Computational models help to understand these processes, but most of the existing models focus on simulating only one of the many parallel operations. One existing spiking neural (SNN) model (Duggins, Krzeminski, Eliasmith, Wichary, 2020) attempts to simulate DM holistically, however it does not take into account significant influence of emotional context of DM. To address this limitation, we propose to examine the impact of arousal-related neural gain modulation on DM using the mentioned SNN model. In this study we outline the methodology and perform successful in-silico validation of global gain modulation hypothesis with the SNN model of DM. To perform the simulation, we use a well-studied multi-attribute choice task and we validate simulation results against human behavioral data.

Modeling the impact of arousal on decision making with spiking neural networks

How do high trusters cognitively process integrative information regarding others while interacting with them? This exploratory study investigated the difference between high and low trusters in influencing top-down and bottom-up processes to score points in the ultimatum game against a computer agent. We designed an experiment wherein (1) participants expected their partners to be humans or agents (top-down processes) and (2) agents used one of three different types of algorithmic behavior (bottom-up processes) to propose and respond. We found that high trusters made rational proposals when they believed their opponents were human and when their opponents’ behaviors were ambiguous; they also modulated each other’s points when they believed their opponents were human and when their opponents’ behaviors were altruistic. Results suggest that, in interpersonal communication, high trusters flexibly use both types of cognitive processing based on reciprocity when developing representations of others and deciding on a response.

Behavioral characteristics in general trust: an exploratory laboratory-based analysis using the ultimatum game

Many real-life decisions, such as booking a vacation or selecting a partner, involve relatively cost-free sampling of options up to a terminal decision point, beyond which the choice becomes costly to reverse. Such problems can be formulated as optimal stopping problems (OSPs), such as the famous secretary problem. Although human behavior on optimal stopping problems has been studied extensively, much of the literature has focused on the behavior of individual decision-makers operating using a binary payoff function. In this study, we use an OSP with a continuous payoff function to study how individuals’ decisions differ from the collective decision of groups of three members working together. An independent threshold model offered the best explanation for the behavior of both individuals and groups. We found groups performed significantly better than individuals, with individuals consistently waiting too long to make a choice relative to the optimal strategy. Finally, we also found a lack of long-term learning in OSPs for groups, a trend previously documented in individuals.

Groups are Better than Individuals at Solving Optimum Stopping Problems

The empirical measurement of mental effort is an important problem in the cognitive sciences. Recently, researchers have adopted econometric tools to attempt to characterize mental effort in terms of monetary costs foregone. Such efforts yield a very helpful calculation device - a money utility of mental effort. However, since the opportunity cost of applying mental effort in any given situation is measured with respect to time rather than money in most ecologically reasonable settings, it is even more desirable to obtain a measure of the time utility of mental effort. In the absence of direct measurements of mental effort, such a task has proved econometrically challenging. We use a recently developed direct measure of mental effort to characterize its time utility, finding that it is approximately linear in effort. We discuss some implications of this result for current theories of mental effort, as well as for practical applications.

Premium content

Downloads

Next from CogSci 2023

Predicting judgments of food healthiness with deep latent-construct cultural consensus theory

Similar lecture

How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian