Pigeons spontaneously form three-dimensional shape categories☆
Introduction
A three-dimensional object of even moderate complexity can project a near infinity of images when we consider the variations that are produced by changes in orientation, scale, and lighting direction. Humans’ accuracy in classifying an object, as when it is named, is only minimally improved, if at all, by the addition of color and surface texture (Biederman and Ju, 1988). Moreover, the speed and accuracy of object identification or matching is only minimally affected by rotation around the vertical axis as long as the same part structure is present in the image (e.g., Biederman and Gerhardstein, 1993). These findings for human participants suggest that recognition heavily weights those aspects of shape that are invariant (or “nonaccidental”) with orientation in depth, such as whether a contour is straight or curved, or whether pairs of contours are parallel or not (Biederman, 1987).
Nonverbal observers perform visual object recognition when they choose to approach one object, e.g., a token that they can exchange for food, versus another, a token that cannot be so traded (Wolf, 1936). This differential behavior is a clear indication that a visual stimulus has been recognized as a member of a class (e.g., exchangeable or not for an edible morsel). Researchers studying object recognition in nonverbal species can assign different responses to different stimuli to determine if their subjects will respond in a consistent way to one object rather than another (Jitsumori and Delius, 2001; Wood and Wood, 2017).
In previous object recognition research involving pigeons, scientists have examined generalization across viewpoint (Peissig et al., 2002; Spetch and Friedman, 2003), lighting (Young et al., 2001), size (Delius, 1992; Peissig et al., 2006), and mild deformations (Cerella, 1979; Delius, 1992; Herrnstein and Loveland, 1964), inter alia. To determine whether the same object is responded to independently of variations in its orientation, the most common approach is to train a subject to make the same response to multiple views of the same object. For example, Peissig et al. (2000a) trained pigeons to choose the upper left key when the stimulus was a barrel, the upper right key when the stimulus was an arch, etc. Each object type, or geon (Biederman, 1987), was presented at one of five viewpoints by rotating the geon in depth. If the stimuli corresponding to different objects are sufficiently distinct, then subjects should readily learn to respond differentially to the stimuli. Furthermore, if a new orientation of an object is encountered, then subjects should choose the response that was paired with the trained exemplars of the same object.
This many-to-one choice task assesses whether a subject can respond in the same way to one collection of stimuli and in a different way to other collections of stimuli. Presumably, the ability to do so is facilitated by subjects identifying something in common among the various stimuli representing the same object—a generalized object representation. The generalization to novel exemplars supports this conclusion, although it is possible that this behavior is based on the similarity of a novel object to only one of the exemplars (Tarr, 1995).
To assess the relative similarity of multiple exemplars of objects, a go/no-go task has been used, in which responding to one exemplar of one object is reinforced (the S+), whereas responding to other exemplars of that same object or to exemplars of different objects are not reinforced (the S-s). This task allows the researcher to determine the relative similarity of stimuli in the absence of any explicit training designed to create a generalized class of objects (as occurs in the many-to-one choice task). Peissig et al. (2000b) explored object recognition in pigeons using this go/no-go paradigm, in which one view of a target object served as the S+ and nine views (created by rotating the objects in depth by 36° increments) of the same object were S-s; in addition to the target object, pigeons were shown 10 views of a foil object for which all views were S-s. Pigeons made significantly fewer errors (pecks to an S−) to nonreinforced views of the foil object than to nonreinforced views of the target object. These data suggest that pigeons do indeed perceive different views of the same object as more similar to one another than to views of different objects.
The present project re-examined the issue of object recognition in the pigeon to assess a related issue from a fresh perspective. We used a task designed to determine whether any arbitrary collection of stimuli of the kind used in previous studies can be grouped together in the creation of a new object category (cf. Wasserman et al., 1988). Given that previous object recognition research in the pigeon has involved training with a relatively small number of exemplars of each object (usually fewer than 20, e.g., Delius and Hollard, 1995; Peissig et al., 2000a,b; Spetch and Friedman, 2003; Wasserman et al., 1996), it is possible that the pigeons were simply memorizing the correct response to each stimulus. If so, then the ability to generalize discriminative responding to novel exemplars could have been driven by the greater similarity of these stimuli to one or more of the trained exemplars (although the pigeon’s generalization to novel views created by rotating an object along an orthogonal axis undermines this argument, Peissig et al., 2002).
We determined the extent to which pigeons would group exemplars of different objects into the same category when such categorization was reinforced as a basis of comparison for the extent to which different views of the same object would be grouped to form a natural category for pigeons. Although the earlier work documenting greater similarity among different views of the same object suggests that it should be quite easy for the pigeon to group together different viewpoints of the same object, it is still possible that this category coherence may produce only a small improvement in performance.
Pigeons have occasionally been reported to have difficulty learning categories for artificial stimuli. For example, Cerella (1980) found that pigeons struggled to learn to categorize different images of Charlie Brown. However, Herrnstein and de Villiers (1980) had no trouble teaching pigeons to categorize images of fish. The objects used in the present study are two-dimensional images of three-dimensional objects. They include shading and surface reflectance, but it is uncertain whether pigeons perceive these stimuli as more like natural images or artificial images. If we are to feel confident that previous studies using the same or similar stimuli involve the use of normal object recognition mechanisms, we need to be assured that these objects are treated similarly to other natural categories. In addition, we have ample evidence that humans easily recognize that different views of the same object belong together (e.g., Biederman and Gerhardstein, 1993). If pigeons possess visual recognition mechanisms similar to those of humans, then it is important to see that they too exhibit the same perceptual proclivities.
In this study, we employed the category-pseudocategory technique previously used by Wasserman et al. (1988) to test whether the groupings of different views of the same object (a geon) would be learned reliably faster than groupings of different views of different objects (i.e., different geons). The method of using pseudocategories to test perceptual coherence has a long history in the field of animal cognition. To our knowledge, this method was first used by Herrnstein and de Villiers (1980), who explored whether pigeons learned to discriminate between images that contained a fish and those that did not. This group was compared to a group that Herrnstein and de Villiers called a “quasi-category” group, in which the same two sets images (fish and non-fish) were randomly assigned to reinforced and nonreinforced clusters. They found that pigeons learned the quasi-categories much more slowly than they learned the natural categories (images with fish and images without fish).
Numerous other researchers have also used pseudocategories to study concept formation in nonhuman animals, and this methodology continues to be useful in understanding the nature of categorization in nonhuman animals (Cook, 2002; Ditrich et al., 1998; Roach et al., 2017; Sturdy et al., 1999a,b; Wright et al., 2017). According to Lea and Ryan (1990), there are two possible ways of implementing pseudocategories: random and perverse. In random pseudocategories, the categories are created in a truly random fashion; thus two items from the same category could end up randomly assigned together. In perverse pseudocategory assignment, the groupings are formed such that items from the same category are explicitly assigned to different groupings.
In two different studies, Vaughan and Greene (1983, 1984) used random groupings to study concept formation in pigeons. They found that pigeons were able to successfully learn these random group assignments. In another study using pseudocategories, Sturdy et al. (1999a,b) tested whether distinct song note categories of zebra finch songs (as determined by a song note classification scheme developed by Sturdy et al., 1999a,b) would be relevant to zebra finches. To test this possibility, they selected four-note song subcategories to use to test zebra finches in a discrimination task, with 20 sample notes in each category. In the categorization group, one of the song note categories was assigned as the S+ and the remaining three categories were assigned as the S-s. In the pseudocategory group, the S+ group consisted of 20 randomly selected notes, and the remaining 60 notes were the S-s. The random assignment led to nearly equal distribution of the four-note categories across the S+ and S− groups. Sturdy et al. found that the birds in the pseudocategory condition learned the discrimination 36% more slowly than the birds in the categorization condition.
In studies in which perverse groupings are assigned (e.g., Wasserman et al., 1988 and the current study), the stimuli were grouped so that different orientations of the same geon were assigned to different responses; this grouping can be done either by systematically or by randomly assigning stimuli to different groups using a rule that assures that the different categories are equally represented in each group. In Wasserman, Kiedinger, and Bhatt, the categorization group was given assignments of the four visual categories to different responses, e.g., all cars were assigned to the upper right button, all the cats to the upper left button, all the chairs to lower right button, and all the flowers to the lower left button. In the pseudocategorization group, the stimuli were intentionally assigned so that the visual categories would cut across the button assignments. For example, the upper right key would have assigned to it an equal number of cars, cats, chairs, and flowers. The categorization group learned far faster and to much higher levels of accuracy than did the pseudocategorization group.
In many ways, the randomly assigned categories often function similarly to the perverse categories (e.g., those in which examplars are explicitly assigned to different responses), in that what we presume to be natural category boundaries are violated by the assignment of members of the same category to these two different response groups. Doing so allows us to test whether the animals behave according to the category boundaries, as defined by the experimenter, or if they treat all of the stimuli as perceptually independent of one another.
In general, virtually all of the studies have found that animals experience much great difficulty learning the pseudocategories than the “true” categories (Cook, 2002; Ditrich et al., 1998; Roach et al., 2017; Sturdy et al., 1999a,b; Wright et al., 2017). This finding suggests that the nonhuman animals that were assigned to the true category groups indeed abided by the perceptual boundaries established by the human experimenters; working against these perceptual categories must have made the task exceedingly difficult for the nonhuman animals assigned to the pseudocategorization groups.
In most studies using pigeons as research participants, including our own, experimenters have assumed that the pigeons perceived different views of an object as all deriving from a single object, similar to how they would treat different views from any natural object category. It is possible, however, that when pigeons view stimuli one at a time on a computer monitor, the stimuli may appear distinctly different from one another. If pigeons can rapidly learn to group together an arbitrary collection of views of different objects, then at least part of the learning of a more natural grouping of different views of the same object could merely be a byproduct of learning simple associations between each individual stimulus and its correct response. By comparing the rate of learning categories when the constituent stimuli are grouped according to object object identity to that when the stimuli are grouped arbitrarily, we were able to assess both the learnability of such arbitrary groupings and the degree of advantage that holds when the stimuli represent different views of the same object.
In the present study, we taught pigeons to peck four different buttons to each of four different collections of computer renderings of four different geons each at four different orientations. The entire set of 16 drawings involved four depth-rotated views of each of four discriminatively different objects: an arch, a barrel, a brick, and a wedge (see Fig. 1). These objects were chosen from the larger set of 36 basic object components, or geons, described by Biederman (1987). One group of pigeons (the Categorization group) was trained to peck the same button to the four different views of the same geon, whereas the second group of pigeons (the Pseudocategorization group) was trained to peck the same button to one view of each of the four different geons. If pigeons perceive the related nature of different views of the same geon, then the first task should be far easier to master because each of these different views of the same geon is associated with the same response; for example, the pigeon would only have to learn to peck the upper left button whenever it saw the barrel-shaped object at any orientation. Looked at in a different way, if pigeons perceive the related nature of different views of the same geon, then the second task should be far more difficult to master because each of these different views of the same geon is associated with different responses; here, for example, the birds should be quite confused as to which button is the correct one to peck to the particular view of the barrel-shaped object they happen to be viewing on that trial.
Of course, if the pigeons see all of the 16 drawings as equivalently similar to one another, then both groups of pigeons should learn their respective discriminations at the same speed. Whether this equivalent learning would be fast or slow would depend on the relative uniqueness of the stimuli. Given that the stimuli used in the present study were of a similar size, color, and shading, they may have been too similar to support rapid discrimination of all 16 stimuli, so we gave the pigeons an extended period (45 days) of training.
Section snippets
Subjects
The participants were eight feral pigeons maintained at 85% of their free feed weights by controlled daily feeding. The pigeons were obtained from the Office of Animal Resources at The University of Iowa. This office abides by all federal regulations for procurement, housing, and care of animals on the campus. The protocol for this study was approved by the Institutional Animal Care and Use Committee of The University of Iowa. The pigeons had previously participated in unrelated visual
Results
Two pigeons in the Pseudocategorization group failed to complete all 160 trials every day. Bird 87Y did not complete 2 of its sessions (it finished 133 and 143 trials on these days). Another pigeon, 55Y, did not complete 12 of its 45 sessions with 84 as the fewest number of trials completed. To minimize differential attrition, all of the data were analyzed. The statistical approach used, multilevel modeling using likelihood maximization, appropriately weights the imbalance produced by such
Discussion
The purpose of this study was to use the categorization-pseudocategorization method for assessing the extent to which the pigeon would spontaneously represent the underlying 3D shape of an object, in this case a geon. This representation allowed fast and accurate responding when the four different views of a geon were assigned to the same response, consistent with the underlying representation. In contrast, the Pseudocategorization group barely, if at all, learned the stimulus-response
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These data were included in the doctoral dissertation of Jessie J. Peissig at The University of Iowa. This research was supported by a National Institute of Mental Health Grant, MH 47313. We would like to thank C. Antes, T. Barclay, M. Prier, and B. J. Terrones for their assistance in collecting and analyzing these data.