High temporal resolution decoding of object position and category

Abstract

We effortlessly and seemingly instantaneously recognize thousands of objects, although we rarely--if ever--see the same image of an object twice. The retinal image of an object can vary by context, size, viewpoint, illumination, and location. The present study examined how the visual system abstracts object category across variations in retinal location. In three experiments, participants viewed images of objects presented to different retinal locations while brain activity was recorded using magnetoencephalography (MEG). A pattern classifier was trained to recover the stimulus position (Experiments 1, 2, and 3) and category (Experiment 3) from the recordings. Using this decoding approach, we show that an object's location in the visual field can be recovered in high temporal resolution (5 ms) and with sufficient fidelity to capture topographic organization in visual areas. Experiment 3 showed that an object's category could be recovered from the recordings as early as 135 ms after the onset of the stimulus and that category decoding generalized across retinal location (i.e., position invariance). Our experiments thus show that the visual system rapidly constructs a category representation for objects that is invariant to position.

The Temporal Evolution of Coarse Location Coding of Objects: Evidence for Feedback

“…It is to be noted that some studies have used this method to assess cognitive processes, but they were not interested in extracting the detailed temporal dynamics of the processes (Chan, Halgren, Marinkovic, & Cash, 2011;Murphy et al, 2011). However, a few recent studies have begun to address this lack (Carlson, Hogendoorn, & Kanai, et al, 2011. For example, a recent study (Carlson, Hogendoorn, & Kanai, et al, 2011) showed that MEG responses could be used to categorize objects such as faces and cars.…”

“…However, a few recent studies have begun to address this lack (Carlson, Hogendoorn, & Kanai, et al, 2011. For example, a recent study (Carlson, Hogendoorn, & Kanai, et al, 2011) showed that MEG responses could be used to categorize objects such as faces and cars. Furthermore, the location of such objects could also be decoded, and the precision of this decoding depended on their cortical separation, arguing for representations in the early visual cortex, consistent with current results.…”

“…In this study, we tested these alternatives-the feedforward and feedback hypotheses-by applying classification techniques to EEG (see Carlson, Tovar, Alink, & Kriegeskorte, 2013;Carlson, Hogendoorn, Kanai, Mesik, & Turret, 2011) while participants viewed various types of objects. This allows us to extract information about object location from ongoing neural activity on a millisecondby-millisecond basis, thus tracing the time course of location representation.…”

The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques

“…We also conducted a discriminant cross-training (DCT) analysis [1]: A classifier is trained using patterns of neural activity at one time point t o and tested on all time points in the epoch, producing a matrix of decoding accuracy for each combination of training/testing time points (figure 4A). Note that the diagonals of these two matrices correspond to the decoding accuracy curves shown in figure 2C.…”

MEG sensor patterns reflect perceptual but not categorical similarity of animate and inanimate objects

“…Replicating previous reports, MEG sensor patterns could be used to accurately decode individual objects (Carlson et al, 2011;Carlson et al, 2013;van de Nieuwenhuijzen et al, 2013;Cichy et al, 2014;Isik et al, 2014;Clarke et al, 2015;Ritchie et al, 2015;Coggan et al, 2016;Kaiser et al, 2016a). Using representational similarity analysis, we then related MEG neural similarity to the objects' perceptual and categorical similarity.…”