NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of ...bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal ...processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry‐level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
In light of the increasing popularity of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals, we provide an overview of existing EEG complexity measures, broadly categorized as measures of predictability and regularity. We also synthesize complexity findings across different areas of psychological science (e.g., neuropsychiatric disorders and consciousness research), addressing theoretical and methodological issues underlying discrepancies in data.
The conceptualization of deception as a dispositional trait is under-represented in the literature. Despite scientific evidence supporting the existence of individual differences in lying, a ...validated measure of dispositional deception is still lacking. This study aims to explore the structure of dispositional deception by validating a 16-item questionnaire to characterize individuals’ lying patterns. The final sample included 716 participants (Mean age = 25.02; 55.87% females) who were recruited via posters, flyers, and online social media platforms in Singapore. Our findings suggested four distinct latent dimensions: frequency, ability, negativity, and contextuality. We established the convergent validity of our measure by showing significant relationships with social desirability, malevolent traits, cognitive control deficits, normal and pathological personality traits, as well as demographic variables such as sex, age, and religiosity. Overall, the present study introduced a general framework to understanding deception as a dispositional trait.
Visual illusions are fascinating phenomena that have been used and studied by artists and scientists for centuries, leading to important discoveries about the neurocognitive underpinnings of ...perception, consciousness, and neuropsychiatric disorders such as schizophrenia or autism. Surprisingly, despite their historical and theoretical importance as psychological stimuli, there is no dedicated software, nor consistent approach, to generate illusions in a systematic fashion. Instead, scientists have to craft them by hand in an idiosyncratic fashion, or use pre-made images not tailored for the specific needs of their studies. This, in turn, hinders the reproducibility of illusion-based research, narrowing possibilities for scientific breakthroughs and their applications. With the aim of addressing this gap, Pyllusion is a Python-based open-source software (freely available at https://github.com/RealityBending/Pyllusion), that offers a framework to manipulate and generate illusions in a systematic way, compatible with different output formats such as image files (.png, .jpg, .tiff, etc.) or experimental software (such as PsychoPy).
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