Neural signatures of evidence accumulation in temporal decisions

Ofir, Nir; Landau, Ayelet N.

doi:10.1016/j.cub.2022.08.006

Cited by 27 publications

(46 citation statements)

References 43 publications

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“…Both CNV and LPCt were linked to the given test duration, but were not limited to absolute durations. Our findings, consistent with the previous studies (Baykan & Shi, 2022;Boehm et al, 2014;Ofir & Landau, 2022), indicate that the CNV represents an expectancy wave for upcoming decision-making, while LPCt reflects the decision-making process, both CNV and LPCt influenced by the temporal context.…”

Section: Discussionsupporting

confidence: 93%

“…For better comparison with the literature (Bueno & Cravo, 2021;Ofir & Landau, 2022), we extracted the mean LPCt amplitude from the time window of [300,500] 1). As seen in Figure 8d, the interaction was caused by different amplitudes for the short durations but plateaued at a similar level for the long durations.…”

Section: Electrophysiological Resultsmentioning

confidence: 99%

“…For example, in a bisection task, the contingent negative variation (CNV) -a negative polarity waveform that appears in the supplementary motor area (SMA) -has been found to increase in negativity as the interval progresses and levels off when the duration exceeds the geometric mean of the short and long anchors (Ng et al, 2011;van Rijn et al, 2011;Wiener & Thompson, 2015). Additionally, post-interval positivity ERPs, which appear in the range between 200 to 600 ms after the stimulus offset in the same electrode clusters as CNV after the stimulus offset, have been found to vary with duration judgments and temporal decisions (Damsma et al, 2021;Ofir & Landau, 2022). The early positivity component P2, peaking around 200 ms after the stimulus, has been suggested to be linked with perceived duration length , although the relationship between P2 and the probe duration remains unclear (Kononowicz & van Rijn, 2014;Lindbergh & Kieffaber, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Electrophysiological signatures of temporal context in the bisection task

Baykan

Zhu

Zinchenko

et al. 2023

Preprint

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Despite relatively accurate time judgment, subjective time is susceptible to various contexts, such as sample spacing and frequency. Several electroencephalographic (EEG) components have been linked to timing, including the contingent negative variation (CNV), offset P2, and late positive component of timing (LPCt). However, the specific role of these components in the contextual modulation of perceived time remains unclear. In this study, we conducted two temporal bisection experiments, where participants had to judge if a test duration was close to a short or long standard. Unbeknownst to participants, the sample spacing (Experiment 1) and frequency (Experiment 2) were altered to create short and long contexts while keeping the test range and standards the same in different sessions. The results showed that the bisection threshold shifted toward the ensemble mean and that CNV and LPCt were sensitive to context modulation. Compared to the long context, the CNV climbing rate increased in the short context, and the amplitude and latency of the LPCt were reduced. These findings suggest the CNV represents an expectancy wave for upcoming decision-making, while LPCt reflects the decision-making process, both influenced by the temporal context.

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Section: Discussionsupporting

confidence: 93%

Section: Electrophysiological Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electrophysiological signatures of temporal context in the bisection task

Baykan

Zhu

Zinchenko

et al. 2023

Preprint

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“…We segmented the data to blocks according to the presented clips, starting from two seconds before the story began until two seconds after the story ended. We removed slow drifts in the EEG using spline interpolation as implemented in the function (part of the MATLAB Bioinformatics toolbox) and similarly to (Ofir & Landau, 2022). The window size was 4 seconds, with a step of 0.75 second between two consecutive windows.…”

Section: Methodsmentioning

confidence: 99%

“…and similarly to (Ofir & Landau, 2022). The window size was 4 seconds, with a step of 0.75 second between two consecutive windows.…”

Section: Eeg Preprocessingmentioning

confidence: 99%

Intonation Units in spontaneous speech evoke a neural response

Inbar

Genzer

Grossman

et al. 2023

Preprint

Self Cite

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Spontaneous speech is produced in chunks called Intonation Units (IUs). IUs are defined by a set of prosodic cues and occur in all human languages. Linguistic theory suggests that IUs pace the flow of information and serve as a window onto the dynamic focus of attention in speech processing. IUs provide a promising and hitherto unexplored theoretical framework for studying the neural mechanisms of communication, thanks to their universality and their consistent temporal structure across different grammatical and socio-cultural conditions. In this article, we identify a neural response unique to the boundary defined by the IU. We measured the EEG of participants who listened to different speakers recounting an emotional life event. We analyzed the speech stimuli linguistically, and modeled the EEG response at word offset using a GLM approach. We find that the EEG response to IU-final words differs from the response to IU-nonfinal words when acoustic boundary strength is held constant. To the best of our knowledge, this is the first time this is demonstrated in spontaneous speech under naturalistic listening conditions, and under a theoretical framework that connects the prosodic chunking of speech, on the one hand, with the flow of information during communication, on the other. Finally, we relate our findings to the body of research on rhythmic brain mechanism in speech processing by comparing the topographical distributions of neural speech tracking in model-predicted and empirical EEG. This qualitative comparison suggests that IU-related neural activity contributes to the previously characterized delta-band neural speech tracking.

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Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees

Balcı,

Simen

2024

Advances in Experimental Medicine and Biology

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Neural signatures of evidence accumulation in temporal decisions

Cited by 27 publications

References 43 publications

Electrophysiological signatures of temporal context in the bisection task

Electrophysiological signatures of temporal context in the bisection task

Intonation Units in spontaneous speech evoke a neural response

Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees

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