Motivation
Feature Integration theory is a theory of Attention which suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing. This theory has been used to explain the phenomenon of
Crowding. There is a definite relationship between Feature Integration and Crowding, however as of now no consensus has been reached that dictates how exactly these two are related. I aim to perform a small empirical study to ascertain the applicability of different theories to the data set that shall be generated.
Related Work
Works of Dennis G Pellis has largely been done on cued and uncued target identification and they found out that the target that has been cued is far easier to identify than the uncued one. Because the more unfamiliar the letters few will fit in working memory, more computation will be required and while if cued the subject need remember the cued letter only hence drastically increasing the performance.
Pelli, Palomares, and Majaj (2004) suggested that feature binding is mediated by hard-wired integration fields instead of a spotlight of spatial attention as was previously assumed when the theory of feature integration was first proposed.
Endel Poder worked with differently colored, differently shaped flankers. However when working with variable number of flankers they noticed a remarkable fact that increasing the number of distractors, the crowding effect was reduced. He proposed a bottom-up attention that facilitates the processing of information from salient locations in the visual field. Van den berg et al. have found out the neurological population code model basis of feature integration to explain the phenomenon of visual crowding.
Proposed Methodology
I plan to make a simple flash application that will have a fixation cross, and a pinot of interest marked by some noise. Subject would be exposed to this test and their performance shall be noted. There will be at least 20 subjects spanned widely across geographical origin, department, sex, and if possible age. Based on this data set, I would try to match the outcomes with the existing theories that have been proposed as to how feature integration explains crowding. This will then be used to measure reading speed of the subject because there is a direct co-relation between integrating the visible features few characters ahead in the word and correctly inferring it from all the different characters that surround the character of interest. At least one subject will also be trained to learn to discard noise and identify the flanker, corresponding changes in his/her performance on standard reading tests will be noted.
Empirical Study
There will be variable parameters like eccentricity (defined as the distance from the fixation point to the character of interest), spacing (defined as the distance between the two characters, one of interest called target while the other noise called Flanker), Position of the target will also be varied across both horizontal to vertical axes. Then there will be assessment of crowding and its relationship with the accuracy with which the target is ascertained. Flankers will be both fovial and prepheral. Flankers will be made of variable similarity to the target. Flanker configuration will also be incorporated in the study to ascertain how much, if at all there is a difference in target identification accuracy once the subject is able to identify the flanker configuration as something known and hence able to disregard it completely from the attention.
Some test are already available
here.
References
- Van den Berg R, Roerdink JBTM, Cornelissen FW (2010) A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual
Crowding. PLoS Comput Biol 6(1): e1000646. doi:10.1371/journal.pcbi.1000646
- Freeman, J., & Pelli, D. G. (2007). An escape from crowding.Journal of Vision, 7(2):22, 1–14, http://journalofvision.
org/7/2/22/, doi:10.1167/7.2.22
- EndelPo˜der. Crowding, feature integration, and two kinds of ‘‘attention’’.Journal of Vision(2006) 6, 163–169
- Pelli D G. Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journalof Vision(2004) 4,1136-1169