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Cue-based retrieval theories of sentence processing assume that subject-verb dependencies are resolved through a content-addressable search in memory. The model assumes that multiple nouns with similar syntactic or semantic features increase dependency completion difficulty. English eyetracking data (reading) are consistent with model predictions; interestingly, a similar experiment with German --a language marking case overtly-- suggests that only syntactic features affect dependency completion difficulty. Why would German show different behavior than English? Using a computational implementation of the cue-based retrieval model and model comparison using Bayes factors, we show that the reason is systematic variation at the individual-participant level: German participants overwhelmingly give higher weighting to syntactic cues over semantic cues, whereas English participants mostly give equal weighting to syntactic and semantic cues. The richer morphosyntax of German leads to syntactic cues being favoured; if such cues are largely absent (as in English) the parser relies on both cue types equally.

CogSci 2022

Individuals differ cross-linguistically in cue weighting: A computational evaluation of cue-based retrieval in sentence processing

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. 
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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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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)
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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. 





Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene’s odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene’s test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene’s test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry.

Symmetry as a Representation for Intuitive Geometry?

Within-species variability is a fundamental biological concept, essential in understanding advanced concepts like evolution. Students formally encounter variability in their study of genetics in 4th grade; however, children’s cognitive biases can constrain their learning of variability. Diagrams are often used to facilitate learning of biological concepts, so it is important to understand how genetic inheritance diagrams depict variability. We analyzed 647 diagrams collected from textbooks and websites to examine how much variation diagrams present. We coded (1) depictions of within-species variability, and (2) other perceptual features implicated in learning (e.g., concreteness, richness). Preliminary findings indicate that many diagrams depict some within-species variability. Additionally, most diagrams used abstract representations of organisms and traits, which research suggests may be less optimal for younger students’ learning but may encourage transfer in older students. We will discuss how diagram features may reinforce children’s cognitive biases and may influence students’ learning of inheritance and variability.

Perceptual Features in Visual Representations: A Content Analysis of Inheritance Diagrams

Path integration, the ability to maintain an estimate of one's location by continuously integrating self-motion cues, is a vital component of the brain's navigation system. We present a spiking neural network model of path integration derived from a starting assumption that the brain represents continuous variables, such as spatial coordinates, using Spatial Semantic Pointers (SSPs). SSPs are a representation for encoding continuous variables as high-dimensional vectors, and can also be used to create structured, hierarchical representations for neural cognitive modelling. Path integration can be performed by a recurrently-connected neural network using SSP representations. Unlike past work, we show that our model can be used to continuously update variables of any dimensionality. We demonstrate that symbol-like object representations can be bound to continuous SSP representations. Specifically, we incorporate a simple model of working memory to remember environment maps with such symbol-like representations situated in 2D space.

A model of path integration that connects neural and symbolic representation

A recent sampling-based model of causal cognition, the Mutation Sampler, can explain the average responses of participants on a wide variety of tasks (Davis & Rehder, 2020). Careful analysis, however, shows that the predicted response distributions do not match empirical data. As an explanation for the observed response distributions, we propose that people engage in mutation sampling when confronted with a causal query and subsequently integrate this information with a prior. Several theoretical arguments can be made for the notion that people use priors in causal judgements; it is rational and matches the phenomenology of making such judgements. We have modelled this process with a new model called the Bayesian Mutation Sampler (BMS). We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple ‘distributional phenomena’, such as the moderate conservatism of the bulk of responses and the lack of extreme responses.

Explaining Full Response Distributions in Causal Reasoning Tasks: The Bayesian Mutation Sampler

Many of the real world phenomena that cognizers must grapple with are continuous, not only in the values they can take, but also in how these values change over time. The mind must somehow abstract from these inputs to extract useful discrete concepts such as objects, events and causal relationships. We investigate several factors that affect basic inferences about causal relationships between continuous variables based on observations in continuous time. In a novel experiment, we explore the ways in which causal judgments are sensitive to factors that relate to causal inductive biases (e.g. causal lags, the direction of variation) and causal perception (e.g. the range and rapidity of variation). We argue standard statistical time-series models have limited utility in accounting for human sensitivity to these factors. We suggest further work is needed to fully understand the cognitive processes that underlie causal induction from time-series information.

Intuitions and Perceptual Constraints on Causal Learning from Dynamics

When assessing the quality of a machine, people might consider the machine’s outputs—how well it serves its function. Alternatively, people might also consider the efficiency of the machine. We investigated this possibility in two experiments (N = 392). In each experiment, participants saw pairs of machines, one with simple inside parts and one with more complex inside parts. Machines either had the same output or unknown outputs, and people judged which of the two machines was better. When the machines had the same output, participants in both experiments judged that machines with simpler inside parts were better than ones with more complex insides. However, when machines’ functions were unknown, people predominantly judged that machines with complex insides were better. Together, our work shows that people consider both parts and functions of machines when inferring quality.

Using efficiency to infer the quality of machines

Human learning and generalization benefit from bootstrapping: we arrive at complex concepts by starting small and building upon past successes. In this paper, we examine a computational account of causal conceptual bootstrapping, and describe a novel experiment in which the sequence of training data results in a dramatic order effect: participants succeed in identifying a compound concept only after experiencing training data in a “helpful” order. Our computational model represents causal relations as reusable, modular programs, which can themselves be “chunked” and flexibly reused to tackle more complex tasks. Our specific approach is based in combinatory logic and adaptor grammars, building on previous theories that posit a “language of thought” for concept representation, but making the learning process more sensitive to a learner’s experiences than any particular choice of conceptual primitives. Crucially, we demonstrate that a caching mechanism like that used in adaptor grammars is key to explain human-like bootstrapping patterns in causal generalization.

Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars

This study aimed to characterize the developmental trajectories of different cognitive component processes underlying planning decisions. Participants (ages 8-25 years) completed a planning task called Four-in-a-row. We used computational modeling to distinguish between three cognitive component processes of planning: planning depth, heuristic quality, and attentional oversights, each of which three contributed to better playing strength, but differed in their developmental trajectories. Specifically, from early to mid-adolescence, heuristic quality rapidly improved and contributed to better playing strength. From mid to late-adolescence, planning depth increased and supported better playing strength. Fewer attentional oversights were associated with better playing strength and this relation did not show age differences. Together, these results reveal sequential development of the cognitive component processes underlying planning, with early refinement of heuristic strategies, and gradual increases into young adulthood in the number of considered future actions, states, and outcomes. These findings provide a more complete account of the development of planning and its component processes.

Distinct Developmental Trajectories In The Cognitive Components Of Complex Planning

As the history of science has documented, there is an important role for thought experiments in scientific progress. Yet, there is very little empirical research about whether and how children learn from thought experiments. Here, we asked that question in the context of 6-year-olds’ developing theory of matter. At the outset of the study, over half of the children claimed that small pieces of matter weigh nothing at all. Children were randomly assigned to a Real (RE) and a Thought Experiment (TE) condition. The goal of each condition was to show – via demonstration in the RE and via mental simulation in the TE – that the weight of a single grain of rice can cause a card resting on a fulcrum to topple. We found that children simulated accurately in the TE, and they changed their judgments and justifications concerning the weight of small pieces equally from the TE and RE.

Young children learn equally from real and thought experiments

Research has documented children’s understanding that a choice made when constrained to a single option is a poor indicator of another person’s preference. However, when constraints are constant over time—as they often are in social contexts—they may lose their salience. We examined whether children (N = 133, 5- to 12-year-olds) were more likely to refrain from inferring that a constrained actor prefers their choice if they first observe unconstrained actors (Alternatives condition) compared to if they only observe constrained actors (Constant condition). Presence of alternatives was crossed with constraint type: either the second option was hard to access or there was no other option. In line with our predictions, results indicated that observing alternative situations with greater choice increased children’s subsequent attention to constraints. Effects were stronger for the hard to access constraint and for older children.

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Symmetry as a Representation for Intuitive Geometry?

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