Temporal scaling of human scalp-recorded potentials

Hassall, Cameron D.; Harley, Jack; Kolling, Nils; Hunt, Laurence T.

doi:10.1101/2020.12.11.421180

Cited by 10 publications

(10 citation statements)

References 68 publications

(168 reference statements)

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“…Finally, and perhaps most importantly, the experimenter-controlled sensory noise allows us to characterise how continuous sensory evidence fluctuations cause changes in the simultaneously recorded continuous EEG signal. To study this, we used a deconvolutional general linear model (GLM) approach (Crosse et al, 2016; Ehinger and Dimigen, 2019; Gonçalves et al, 2014; Hassall et al, 2021) to estimate temporal response functions (TRFs) to various events relating to the time-varying sensory evidence. We describe this approach and the resulting TRFs in more detail below.…”

Section: Resultsmentioning

confidence: 99%

Quantifying decision-making in dynamic, continuously evolving environments

Ruesseler

Weber

Marshall

et al. 2022

Preprint

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During perceptual decision-making tasks, centroparietal EEG potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. Confronted with time-varying stimuli, humans can appropriately adapt their weighting of recent evidence according to the statistics of the environment. The neural mechanisms supporting this adaptation currently remain unclear. Here, we show that humans' ability to adapt evidence weighting to different sensory environments is reflected in changes in centroparietal EEG potentials. We use a novel continuous task design to show that the Centroparietal Positivity (CPP) becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with the duration over which different individuals integrate sensory evidence. Our findings reveal how adaptations in centroparietal responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.

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Section: Resultsmentioning

confidence: 99%

Quantifying decision-making in dynamic, continuously evolving environments

Ruesseler

Weber

Marshall

et al. 2022

Preprint

Self Cite

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“…Another phenomenon to consider is that of temporal scaling. Prior work has shown that rather than occurring at fixed latencies relative to events, certain neural responses can stretch or compress to fill a stimulus-response gap [96][97][98]. When characterizing particular neural responses, it is important to understand whether the response occurs at a fixed latency, or if it scales with the stimulusresponse gap.…”

Section: The Importance Of a Post-stimulus Gapmentioning

confidence: 99%

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Sadras,

Sani,

Ahmadipour

et al. 2023

J. Neural Eng.

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Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main Results. We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

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“…Another phenomenon to consider is that of temporal scaling. Prior work has shown that rather than occurring at fixed latencies relative to events, certain neural responses can stretch or compress to fill a stimulus-response gap [95][96][97]. When characterizing particular neural responses, it is important to understand whether the response occurs at a fixed latency, or if it scales with the stimulus-response gap.…”

Section: The Importance Of a Post-stimulus Gapmentioning

confidence: 99%

Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making

Sadras

Sani

Ahmadipour

et al. 2022

Preprint

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When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

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Temporal scaling of human scalp-recorded potentials

Cited by 10 publications

References 68 publications

Quantifying decision-making in dynamic, continuously evolving environments

Quantifying decision-making in dynamic, continuously evolving environments

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making

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