Orientation Decoding in Human Visual Cortex: New Insights from an Unbiased Perspective

Abstract

The development of multivariate pattern analysis or brain "decoding" methods has substantially altered the field of fMRI research. Although these methods are highly sensitive to whether or not decodable information exists, the information they discover and make use of for decoding is often concealed within complex patterns of activation. This opacity of interpretation is embodied in influential studies showing that the orientation of visual gratings can be decoded from brain activity in human visual cortex with fMRI. Although these studies provided a compelling demonstration of the power of these methods, their findings were somewhat mysterious as the scanning resolution was insufficient to resolve orientation columns, i.e., orientation information should not have been accessible. Two theories have been put forth to account for this result, the hyperacuity account and the biased map account, both of which assume that small biases in fMRI voxels are the source of decodable information. In the present study, we use Hubel and Wiesel's (1972) classic ice-cube model of visual cortex to show that the orientation of gratings can be decoded from an unbiased representation. In our analysis, we identify patterns of activity elicited by the edges of the stimulus as the source of the decodable information. Furthermore, these activation patterns masquerade as a radial bias, a key element of the biased map account. This classic model thus sheds new light on the mystery behind orientation decoding by unveiling a new source of decodable information.

Decoding eye-of-origin outside of awareness

“…It has been suggested that decoding performance could be due to coarse-scale effects such as radial biases (Mannion et al, 2010), edge effects (Carlson, 2014), or differential allocation of attention . Some of these possibilities for orientation decoding using MEG have been ruled out by careful control experiments (Cichy et al, 2015), but might there be similar confounds for decoding eye-of-origin?…”

Orientation decoding: Sense in spirals?

“…Here, we elaborate the mathematical relationship between spirals of opposite sense to confirm that they cannot be discriminated on the basis of the pooled output of unbiased or radially biased orientation filters. We then demonstrate that Carlson's (2014) reported decoding ability is consistent with the presence of inadvertent biases in the set of orientation filters; biases introduced by their digital implementation and unrelated to the brain's processing of orientation. These analyses demonstrate that spirals must be processed with an orientation bias other than the radial bias for successful decoding of spiral sense.…”

“…Despite this, Carlson (2014) has recently claimed that spirals of opposite sense can be discriminated from a representation of local image structure that is unbiased with respect to orientation. On this basis, it was argued that "there is no need to posit a bias either at fine grain or coarse-scale representation to account for decoding spiral sense" (Carlson, 2014).…”

“…We and others have advocated the use of spiral stimuli to eliminate a potential coarse-scale bias-the radial bias toward local orientations that are collinear with the centre of gaze-and hence narrow down the potential coarse-scale biases that could contribute to orientation decoding. The usefulness of this strategy is challenged by the computational simulations of Carlson (2014), who reported the ability to successfully decode spirals of opposite sense (opening clockwise or counter-clockwise) from the pooled output of purportedly unbiased orientation filters. Here, we elaborate the mathematical relationship between spirals of opposite sense to confirm that they cannot be discriminated on the basis of the pooled output of unbiased or radially biased orientation filters.…”

Practices and pitfalls in inferring neural representations

“…In multivoxel pattern analysis (right), machine learning techniques are used to train a classifier to distinguish categories based on fine-grained activation patterns (yellow box) and its classification performance is evaluated on a separate test set (red box). from visual cortex because imperfect sampling of orientation-selective columns leads to hyperacuity (Kamitani & Tong, 2005), because of radial biases in the retinotopic map (Freeman et al, 2011), or because of edge-related activity (Carlson, 2014). Ritchie et al (2017) further suggest that relating classifier performance to behavior only partly remedies the problem as "a brain region might carry information which is reliably correlated with the information that is actually used, but which is not itself used in behavior".…”