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Feature - Shape

Feature - Color

Conjunction - Color and Shape

Figure 4.1. Stimuli used to study texture segregation. Subjects search for a target (the small rectangle) within the display. Displays A and B illustrate targets that differ in a single feature (shape or color) from the background. Note the "pop-out" effect forthese single-feature displays. Display C contains a target that differs from the background in a conjunction of features: black circles and white squares in a background of white circles and black squares. Note the difficulty in locating this target. Both pigeons and humans show decrements in performance on such conjunctive searches. (After Cook 1992.)

Conjunction - Color and Shape

Figure 4.1. Stimuli used to study texture segregation. Subjects search for a target (the small rectangle) within the display. Displays A and B illustrate targets that differ in a single feature (shape or color) from the background. Note the "pop-out" effect forthese single-feature displays. Display C contains a target that differs from the background in a conjunction of features: black circles and white squares in a background of white circles and black squares. Note the difficulty in locating this target. Both pigeons and humans show decrements in performance on such conjunctive searches. (After Cook 1992.)

and the background contained the two remaining combinations. Both humans and pigeons performed poorly in conjunctive searches. Another visual search experiment (Blough 1992) found evidence of serial processing during conjunctive searching in pigeons. Blough used alphanumeric characters as distractors and the letter "B" and a solid heart shape as targets. The number of distractors did not affect search time for the dissimilar heart shape, but increased search time for the cryptic letter "B." Together, these studies suggest that in pigeons and humans, two disparate species that rely on vision, integration of features may require attention. Challenges and extensions to this theory are reviewed in Palmer (1999) and, with additional pigeon experiments, in Avian Visual Cognition (see section 4.8 for URL).

Search Image

Luuk Tinbergen (1960) observed great tits in the field delivering insect prey to their young and compared these observations with changing abundances of prey. When a new prey species became available, Tinbergen found that parents collected it at a low rate for a while before the collection rate caught up to its abundance. Tinbergen interpreted this pattern as revealing a cognitive constraint on search: the food-collecting parents behave as if they are temporarily "blind" to the abundance of a newly emerged prey type. He argued that foraging animals form a perceptual template ofprey items over time. We now call this phenomenon search image.

Laboratory studies have shown that search image effects occur only when prey are cryptic (Langley et al. 1996), suggesting that animals require search images only for conjunctive searching. As reviewed by Shettleworth (1998; see also Bond and Kamil 1999), search image is probably an attentional phenomenon that selectively amplifies certain features relative to others. Sequential priming may be the mechanism involved. Every time a predator encounters a feature (e.g., a blue jay encounters the curved line of a moth wing), the perceptual system becomes partially activated (primed) for that feature. Priming is a preattentive process that temporarily activates a cognitive representation, often facilitating perception and attracting attention. A classic study by Pietrewicz and Kamil (1979) ofbluejays searching projected images for cryptic moths supports the role of sequential priming in search image formation. In these experiments, jays saw photographs of Catocala relicta (a light-colored moth) on a light birch background, C. retecta (a dark-colored moth) on a dark oak background, and pictures of both types of tree bark with no moth. The apparatus rewarded the jays with a mealworm for pecking at pictures that contained moths. The birds' ability to detect a single moth species improved with consecutive experiences, consistent with sequential priming. Mixing two prey types in a series blocked the improvement.

Bond and Kamil (1998) showed that this search image effect can select for prey polymorphisms because search image formation lags changes in the relative frequency of morphs. The experimental predators, again bluejays in an operant chamber, generated frequency-dependent selection that maintained three prey morphs in a population of digitized images. Jay predation selects for both polymorphisms and crypticity in moths, which may fuel the evolution of thejay's perceptual capacities in turn (Bond and Kamil 2002).

Figure 4.2. Stimulus generalization to a lightwith a wavelength of 550 nm (the conditioned stimulus, or CS) with no discrimination training and with training to avoid a light of greater wavelength (S -). Pigeons trained to respond only to the CS (control) showed a peak response (highest number of pecks) to wavelengths very near the CS. Note the "peak shift" effect caused by discrimination training: the peak response moves away from the negatively trained stimulus. (After Hanson 1959.)


Figure 4.2. Stimulus generalization to a lightwith a wavelength of 550 nm (the conditioned stimulus, or CS) with no discrimination training and with training to avoid a light of greater wavelength (S -). Pigeons trained to respond only to the CS (control) showed a peak response (highest number of pecks) to wavelengths very near the CS. Note the "peak shift" effect caused by discrimination training: the peak response moves away from the negatively trained stimulus. (After Hanson 1959.)

Stimulus Generalization

Because no two moths are identical, the foraging jay must generalize. Stimulus generalization allows a forager to discount minor differences in stimuli. In a classic study, Hanson (1959) trained pigeons to peck at a key that emitted light at 550 nm, a greenish yellow color. When presented with random wavelengths, the trained pigeons also responded to wavelengths close to 550 nm and less strongly to wavelengths farther away (fig. 4.2).

An important characteristic of stimulus generalization is its flexibility. Discrimination training can shift the response peak away from a trained stimulus. When Hanson further trained groups of pigeons to inhibit their response to a second wavelength greater than 550 nm, the pigeons preferred wavelengths less than 550 nm (see fig. 4.2). This peak shift effect shows the flexibility of stimulus generalization, which allows animals to group similar stimuli according to behavioral requirements or experience. Peak shift has been shown in animals from goldfish to humans (see Ghirlanda and Enquist 2003 for a review of stimulus generalization).


Stimulus generalization may underlie some categorizations. Wasserman and colleagues used a sorting task to investigate visual categorization in pigeons.

First, they trained pigeons to match four classes of objects (cats or people, cars, chairs, and flowers) with the positions offour pecking keys (left or right, upper or lower), where each key corresponded to one object class. Intermittently during training with one set ofdrawings, the experimenters tested the pigeons with a set of new images from these object classes. This testing demonstrated that the pigeons had not simply memorized the correct response for each image, but were generalizing (Bhatt et al. 1988). In a further demonstration, Wasserman and colleagues required pigeons to sort these same images into "pseudocategories" (classes with an equal number of cats, flowers, cars, and chairs). This greatly impaired the pigeons' performance, suggesting that categorization underlies this behavior (Wasserman et al. 1988). Although this result shows that pigeons can use visual criteria to categorize pictures, because all car drawings resemble one another in many ways, we cannot eliminate an explanation based on stimulus generalization.

To eliminate stimulus generalization, Wasserman and colleagues performed a three-stage experiment. In stage 1, they created superordinate categories of perceptually dissimilar objects. One group of pigeons learned to peck at a key near the upper right corner ofa screen ifthey saw a person or a flower and to peck at a key near the lower left corner if they saw a chair or a car (fig. 4.3). In stage 2, the experimenters changed the response required for each category. The pigeons above saw only people or chairs. When the apparatus showed images of people, the pigeons had to peck the key at the upper left. Similarly, when the screen showed images of chairs, the pigeons had to peck the key at the lower right. What happened when these pigeons saw flowers again in stage 3? Did they peck at the upper left because that was the correct response for the person-flower category in stage 2, or did they choose between the two new responses randomly? On 72% of stage 3 trials, pigeons in this experiment chose the key corresponding to their category training in stage 2 (e.g., upper left key for flowers and lower right key for cars) (Wasserman et al. 1992). This result demonstrates that pigeons can form a functional equivalence between perceptually dissimilar items, a characteristic of true categorization (see Khallad 2004 for review).

Do animals have natural functional categories ? Watanabe (1993) trained one set of pigeons to group stimuli into food versus nonfood categories and another set ofpigeons to group stimuli into arbitrary categories (with equal numbers of food and nonfood items). Watanabe also trained some individuals with real objects and others with photographs. After training, the experimenter tested subjects on transfer to the opposite condition (real objects to photographs and photographs to real objects). The pigeons trained to distinguish food from nonfood easily transferred their skills from one type of stimulus to the other, but those trained with arbitrary categories did not transfer their skill.

Stage 1

■ ■ people . ^ ' flowers cars . o chairs ■ . •

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Stage 2

Stage 3

Figure 4.3. Testing for categorization in pigeons using an operant chamber. Subjects pecked at one of two illuminated keys (open circles) in response to a photographic stimulus (listed inside the square) to receive a reward. Correct answers and predicted responses are indicated beside the keys. In stage 1, subjects learned to make a common response to perceptually different pairs of stimuli (cars and chairs or people and flowers). In stage 2, subjects learned a new response for one type of stimulus in each pair. Stage 3 tested whether subjects would generalize this new response to the other stimulus type (cars or flowers). (Experimental design from Wasserman etal. 1992.)

This finding suggests that the subjects in the food/nonfood condition used categories, but those in the arbitrary category condition were making memorized responses to particular stimuli. Moreover, Bovet and Vauclair (1998) found that baboons could categorize both objects and pictures of those objects into food and nonfood groups after only one training trial. Functional categorization is another type of generalization. A forager that can parse its world into groups of related objects can recognize the properties of novel exemplars and predict how they will behave.


After determining what objects are around, a forager may need to process information about quantity: How many moths did I encounter in that patch? How many individuals are in my group? An animal might use any of several methods to solve problems about quantity. Detecting relative numerousness is simply determining that one set contains more than another. Several species can use relative numerousness to make judgments about quantity, including laboratory rats, pigeons, and monkeys (see discussion in Roberts 1998). In contrast, to discriminate absolute number, the animal must perceive, for example, that four stimuli differ from three and five. Davis and colleagues have demonstrated that laboratory rats can discriminate the absolute number of bursts of white noise, brushes on their whiskers, wooden boxes in an array, and even the number of food items they have eaten (Davis 1996).

How animals accomplish such feats has been the subject of considerable debate. Humans can subitize, or perceive the size of small groups of items that are presented for less time than would be needed to count them. Subitizing may be a perceptual process in which certain small numbers are recognized by their typical patterns (or rhythms in the case of nonvisual stimuli). Humans subitize so quickly that the process appears to be preattentive. Animals may subitize, but there is also evidence that they count. Alex, an African gray parrot, could identify the number of objects (wood or chalk pieces, colored orange or purple) by color and/or material on command (Pepperberg 1994). Since selecting the objects to count involves a conjunction ofshape and color, Alex may have to count each item serially. Capaldi and Miller (1988) argue that laboratory rats automatically count the number of times they traverse a runway to obtain food because they behave as if they expect reward after a certain number of runs, whether they travel the runway quickly or slowly. This number expectation was transferred when the investigators changed the type ofreward, suggesting that rats count using abstract representations rather than specific qualities of the reinforcer. Notwithstanding these impressive numerical feats, some researchers are not ready to conclude that nonhumans meet the strict standard of counting in which each item in a list has a unique tag or identifier (see Roberts 1998 for discussion).


Cognition begins with sensation and perception. Animals possess diverse senses, such as vision, audition, touch, electroception, and proprioception, which provide the information an animal needs to forage effectively. Attention binds complex conjunctions of sensory information. Search image results from these perceptual and attentional processes. Stimulus generalization allows an animal to group stimuli based on sensory similarity. Categorization allows animals to group objects functionally. Finally, numerical competencies allow animals to quantify food items. These processes enable the forager to perceive its environment.

4.4 Learning What to Eat

If a new prey item replaces an old one, a jay that can learn to eat this new prey will be more successful. We will define learning as a change in cognition caused by new information—not by fatigue, hunger, or maturation, which can also cause cognitive changes. Learning has no adaptive value when the environment is completely static or completely random, since learned information cannot be applied (Stephens 1991). In the appropriate environment, learning allows adaptation to occur on an ontogenetic time scale rather than a phylogenetic one. Learning is related to memory: learning is a change in information processing, while memory is the maintenance of an information state. In practice, students of learning and memory find it difficult to distinguish the two. A forager must, in the end, both learn what to eat and remember what it has learned.

Classical Conditioning

An experienced blue jay may form an association between the shape of a moth and food or between shaking a branch and the appearance of this food item. Known as associativelearning or conditioning, the formation of associations plays an important role in behavior. Classical or Pavlovian conditioning involves passive associations (as in the first case), while instrumental or operant conditioning (which we will discuss later) involves associations between the animal's own behavior and its results. In classical conditioning, the animal learns that something that had been neutral (the conditioned stimulus, or CS; e.g., moth shape) seems to appear predictably with something that it has an innate interest in (the unconditioned stimulus, or US; e.g., food) and to which it will make an innate response (the unconditioned response, or UR; e.g., salivation in the case of Pavlov's original experiments with dogs). Based on this relationship, simply perceiving the conditioned stimulus leads to a response, called the conditioned response (CR), which is often identical to the UR. Common conditioning procedures are described in box 4.1. Modern conditioning researchers generally consider the mechanism underlying the CR to be a cognitive representation of expectancy, rather than the Pavlovian "reflex."

These researchers also recognize that all traditional conditioning phenomena may not be explainable by one mechanism, and they acknowledge alternative forms of learning, such as learning by observation, which we will discuss below (see Kirsch et al. 2004 and Rescorla 1988 for excellent discussions).

BOX 4.1 Learning in the Laboratory

Researchers studying learning in the laboratory have developed many standard procedures and uncovered numerous replicable phenomena. Here we review some of the best known of these phenomena.

Second-Order Conditioning

A blue jay learns that a rainfall precedes wet leaves, which in turn predict greater abundance of certain invertebrates. Soon, rain by itself will stimulate the jay to look for those prey species. In the laboratory, we first condition a hungry rat to expect food (US) when we switch on a light (CS1). Then we pair the light with a tone (CS2), and soon the tone by itself will come to elicit salivation (CR). The conditioning to the tone is second-order conditioning. We have, in effect, chained two conditioned stimuli together.

Conditioned Inhibition

A blue jay that has learned to hunt brown moths on oak trees now learns a new association—that the presence of another blue jay on the same tree is almost always correlated with an absence ofmoths. This association causes conditioned inhibition of its foraging response. Conditioned inhibition occurs when we pairaCS, such as a tone, with the US (e.g., food) only when the CS appears alone, but not when it appears with a second stimulus, such as alight. This experience inhibits the response to the light-tone combination. Conditioned inhibition allows the forager to learn the circumstances in which a CS (oak tree) does not signal the US (moth).

Sensory Preconditioning

A blue jay encounters an orange butterfly resting on a clump of moss, but sated, it flies away. Later, the blue jay learns that the orange butterfly is toxic. Afterward, the blue jay may show a withdrawal response to the moss, even in the absence of the butterfly. In the laboratory, we present two CSs (such as a light and a tone) together prior to any conditioning procedure. When later, we pair one of these (e.g., the tone) with a US (e.g., food) in a conditioning procedure, the second one will also elicit the CR (e.g., salivation) with no direct training. Though this phenomenon seems similar to second-order conditioning, it is actually a form of latent learning in which animals gain information (such as an association) in the absence of any apparent immediate benefit for doing so.


A blue jay searches for acorns in an oak tree. Every time it finds a branch of a certain diameter, the branch also contains many acorns. It then searches out branches of that diameter. However, on the other side of the tree, branches of this diameter are also covered with lichens. A second blue jay happens to find many acorns on this side, and learns to search for branches of a certain diameter that are covered with lichens. The first blue jay, when it then moves into the lichen area, does not learn that lichens predict acorns. In the laboratory, we condition a subject by pairing a tone with food until the tone reliably produces salivation. After we have completed this conditioning, we present a compound stimulus made up of our old tone and a new light. When we test the subject with the light and tone separately, we find that the tone produces salivation as before, but the light has no effect. We say that the prior conditioning to the tone blocks conditioning to the light. Psychologists view blocking as an important conditioning phenomenon because it demonstrates that correlation with the US is not sufficient for learning to occur; after all, the light has been correlated with food, so one might expect salivation to the light as well, but this is not what we find. Blocking suggests an information model of conditioning: the second CS (the light) adds no new information because the first CS (tone) already perfectly predicts the US (food).


A blue jay learns that orange wings predict toxicity in butterflies. Black spots also predict toxicity, but thejay has not learned this. In the laboratory, we begin such a conditioning experiment by pairing a compound light-tone stimulus with food until our compound stimulus reliably produces salivation. When we test the light and tone separately, we typically find that one stimulus elicits salivation much more strongly. If we find that the tone and not the light elicits salivation, then we say that the tone overshadows the light. Ifthe light and the tone differ greatly in intensity, size, or saliency (as with a dim light and a loud tone), it is the larger, brighter, louder, or more critical CS that gains the most strength in eliciting the CR. Studies suggest that subjects learn both CSs, but not equally well. Biological relevance, as found in the Garcia effect (see section 4.4), can be a cause of overshadowing.

Latent Inhibition

A blue jay searching for food never finds any at its nest tree. One morning an infestation of bark beetles takes hold in the tree. The blue jay sees one, but does not stay to forage at the tree. In fact, it takes the jay quite a while to learn that its own tree is now a source of food. In the laboratory, we play a tone to an experimental subject. The subject hears the tone frequently, but it is not correlated with food or other salient events in the subject's environment. Ifwe then try to condition the subject by pairing the tone with food, we find that this prior exposure to an irrelevant tone inhibits conditioning. It is as ifwhat has been learned (that the tone predicts nothing and therefore can be ignored) must be unlearned before the new association can be made. Latent inhibition supports an information model of conditioning and contradicts the expectation that familiarity would facilitate learning.


A blue jay foraging for acorns on a particular tree always finds an acorn when it searches in that tree. As the season progresses, the jay is less likely to find an acorn. Eventually, the tree is empty. At the same time, the blue jay becomes less likely to search that tree. In the laboratory, we pair a light with food until a rat reliably presses a lever to get food when the light appears. Now we begin to switch on the light without food. Over subsequent trials, the rat no longer responds to the light. The stimulus that used to provide information about the arrival of food is now useless, and the subject stops responding to it. Like latent inhibition, extinction involves learning not to respond to an unpredictive CS. Psychologists often use the speed of extinction to measure the strength of the original association.

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