Open Mind-Discoveries in Cognitive Science
Open Mind-Discoveries in Cognitive Science(英文缩写 OPEN MIND-CAMBRIDGE),ISSN 2470-2986,eISSN 2470-2986 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
Open Mind-Discoveries in Cognitive Science 最新收录文献
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1. Modeling Memory Effects in a Head-Final Language With Category Locality.
PMID:日期:2026-08-21Memory limitations have been assumed to be a major factor shaping human sentence comprehension, but characterizing memory-demanding structures in a broad-coverage and cross-linguistic manner has remained a challenge. A recent study suggested that such a general characterization can be obtained by assuming efficient compression of the syntactic information from the input guided by a logic-based grammar. Specifically, it was shown that the memory cost of syntactic integration measured in terms of Combinatory Categorial Grammar (CCG) accounts for variance in reading times for naturalistic texts in English, unlike the more traditional dependency-based integration cost. The crucial test for the new theory is yet to be done, however, since the predictions of CCG- and dependency-based integration cost most drastically diverge in head-final languages. This study conducts this crucial test using a naturalistic reading time dataset in Japanese. Our results favor CCG over dependency grammar: CCG-based integration cost predicted reading time variance of held-out data, while the dependency-based version failed to show such a predictive power. Our results, combined with previous ones, suggest that the logic-based grammar captures some cross-linguistic, potentially universal aspect of human sentence comprehension.
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2. The Effect of Hearsay on Speaker Commitment: An fEMG Study.
PMID:日期:2026-08-21When reporting speech, speakers not only relay information, but may also signal their degree of commitment to that information. This study aims to gather reliable empirical data on the role of so-called reportative evidentials (like 'I heard that Jack is a bachelor') in impacting the degree of perceived speaker commitment (here, their commitment to the truth of Jack being a bachelor). To measure perceived speaker commitment we use the physiological method of or fEMG. We show how this method can be used to trace perceived norm violations in conversation, from which we can then derive the commitments attributed to the discourse participants. In addition to comparing assertions with and without the use of reportative evidentials, we zoom in on the differences between two common reportative constructions: embedding ('I heard that Jack is a bachelor') versus parenthetical ('Jack is a bachelor, I heard'). In the literature there is controversy as to whether these two constructions have the same effect on speaker commitment. Results show that the use of reportative evidentials indeed lowers (but does not fully remove) perceived speaker commitment, but do not provide evidence that the two constructions have different effects. The findings can be understood within discourse frameworks that both model the contents of what is said and the degree of commitment to these contents.
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3. On Benchmarking Human-like Intelligence in Machines.
PMID:日期:2026-08-21Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition. However, we argue that many current evaluation paradigms for AI are insufficient for assessing human-like cognitive capabilities in these models. We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically invalid tasks. We support our claims by conducting a human evaluation study on nine existing AI benchmarks, suggesting major limitations in task and label designs. To address these limitations, we propose five concrete recommendations for future AI evaluation efforts that will enable more rigorous and meaningful understanding of human-like cognitive capacities in AI models.
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4. Child-Directed Speech Is Semantically Constrained: Evidence From Metaphoricity and Concreteness.
PMID:日期:2026-08-21Child-directed speech (CDS) is widely theorized to provide a structured scaffold for early language acquisition, yet the semantic properties of this register remain insufficiently quantified. In particular, it remains unclear how figurative language and semantic grounding interact within caregiver input to stabilize early form-meaning mappings. We present a large-scale computational analysis of metaphoricity and lexical concreteness in a naturalistic corpus of CDS, evaluated against a size-matched baseline of adult-directed speech (ADS). Using a token-level metaphor detection model and human-indexed semantic norms, we test two primary accounts: the reduced metaphoricity hypothesis (H1), which predicts a systematic reduction of nonliteral expressions in caregiver input, and the lexical concreteness hypothesis (H2), which predicts an intensified reliance on perceptually grounded vocabulary. Cross-register comparisons revealed robust support for both accounts: CDS contained a significantly lower density of metaphorical expressions and a higher baseline of both continuous and categorical lexical concreteness than ADS. Longitudinal mixed-effects models further showed that these semantic constraints undergo gradual, age-dependent tuning. As children mature, caregivers appear to relax these constraints incrementally, diversifying their figurative repertoire and reducing overall vocabulary concreteness over time. Taken together, these findings suggest that the semantic structure of caregiver speech functions as a dynamic, responsive learning environment. By initially favoring concrete, transparent, and literal forms, CDS may reduce interpretive ambiguity during early phases of reference anchoring before gradually shifting toward the more abstract and flexible lexical patterns characteristic of adult speech.
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5. When the City Speaks: How Urban Form Shapes the Cultural Transmission of Spatial Conceptualization.
PMID:日期:2026-08-13How do urban environments shape the way people conceptualize and communicate space? Using a cultural transmission experiment in virtual reality, we asked participants to navigate one of two contrasting cityscapes-a regular, grid-like "Manhattan" or an irregular, landmark-rich "Barcelona"-and to produce route descriptions and sketch maps. We examined participants' spatial conceptualizations and tracked how spatial communication was transmitted across six simulated generations. Results show that city layout shaped spatial language and map production. Participants used more horizontal (e.g., numbered routes and intersections) descriptors in the "Manhattan" environment and more vertical (e.g., buildings distinguished by colors and textures) descriptors in the "Barcelona" environment. These strategies were also more effective: participants completed navigation tasks faster when the descriptions they received matched the structure of the environment. We observed robust cultural transmission of spatial conventions, with participants' productions predicted by the linguistic input they received. However, rather than showing cumulative elaboration over time, these conventions stabilized quickly. Individual-level analyses revealed consistency between language and maps, as well as across environments: participants formed coherent spatial models that persisted and shaped adaptation to a new cityscape. This suggests that individual cognitive stability may act as a mechanism for preserving, rather than progressively refining, cultural conventions. Together, these findings highlight urban environments as cognitive-cultural niches that shape how people perceive, conceptualize, and communicate space. We discuss implications for theories of spatial language, cultural evolution, and urban design, showing how city layout shapes not only navigation behavior, but also the structure of spatial thought.
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6. Confidence Is Detection-Like in High-Dimensional Spaces.
PMID:日期:2026-08-13Confidence estimates are often "detection-like"-driven by positive evidence in favour of a decision. This empirical observation has been interpreted as showing that human metacognition is limited by biases or heuristics. Here, we show that Bayesian confidence estimates also exhibit heightened sensitivity to decision-congruent evidence in higher-dimensional signal detection theoretic spaces, leading to detection-like confidence criteria. This effect is due to a nonlinearity induced by normalisation of confidence by a large number of unchosen alternatives. Our analysis suggests that detection-like confidence is rational when participants consider a greater number of hypotheses than assumed by the experimenter. Further, we show that a similar dimensionality-driven mechanism can give rise to and modulate the strength of the positive evidence biases in convolutional neural networks, linking our signal detection theoretic analysis to confidence behaviour in artificial systems.
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7. On the Robustness of Modeling Grounded Word Learning Through a Child's Egocentric Input.
PMID:日期:2026-08-09What insights can machine learning bring to understanding human language acquisition? Large language and multimodal models have achieved remarkable capabilities, but their reliance on massive training datasets creates a fundamental mismatch with children, who succeed in acquiring language from comparatively limited input. To help bridge this gap, researchers have increasingly trained neural networks using data similar in quantity and quality to children's input. Taking this approach to the limit, Vong et al. (2024) showed that a multimodal neural network trained on 61 hours of visual and linguistic input extracted from just one child's developmental experience could acquire word-referent mappings. However, whether this approach's success reflects the idiosyncrasies of a single child's experience, or whether it would show consistent and robust learning patterns across multiple children's experiences was not explored. In this article, we applied automated speech transcription methods to the entirety of the SAYCam dataset, consisting of over 500 hours of video data spread across all three children. Using these automated transcriptions, we generated multi-modal vision-and-language datasets for both training and evaluation, and explored a range of neural network configurations to examine the robustness of simulated word learning. Our findings demonstrate that networks trained on automatically transcribed data from each child can acquire word-referent mappings, generalizing across videos, children, and image domains. These results validate the robustness of multimodal neural networks for grounded word learning, while highlighting the individual differences that emerge in how models learn when trained on each child's developmental experiences.
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8. Large Language Models as Distributional Baselines for Language Tasks.
PMID:日期:2026-07-18In order to ask questions about the mechanisms underpinning human cognition, researchers must control for properties of stimuli that could confound detected effects. In experiments involving linguistic stimuli, this includes properties like frequency, length, and neighborhood size of those stimuli, which are known to affect behavioral and neural responses. With improvements in the performance and usability of language models, it is now possible to also control for how predictable stimuli and their parts are, on the basis of the distributions of words alone: their . This coincides with a resurgence of interest in the possibility that statistical language learning may underlie a broad range of human cognitive phenomena; indeed, there are both theoretical and empirical reasons to believe that humans rely on distributional information during certain cognitive tasks. This creates a confound, whereby experimental operationalizations of psychological constructs with linguistic stimuli may not in fact be testing what they are intended to test. Thus, the central contributions of this paper are twofold: first, we articulate the conditions under which distributional predictability threatens the internal validity of an experiment; and second, we provide concrete recommendations for how to control for this potential confound. Beyond these primary contributions, we survey techniques for measuring distributional predictability, review theoretical and empirical work supporting the role of distributional statistics in human cognition, and present several case studies illustrating the range of possible outcomes-from the "distributional baselines" only marginally affecting theoretical inferences to constituting fully deflationary confounds. We also enumerate and address potential objections to this approach. This paper is primarily intended for researchers in psychology, cognitive science, and linguistics who use linguistic stimuli but have not yet incorporated distributional baselines into their work.
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9. Agreement Marking May Facilitate Noun Class Learning in Children But Not Adults.
PMID:日期:2026-07-17Agreement, defined as the systematic covariance between elements (e.g., between the 3rd person singular subject and the -s verb ending in 'she writes'), is present in many languages despite introducing redundant complexity. A prominent hypothesis suggests that the ubiquity of complex morphological features such as agreement might be attributable to a functional advantage it provides specifically to child learners (Lupyan & Dale, 2010). We tested this hypothesis through two artificial language learning experiments involving both children and adults. In both studies we compared noun class learning in artificial languages with and without agreement marking. While no overall advantage of agreement in noun classification was observed, our findings indicate that agreement may have a differential effect on children and adults. Specifically, agreement may facilitate the generalization of noun classification to novel nouns in children, but not in adults. These findings add to the growing body of evidence that highlights differences in the cues children and adults rely on in language acquisition. We discuss the implications of these results for theories of language acquisition and language change.
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10. POPKI: Modeling Surprise in a Physically-Grounded Joint Inference of Observation, Preference, and Knowledge.
10. POPKI：在观察、偏好和知识的物理联合推理中模拟惊喜PMID:日期:2026-07-17People naturally take into account others' limited perceptual access when attributing mental states such as preferences and knowledge. This ability is central to intuitive psychology, and even infants recognize when others have only partial observability. Understanding how mental state attributions adapt under limited perceptual access is thus crucial for advancing models of intuitive psychology. Here, we introduce POPKI (Physically-grounded Observation, Preference, and Knowledge Inference), a Bayesian inverse-planning framework designed to model graded surprise based on agents' inferred preferences and knowledge in visually constrained 3D environments. Building on and expanding the AGENT dataset, we develop new scenarios that test how partial observability shapes judgments of surprise. Despite the tremendous progress of state-of-the-art vision-language models (VLMs), our experiments show that they fail to capture the nuances of perceptual access when judging surprise. POPKI, in contrast, offers a principled approach to modeling human-like uncertainty under partial observability and closely replicates human surprise patterns. It provides a foundation for advancing psychological reasoning models and enhancing human-AI interaction.