ORCID Profile
0000-0001-5375-9967
Current Organisation
University of Sussex
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Publisher: Wiley
Date: 02-09-2022
DOI: 10.1111/EJN.15800
Abstract: 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.
Publisher: Center for Open Science
Date: 06-2022
Abstract: Complexity quantification, through entropy, information and fractal dimension indices, is gaining a renewed traction in psychophysiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. In this study, we systematically compared 125 indices of complexity by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. We propose that a selection of indices, including ShanEn (D), MSWPEn, CWPEn, FuzzyMSEn, AttEn, NLDFD, Hjorth, MFDFA (Width), MFDFA (Max), MFDFA (Mean), SVDEn, MFDFA (Increment), might offer a complimentary choice in regards to the quantification of the complexity of time series.
Publisher: Center for Open Science
Date: 26-10-2021
Abstract: Heart Rate Variability (HRV) can be estimated using a myriad of mathematical indices, but the lack of systematic comparison between these indices renders the interpretation and evaluation of results tedious. In this study, we assessed the relationship between 57 HRV metrics collected from 302 human recordings using a variety of structure-analysis algorithms. We then applied a meta-clustering approach that combines their results to obtain a robust and reliable view of the observed relationships. We found that HRV metrics can be clustered into 3 groups, representing the core variability features, extreme variability features and frequency/complexity features. From there, we described and discussed their associations, and derived recommendations on which indices to prioritize for parsimonious, yet comprehensive HRV-related data analysis and reporting.
Publisher: Center for Open Science
Date: 13-09-2021
Abstract: While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend towards the use of complexity analysis in quantifying neural activity. 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 (1) predictability and (2) 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.
Publisher: MDPI AG
Date: 05-05-2021
DOI: 10.20944/PREPRINTS202105.0070.V1
Abstract: The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiogram (ECG) in research and advancement in sensors technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, posing a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas that these indices have shown to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.
Publisher: Center for Open Science
Date: 10-03-2020
Abstract: The conceptualization of deception as a dispositional trait is under-represented in the literature. Despite scientific evidence supporting the existence of in idual 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 in iduals' lying patterns. Our findings suggest four distinct latent dimensions: frequency, ability, negativity, and contextuality. We establish 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 introduces a general framework to understanding deception as a dispositional trait.
Publisher: MDPI AG
Date: 27-07-2022
DOI: 10.3390/E24081036
Abstract: Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. Using the NeuroKit2 Python software, we computed a list of 112 (predominantly used) complexity indices on signals varying in their characteristics (noise, length and frequency spectrum). We then systematically compared the indices by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. Based on these considerations, we propose that a selection of 12 indices, together representing 85.97% of the total variance of all indices, might offer a parsimonious and complimentary choice in regards to the quantification of the complexity of time series. Our selection includes CWPEn, Line Length (LL), BubbEn, MSWPEn, MFDFA (Max), Hjorth Complexity, SVDEn, MFDFA (Width), MFDFA (Mean), MFDFA (Peak), MFDFA (Fluctuation), AttEn. Elements of consideration for alternative subsets are discussed, and data, analysis scripts and code for the figures are open-source.
Publisher: Springer Science and Business Media LLC
Date: 22-04-2021
Publisher: Center for Open Science
Date: 23-04-2023
Abstract: While a large part of the deception literature focuses on lying detection, the factors contributing to one's ability to lie remain unclear. The present study examined the contribution of Theory of Mind (ToM) and interoception on our ability to lie using a directed lie paradigm with two conditions ("Interrogation" and "Polygraph"), designed to enhance each of the two mechanisms. Using a mixed-design Bayesian analysis, we show that various facets of interoceptive abilities are positively related to the self-rated confidence in one's own lies, especially when under the belief that bodily signals are being monitored (i.e., in the "Polygraph" condition). Beyond providing evidence for the role of the body in lying and raising interesting questions for deception science, these results carry practical implications for criminology and lie detection protocols.
Publisher: SAGE Publications
Date: 11-2021
DOI: 10.1177/03010066211057347
Abstract: 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 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).
Publisher: Springer Science and Business Media LLC
Date: 02-02-2021
Publisher: MDPI AG
Date: 09-06-2021
DOI: 10.3390/S21123998
Abstract: The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Dominique Makowski.