Superordinate Level Processing Has Priority Over Basic-Level Processing in Scene Gist Recognition

Superordinate Level Processing Has Priority Over Basic-Level Processing in Scene Gist Recognition

By combining a perceptual discrimination task and a visuospatial working memory task, the present study examined the effects of visuospatial working memory load on the hierarchical processing of scene gist. In the perceptual discrimination task, two scene images from the same (manmade-manmade pairing or natural-natural pairing) or different superordinate level categories (manmade-natural pairin...

The natural/man-made distinction is made before basic-level distinctions in scene gist processing

Instruction-level parallel processing.

The performance of microprocessors has increased steadily over the past 20 years at a rate of about 50% per year. This is the cumulative result of architectural improvements as well as increases in circuit speed. Moreover, this improvement has been obtained in a transparent fashion, that is, without requiring programmers to rethink their algorithms and programs, thereby enabling the tremendous ...

The role of gist in scene recognition

Studies of change blindness suggest that we bring only a few attended features of a scene, plus a gist, from one visual fixation to the next. We examine the role of gist by substituting an original image with a second image in which a substitution of one object changes the gist, compared with a third image in which a substitution of that object does not change the gist. Small perceptual changes...

An interactive model accounts for both ultra-rapid superordinate classification and basic-level advantage in object recognition

While people are faster to categorize objects at an intermediate or basic level of specificity (e.g. “bird”), several recent studies have shown them to have much earlier access to more general category information (e.g. “animal”). Ultra-rapid superordinate classification has been taken as evidence that recognition processes are largely feed-forward. In simulations with a deep neural network mod...