Coding of low-level position and orientation information in human naturalistic vision
Authors: József Fiser
Jeppe H Christensen
Peter J Bex
Journal: PLoS ONE (2019)
Discipline: Multidisciplinary
Keywords: Orientation; position; image segments; low-level attributes; visual processing
Affiliations: Department of Psychology, University of Copenhagen, Copenhagen, Denmark., Department of Psychology, Northeastern University, Boston, Massachusetts, United States of America., Department of Cognitive Science, Central European University, Budapest, Hungary., Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America.

Abstract:  Orientation and position of small image segments are considered to be two fundamental low-level attributes in early visual processing, yet their encoding in complex natural stimuli is underexplored. By measuring the just-noticeable differences in noise perturbation, we investigated how orientation and position information of a large number of local elements (Gabors) were encoded separately or jointly. Importantly, the Gabors composed various classes of naturalistic stimuli that were equated by all low-level attributes and differed only in their higher-order configural complexity and familiarity. Although unable to consciously tell apart the type of perturbation, observers detected orientation and position noise significantly differently. Furthermore, when the Gabors were perturbed by both types of noise simultaneously, performance adhered to a reliability-based optimal probabilistic combination of individual attribute noises. Our results suggest that orientation and position are independently coded and probabilistically combined for naturalistic stimuli at the earliest stage of visual processing. 

Retracted: No

PubPeer comments:
  Orientation and position of small image segments are considered to be two fundamental low-level attributes in early visual processing. Is that a valid statement? It has [no empirical or logical basis] .  The NIO model makes explicit predictions derived from signal detection theory. Briefly, the model assumes that the observer sample M independent elements from each of the two test stimulus images and compute sample variances by comparing the attribute of the elements with those from a noisy template. The noisy template implies that during perception, sample elements of the stimulus are perturbed by zero-mean Gaussian internal noise with variance. Are the assumptions of  signal detection theory reasonable? [No] . But aren t they tested by prediction? Again, no; signal detection theory consists of a set of (pseudoscientific) procedures that [protect assumptions] from falsification, rendering projects like this one just exercises in mathematical acrobatics. It s a terrific scheme that s lasted decades. And then there s the post hoc data-fitting. Post hoc analysis means you have no license to claim any correlations you find are not spurious or the result of chance. As in this case, even the title of correlation-fishing papers betray their open-ended, hodge-podge nature - the title tells you nothing about what was  found. The concluding statements say it all:  In conclusion, using novel stimuli and a noise discrimination task, we found top-down modulations and differences in psychophysical measurements of low-level position and orientation coding in naturalistic images. This argues for a more adaptive and yet more specialized dynamic perceptual process compared to the traditional filter-based feed-forward model of human vision. In other words, we got lots of different results we can t explain on the basis of our (empirically and logically invalid)  low-level story. We need a more complicated concept! (I didn t think anyone took  feedforward models seriously anymore). , 
 Hi, Maybe instead of referering to a blog website in your comment, you could refer us to either your own or others peer-reviewed work to support your claims? 1) You are asking if it is a valid statement to claim that the orientation and position of low-level elements are considered fundamental for early vision. It is, and you would know if you knew the literature. 2) Do you have any alternatives (peer-reviewed, no blog posts) to computionally explain the data, keeping in mind parsimony? Remember,  All models are wrong, but some are useful . 3) I m not following you on this point. You argue against fitting a model to data? The whole idea is exactly to filter out effects occuring as a result of chance. 4) Hey, seems you got our final argument right :-) , 
 #2  Maybe instead of referering to a blog website in your comment, you could refer us to either your own or others peer-reviewed work to support your claims? It s my website, and I include published references when I think they re important for the arguments. Many of my arguments are based on uncontroversial facts and logical arguments; given the widespread acknowledgment that the peer-review system is broken - ergo the success of PubPeer - the implication that peer-reviewed papers are more valid than other documents is just elitism. Also, I can t count the times when peer-reviewed papers throw out references that turn out to be merely cosmetic.  You are asking if it is a valid statement to claim that the orientation and position of low-level elements are considered fundamental for early vision. It is, and you would know if you knew the literature. You are misrepresenting my claim. The question isn t what s currently (and quite mindlessly)  considered valid, but what holds up to fact and logic. I ve gone to some trouble on my blog to explain why e.g. the notion of [early ( low-level ) vision] is invalid, as are the [methods on the basis of which] orientation detector, etc claims are arrived at. It is an extraordinary fact that the field of visual perception has for decades been functioning as a pseudoscience, turning a blind eye to inconvenient facts and employing methods that are insensitive to the truth or falsity of its assumptions. One example [I ve discussed] is Adelson Movshon s (1982)  classic Nature paper on motion perception. Another is [ signal detection theory ] , as well, more specifically, as its sham concept of [ ideal observer ] . I m painfully aware that popularity of opinion, rather than rationality, is the current standard of validity of scientific views, but it shouldn t be. In short, we don t disagree on what s  considered fundamental, but on what is defensible on the basis of clear and explicit arguments that respect fact and reason.  Do you have any alternatives (peer-reviewed, no blog posts) to computionally explain the data, keeping in mind parsimony? Remember,  All models are wrong, but some are useful . There s no point to  computationally explaining the data, it s irrelevant to understanding the brain. This  all models are wrong trope is horribly abused in the current neuro literature, to excuse  models that are poorly-conceived as well as wrong. You can t put Newton s theory and astrology in the same basket just because both are wrong.  I m not following you on this point. You argue against fitting a model to data? The whole idea is exactly to filter out effects occurring as a result of chance. You cannot  filter out chance effects or patterns post hoc. Even some  computational neuroscientists have started to appreciate this age-old truth, specifically Mehler Kording (2018). Empirically speaking, such procedures never produce reliable results in a situation of any complexity.  Hey, seems you got our final argument right :-) We didn t need to do any data-collecting to arrive at that conclusion. ,