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Ryshan Equilibrium Framework: A Minimal Bridge Between Classical, Einsteinian, and Quantum Physics
No full textThis project presents a minimal equilibrium-based physical framework proposed by an independent high school student.
The work explores how equilibrium dynamics may provide a conceptual bridge between classical mechanics,
Newtonian gravity, weak-field Einstein limits, and quantum harmonic behavior.
This is an exploratory theoretical contribution intended for open scientific discussion and future testing
Semispontaneous speech in speakers of Kalaallisut (West Greenlandic) with non-fluent aphasia
No full textBackground: Few studies in aphasiology have investigated languages with a high degree of morphological complexity, and only one previous study has investigated aphasia in a polysynthetic language. Morphologically complex languages have great potential to inform theories of aphasia in general and agrammatic aphasia in particular. In the present study, we focus on one such language, namely Kalaallisut (West Greenlandic), which has unusually rich inflectional and derivational morphology.
Aims: The present study aimed to describe the semispontaneous speech of speakers of Kalaallisut with non-fluent aphasia with a specific focus on morphosyntactic characteristics. Based on previous studies, we hypothesised that their aphasic speech would consist of relatively slow and short utterances, that morphological features would be less affected than syntactic features, and that verbs would be particularly affected. In case of grammatical errors, we did not expect them to consist in omission of inflectional endings but rather erroneous substitution of other forms.
Methods & Procedures: We elicited semispontaneous speech from 11 Kalaallisut speakers with non-fluent aphasia and 6 speakers without aphasia using pictures and questions about personal experiences. The speech samples underwent extensive morphosyntactic analyses.
Outcomes & Results: Participants with aphasia produced utterances that were slower, shorter, and contained fewer subordinate verbs than those produced by control participants. They also produced shorter words, although this difference was less marked. The derivational morphology appeared relatively spared while the inflectional forms on verbs displayed reduced diversity. Participants with aphasia did not systematically produce morphological errors.
Conclusions: Non-fluent aphasia in Kalaallisut is characterised by slow, short and syntactically simple utterances, which is similar to the characteristics of non-fluent aphasia in other languages. However, the morphology produced by individuals with non-fluent aphasia appears relatively spared which goes against the view that impaired functional morphology characterises non-fluent aphasia. Our findings are best explained by crosslinguistically-informed morphosyntactic account grounded in cognitive mechanisms
Manipulating Prior Causal Beliefs Through Causal Mechanism Information Affects the Outcome-Density Bias
No full textNon-contingent learning data can lead to the (seemingly) illusory perception of causality if the potential cause and effect often co-occur (i.e., the effect prevalence is high) -- an effect known as "outcome-density bias". Bayesian models of causal induction explain this effect as the result of a rational learning process in which the data do not fully override held non-zero causal priors. Convincing evidence of this rational explanation requires the demonstration of an experimentally manipulated effect of causal priors, which has been lacking. We successfully manipulated participants' (N =300) causal priors through a combination of statistical and mechanism information. This manipulation moderated the outcome-density effect in the predicted way and, in one condition, even eliminated it entirely by inducing causal priors close to zero. The results strengthen the rational Bayesian view of causal induction and support computational models that formalize this view
Evaluating Racial Bias in LLM Reasoning: Implications for Equitable AI Use in Education
No full textLarge language models (LLMs) with explicit reasoning capabilities represent an important frontier in artificial intelligence development, yet their potential to perpetuate racial bias through displayed chain-of-thought reasoning processes remains understudied. This study provides systematic examination of how racial bias manifests in the step-by-step reasoning of reasoning models when addressing race-related questions, with particular attention to implications for educational technology applications. Using 3,440 race-related questions from the Bias Benchmark for Question Answering (BBQ) dataset, we evaluated reasoning chains generated by DeepSeek-R1-Distill-Llama-8B using an LLM-as-a-judge approach with GPT-4o. Results revealed that 16.4% of questions exhibited racial bias in their reasoning processes, with bias intensifying through reasoning chains—a pattern we term “bias amplification.” Notably, even correctly answered questions (96.5% accuracy) contained biased reasoning steps, with 10% showing at least slight bias, demonstrating “hidden bias” that conventional output-focused evaluations miss. Questions involving academic merit demonstrated the highest average bias scores, raising particular concerns for AI-assisted grading and student assessment applications. Name-based questions accounted for over half of all biased instances despite showing moderate average bias, suggesting implicit racial cues activate stereotyped reasoning. An inverse relationship emerged between reasoning bias and answer accuracy, suggesting that bias detection could improve model performance through selective abstention. These findings have significant implications for educational technology, where students may internalize not only AI-generated content but also the demonstrated problem-solving approaches. Policy recommendations include developing standards that evaluate AI reasoning processes alongside outputs, implementing pre-deployment bias audits for educational AI systems, and establishing transparency requirements for reasoning-displaying models in classroom settings
Local curcumin delivery systems for bone healing: a scoping review
No full textThis scoping review will map studies that utilize carriers (e.g., scaffolds, hydrogels, coatings, and nanoparticles) for the local administration of curcumin in bone repair and regeneration