Canada

The paper investigates whether English native speakers (NSs)’ and non-native speakers (NNSs)’ online processing of prepositional phrases were primed by schematic diagrams and whether the priming effects were modulated by different prepositions and senses. 125 adult NS and NNS participants judged on over and in phrases that encode spatial and extended senses in a semantic priming task. Results showed an inhibitory effect of diagrams on NNSs’ processing but a marginal facilitatory effect on NSs’ processing. In addition, the inhibitory effect of diagrams only applied to the processing of extended senses, but not spatial senses. The current findings indicate that NNSs’ processing of English PPs could be negatively influenced by schematic diagrams particularly when the target phrases are more complex (Boers, 2011), presumably due to NNSs’ more limited cognitive capacity of processing complex second language expressions under time pressure than NSs.

CogSci 2022

Schematic Diagrams and Semantic Processing of English Prepositional Polysemy

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

Registration Is Required

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. 





The paper explores the impact of social role assignment and the corresponding payoff distribution on cooperation in the Prisoner’s dilemma following the types of relations according to Fiske’s relational models’ theory: communal sharing, authority ranking, equality matching, and market pricing. The corresponding roles and payoff distribution are teammates (each player receives the sum of the payoffs), partners (each player receives half of the sum of the payoffs), boss and subordinate (the boss receives 2/3 and the subordinate 1/3 of the sum of the payoffs), and opponents (each player receives the standard payoff). The results show that in the teammates’ and partners’ conditions cooperation was significantly higher than in the other conditions. Surprisingly, the results for the boss and subordinate condition although sharing a similar payoff distribution rule to the teammates’ and partners’ conditions were more similar to the opponents’ condition with significantly lower cooperation rate.

Can Social Relations Influence Cooperation in Prisoner’s Dilemma?

The Shapley value is one of the most important normative division schemes in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans.
In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms.
Given any coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. 
Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well.
We run an experiment with 630 human participants and show that when applying our method, humans perceive the Shapley-based payoff allocation as more fair than the Shapley-based payoff allocation without any explanation or with explanations generated by other methods.

Improving the Perception of Fairness in Shapley-Based Allocations

Linguistic similarities increase cooperative behavior between people, while linguistic differences can reduce cooperativeness, and even promote aversion towards outsiders. This may not only be true for sharing the same native language, but also for more subtle similarities such as using similar syntactic constructions. In our experiment, participants communicated with two interaction partners (which were in fact standardized chat bots), one of which syntactically aligned their language with the participants and one of which didn’t. Afterwards, participants decided with which of the two partners they would like to play a cooperation game that determined the financial bonus that they would receive for participation. Against our predictions, linguistically aligned communication partners were not preferred as cooperation partners over non-aligned communication partners. This suggests that formal linguistic similarity may be less relevant for the outcome of negotiations than previously expected, and has real-life implications for conflict resolution, especially in intercultural communication settings.

Does linguistic alignment promote cooperation?

The Covid-19 pandemic altered workplaces. For those with ‘office jobs,’ this meant working ‘virtually,’ or remotely, from home. This transition forced organizations and workplaces to exercise flexibility, adapt workflows and rely on Information and Communication Technologies (ICTs) to work remotely. However, Blind and Partially Sighted Individuals (BPSI) face challenges accessing work digitally and remote communications through ICTs. In response, we report on the results of our longitudinal participatory design study investigating the impact of working and training over a distance for BPSI. What emerged is a conceptual model to assist in understanding how ICT interfaces convey spatial-topological cues for the construction of shared intentionality in virtual work environments. The implications of our model could be significant, as it aids understanding of what is lost and gained when transitioning to virtual work environments. This could inform the development of ICTs with cross-sensory interaction and national accessibility policies for the workplace.

How Virtual Work Environments Convey Perceptual Cues to Foster Shared Intentionality During Covid-19 for Blind and Partially Sighted Employees

The purpose of this paper is to present a novel conceptualisation of an intuitive, primitive form of trust termed proto-trust. This concept is proposed in order to account for the many different senses, types and domains in which trust has traditionally been defined and theorised. A brief review of the literature on affective and intuitive trust is presented, informing the definition and formalisation of proto-trust. Following this, a preliminary empirical investigation of proto-trust is described, where intuitive trust assessments are compared to analytical trust decisions, under various attribution prompts. Results showed effects of attribution prompts on changes to trust assessments from intuitive to deliberative decisions. In addition, qualitative data are presented for the various reasons participants gave for their trust decisions. One of these reasons (emotional reaction) was found to affect the degree of difference between intuitive and deliberative trust assessments.

Proto-trust and trust attribution: a theory of intuitive, affective forms of trust and the means by which trust decisions are made

The external provision of examples has proven the most successful approach to aid learning and application of declarative concepts (i.e., abstract concepts denoted by key terms and short definitions that can be applied to a wide variety of scenarios). The current experiment sought to further this line of research by exploring the effect of using thematically varied examples on learners' ability to classify novel exemplars and near misses of five system principles that cut across thematic domains. Results revealed that thematic variation increased learners’ ability to reject near-misses and, more crucially, to classify novel exemplars from domains not covered by the studied examples. The fact that this enhanced flexibility was unaccompanied by poorer performance in rejecting near misses or classifying new items from domains covered by the learned examples renders this strategy readily applicable in instructional settings. We end by discussing possible mechanisms that could potentially explain the observed advantage of thematically varied examples.

Defending Diversity: Providing Examples from Different Domains Enhances Application of System Principles Beyond the Domains Covered by the Examples

The learning of rule-plus-exception categories relies on pattern integration and differentiation, but how the representations of rule-followers and exceptions develop through these two operations remains obscure. Here, we inspected the representational shifts in rule-plus-exception category learning by fitting a computational model to behavioral categorization data. We found that exceptions were differentiated from rule-followers within and between categories through learning. The distanced rule-follower and exception representations in each category formed distinct clusters that together constituted a hierarchically structured categorical representation. Moreover, exception learning increased the representational overlap between rule-followers of opposite categories, thereby blurring the category boundary. Our findings illuminate the representational dynamic underlying the acquisition of rule-plus-exception categories and highlight the roles of pattern integration and differentiation in category learning.

Differentiating Exceptions in Rule-Plus-Exception Category Learning

Human behavior is modulated by financial incentives, but it is not well understood what types of behavior are immune to incentive and why. The cognitive processes underlying behavior appear to create restrictions on the effect an individual's motivation will have on their performance. We investigate a classic category learning task for which the effect of financial incentives is still unknown (Shepard, Hovland, & Jenkins, 1961). Across four renditions of a category learning experiment, we find no effect of incentive on performance. On a fifth experiment requiring category recognition but not learning, we find a large effect on response time and small effect on task performance. Humans appear to selectively apply more effort in valuable contexts, but the effort is disproportionate with the performance improvement. Taken together, the results suggest that performance in tasks which require novel inductive insights are relatively immune to financial incentive, while tasks that require rote perseverance of a fixed strategy are more malleable.

Rule discovery performance unchanged by incentives

Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly how small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot'' learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems.

Can Humans Do Less-Than-One-Shot Learning?

Paradigms using the free recall of word lists have furthered our understanding of the organizational structure of memory by elucidating the role of contextual associations on memory search. We adapted the traditional word list-learning paradigm to investigate whether emphasizing contextual associations between items influences subsequent retrieval. Specifically, we introduced a review period between encoding and recall of word lists where items were repeated to highlight either the temporal or semantic associations at encoding. We found that temporal review led to stronger temporal clustering compared to a semantic or control review, and semantic review led to stronger semantic clustering compared to a temporal or control review. Moreover, participants recalled more list items when semantic associations were emphasized, with the degree of semantic clustering at recall predicting memory performance. These results demonstrate that emphasizing contextual associations during a repeated viewing after initial encoding can affect subsequent memory organization and recall.

Premium content

Downloads

Next from CogSci 2022

Can Social Relations Influence Cooperation in Prisoner’s Dilemma?

Similar lecture

Evaluating LLM-Generated Diagrams as Graphs