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A significant aspect of object recognition is that of object constancy: the ability to recognize an object across varying viewing conditions. These varying conditions include object orientation, lighting, and object variability (size, colour, and other within-category differences). For the visual system to achieve object constancy, it must be able to extract a commonality in the object description across different viewpoints and the retinal descriptions.[9] Participants who did categorization and recognitions tasks while undergoing a functional magnetic found as increased blood flow indicating activation in specific regions of the brain. These regions implicated in mental rotation, such as the ventral and dorsal visual pathways and the prefrontal cortex, are critical for the ability to view objects from multiple angles.^[1] Several theories have been generated to provide insight on how object constancy may be achieved for the purpose of object recognition including, viewpoint-invariant, viewpoint-dependent and multiple views theories.

Viewpoint-invariant theories

Viewpoint-invariant theories suggest that object recognition is based on structural information, such as individual parts, allowing for recognition to take place regardless of the object's viewpoint. Accordingly, recognition is possible from any viewpoint as individual parts of an object can be rotated to fit any particular view.[10] This form of analytical recognition requires little memory as only structural parts need to be encoded, which can produce multiple object representations through the interrelations of these parts and mental rotation.[10] Participants in a study viewed objects in the central visual field and then named either same or different depth- orientation views of these objects presented briefly in the left or the right visual field.^[2] Viewpoint-dependent priming was observed when test views were presented directly to the right hemisphere, but not when test views were presented directly to the left hemisphere. The results support the model that objects are stored in a manner that is viewpoint dependent because the results did not depend on whether the same or a different set of parts could be recovered from the different-orientation views.^[2]

3-D model representation[edit | edit source]

This model, proposed by Marr and Nishihara (1978), states that object recognition is achieved by matching 3-D model representations obtained from the visual object with 3-D model representations stored in memory as veridical shape precepts^[3]. Through the use of computer programs and algorithms, Yi Yungfeng (2009) was able to demonstrate the ability for the human brain to mentally construct 3D images using only the 2D images that appear on the retina. Their model also demonstrates a high degree of shape constancy conserved between 2D images, which allow the 3D image to be recognized.^[3] The 3-D model representations obtained from the object are formed by first identifying the concavities of the object, which separate the stimulus into individual parts. Recent research suggests that an area of the brain, known as the caudal intraparietal area (CIP), is responsible for storing the slant and tilt of a plan surface that allow for concavity recognition.^[4] Rosenburg et al. implanted monkeys with a scleral search coil for monitoring eye position while recording single neuron activation from neurons within the CIP. These single neuron activations for specific concavities of objects lead to the discovery that each axis of an individual part of an object containing concavity are found in memory stores.^[4] Identifying the principal axis of the object assists in the normalization process via mental rotation that is required because only the canonical description of the object is stored in memory. Recognition is acquired when the observed object viewpoint is mentally rotated to match the stored canonical description.[11]