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Text skimming is a common reading behavior that occurs when readers scan text at a faster than normal rate to attempt to form understanding when not able to read at normal speed. Research has suggested that reading time varies across a skimmed text, guided by attention and comprehension goals. However, do individual differences in the ability to manage attention affect skimming? Are those better at managing attention (i.e., high working memory) also better at managing text skimming? Two experiments were conducted where participants who varied in WMC were asked to skim an unfamiliar expository text, both on a computer, and while being eyetracked. In both experiments, while participants did spend more time reading earlier portions of the text, this did interact with WMC. Those higher in WMC balanced their reading efforts more equally across the entire text, suggesting that text skimming behavior is sensitive to differences in WMC.

CogSci 2021

Readers text skimming behavior changes with variation in working memory capacity

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Despite the increase in studies investigating people’s explana- tory preferences in the domains of psychology and philoso- phy, little is known about their preferences in more applied do- mains, such as the criminal justice system. We show that when people evaluate competing legal accounts of the same evidence that vary in complexity, their explanatory preferences are af- fected by: i) whether they are required to draw causal mod- els of the evidence, and ii) the actual structure that is drawn. Although previous research has shown that people can reason correctly about causality, ours is one of the first studies that shows that generating and drawing causal models directly af- fects people’s evaluations of explanations.

The role of causal models in evaluating simple and complex legal explanations

Keeping track of time is essential for everyday behavior. Theoretical models have proposed a wide variety of neural processes that could tell time, but it is unclear which ones the brain actually uses. Low-level neural models are specific, but rarely explicate how cognitive processes, such as attention and memory, modulate prospective and retrospective timing. Here we develop a neurocomputational model of prospective and retrospective timing, using a spiking recurrent neural network. The model captures behavior of individual spiking neurons and population dynamics when producing and perceiving time intervals, thus bridging low- and high-level phenomena. When interrupting events are introduced, the model delays responding in a similar way to pigeons and rats. Crucially, the model also explains why attending incoming stimuli decreases prospective estimates and increases retrospective estimates of time. In sum, our model offers a neurocomputational account of prospective and retrospective timing, from low-level neural dynamics to high-level cognition.

A Neurocomputational Model of Prospective and Retrospective Timing

This paper examines whether advanced law students are resistant to the blame blocking effect—a tendency to assign higher punishments for failed attempts than failed attempts with independent causal chains leading to intended harm (Cushman, 2008). This effect goes against the criminal law principle that intentionally acting towards committing a crime, and not accidental outcomes, determine liability for attempts. To further investigate whether advanced students of law are judging blame blocking scenarios correctly (in line with their legal expertise), in two experiments, we compared their punishment responses with four populations: beginning law students, advanced philosophy students, advanced natural science students, and laypeople with no academic background. We did not observe the blame blocking effect in either of the four student populations, and it was only partially present in the lay population. We discuss the implications of these findings for research on legal expertise and the blame blocking effect in general.

Blame Blocking and Expertise Effects Revisited

The associative theory of creativity proposes that creative ability relies on the organization of semantic memory, yet the relationships between semantic memory structure and brain connectivity, in relation to creativity, remain poorly understood. Here, we explored the relationships between the network properties of individual semantic memory, patterns of functional brain connectivity, and real-life creativity. To this end, we acquired functional magnetic resonance imaging data while participants underwent a semantic relatedness judgment task. Participants’ relatedness ratings between word-pairs were used to estimate their individual semantic networks, whose network properties were significantly related to their real-life creativity. Using a connectome-based predictive modeling approach, we identified patterns of on-task functional connectivity that predicted creativity-related semantic memory network properties in novel individuals. Furthermore, the predicted semantic network properties partially mediated the relationship between functional connectivity and real-life creativity. These results provide new insights on how brain connectivity supports the associative mechanisms of creativity.

Brain Connectivity-Based Prediction of Semantic Network Properties Related to Creativity

In three experiments (total N = 1302) we investigated whether causal proximity affects moral judgments. We manipulated causal proximity by varying the length of chains mediating between actions and outcomes, and by varying the strengths of causal links. We demonstrate that moral judgments are affected by causal proximity with longer chains or weaker links leading to more lenient moral evaluations. Moreover, we identify outcome foreseeability as the crucial factor linking causal proximity and moral judgments. While effects of causal proximity on moral judgments were small when controlling for factors that were confounded in previous studies, knowledge about the presence of causal links substantially alters judgments of permissibility and responsibility. The experiments demonstrate a tight coupling between causal representations, inferences about mental states, and moral reasoning.

A Causal Proximity Effect in Moral Judgment

We present a neural network model of referent identification in a preferential looking task. The inputs are visual representations of pairs of objects concurrent with unfolding sequences of phonemes identifying the target object. The model is trained to output the semantic representation of the target object and to suppress the semantic representation of the distractor object. Referent identification is achieved in the model based only on bottom-up processing. The training set uses a lexicon of 200 words and their visual and semantic referents, reported by parents as typically known by toddlers. The phonological, visual and semantic representations are derived from real corpora.

The model successfully replicates experimental evidence that phonological, perceptual and categorical relationships between target and distractor modulate the temporal pattern of visual attention. In particular, the network captures early effects of phonological similarity, followed by later effects of semantic similarity on referent identification.

A neural network model of referent identification in the inter-modal preference looking task

Learning the contingencies of a complex experiment is hard, and animals likely revise their strategies multiple times during the process. Individuals learn in an idiosyncratic manner and may even end up with different asymptotic strategies. 
Modeling such long-run acquisition requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden Markov model whose latent states capture behaviours. We fit this model to data collected from mice who learnt a contrast detection task over tens of sessions and thousands of trials. Different stages of learning are quantified via the number and psychometric nature of prevalent behavioural states. Our model indicates that initial learning proceeds via drastic changes in behavior (i.e. new states), whereas later learning consists of adaptations to existing states, even if the task structure changes notably at this time.

Exploring learning trajectories with dynamic infinite hidden Markov models

We explore the separation of decision confidence and familiarity components in EEG data from recognition memory experiments. We first develop and test a classifier designed to classify decision confidence on new trials. We then use this classifier to control for confidence in the selection of trials of familiarity and correct rejection. This allows us to reveal a familiarity component that is of similar magnitude for recollection and familiarity judgements. This familiarity component reveals more of a frontal extent than obtained without confidence matching. We believe that this preliminary result can serve as a guide for designing future electrophysiological experiments to better separate the different components of recognition memory and that the technique of using classifiers to control for response-related covariates can be used for early exploration of these components in existing data.

EEG Reveals Familiarity by Controlling Confidence in Memory Retrieval

Modus tollens is a rule of inference in classical, two-valued logic which allows to derive the negation of the antecedent from a conditional and the negation of its consequent. In this paper, we investigate when humans draw such conclusions and what modulates the application of modus tollens. We consider conditionals which may or may not be obligations and which may or may not have necessary antecedents. We show that humans make significantly more modus tollens inferences in case of obligation conditionals and that the time to make a modus tollens inference is shorter than the time to answer ``nothing follows''. We illustrate how these differences can be modeled within the weak completion semantics.

When are Humans Reasoning with Modus Tollens?

What can stories teach us about the real world? How can stories change our behavior? From research on cognitive maps, we know that hippocampal neurons form maps of physical space and time. Here, we propose that the same neural processes underlie engagement with fictional stories: during story processing we create cognitive maps of various domains—spatial, social, moral, etc.—that we can later use for real-world navigation. This perspective can also inspire new methodological tools for assessing story processing, and we give an example showing how a story can be modeled as paths taken on a cognitive map. In summary, we see stories as affording paths that guide our navigation through multiple dimensions of life, which raises the implication that the function of stories, including their moral content, is not only to be understood at the level of abstract and linguistically coherent propositions, but at the fundamental level of navigation.

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The role of causal models in evaluating simple and complex legal explanations

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