Figure 3.19
The extent to which perceived (left side of figure) or imagined (right side of figure) objects could be classified accurately on the basis of brain activity in early visual cortex and object-selective cortex. (ES = extrastriate retinotopic cortex; LO = lateral occipital cortex; pFs = posterior fusiform sulcus.)
From S.H. Lee et al. (2012). Reproduced with permission from Elsevier.
Imagery does not resemble perception
Have a look at Figure 3.18, which consists of the outlines of three objects. Start with the object on the left and form a clear image. Then close your eyes, mentally rotate the image by 90 degrees clockwise and decide what you see. Then repeat the exercise with the other objects. Finally, rotate the book through 90 degrees. You found it easy to identify the objects when perceiving them even though you probably could not when you only imagined rotating them.
Slezak (1991, 1995) carried out research using stimuli very similar to those shown in Figure 3.18. No observers reported seeing the objects. This was not a deficiency in memory – participants who sketched the image from memory and then rotated it saw the new object. Thus, the information contained in images cannot be used as flexibly as visual information.
S.-H. Lee et al. (2012) found evidence for important differences between imagery and perception. Participants viewed or imagined various common objects (e.g., car, umbrella) and activity in early visual cortex and areas associated with later visual processing (object-selective regions) was assessed.
Attempts were made to work out which objects were being imagined or perceived on the basis of activation in these areas.
What did Lee et al. (2012) find? First, activation in all brain areas assessed was considerably greater when participants perceived rather than imagined objects. Second, objects being perceived or imagined could be identified with above-chance accuracy on the basis of patterns of brain activation except for imagined objects in primary visual cortex (V1; see Figure 3.19).
Third, the success rate in identifying perceived objects was greater based on brain activation in areas associated with early visual processing than those associated with later processing. However, the opposite was the case with respect to identifying imagined objects (see Figure 3.19). These findings point to an important difference between imagery and perception: processing in early visual cortex is very limited during imagery for objects but is extremely important during perception. Imagery for objects depends mostly on top-down processes based on object knowledge rather than on processing in early visual cortex.
These patients (and others with impaired visual imagery but intact visual perception) have damage to the left temporal lobe. Visual images are probably generated from information about concepts (including objects) stored in the temporal lobes (Patterson et al., 2007). However, this generation process is not needed (or is less important) for visual perception.
Other patients have intact visual imagery but impaired visual perception. These patients typically have severe damage to primary visual cortex, as in the case of SBR discussed earlier (Bridge et al., 2012). Another patient suffering from Anton’s syndrome (blindness denial) was also discussed earlier (Goldenberg et al., 1995). Zago et al. (2010) reported similar findings in another patient with Anton’s syndrome having total damage to primary visual cortex.
How can we interpret the findings from brain-damaged patients? In essence, visual perception mostly involves bottom-up processes triggered by the stimulus, whereas visual imagery primarily involves top-down processes based on object knowledge. Thus, it is not surprising that brain areas involved in early visual processing are more important for perception than imagery. It is also unsurprising that brain areas associated with storage of information about visual objects are more important for imagery.
Evaluation
Much progress has been made in understanding the relationship between visual imagery and visual perception. There is strong empirical support for the notion that similar processes are involved in imagery and perception. For example, imagery and perception are both associated with somewhat similar patterns of brain activity. In addition, the predicted facilitatory and interfering effects between imagery and perception tasks have been reported. These findings are more consistent with Kosslyn’s theory than that of Pylyshyn.
On the negative side, visual perception and visual imagery are less similar than assumed by Kosslyn. For example, there is the neuroimaging evidence reported by S.-H. Lee et al. (2012) and the frequent dissociations between perception and imagery found in brain-damaged patients. What is required in future is a theory explaining the differences between imagery and perception as well as the similarities. We already know that there is differential involvement of bottom-up and top-down processes in perception and imagery.
CHAPTER SUMMARY
• Pattern recognition. Pattern recognition involves processing of specific features and global processing. Feature processing typically precedes global processing but there are exceptions. Several types of cells (e.g., simple cells, complex cells, end-stopped cells) involved in feature processing have been identified. Other cells responsive to different spatial frequencies are also important in pattern and object recognition. It is often assumed fingerprint identification is typically very accurate. In fact, there is substantial evidence for forensic confirmation bias, which involves contextual information distorting fingerprint identification via top-down processes. Fingerprint experts have a greater ability than novices to discriminate between matches and non-matches and also adopt a more conservative response bias.
• Perceptual organisation. The Gestaltists put forward several principles of perceptual grouping and emphasised the importance of figure–
ground segregation. They argued that perceptual grouping and figure–ground segregation depend on innate factors. They also argued that we perceive the simplest possible organisation of the visual field, an important notion they failed to develop fully. The Gestaltists provided descriptions rather than explanations. Their approach was inflexible and they underestimated the complex interactions of factors underlying perceptual organisation. The Gestaltists de-emphasised the role of experience and learning in perceptual organisation, but this neglect has been rectified subsequently (e.g., the Bayesian approach).
• Approaches to object recognition. Visual processing typically involves a coarse-to-fine processing sequence. This sequence occurs in part because low spatial frequencies in visual input (associated with coarse processing) are conveyed to higher visual areas more rapidly than high spatial frequencies (associated with fine processing).
Biederman assumed in his recognition-by-components theory that objects consist of basic shapes known as geons. An object’s geons are determined by edge-extraction processes focusing on invariant properties of edges and the resultant geon-based description is viewpoint-invariant.
Biederman’s theory applies primarily to easy categorical discriminations, whereas object recognition is typically viewer-centred when identification is required. The theory is also too inflexible.
Object recognition is often viewpoint-invariant when categorisation is required. However, it is typically viewer-centred when complex identification (within-category discrimination) is required. Inferotemporal cortex is of crucial importance in visual object recognition. Some inferotemporal neurons seem to be viewpoint-dependent, whereas others are viewpoint-invariant.
Research with ambiguous figures indicates that object recognition often depends on top-down processes. Top-down processes are frequently necessary for object recognition to occur, especially when recognition is hard.
• Face recognition. Face recognition involves more holistic processing than object recognition. Deficient holistic processing partly explains why prosopagnosic patients have much greater problems with face recognition than object recognition. The brain areas involved in face recognition (e.g., fusiform face area) may differ somewhat from those involved in object recognition. This may be due in part to the fact that they have special expertise with faces – there is some evidence the brain and processing mechanisms involved in face recognition are also used when recognising objects for which we have expertise.
Bruce and Young’s model assumes there are major differences in the processing of familiar and unfamiliar faces and that processing of facial identity is separate from processing of facial expression. There is good support for the former assumption but not the latter. Super-recognisers have outstanding face-recognition ability, which is due in part to genetic factors specific to faces.
• Visual imagery. Visual imagery is useful because it allows us to predict the visual consequences of performing certain actions. According to Kosslyn’s perceptual anticipation theory, visual imagery closely resembles visual perception. In contrast, Pylyshyn in his propositional theory argued that visual imagery involves making use of tacit knowledge and does not resemble visual perception.
Visual imagery and visual perception influence each other in ways predictable from Kosslyn’s theory. Neuroimaging studies and studies on brain-damaged patients indicate that similar areas are involved in imagery and perception. However, areas involved in top-down processing (e.g., left temporal lobe) are more important in imagery than perception, and areas involved in bottom-up processing (e.g., early visual cortex) are more important in perception. Thus, there are major similarities and differences between imagery and perception.
Further reading
• Bruce, V. & Young, A. (2012). Face perception. Hove: Psychology Press. Vicki Bruce and Andy Young provide a thorough and authoritative account of our current knowledge of face perception.
• Dror, I.E., Champod, C., Langenburg, G., Charlton, D., Hunt, H. & Rosenthal, R. (2011). Cognitive issues in fingerprint analysis: Inter- and intra-expert consistency and the effect of a “target” comparison. Forensic Science International, 208: 10–17. Some of the major problems that arise in fingerprint analysis are discussed by Itiel Dror (a leading expert in this field) and his colleagues.
• Ganis, G. & Schendan, H.E. (2011). Visual imagery. Wiley Interdisciplinary Reviews – Cognitive Science, 2: 239–52. In this article, the authors provide a comprehensive account of our current knowledge and understanding of visual imagery.
• Hayward, W.G. (2012). Whatever happened to object-centred representations? Perception, 41: 1153–62. William Hayward discusses important theoretical issues relating to how observers identify objects presented from different viewpoints.
• Hummel, J.E. (2013). Object recognition. In D. Reisberg (ed.), The Oxford handbook of cognitive psychology. Oxford: Oxford University Press. This chapter by John Hummel provides a comprehensive overview of theory and research on object recognition.
• Reisberg, D. (2013). Mental images. In D. Reisberg (ed.), The Oxford handbook of cognitive psychology. Oxford: Oxford University Press. Major issues concerning visual imagery are discussed at length by Daniel Reisberg.
• Wade, N.J. & Swanston, M.T. (2013). Visual perception: An introduction (3rd edn). Hove: Psychology Press. This textbook provides a good overview of visual perception including object recognition.
• Wagemans, J., Feldman, J., Gepshtein, S., Kimchi, R., Poemerantz, J.R. & van der Helm, P.A. (2012). A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations. Psychological Bulletin, 138: 1218–52. The Gestaltists’ theoretical approach is compared with more contemporary theories of perceptual grouping and figure–ground segregation.
• Wallis, G. (2013). Toward a unified model of face and object recognition in the human visual system. Frontiers in Psychology, 4 (Article 497). Guy Wallis argues persuasively that the influential notion that face and object recognition involves different processes may be incorrect.