If you have any problems related to the accessibility of any content (or if you want to request that a specific publication be accessible), please contact (email@example.com). We will work to respond to each request in as timely a manner as possible.
The neural representation of ensemble mean.
AuthorKillebrew, Kyle William
AdvisorCaplovitz, Gideon P.
StatisticsView Usage Statistics
Our perceptual systems are capacity limited by the bottleneck of attention and, as a result, we can only process a limited amount of information at any given time. In order to help overcome this limitation, our perceptual systems can quickly summarize and extract information over a large area of visual space. In other words, we have the remarkable ability to extract the ‘gist’ of a scene or group of objects. Ensemble encoding, a proxy of gist perception, is the ability to rapidly extract the average feature of a group of items. For example, people can extract the average orientation, size, direction of motion, hue, and even facial expression among a group of similar objects. This ability has been demonstrated behaviorally many times using many different experimental paradigms. However, little is known about how these ensemble averages are extracted and how they are neurally encoded. We predict that if there is a representation of the ensemble, we can measure it in response to systematically varying the average feature of a group objects using high-density electroencephalography (hdEEG) and functional magnetic resonance imaging (fMRI). Specifically, the current series of experiments attempts to identify the neural correlates and temporal dynamics of ensemble encoding of orientation and size as well as measuring changes to that representation by manipulating spatial attention and the type of averaging task performed by the participant. In experiment 1, we measured neural adaptation to repeated presentations of adapting ensembles with a reference average orientation and size and test ensembles of progressively larger or more tilted averages using fMRI repetition suppression. In experiment 2, we used hdEEG to measure evoked potentials in response to ensembles of framed ellipses with different mean sizes and orientations. We then performed univarite and multivariate analysis in an attempt to find differences over time between the signals of these ensembles. In experiment 3, we attempted to tease out the effects of attention and relevant averaging task on the representation of these ensemble averages. We used a multiplexed frequency tagging oddball paradigm in which we ‘tagged’ ensembles by flickering them at specific frequencies. We then transform the EEG waveforms from the time to frequency domain using a fast Fourier transform and measure the resulting amplitude to the specific presentation frequency. Although we do see some results consistent with the view of ensemble encoding as a rapid parallel process, our results largely show no consistent differentiable response in the neural signal between ensembles of different levels. Our data are most consistent with a theory of ensemble encoding as an encoding strategy as opposed to a pre-attentive, automatic, and parallel process. More work will need to be done in order to make a firmer conclusion about the neural representation of the ensemble average.