Detecting behavioural oscillations with increased sensitivity: A modification of Brookshire's (2022) AR-surrogate method DOI Creative Commons
Anthony M. Harris, Henry A. Beale

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 23, 2024

Abstract A core challenge of cognitive neuroscience is to understand how cognition changes over time within the same individual. For example, tendency for behavioural responses in a range domains oscillate has been studied extensively. Recently, however, phenomenon oscillations called into question by indications that past findings might reflect aperiodic temporal structure rather than true oscillations. Brookshire (2022) proposed methods control while examining time-courses and found no evidence reanalyses four published datasets. However, Brookshire’s method criticised having low sensitivity detect effects realistic magnitude, so it currently unclear whether these suggest are not present perhaps many other datasets, or they false negatives. Here, we modification AR-surrogate with increased adequate positives, desirable properties such as ability increase statistical power adding more participants. Using this method, reanalyse publicly available datasets show significant each them, suggesting behaviour robust upon which draw theoretical inferences. The participant-level most sensitive analysing controlling contribution data fluctuations.

Язык: Английский

Detecting behavioural oscillations with increased sensitivity: A modification of Brookshire’s (2022) AR-surrogate method DOI Open Access
Anthony M. Harris, Henry A. Beale

Опубликована: Апрель 24, 2025

A core challenge of cognitive neuroscience is to understand how cognition changes over time within the same individual. For example, tendency for behavioural responses in a range domains oscillate has been studied extensively. Recently, however, phenomenon oscillations called into question by indications that past findings might reflect aperiodic temporal structure rather than true oscillations. Brookshire (2022) proposed methods control while examining time-courses and found no evidence reanalyses four published datasets. However, Brookshire’s method criticised having low sensitivity detect effects realistic magnitude, so it currently unclear whether these suggest are not present perhaps many other datasets, or they false negatives. Here, we modification AR-surrogate with increased adequate positives, desirable properties such as ability increase statistical power adding more participants. Using this method, reanalyse publicly available datasets show significant each them, suggesting behaviour robust upon which draw theoretical inferences. The participant-level most sensitive analysing controlling contribution data fluctuations.

Язык: Английский

Процитировано

0
Detecting behavioural oscillations with increased sensitivity: A modification of Brookshire’s (2022) AR-surrogate method DOI Open Access
Anthony M. Harris, Henry A. Beale

Опубликована: Апрель 24, 2025

A core challenge of cognitive neuroscience is to understand how cognition changes over time within the same individual. For example, tendency for behavioural responses in a range domains oscillate has been studied extensively. Recently, however, phenomenon oscillations called into question by indications that past findings might reflect aperiodic temporal structure rather than true oscillations. Brookshire (2022) proposed methods control while examining time-courses and found no evidence reanalyses four published datasets. However, Brookshire’s method criticised having low sensitivity detect effects realistic magnitude, so it currently unclear whether these suggest are not present perhaps many other datasets, or they false negatives. Here, we modification AR-surrogate with increased adequate positives, desirable properties such as ability increase statistical power adding more participants. Using this method, reanalyse publicly available datasets show significant each them, suggesting behaviour robust upon which draw theoretical inferences. The participant-level most sensitive analysing controlling contribution data fluctuations.

Язык: Английский

Процитировано

0
Detecting behavioural oscillations with increased sensitivity: A modification of Brookshire's (2022) AR-surrogate method DOI Creative Commons
Anthony M. Harris, Henry A. Beale

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 23, 2024

Abstract A core challenge of cognitive neuroscience is to understand how cognition changes over time within the same individual. For example, tendency for behavioural responses in a range domains oscillate has been studied extensively. Recently, however, phenomenon oscillations called into question by indications that past findings might reflect aperiodic temporal structure rather than true oscillations. Brookshire (2022) proposed methods control while examining time-courses and found no evidence reanalyses four published datasets. However, Brookshire’s method criticised having low sensitivity detect effects realistic magnitude, so it currently unclear whether these suggest are not present perhaps many other datasets, or they false negatives. Here, we modification AR-surrogate with increased adequate positives, desirable properties such as ability increase statistical power adding more participants. Using this method, reanalyse publicly available datasets show significant each them, suggesting behaviour robust upon which draw theoretical inferences. The participant-level most sensitive analysing controlling contribution data fluctuations.

Язык: Английский

Процитировано

0