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Pigeons and perceptrons
Pattern Recoonition, VoL 19. No. 6. pp 431 438. 1986. Printed in Great Britain.
0031 3203/86 $3.00-*- .00 Pergamon Journals Ltd. Pattern Recognition Society
PIGEONS A N D PERCEPTRONS* JOHN CERELLA Veterans Administration Outpatient Clinic, Boston, MA 02108, U.S.A.
{Received 13 September 1985: receiredfor publication 11 February 1986) Abstract--The laboratory pigeon is able to classify images into natural categories such as people, trees, fish, etc. A series of experiments suggested that the pigeon does so by cueing on specific features. There was no tolerance of noise or of perspective distortion, and no recovery of the three-dimensional target. It appeared that the pigeon's capacity could be described by a diameter-limited perceptron. A second series of experiments revealed that feature detection was. in fact, translation invariant: responding extended to any permutation of the feature set. Thus, the pigeon can be more accurately described as extracting the first order invariants from an image class. This first order limitation may be tied to the nearfield grain acquisition system, and may not apply to the far-field flight control system.
Pattern recognition Animal perception
Image classification
Biological systems
I. INTRODUCTION The laboratory pigeon can be easily trained to identify natural objects in 35mm color shades. In a typical experimend 1~ a particular class, such as "people", is designated positive. A large collection of slides, some with people and some without, is presented to the pigeon. The slides display outdoor scenes, perhaps from the libraries of the National Geographic or the Canadian Tourist Bureau. They are projected onto a small screen mounted in the wall of a Skinner box, and are prepunched with a code read by photocells into a microcomputer which conducts the training session. If a person is present in the image the pigeon is reinforced for pecking the screen by a grain delivery mechanism. If no person is present, pecking is not reinforced and serves to prolong the image, which does not turn off until pecking ceases. After one or two 80-slide sessions, the pigeon's pecking comes under control of the slides: pecking to positives is rapid and pecking to negatives is negligible. The rate of pecking to the screen before any feedback from the reinforcement schedule has occurred provides a measure of the bird's classification of the image. The classification is usually correct for 76 or 77 of the 80 slides of a session. Three or four slides are likely to be misclassified, a positive frame inducing a low response rate or a negative frame a high. The errors turn out to be non-informative. The affected frames vary randomly from session to session if the same slides are repeated, and are apparently due to lapses in attention. Indeed similar errors occur in the simple red vs green discriminations investigated by * Research supported by NIH Grant MH 32628 and the VA Medical Research Service. Veterans Administration, 17 Court St., Boston, MA 02108, U.S.A. 431
Objectidentification
Skinner in the 1940s. The learning rate is about the same in the two cases as well (one or two sessionst. This procedure has been applied to a variety of visual classes with equal success: ~2~people, a particular person, trees, bodies of water, pigeons and fish. Thus, the pigeon, with its pea-sized brain, can sort slides into the same natural categories into which we divide the world, an achievement that lies beyond current computer systems. Recent research has shed some light on how this is achieved. These experiments have tested the pigeon on a variety of specially devised stimulus sets. Depending on which pattern classes can be separated and which cannot be, inferences can be made about the kind of analysis applied to the image. The outcomes suggest that some rather simple mechanisms underlie the enormously complex visual classes ultimately attained by the pigeon. 2. NATURALISTIC IMAGES We begin by considering some earlier work on images which, although they were not natural, were, nonetheless, naturalistic. The first experiment involved a set of imaginary creatures called Caminalcules, devised by a numerical taxonomist to illustrate evolutionary principles. Pigeons were asked to distinguish each Caminalcule from every other. Depending on which pairs of Caminalcules were easy to distinguish and which pairs were difficult, the stimuli could be situated in a "perceptual space" such that the distance between stimuli reflected their perceived similarity. (See Fig. I). One could then ask, which of the 25 physical features of the stimuli would generate the same proximity matrix'? The answer was surprisingly s i m p l e - - t h r e e physical features were sufficient to generate a corresponding matrix. These were: the eye type, the forelimb
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Fig. 1. Twelve Caminalcules positioned such that the distance between any pair predicts the extent to which they were confused by the pigeon. The data from four pigeons were similar, and indicate that only eye spots, back markings and forelimbs were noticed. type and the back markings. The analysis suggested that in their efforts to differentiate these complex patterns, the pigeons ignored their more extended characteristics such as body configuration, and focused on specific blobs and protuberances. This kind of "particulate perception" also appeared in a second experiment,¢3~ again involving a set of imaginary creatures. The creatures were somewhat more familiar. Hundreds of Peanuts frames were photographed, and the pigeons were asked to pick out Charlie Brown as the positive, in contrast to the other Peanuts characters, Lucy, Linus, etc. as negatives. This discrimination, across Charlie Brown's countless attitudes and attires, was readily achieved. At the conclusion of the training, test images were presented that were composed of randomly arranged pieces of Charlie Brown (heads, torsos, etc.}. The scrambled images were unhesitantly accepted as positive by the pigeons. (See Fig. 2). Thus, the stimulus could be broken into bits and
pieces without disrupting the discrimination. The result again raised the possibility that the pigeons may have been seeing only bits and pieces with no comprehension of the whole. The experiment, though, was inconclusive because there was no penalty for responding to the scrambled test patterns. The pigeons may have been aware of the global disruption of the stimulus, but have chosen to respond anyway. The second experiment thus prompted a third, in which pigeons were explicitly reinforced to accept the intact projections of a target as positive, and to reject disrupted projections as negative,c4~The target was a cube, presented in wire-frame projection on a small video monitor. Disruptions were introduced in such a way that the local features of the positives, the L-, Y-, and fork-vertices, were preserved in the negatives. Only by considering relations between features could the two classes of patterns be separated. While this was a trivial task for me, it proved to be an impossible task for the pigeons. Despite intensive training and ready
Pigeons and perceptrons
Fig. 2. After being trained to identify Chartie Brown, pigeons treated scrambled and incomplete test figuresas equivalent to intact figures.
acquisition of individual patterns under the same circumstances, the discrepant cubes could not be identified as a class. (See Fig. 3). Thus, pigeons were able to distinguish two pattern classes, Charlie Brown vs Lucy, Linus, etc. that differed on local features, even when global features were disrupted. At the same time, pigeons could not distinguish two pattern classes, cubes and non-cubes, composed of the same local features that differed only on global features. The combination of results strengthens the idea that the pigeon sees only bits and pieces. Natural image processing by computer is typically performed in two stages. There is an initial analysis stage, in which the image is decomposed into a set of features, followed by a synthesis stage in which a threedimensional configuration is reconstructed. The latter is a formidable computation, one that is evidently beyond the capacity of the pigeon. A final experiment considered some simpler global properties, involving two-dimensional rather than three-dimensional relations among pattern elements.'5~In the first condition of the experiment, pigeons were asked to distinguish a particular oak leaf (Quercus alba, in silhouette) from tree leaves of other species. This was readily achieved. The prototype was conspicuous for its rounded lobes, not present in any negative leaf patterns. If the pigeon had extracted this local feature, responding might be expected to transfer to other oak leaves, as rounded
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lobes are a species characteristic. This expectation was confirmed--other oaks were accepted as positive after the initial discrimination was established, although they differed greatly from the prototype in overall shape. (See Fig. 4). In the second condition of the experiment, another group of pigeons was asked to distinguish the same oak, not from non-oaks, but from other oaks. This 1 could do by noting its overall shape, but the pigeons could not. A pattern description involving both local features and their global arrangement could not be formulated.
3. PERCEPTRON MODEl, Taken together, these data suggest that the visual capacity of the pigeon may be described by a diameterlimited perceptron, "~ a system that readily detects local features and classifies patterns on their basis, but is unable to compute relations between features. Some more recent experiments have tested this model. The experiments involved drabber but better controlled patterns than the naturalistic ones used earlier. The strategy was to establish a specific pattern as positive and then to challenge the pigeon with transformations of the prototype known to be beyond the equivalence relations achievable by a perceptron. Consider an elementary pattern discrimination, akin to the red vs green of Skinner: X vs O, where each stimulus is a single capital letter centered on a small video screen. Such a discrimination, involving one positive and one negative, can be learned in a single session. Now consider a slightly more elaborate problem: the stimuli are letter pairs: X ... O vs X ... X or O ... O, where ... indicates that the two letters are separated on the screen by five blank spaces. Here there is one positive, presented in a random sequence of trials intermixed with two possible negatives. This, too, is an easy problem for the pigeon, as it would be for a perceptron. A perceptron, for example, could be constructed to assign a value of 1 to an X in the left
Fig. 3. Pigeons could not distinguish intact cubes from distorted cubes. Notice that both were composed from the same local features.
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Fig. 4. The center oak leaf (top row) could be readily distinguished from non-oak leaves [bottom row) but could not be distinguished from other oak leaves{top left and right). screen position and a 1 to an O in the right screen position; anything else is assigned a 0 value. Under this scheme the composite XO will sum to 2, whereas X X and OO each sum to 1. The two sets of patterns can thus be straightforwardly separated under linear threshold logic. Feature definitions in a perceptron are realized directly as configurations of white and black cells on the retinal mosaic. One consequence of this "singlelayered" logic is a lack of tolerance to noise cells on the retina. Patterns that register in isolation will not be detected when contaminated with noise. The effect of noise on the X ... O discrimination was examined by filling blank character spaces with random letters, resulting in letter strings such as LCXBZDJIOTJ (positive) or CHXFUTYLXAM (negative). The manipulation was attempted on 10 pigeons; in every case the established discrimination collapsed, and performance remained at chance for some 30 ad-
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ditional sessions, recovering only when the filler characters were removed. A similar manipulation on a different set of patterns had a similar effect. Pigeons readily discriminated pairs of random checkerboards when the checkerboards were small, containing 4 x 6, 8 x 12, or 16 x 24 cells. These random configurations of cells constituted effective "local features". Large checkerboards. containing32 x 48,64 x 96, or 128 x 192cells. could not be discriminated. The large checkerboards must have been replete with potential features of the sort found in small checkerboards. Because they were not physically isolated, the potential features evidently could not be extracted. (See Fig. 5). The inability of the pigeon to separate target from surround is not total. Returning to the X ... O problem, it was found that one or two filler characters could be introduced without reducing performance. As additional distractors were added, the discrimination deteriorated. Both the pigeon and the perceptron fail on noisy targets. What other parallels may exist? Another limitation of the perceptron is the inability to generalize across the perspective transformations of a target. Any set of feature detectors sufficient to capture all of the translation group or the rotation group of a prototype will capture many non-target transforms as well, linear separation logic based on a vocabulary of small features is insufficient to circumscribe the target group. Generalization to perspective transforms was tested in the pigeon in the course of an elementary letter discrimination.~7}A single letter was trained as positive (either F or G or J or R) in contrast to the remaining letters as negative. Letters had a square format so that rotations would not alter the height/width ratio. An assortment of 10 discrete transformations of the target was then presented. Of these, seven were treated as negative. These included all of the rotations and the dilations. For example, an upside down F was considered no more equivalent to an upright F than was a P or a Q. (See Fig. 6). The remaining three transforms were treated more or less as positive. One of them was the reflection around a vertical axis: F and q were treated as equivalent. This peculiarity of the pigeon's visual system is true of the human visual system too. Human observers cannot discriminate between normal and reflected alphabet letters, at least when presented in an unfamiliar orientation, except by an elaborate mental computation. I
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Fig. 5. P i g e o n s c o u l d d i s c r i m i n a t e a pair of n x m r a n d o m c h e c k e r b o a r d s ifthe c h e c k e r b o a r d s were small{4 x 6. 8 x 12, or 16 x 24 [right column)) but not if the c h e c k e r b o a r d s were large [32 x 48, 64 x 96. or 128 x 192 [left column)].
Fig. 6. Pigeons treated the three t r a n s f o r m a t i o n s in the top row as e q u i v a l e n t to the target, a n d the seven t r a n s f o r m a t i o n s in the b o t t o m row as different from the target. I N o t e . indicates a shift in position).
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consisted of two large shapes composed of asterisks. The other pair consisted of two small shapes both, repeated in the form of a large plus sign. The pigeons learned the small shapes immediately: they never learned the large shapes well. (See Fig. 7). The second condition on first order invariants has not yet been treated.. The invariance may be only statistical--a given feature need not occur in every positive, only in some of the positives. This condition certainly applies to natural categories, for example, some images of trees will display leaves, others will not. The pigeon has no difficulty in dealing with this variability in natural concept formation experiments. Two experiments have demonstrated such statistical summation on controlled pattern sets. The first experiment used letter triplets as stimuli, presented at the center of the screen. The positives were six of the 120 permutations of the letters A, B. C. D. E, G taken three at a time, namely, ADE, EDG, GBD, CEA, BAC and GBC. Negatives were random triplets formed from the remainder of the alphabet, e.g. PXT, RIS, etc. The six positives were rapidly acquired by the pigeons, and responding subsequently transferred to the remaining ! 14 permutations of the positive letters. This data suggested two things. First, local features, that is individual letters, were extracted as expected rather than whole triplets, because responding extended from the training triplets to their permutations. Second, individual letters were extracted although they only occurred in every other positive. (Positives, in turn, alternated with negatives, so a given letter~ e.g. "A", actually occurred once in every four patterns on the average.) A final experiment ~1°~ provided further evidence of the pigeon's sensitivity to sporadically occurring pattern elements, and also illustrated several of the other points that have been made. The experiment compared the rate of acquisition of a variety of pattern classes, all based on a common format. The classes were defined on a vocabulary of 256 local features, each being a minute random walk fragment akin to the small, easily discriminated random checkerboards illustrated 4. FIRST ORDER INVARIANTS above. Twelve of the 256 features were designated The correspondence between the pigeon and the positive, and the remaining 244 were negative. A perceptron breaks down at this point. Both employ a complete positive pattern consisted of four positive vocabulary of small features: in the one the features are features chosen randomly without replacement from position invariant, in the other they are not. The the set of 12, and placed randomly into four of the cells pigeon thus commands more computational power of a 4 x 4 matrix. The remaining 12 cells were left than a single-layered perceptron; power that allows it blank. A negative pattern consisted of four features to extract the first order invariants from a pattern set, chosen from the negative feature set, placed randomly that is, the repeated subpatterns. Neither device, in the same fashion. (See Fig. 8J. however, is able to extract higher order invariants, Because features were sampled with equal probthose involving the relations between subpatterns. ~8~ ability, a given positive feature occurred once in every Two conditions apply to the subpatterns extracted six patterns on the average, in a random location. by the pigeon. First. they are small. This has already Despite this intermittency, the discrimination was been suggested by the scale of the features that learned successfully. Thus, the pigeon shows consideremerged from the tests on Caminaicules, Charlie able facility in correlating the elements of one pattern Brown and oak leaves. The size constraint was also with those in another. This facility was, nonetheless, taxed. In another tested directly in an experiment that required pigeons to discriminate two pairs of patterns. ~91 One pair condition, some of the 12 positive features were The other two positive transforms held more interest, the left and right shifts. Displacement by half a character space did not disrupt responding to the target. The pigeon, therefore, demonstrated translation invariance. The extent of the invariance was mapped in another experiment, which encouraged the pigeon to peck at an X anywhere on the screen as fast as possible while refraining to peck at an O. Xs within the inner 50°., of the screen area were attained "instantaneously", without an increase in latency over the specially trained centermost position. More remote Xs were successfully detected, but with an increase in latency of about 40ms per characterposition displacement. The pigeon's eyes are relatively immobile, forcing a whole head movement ifa shift in gaze is necessary. These data suggest that large displacements triggered such a movement, allowing the target to be recentered at a cost of increased response time. By the same token, the pigeon enjoyed true shift invariance over a fair-sized area. in that target displacements were not compensated by a head movement. Apparently, detection occurred at the eccentric location directly. A further experiment confirmed the robustness of shift invariance in the pigeon. After training the X . . . . . O discrimination described above, the spacing between the two letters was varied from X . O to X . . . . . . . . . O. Performance was unaffected, indicating the pigeon had not employed position information in its description of the patterns. An additional group of pigeons was explicitly asked to formulate position dependent descriptions. The birds attempted to distinguish the positive, X ..... O, from the negatives, X . O, X .. O, X ... O, X . . . . . . . O. X . . . . . . . . O, X . . . . . . . . . O, which could only be done by encoding the spacing of the elements. The pigeons were unable to do this. Thus, the pigeon is sensitive to the occurrence but not the relative position of pattern elements.
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sampled with a higher probability than others, creating "typical" features. Classes structured in this way were easier to learn. The effects of figure/ground separation were also examined. When the 12 empty cells of a frame were filled with negative features there was no learning. As in previous experiments noise preempted feature extraction. Again, however, a limited amount of noise was tolerated. When a smaller number of background features, varied randomly from one to eight, was added to each frame the discrimination could be formed, but not as rapidly as in the no background condition. In a final condition, positive features were clustered rather than dispersed over the 4 x 4 array. The cells immediately surrounding the cluster were left vacant, while the more remote cells were filled with noise features. Negative patterns, of course, were clustered in the same way. Isolating the target in this way proved to be completely restorative. This discrimination was no more difficult than the no background condition.
5. CONCLUSIONS
Our efforts to unravel natural image processing in the pigeon have led us from the naturalistic representations of Peanuts and Caminalcules to the decidedly unnatural squiggles and blobs of the last experiment. Paradoxically, the last set of patterns better expresses the characteristics of natural images as they are transformed by the visual system. These patterns actually constitute a model of natural images and image classes as apprehended by the pigeon. Let us review their characteristics. First, a figure/ground transformation has been applied, separating critical regions of the image from the surround on the basis of gray levels. As we have seen, even practiced targets cannot be detected if lowlevel segmentation is not possible. Second, critical regions have been decomposed into a set of local features. We have repeatedly seen the pigeon focus on fragments of an image, and fail in attempts to write descriptions of extended regions. Third, a class has been defined on the basis of distinctive local features. Such shared features seem to be the common element in all of the pattern sets the pigeon has successfully acquired. Fourth, the feature set was scrambled from instance to instance. Feature detection is position invariant. Furthermore, the scrambling takes the form of a total permutation. We have presented evidence that constraints in the repositioning of features fail to be represented. For example, a cyclic permutation is not distinguished from a random permutation. Fifth, although features are displaced randomly, they have not been rotated, reduced, or otherwise transformed. As we have seen, the pigeon treats such a transformed feature as a different feature. Sixth, features recur only intermittently. The pigeon is able to extract repeated elements across at least half a dozen intervening frames. In summary, a natural class appears to be represented by the pigeon as a large unstructured set of local features. Individual instances present a sample of features--a sample drawn randomly from the set and positioned randomly in the frame, so far as the pigeon is aware. This model of image processing coincides very neatly with our current understanding of the electro-
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Fig. 8. Pattern classeswere created from a set of 12 positive features and a set of 244 negative features. Positive frames always presented a sample of four positive features;negative frames always presented a sample of four negative features. Such classes could be learned by the pigeon (left). When 12 additional negative features were added to every frame, the resulting classes could not be learned (center). Isolating positive features by suppressing adjacent background features permitted learning as usual (right).
Pigeons and perceptrons physiology of the visual system. The classic work of Hubel and Wiesel~ demonstrated that neurons in primary striate cortex respond to simple features such as dots and strokes at particular locations or receptive fields on the retina. More recent work ~t2~has described the subsequent processing of this information in poststriate cortex. Primary feature detectors are combined in two ways: simple features are compounded into more complicated features; and narrow receptive fields are compounded into wide receptive fields. Poststriate visual cortex seems ideally suited to the detection of the first order invariants in a pattern set; this functionality has, in fact, been demonstrated in computer simulations.~ 3~
6. SPECULATIONS Evidently the first order invariants in natural images are sufficiently distinctive as to allow successful classification on their basis. Nevertheless, this "bits and pieces" approach is a simplified one, and will fail on selected stimuli as has been shown. Most often these stimuli have involved a pathological rearrangement of parts. Perhaps such cases are sufficiently rare in real life for the errors not to matter. What errors might matter? Let us speculate a moment on this issue from the pigeon's point of view. Overlooking the specialized activities of the reproductive cycle, the urban pigeon appears to spend most of its active time foraging. Offhand, two perceptual systems seem crucial to foraging, and neither is very tolerant oferrors. First, a whole body guidance system, directing movements through a cluttered environment (Times Square is a favorite foraging site!). Second, a food detection system, triggering pecks at edible grains (target acquisition is meant literally here!) Of the food detection system we have had some inkling, in one of the alphabet discriminations. Recall that the pigeon effortlessly "attains" an X anywhere within a fairly large field. The target is attained by pecking at it--that is to say, a single point in space can be localized very precisely. Indeed, the two frontal eye fields of the pigeon overlap, so that stereoscopic depth is likely available. Of the pigeon's guidance system we have had no inkling. Indeed, the system seems to revolve around the comprehension of spatial relationships, the very capacity found wanting in the pigeon. Here the neurophysiology is informative. The positioninvariant, complex-feature detecting neurons already described turn out to be dedicated to object identification. A different set of neurons is specialized for spatial processing. ~t'*~ These neurons respond to simple spots and strokes, just like primary neurons, and have narrow, rather than wide, receptive fields. In aggregate they appear to represent the surface layout of the surround. The two neural populations are separated with special clarity in the pigeon,~5"~6~ whose eyes each
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have two foveae. One pair of foveae looks forward with a permanent focus on near objects and drives "identity'" neurons further on the visual pathway. The other pair of foveae looks sideways with a far focus, and drives "spatial" neurons. Surely these are the machinery for the food detection and movement guidance systems, respectively. Now, in presenting images on a small screen in front of the beak, we are entering the near-focus system, whose primary adaptation may be grain detection. In the pigeon, detail-based object identification of any other sort may be entirely secondary. This would explain the overwhelming emphasis on readily isolated fragments such as the spots and protuberances of the Caminalcules, and the lack of concern with any larger structure. Where we see people and trees in front of our noses, the pigeon may see only some exotic species of grain."71 If such is the case, the application of this primitive mechanism to natural image classification may be altogether fortuitous. Be it fortuitous or planned, the effectiveness of this first-order solution must be acknowledged, or perhaps, even imitated.
REFERENCES
1. R. Herrnstein and D. Loveland, Complex visual concept in the pigeon, Scienee 146, 549 (1964). 2. R. Herrnstein, D. Loveland and C. Cable, Natural concepts in pigeons,J. Exp. Psych: Animal Beh. Proce.sses 2, 285 (1976}. 3. J. Cerella, The pigeon's analysis of pictures, Pattern Recognition 12, 1 (1980). 4. J. Cerella, Absence of perspective processing in the pigeon, Pattern Recoynition 9, 65 (1977). 5. J. Cerella, Visual classes and natural categories in the pigeon, J. Exp. Psych: Human Pereep. Perlorm. 5, 68 (1979). 6. M. Minsky and S. Papert, Pereeptrons. M.I.T. Press, Cambridge, MA (1972). 7. J. Cerella, Shape constancy in the pigeon, to appear in Pattern Recoqnition and Concepts in Animals. People and Machines. M. Commons, S. Kosslyn and R. Herrnstein, eds. Erlbaum, NJ. 8. F. Hayes-Roth, Representation of structured events and efficient procedures for their recognition, Pattern Recoynition 8, 141 (1976). 9. J. Cerella, Pigeon's response to Navon's patterns, proc. Eastern Psyeh. Ass. 56, 56 (19851. 10. J. Cerella, Simulating natural concepts in the pigeon, Proc. Am. Psych. Ass. 91, 29 (1983). 11. D. Hubel and T. Wiesel, Receptive fields and functional architecture of monkey striate cortex, J. Physiol. 195, 215 (1968). 12. C. Gross, C. Rocha-Miranda. and D. Binder, Visual properties of neurons in inferotemporal cortex of the macaque, J. Neuroph),siol. 35, 96 (1972). 13. K. Fukushima and S. Miyake, Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Reco.qnition 15, 455 (1982). 14. L. Ungerleider and M. Mishkin, Two cortical visual systems, Analysis of Visual Beha~,ior. D. Ingle, M. Goodale and R. Mansfield,eds. M.I.T. Press, Cambridge, MA (1982). 15. P. Blough, Functional implications of the pigeon's peculiar retinal structure, Neural Mechanisms o]Behat~ior in the Pi.qeon. A. Granda and J. Maxwell, eds. Plenum Press, NY (1979).
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16. D. Cohen and H. Karten. The structural organization of avian brain, Birds: Brains and Behariar. Vol. I. Goodman and M. Schein, eds. Academic Press, NY 11974). 17. This view has been previously developed for other non-
mammalian vertebrates: D. Ingle, Mechanisms of shaperecognition among vertebrates. Handbook Of Sensory Psycholo~/y. Vol. 7, R. Held, ed. Springer, Berlin (1978).
About the Author--Jo..~ CERELLA received a B.A. in 1969 and a Ph.D. in 1975 from the Psychology Department of Harvard University, where some of his research was done in apparatus used originally by Skinner. His current interests include the effects of age on human vision. Extreme age may precipitate deficits similar to those seen in the pigeon.
0031 3203/86 $3.00-*- .00 Pergamon Journals Ltd. Pattern Recognition Society
PIGEONS A N D PERCEPTRONS* JOHN CERELLA Veterans Administration Outpatient Clinic, Boston, MA 02108, U.S.A.
{Received 13 September 1985: receiredfor publication 11 February 1986) Abstract--The laboratory pigeon is able to classify images into natural categories such as people, trees, fish, etc. A series of experiments suggested that the pigeon does so by cueing on specific features. There was no tolerance of noise or of perspective distortion, and no recovery of the three-dimensional target. It appeared that the pigeon's capacity could be described by a diameter-limited perceptron. A second series of experiments revealed that feature detection was. in fact, translation invariant: responding extended to any permutation of the feature set. Thus, the pigeon can be more accurately described as extracting the first order invariants from an image class. This first order limitation may be tied to the nearfield grain acquisition system, and may not apply to the far-field flight control system.
Pattern recognition Animal perception
Image classification
Biological systems
I. INTRODUCTION The laboratory pigeon can be easily trained to identify natural objects in 35mm color shades. In a typical experimend 1~ a particular class, such as "people", is designated positive. A large collection of slides, some with people and some without, is presented to the pigeon. The slides display outdoor scenes, perhaps from the libraries of the National Geographic or the Canadian Tourist Bureau. They are projected onto a small screen mounted in the wall of a Skinner box, and are prepunched with a code read by photocells into a microcomputer which conducts the training session. If a person is present in the image the pigeon is reinforced for pecking the screen by a grain delivery mechanism. If no person is present, pecking is not reinforced and serves to prolong the image, which does not turn off until pecking ceases. After one or two 80-slide sessions, the pigeon's pecking comes under control of the slides: pecking to positives is rapid and pecking to negatives is negligible. The rate of pecking to the screen before any feedback from the reinforcement schedule has occurred provides a measure of the bird's classification of the image. The classification is usually correct for 76 or 77 of the 80 slides of a session. Three or four slides are likely to be misclassified, a positive frame inducing a low response rate or a negative frame a high. The errors turn out to be non-informative. The affected frames vary randomly from session to session if the same slides are repeated, and are apparently due to lapses in attention. Indeed similar errors occur in the simple red vs green discriminations investigated by * Research supported by NIH Grant MH 32628 and the VA Medical Research Service. Veterans Administration, 17 Court St., Boston, MA 02108, U.S.A. 431
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Skinner in the 1940s. The learning rate is about the same in the two cases as well (one or two sessionst. This procedure has been applied to a variety of visual classes with equal success: ~2~people, a particular person, trees, bodies of water, pigeons and fish. Thus, the pigeon, with its pea-sized brain, can sort slides into the same natural categories into which we divide the world, an achievement that lies beyond current computer systems. Recent research has shed some light on how this is achieved. These experiments have tested the pigeon on a variety of specially devised stimulus sets. Depending on which pattern classes can be separated and which cannot be, inferences can be made about the kind of analysis applied to the image. The outcomes suggest that some rather simple mechanisms underlie the enormously complex visual classes ultimately attained by the pigeon. 2. NATURALISTIC IMAGES We begin by considering some earlier work on images which, although they were not natural, were, nonetheless, naturalistic. The first experiment involved a set of imaginary creatures called Caminalcules, devised by a numerical taxonomist to illustrate evolutionary principles. Pigeons were asked to distinguish each Caminalcule from every other. Depending on which pairs of Caminalcules were easy to distinguish and which pairs were difficult, the stimuli could be situated in a "perceptual space" such that the distance between stimuli reflected their perceived similarity. (See Fig. I). One could then ask, which of the 25 physical features of the stimuli would generate the same proximity matrix'? The answer was surprisingly s i m p l e - - t h r e e physical features were sufficient to generate a corresponding matrix. These were: the eye type, the forelimb
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Fig. 1. Twelve Caminalcules positioned such that the distance between any pair predicts the extent to which they were confused by the pigeon. The data from four pigeons were similar, and indicate that only eye spots, back markings and forelimbs were noticed. type and the back markings. The analysis suggested that in their efforts to differentiate these complex patterns, the pigeons ignored their more extended characteristics such as body configuration, and focused on specific blobs and protuberances. This kind of "particulate perception" also appeared in a second experiment,¢3~ again involving a set of imaginary creatures. The creatures were somewhat more familiar. Hundreds of Peanuts frames were photographed, and the pigeons were asked to pick out Charlie Brown as the positive, in contrast to the other Peanuts characters, Lucy, Linus, etc. as negatives. This discrimination, across Charlie Brown's countless attitudes and attires, was readily achieved. At the conclusion of the training, test images were presented that were composed of randomly arranged pieces of Charlie Brown (heads, torsos, etc.}. The scrambled images were unhesitantly accepted as positive by the pigeons. (See Fig. 2). Thus, the stimulus could be broken into bits and
pieces without disrupting the discrimination. The result again raised the possibility that the pigeons may have been seeing only bits and pieces with no comprehension of the whole. The experiment, though, was inconclusive because there was no penalty for responding to the scrambled test patterns. The pigeons may have been aware of the global disruption of the stimulus, but have chosen to respond anyway. The second experiment thus prompted a third, in which pigeons were explicitly reinforced to accept the intact projections of a target as positive, and to reject disrupted projections as negative,c4~The target was a cube, presented in wire-frame projection on a small video monitor. Disruptions were introduced in such a way that the local features of the positives, the L-, Y-, and fork-vertices, were preserved in the negatives. Only by considering relations between features could the two classes of patterns be separated. While this was a trivial task for me, it proved to be an impossible task for the pigeons. Despite intensive training and ready
Pigeons and perceptrons
Fig. 2. After being trained to identify Chartie Brown, pigeons treated scrambled and incomplete test figuresas equivalent to intact figures.
acquisition of individual patterns under the same circumstances, the discrepant cubes could not be identified as a class. (See Fig. 3). Thus, pigeons were able to distinguish two pattern classes, Charlie Brown vs Lucy, Linus, etc. that differed on local features, even when global features were disrupted. At the same time, pigeons could not distinguish two pattern classes, cubes and non-cubes, composed of the same local features that differed only on global features. The combination of results strengthens the idea that the pigeon sees only bits and pieces. Natural image processing by computer is typically performed in two stages. There is an initial analysis stage, in which the image is decomposed into a set of features, followed by a synthesis stage in which a threedimensional configuration is reconstructed. The latter is a formidable computation, one that is evidently beyond the capacity of the pigeon. A final experiment considered some simpler global properties, involving two-dimensional rather than three-dimensional relations among pattern elements.'5~In the first condition of the experiment, pigeons were asked to distinguish a particular oak leaf (Quercus alba, in silhouette) from tree leaves of other species. This was readily achieved. The prototype was conspicuous for its rounded lobes, not present in any negative leaf patterns. If the pigeon had extracted this local feature, responding might be expected to transfer to other oak leaves, as rounded
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lobes are a species characteristic. This expectation was confirmed--other oaks were accepted as positive after the initial discrimination was established, although they differed greatly from the prototype in overall shape. (See Fig. 4). In the second condition of the experiment, another group of pigeons was asked to distinguish the same oak, not from non-oaks, but from other oaks. This 1 could do by noting its overall shape, but the pigeons could not. A pattern description involving both local features and their global arrangement could not be formulated.
3. PERCEPTRON MODEl, Taken together, these data suggest that the visual capacity of the pigeon may be described by a diameterlimited perceptron, "~ a system that readily detects local features and classifies patterns on their basis, but is unable to compute relations between features. Some more recent experiments have tested this model. The experiments involved drabber but better controlled patterns than the naturalistic ones used earlier. The strategy was to establish a specific pattern as positive and then to challenge the pigeon with transformations of the prototype known to be beyond the equivalence relations achievable by a perceptron. Consider an elementary pattern discrimination, akin to the red vs green of Skinner: X vs O, where each stimulus is a single capital letter centered on a small video screen. Such a discrimination, involving one positive and one negative, can be learned in a single session. Now consider a slightly more elaborate problem: the stimuli are letter pairs: X ... O vs X ... X or O ... O, where ... indicates that the two letters are separated on the screen by five blank spaces. Here there is one positive, presented in a random sequence of trials intermixed with two possible negatives. This, too, is an easy problem for the pigeon, as it would be for a perceptron. A perceptron, for example, could be constructed to assign a value of 1 to an X in the left
Fig. 3. Pigeons could not distinguish intact cubes from distorted cubes. Notice that both were composed from the same local features.
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Fig. 4. The center oak leaf (top row) could be readily distinguished from non-oak leaves [bottom row) but could not be distinguished from other oak leaves{top left and right). screen position and a 1 to an O in the right screen position; anything else is assigned a 0 value. Under this scheme the composite XO will sum to 2, whereas X X and OO each sum to 1. The two sets of patterns can thus be straightforwardly separated under linear threshold logic. Feature definitions in a perceptron are realized directly as configurations of white and black cells on the retinal mosaic. One consequence of this "singlelayered" logic is a lack of tolerance to noise cells on the retina. Patterns that register in isolation will not be detected when contaminated with noise. The effect of noise on the X ... O discrimination was examined by filling blank character spaces with random letters, resulting in letter strings such as LCXBZDJIOTJ (positive) or CHXFUTYLXAM (negative). The manipulation was attempted on 10 pigeons; in every case the established discrimination collapsed, and performance remained at chance for some 30 ad-
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ditional sessions, recovering only when the filler characters were removed. A similar manipulation on a different set of patterns had a similar effect. Pigeons readily discriminated pairs of random checkerboards when the checkerboards were small, containing 4 x 6, 8 x 12, or 16 x 24 cells. These random configurations of cells constituted effective "local features". Large checkerboards. containing32 x 48,64 x 96, or 128 x 192cells. could not be discriminated. The large checkerboards must have been replete with potential features of the sort found in small checkerboards. Because they were not physically isolated, the potential features evidently could not be extracted. (See Fig. 5). The inability of the pigeon to separate target from surround is not total. Returning to the X ... O problem, it was found that one or two filler characters could be introduced without reducing performance. As additional distractors were added, the discrimination deteriorated. Both the pigeon and the perceptron fail on noisy targets. What other parallels may exist? Another limitation of the perceptron is the inability to generalize across the perspective transformations of a target. Any set of feature detectors sufficient to capture all of the translation group or the rotation group of a prototype will capture many non-target transforms as well, linear separation logic based on a vocabulary of small features is insufficient to circumscribe the target group. Generalization to perspective transforms was tested in the pigeon in the course of an elementary letter discrimination.~7}A single letter was trained as positive (either F or G or J or R) in contrast to the remaining letters as negative. Letters had a square format so that rotations would not alter the height/width ratio. An assortment of 10 discrete transformations of the target was then presented. Of these, seven were treated as negative. These included all of the rotations and the dilations. For example, an upside down F was considered no more equivalent to an upright F than was a P or a Q. (See Fig. 6). The remaining three transforms were treated more or less as positive. One of them was the reflection around a vertical axis: F and q were treated as equivalent. This peculiarity of the pigeon's visual system is true of the human visual system too. Human observers cannot discriminate between normal and reflected alphabet letters, at least when presented in an unfamiliar orientation, except by an elaborate mental computation. I
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Fig. 5. P i g e o n s c o u l d d i s c r i m i n a t e a pair of n x m r a n d o m c h e c k e r b o a r d s ifthe c h e c k e r b o a r d s were small{4 x 6. 8 x 12, or 16 x 24 [right column)) but not if the c h e c k e r b o a r d s were large [32 x 48, 64 x 96. or 128 x 192 [left column)].
Fig. 6. Pigeons treated the three t r a n s f o r m a t i o n s in the top row as e q u i v a l e n t to the target, a n d the seven t r a n s f o r m a t i o n s in the b o t t o m row as different from the target. I N o t e . indicates a shift in position).
Pigeons and pereeptrons
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consisted of two large shapes composed of asterisks. The other pair consisted of two small shapes both, repeated in the form of a large plus sign. The pigeons learned the small shapes immediately: they never learned the large shapes well. (See Fig. 7). The second condition on first order invariants has not yet been treated.. The invariance may be only statistical--a given feature need not occur in every positive, only in some of the positives. This condition certainly applies to natural categories, for example, some images of trees will display leaves, others will not. The pigeon has no difficulty in dealing with this variability in natural concept formation experiments. Two experiments have demonstrated such statistical summation on controlled pattern sets. The first experiment used letter triplets as stimuli, presented at the center of the screen. The positives were six of the 120 permutations of the letters A, B. C. D. E, G taken three at a time, namely, ADE, EDG, GBD, CEA, BAC and GBC. Negatives were random triplets formed from the remainder of the alphabet, e.g. PXT, RIS, etc. The six positives were rapidly acquired by the pigeons, and responding subsequently transferred to the remaining ! 14 permutations of the positive letters. This data suggested two things. First, local features, that is individual letters, were extracted as expected rather than whole triplets, because responding extended from the training triplets to their permutations. Second, individual letters were extracted although they only occurred in every other positive. (Positives, in turn, alternated with negatives, so a given letter~ e.g. "A", actually occurred once in every four patterns on the average.) A final experiment ~1°~ provided further evidence of the pigeon's sensitivity to sporadically occurring pattern elements, and also illustrated several of the other points that have been made. The experiment compared the rate of acquisition of a variety of pattern classes, all based on a common format. The classes were defined on a vocabulary of 256 local features, each being a minute random walk fragment akin to the small, easily discriminated random checkerboards illustrated 4. FIRST ORDER INVARIANTS above. Twelve of the 256 features were designated The correspondence between the pigeon and the positive, and the remaining 244 were negative. A perceptron breaks down at this point. Both employ a complete positive pattern consisted of four positive vocabulary of small features: in the one the features are features chosen randomly without replacement from position invariant, in the other they are not. The the set of 12, and placed randomly into four of the cells pigeon thus commands more computational power of a 4 x 4 matrix. The remaining 12 cells were left than a single-layered perceptron; power that allows it blank. A negative pattern consisted of four features to extract the first order invariants from a pattern set, chosen from the negative feature set, placed randomly that is, the repeated subpatterns. Neither device, in the same fashion. (See Fig. 8J. however, is able to extract higher order invariants, Because features were sampled with equal probthose involving the relations between subpatterns. ~8~ ability, a given positive feature occurred once in every Two conditions apply to the subpatterns extracted six patterns on the average, in a random location. by the pigeon. First. they are small. This has already Despite this intermittency, the discrimination was been suggested by the scale of the features that learned successfully. Thus, the pigeon shows consideremerged from the tests on Caminaicules, Charlie able facility in correlating the elements of one pattern Brown and oak leaves. The size constraint was also with those in another. This facility was, nonetheless, taxed. In another tested directly in an experiment that required pigeons to discriminate two pairs of patterns. ~91 One pair condition, some of the 12 positive features were The other two positive transforms held more interest, the left and right shifts. Displacement by half a character space did not disrupt responding to the target. The pigeon, therefore, demonstrated translation invariance. The extent of the invariance was mapped in another experiment, which encouraged the pigeon to peck at an X anywhere on the screen as fast as possible while refraining to peck at an O. Xs within the inner 50°., of the screen area were attained "instantaneously", without an increase in latency over the specially trained centermost position. More remote Xs were successfully detected, but with an increase in latency of about 40ms per characterposition displacement. The pigeon's eyes are relatively immobile, forcing a whole head movement ifa shift in gaze is necessary. These data suggest that large displacements triggered such a movement, allowing the target to be recentered at a cost of increased response time. By the same token, the pigeon enjoyed true shift invariance over a fair-sized area. in that target displacements were not compensated by a head movement. Apparently, detection occurred at the eccentric location directly. A further experiment confirmed the robustness of shift invariance in the pigeon. After training the X . . . . . O discrimination described above, the spacing between the two letters was varied from X . O to X . . . . . . . . . O. Performance was unaffected, indicating the pigeon had not employed position information in its description of the patterns. An additional group of pigeons was explicitly asked to formulate position dependent descriptions. The birds attempted to distinguish the positive, X ..... O, from the negatives, X . O, X .. O, X ... O, X . . . . . . . O. X . . . . . . . . O, X . . . . . . . . . O, which could only be done by encoding the spacing of the elements. The pigeons were unable to do this. Thus, the pigeon is sensitive to the occurrence but not the relative position of pattern elements.
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Fig. 7. The upper pair of patterns was readily discriminated on the basis of the small feature. The lower pair of patterns was poorly discriminated on the basis of the large shape. (Reflections were also shown, to forestall position cues.)
sampled with a higher probability than others, creating "typical" features. Classes structured in this way were easier to learn. The effects of figure/ground separation were also examined. When the 12 empty cells of a frame were filled with negative features there was no learning. As in previous experiments noise preempted feature extraction. Again, however, a limited amount of noise was tolerated. When a smaller number of background features, varied randomly from one to eight, was added to each frame the discrimination could be formed, but not as rapidly as in the no background condition. In a final condition, positive features were clustered rather than dispersed over the 4 x 4 array. The cells immediately surrounding the cluster were left vacant, while the more remote cells were filled with noise features. Negative patterns, of course, were clustered in the same way. Isolating the target in this way proved to be completely restorative. This discrimination was no more difficult than the no background condition.
5. CONCLUSIONS
Our efforts to unravel natural image processing in the pigeon have led us from the naturalistic representations of Peanuts and Caminalcules to the decidedly unnatural squiggles and blobs of the last experiment. Paradoxically, the last set of patterns better expresses the characteristics of natural images as they are transformed by the visual system. These patterns actually constitute a model of natural images and image classes as apprehended by the pigeon. Let us review their characteristics. First, a figure/ground transformation has been applied, separating critical regions of the image from the surround on the basis of gray levels. As we have seen, even practiced targets cannot be detected if lowlevel segmentation is not possible. Second, critical regions have been decomposed into a set of local features. We have repeatedly seen the pigeon focus on fragments of an image, and fail in attempts to write descriptions of extended regions. Third, a class has been defined on the basis of distinctive local features. Such shared features seem to be the common element in all of the pattern sets the pigeon has successfully acquired. Fourth, the feature set was scrambled from instance to instance. Feature detection is position invariant. Furthermore, the scrambling takes the form of a total permutation. We have presented evidence that constraints in the repositioning of features fail to be represented. For example, a cyclic permutation is not distinguished from a random permutation. Fifth, although features are displaced randomly, they have not been rotated, reduced, or otherwise transformed. As we have seen, the pigeon treats such a transformed feature as a different feature. Sixth, features recur only intermittently. The pigeon is able to extract repeated elements across at least half a dozen intervening frames. In summary, a natural class appears to be represented by the pigeon as a large unstructured set of local features. Individual instances present a sample of features--a sample drawn randomly from the set and positioned randomly in the frame, so far as the pigeon is aware. This model of image processing coincides very neatly with our current understanding of the electro-
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Fig. 8. Pattern classeswere created from a set of 12 positive features and a set of 244 negative features. Positive frames always presented a sample of four positive features;negative frames always presented a sample of four negative features. Such classes could be learned by the pigeon (left). When 12 additional negative features were added to every frame, the resulting classes could not be learned (center). Isolating positive features by suppressing adjacent background features permitted learning as usual (right).
Pigeons and perceptrons physiology of the visual system. The classic work of Hubel and Wiesel~ demonstrated that neurons in primary striate cortex respond to simple features such as dots and strokes at particular locations or receptive fields on the retina. More recent work ~t2~has described the subsequent processing of this information in poststriate cortex. Primary feature detectors are combined in two ways: simple features are compounded into more complicated features; and narrow receptive fields are compounded into wide receptive fields. Poststriate visual cortex seems ideally suited to the detection of the first order invariants in a pattern set; this functionality has, in fact, been demonstrated in computer simulations.~ 3~
6. SPECULATIONS Evidently the first order invariants in natural images are sufficiently distinctive as to allow successful classification on their basis. Nevertheless, this "bits and pieces" approach is a simplified one, and will fail on selected stimuli as has been shown. Most often these stimuli have involved a pathological rearrangement of parts. Perhaps such cases are sufficiently rare in real life for the errors not to matter. What errors might matter? Let us speculate a moment on this issue from the pigeon's point of view. Overlooking the specialized activities of the reproductive cycle, the urban pigeon appears to spend most of its active time foraging. Offhand, two perceptual systems seem crucial to foraging, and neither is very tolerant oferrors. First, a whole body guidance system, directing movements through a cluttered environment (Times Square is a favorite foraging site!). Second, a food detection system, triggering pecks at edible grains (target acquisition is meant literally here!) Of the food detection system we have had some inkling, in one of the alphabet discriminations. Recall that the pigeon effortlessly "attains" an X anywhere within a fairly large field. The target is attained by pecking at it--that is to say, a single point in space can be localized very precisely. Indeed, the two frontal eye fields of the pigeon overlap, so that stereoscopic depth is likely available. Of the pigeon's guidance system we have had no inkling. Indeed, the system seems to revolve around the comprehension of spatial relationships, the very capacity found wanting in the pigeon. Here the neurophysiology is informative. The positioninvariant, complex-feature detecting neurons already described turn out to be dedicated to object identification. A different set of neurons is specialized for spatial processing. ~t'*~ These neurons respond to simple spots and strokes, just like primary neurons, and have narrow, rather than wide, receptive fields. In aggregate they appear to represent the surface layout of the surround. The two neural populations are separated with special clarity in the pigeon,~5"~6~ whose eyes each
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have two foveae. One pair of foveae looks forward with a permanent focus on near objects and drives "identity'" neurons further on the visual pathway. The other pair of foveae looks sideways with a far focus, and drives "spatial" neurons. Surely these are the machinery for the food detection and movement guidance systems, respectively. Now, in presenting images on a small screen in front of the beak, we are entering the near-focus system, whose primary adaptation may be grain detection. In the pigeon, detail-based object identification of any other sort may be entirely secondary. This would explain the overwhelming emphasis on readily isolated fragments such as the spots and protuberances of the Caminalcules, and the lack of concern with any larger structure. Where we see people and trees in front of our noses, the pigeon may see only some exotic species of grain."71 If such is the case, the application of this primitive mechanism to natural image classification may be altogether fortuitous. Be it fortuitous or planned, the effectiveness of this first-order solution must be acknowledged, or perhaps, even imitated.
REFERENCES
1. R. Herrnstein and D. Loveland, Complex visual concept in the pigeon, Scienee 146, 549 (1964). 2. R. Herrnstein, D. Loveland and C. Cable, Natural concepts in pigeons,J. Exp. Psych: Animal Beh. Proce.sses 2, 285 (1976}. 3. J. Cerella, The pigeon's analysis of pictures, Pattern Recognition 12, 1 (1980). 4. J. Cerella, Absence of perspective processing in the pigeon, Pattern Recoynition 9, 65 (1977). 5. J. Cerella, Visual classes and natural categories in the pigeon, J. Exp. Psych: Human Pereep. Perlorm. 5, 68 (1979). 6. M. Minsky and S. Papert, Pereeptrons. M.I.T. Press, Cambridge, MA (1972). 7. J. Cerella, Shape constancy in the pigeon, to appear in Pattern Recoqnition and Concepts in Animals. People and Machines. M. Commons, S. Kosslyn and R. Herrnstein, eds. Erlbaum, NJ. 8. F. Hayes-Roth, Representation of structured events and efficient procedures for their recognition, Pattern Recoynition 8, 141 (1976). 9. J. Cerella, Pigeon's response to Navon's patterns, proc. Eastern Psyeh. Ass. 56, 56 (19851. 10. J. Cerella, Simulating natural concepts in the pigeon, Proc. Am. Psych. Ass. 91, 29 (1983). 11. D. Hubel and T. Wiesel, Receptive fields and functional architecture of monkey striate cortex, J. Physiol. 195, 215 (1968). 12. C. Gross, C. Rocha-Miranda. and D. Binder, Visual properties of neurons in inferotemporal cortex of the macaque, J. Neuroph),siol. 35, 96 (1972). 13. K. Fukushima and S. Miyake, Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Reco.qnition 15, 455 (1982). 14. L. Ungerleider and M. Mishkin, Two cortical visual systems, Analysis of Visual Beha~,ior. D. Ingle, M. Goodale and R. Mansfield,eds. M.I.T. Press, Cambridge, MA (1982). 15. P. Blough, Functional implications of the pigeon's peculiar retinal structure, Neural Mechanisms o]Behat~ior in the Pi.qeon. A. Granda and J. Maxwell, eds. Plenum Press, NY (1979).
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16. D. Cohen and H. Karten. The structural organization of avian brain, Birds: Brains and Behariar. Vol. I. Goodman and M. Schein, eds. Academic Press, NY 11974). 17. This view has been previously developed for other non-
mammalian vertebrates: D. Ingle, Mechanisms of shaperecognition among vertebrates. Handbook Of Sensory Psycholo~/y. Vol. 7, R. Held, ed. Springer, Berlin (1978).
About the Author--Jo..~ CERELLA received a B.A. in 1969 and a Ph.D. in 1975 from the Psychology Department of Harvard University, where some of his research was done in apparatus used originally by Skinner. His current interests include the effects of age on human vision. Extreme age may precipitate deficits similar to those seen in the pigeon.