Paediatric palliative medicine

Lück, Patricia

doi:10.7196/samj.8428

Cited by 6 publications

(18 citation statements)

References 1 publication

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“…S1 ). The right-skewed distribution of RT should lead to a larger within-frames-averaged ERF amplitude where RT variance is small (short RT) and to a smaller average ERF amplitude where RT variance is large (large RT) 25 . Therefore, the distribution of the early negative clusters over motor-related areas can be seen as a sanity check that supports our correlation-based analysis strategy.…”

Section: Resultsmentioning

confidence: 99%

“…4b, right) are in agreement with the probability-sensitive ERP component P300. The P300 (P3b) originates from parietal areas 25,40 and is commonly observed in the time span around 400 ms [19][20][21]23,31 . In contrast, the N2c component, which is also sensitive to probability 24 , is argued to originate from frontal cortex 41 and peaks earlier than the P300, i.e.…”

Section: Event-related Field Components -Correlation With Rt At the S...mentioning

confidence: 99%

“…Thus, it may be that in temporal-probabilistic inference, the brain does not benefit from adaptive stimulus-related processing in early cortical areas. A more straightforward reason for the absence of effect may be that these early components decrease in amplitude as the interval between two stimuli decreases 25 which may interfere with putative effects of probability density. In sum, the analysis of time-locked data did not produce evidence for early cortical stimulus processing that is driven by event probability density.…”

Section: No Effect Of Probability Density On Early Erf Componentsmentioning

confidence: 99%

“…The posterior N2 (N2c) is another ERP component that is sensitive to target probability 24 and is associated with stimulus categorization. These two late components often occur after a response was given 25 which relates these findings less readily to anticipatory processes. Examples of pre-stimulus ERP components include the stimulus-preceding negativity (SPN) 26 , a signal argued to reflect stimulus anticipation, the lateralized readiness potential (LRP) 27 , a preparatory motor signal predictive of response times, and the contingent negative variation (CNV) 28,29 .…”

Section: Introductionmentioning

confidence: 99%

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Neural Correlates of Temporal Anticipation

Grabenhorst

Michalareas

2022

Preprint

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Temporal anticipation is a fundamental process underlying complex neural functions such as associative learning, decision-making, and motor-preparation. Here we study event anticipation in its simplest form in human participants using magnetoencephalography. We distributed events in time according to different probability density functions and presented the stimuli separately in two different sensory modalities. We found that the temporal dynamics in right parietal cortex correlate with reaction times to anticipated events. Specifically, after an event occurred, event probability was represented in right parietal activity, hinting at a functional role of event-related potential component P300 in temporal expectancy. The results are consistent across both visual and auditory modalities. The right parietal cortex seems to play a central role in the processing of event probability density. Overall, this work contributes to the understanding of the neural processes involved in the anticipation of events in time.

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Section: Resultsmentioning

confidence: 99%

Section: Event-related Field Components -Correlation With Rt At the S...mentioning

confidence: 99%

Section: No Effect Of Probability Density On Early Erf Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Correlates of Temporal Anticipation

Grabenhorst

Michalareas

2022

Preprint

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“…From here, the peaks of interest were determined by identifying the local maxima/minima of the expected peaks, using the peak detection function in BrainVision Analyzer. The mean value within a window around the peak was used instead of the peak value, as the mean is more robust against noise (Luck, 2014). A window of 40ms around the mean was chosen as the appropriate window for visual ERPs P1 and N1.…”

Section: Methodsmentioning

confidence: 99%

An EEG study of Detection without Localisation in Change Blindness

Scrivener

Malik

Marsh

et al. 2019

Preprint

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Previous studies of change blindness have suggested a distinction between detection and localisation of changes in a visual scene. Using a simple paradigm with an array of coloured squares, the present study aimed to further investigate differences in event-related potentials (ERPs) between trials in which participants could detect the presence of a colour change but not identify the location of the change (sense trials), versus those where participants could both detect and localise the change (localise trials). Individual differences in performance were controlled for by adjusting the difficulty of the task in real time. Behaviourally, reaction times for sense, blind, and false alarm trials were distinguishable when comparing across levels of participant certainty. In the EEG data, we found no significant differences in the visual awareness negativity ERP, contrary to previous findings. In the N2pc range, both awareness conditions (localise and sense) were significantly different to trials with no change detection (blind trials), suggesting that this ERP is not dependent on explicit awareness. Within the late parietal positivity range, all conditions were significantly different. These results suggest that changes can be 'sensed' without knowledge of the location of the changing object, and that participant certainty scores can provide valuable information about the perception of changes in change blindness.

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