In 1957, linguist J. R. Firth observed that 'you shall know a word by the company it keeps'. That principle — words that co-occur share meaning — is the foundation on which all of generative AI was built, from early Latent Semantic Analysis to today's trillion-parameter Transformers. This post traces the lineage with three interactive LSA-to-PCA visualisations in R (Reuters newswire, State of the Union addresses and IMDB reviews), showing where simple co-occurrence models succeed, where they fail and why scale alone turned a modest insight into the technology behind ChatGPT. It then examines why LLMs are optimised for fluency rather than truth — hallucinations are a structural consequence, not a bug to be patched — and argues that careful prompt engineering is the best tool we have for steering a fundamentally heuristic machine.
A look at the global shift to open science, exploring how transparency, preregistration, and open data are becoming the new standard for credible research, benefiting scientists, funders and society.
Case study showcasing how secure, private transcription at scale can be achieved using Whisper and GitHub Copilot, demonstrating practical applications of AI in research environments while maintaining data privacy and security standards.
A production-ready local transcription workflow leveraging OpenAI's Whisper models that addresses the limitations of cloud-based solutions through complete data sovereignty, unlimited scale, reproducible processing and advanced quality control, while maintaining GDPR compliance.
`4authors-year-doi-url` is a CSL style designed to be as compact as possible while retaining the three most critical pieces of information for a reference: who (authors), when (year), and where to find it (DOI/URL).
Understanding the interplay between speech and gesture is crucial for linguistic and cognitive research. The current prototype, available on GitHub, aims to automate the analysis of temporal alignment between spoken demonstrative pronouns and pointing gestures in video recordings. By integrating computer vision (via Google’s MediaPipe) and speech recognition (using language-specific Vosk models) using Python, the workflow provides enriched video annotations and alignment data, offering valuable insights into deictic communication.
Reducing the impedance in electroencephalography (EEG) is crucial for capturing high-quality brain activity signals. This process involves ensuring that electrodes make optimal contact with the skin without harming the participant. Below are a few tips to achieve this using a blunt needle, electrolyte gel and gentle wiggling.
Researchers often make participants jump through hoops. Due to our personal blind spots, it seems easier to realise the full extent of these acrobatics when we consider the work of other researchers. In linguistic research, the acrobatics are often spurred by unnatural grammatical constructions.
Say, you need to set up a makeshift EEG lab in an office? Easy-peasy---only, try to move the hardware as little as possible, especially laptops with dongles sticking out. The rest is a trail of snapshots devoid of captions, a sink, a shower room and other paraphernalia, as this is only an ancillary, temporary, extraordinary little lab, and all those staples are within reach in our mainstream lab (see Ledwidge et al., 2018; Luck, 2014).
Electroencephalography (EEG) has become a cornerstone for understanding the intricate workings of the human brain in the field of neuroscience. However, EEG software and hardware come with their own set of constraints, particularly in the management of markers, also known as triggers. This article aims to shed light on these limitations and future prospects of marker management in EEG studies, while also introducing R functions that can help deal with vmrk files from BrainVision.
Electroencephalographic (EEG) signals are often contaminated by muscle artifacts such as blinks, jaw clenching and (of course) yawns, which generate electrical activity that can obscure the brain signals of interest. These artifacts typically manifest as large, abrupt changes in the EEG signal, complicating data interpretation and analysis. To mitigate these issues, participants can be instructed during the preparatory phase of the session to minimize blinking and to keep their facial muscles relaxed. Additionally, researchers can emphasize the importance of staying still and provide practice sessions to help participants become aware of their movements, thereby reducing the likelihood of muscle artifacts affecting the EEG recordings.
Part-time research assistant position to help recruit participants and conduct an EEG experiment on language learning and multilingualism at UiT The Arctic University of Norway.
Extension of the rscopus R package with functions that manage search quotas, retrieve DOIs for reference managers, search for additional DOIs, compare publication counts across topics, and visualize bibliometric comparisons over time.
I would like to ask for advice regarding a custom plugin for a serial reaction time task, that was created by @vekteo, and is available in Gorilla, where the code can be edited and tested. By default, trials are self-paced, but I would need them to time out after 2,000 ms. I am struggling to achieve this, and would be very grateful if someone could please advise me a bit.
An Excel workbook template with conditional formatting to facilitate planning, registration, and tracking of sessions in longitudinal studies involving multiple session conductors.
The best argument to motivate a preregistration may be that it doesn’t take any extra time. It just requires frontloading an important portion of the work. As a reward, the paper will receive greater trust from the reviewers and the readers at large. Preregistration is not perfect, but is a lesser evil that reduces the misuse of statistical analysis in science.
Recruitment announcement in Norwegian for participants who speak a Scandinavian language and English (but no other languages) to participate in a paid EEG experiment at UiT The Arctic University of Norway in Tromsø.
Frequently asked questions about mixed-effects models, covering the necessity of random slopes, appropriate p-value calculation methods, parallelization limitations, convergence issues, and optimizer selection.
In the fast-paced world of scientific research, establishing minimum standards for the creation of research materials is essential. Whether it's stimuli, custom software for data collection, or scripts for statistical analysis, the quality and transparency of these materials significantly impact the reproducibility and credibility of research. This blog post explores the importance of adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles, and offers practical examples for researchers, with a focus on the cognitive sciences.