144 research outputs found
Human scalp potentials reflect a mixture of decision-related signals during perceptual choices
Single-unit animal studies have consistently reported decision-related activity mirroring a process of temporal accumulation of sensory evidence to a fixed internal decision boundary. To date, our understanding of how response patterns seen in single-unit data manifest themselves at the macroscopic level of brain activity obtained from human neuroimaging data remains limited. Here, we use single-trial analysis of human electroencephalography data to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling. Interestingly, at time of choice, scalp potentials continue to appear parametrically modulated by the amount of sensory evidence rather than converging to a fixed decision boundary as predicted by our model. We show that trial-to-trial fluctuations in these response-locked signals exert independent leverage on behavior compared with the rate of evidence accumulation earlier in the trial. These results suggest that in addition to accumulator signals, population responses on the scalp reflect the influence of other decision-related signals that continue to covary with the amount of evidence at time of choice
Recommended from our members
Comparing Neural Correlates of Visual Target Detection in Serial Visual Presentations Having Different Temporal Correlations
Most visual stimuli we experience on a day-to-day basis are continuous sequences, with spatial structure highly correlated in time. During rapid serial visual presentation (RSVP), this correlation is absent. Here we study how subjects' target detection responses, both behavioral and electrophysiological, differ between continuous serial visual sequences (CSVP), flashed serial visual presentation (FSVP) and RSVP. Behavioral results show longer reaction times for CSVP compared to the FSVP and RSVP conditions, as well as a difference in miss rate between RSVP and the other two conditions. Using mutual information, we measure electrophysiological differences in the electroencephalography (EEG) for these three conditions. We find two peaks in the mutual information between EEG and stimulus class (target vs. distractor), with the second peak occurring 30–40 ms earlier for the FSVP and RSVP conditions. In addition, we find differences in the persistence of the peak mutual information between FSVP and RSVP conditions. We further investigate these differences using a mutual information based functional connectivity analysis and find significant fronto-parietal functional coupling for RSVP and FSVP but no significant coupling for the CSVP condition. We discuss these findings within the context of attentional engagement, evidence accumulation and short-term visual memory
Circular Clustering with Polar Coordinate Reconstruction
There is a growing interest in characterizing circular data found in
biological systems. Such data are wide ranging and varied, from signal phase in
neural recordings to nucleotide sequences in round genomes. Traditional
clustering algorithms are often inadequate due to their limited ability to
distinguish differences in the periodic component. Current clustering schemes
that work in a polar coordinate system have limitations, such as being only
angle-focused or lacking generality. To overcome these limitations, we propose
a new analysis framework that utilizes projections onto a cylindrical
coordinate system to better represent objects in a polar coordinate system.
Using the mathematical properties of circular data, we show our approach always
finds the correct clustering result within the reconstructed dataset, given
sufficient periodic repetitions of the data. Our approach is generally
applicable and adaptable and can be incorporated into most state-of-the-art
clustering algorithms. We demonstrate on synthetic and real data that our
method generates more appropriate and consistent clustering results compared to
standard methods. In summary, our proposed analysis framework overcomes the
limitations of existing polar coordinate-based clustering methods and provides
a more accurate and efficient way to cluster circular data.Comment: Manuscript is under review in IEEE Transactions on Computational
Biology and Bioinformatics. Copyright holder is credited to IEE
Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
We describe our work using linear discrimination of multi-channel electroencephalography
for single-trial detection of neural signatures of visual recognition events. We demonstrate
the approach as a methodology for relating neural variability to response variability, describing
studies for response accuracy and response latency during visual target detection.
We then show how the approach can be utilized to construct a novel type of brain-computer
interface, which we term cortically-coupled computer vision. In this application, a large
database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’
improves image search over a strictly behavioral response
- …