Peripheral vision and pattern recognition: a review - strasburger - main

Peripheral_Vision.doc
empirical values and a logarithmic retino-cortical mapping function which matches the inverselinear
law Further low-level tasks reviewed are the measurements of visual reaction time,
apparent brightness, temporal resolution, flicker detection, and spatial summation. These tasks
have found application as diagnostic tools for perimetry, both in clinical and non-clinical
settings.
Peripheral letter recognition is a central topic in our review. In Chapter 4, we first consider its
dependence on stimulus contrast. We then proceed to crowding, the phenomenon traditionally
defined as loss of recognition performance for letter targets appearing in the context of other,
distracting letters (Chapter 5). Crowding occurs when the distracters are closer than a critical
distance specified by Bouma’s law (1970). We demonstrate its relationship with size-scaling
according to cortical magnification and derive the equivalent of Bouma’s law in retinotopic
cortical visual areas. Furthermore, we discuss how crowding is related to low-level contour
interactions, such as lateral masking and surround suppression, and how it is modulated by
attentional factors.
Regarding the recognition of scenes, objects, and faces in peripheral vision, a key question is
whether observer performance follows predictions based on cortical magnification and acuity
measures (Chapter 6). Alternatively, it might be that configural information plays a role in the
peripheral recognition of complex stimuli. Such information could result from mid-level
processes of perceptual organization integrating local features into contours and contours into
parts of objects or scenes.
Of particular relevance for basic and clinical research is the possibility of improving peripheral
form vision by way of learning (Chapter 7). Perceptual learning may enhance elementary
functions such as orientation discrimination, contrast sensitivity, and types of acuity. This entails
the question of whether crowding can be ameliorated or even removed by perceptual learning.
We shall then proceed to consider possibilities of acquiring pattern categories through learning
in indirect view. Of special interest is the extent of shift invariance of learned recognition
performance, and whether this imposes similar limitations on low-level and cognitive functions in
peripheral vision.
In Chapter 8 we review modeling peripheral form vision by employing concepts from computer
vision, artificial neural networks, and pattern recognition. The most successful of these
approaches are rooted in the above-mentioned work of Lettvin and Julesz and co-workers. That
is, they modeled peripheral form vision by deteriorating structure within image parts using some
sort of summary statistics. An alternative approach, termed the method of classification images,
uses techniques of system identification. Finally, cognitive limitations of peripheral form vision
are explored using the analysis of category learning by means of psychometric methodologies
based on statistical pattern recognition.
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