217-228 Beran Apr 07

The Journal of General Psychology, 2007, 134(2), 217–228 Copyright © 2007 Heldref Publications

A Stroop-Like Effect in Color-Naming of Color-Word Lexigrams by a Chimpanzee (Pan troglodytes) MICHAEL J. BERAN DAVID A. WASHBURN Language Research Center Georgia State University, Atlanta DUANE M. RUMBAUGH Great Ape Trust of Iowa, Des Moines Georgia State University, Atlanta

ABSTRACT. The Stroop effect (J. R. Stroop, 1935) reflects the difficulty in ignoring irrelevant, but automatically processed, semantic information that is inherent in certain stimuli. With humans, researchers have found this effect when they asked participants to name the color of the letters that make up a word that is incongruent with that color. The authors tested a chimpanzee that had learned to associate geometric symbols called lexigrams with specific colors. When the chimpanzee had to make different responses that depended on the color of stimuli presented to her, she showed a Stroop-like effect when researchers presented to her the previously learned symbols for colors in incongruent font colors. Her accuracy performance was significantly poorer with these stimuli than with congruent color-referent lexigrams, noncolor-referent lexigrams, and nonlexigram stimuli, although there were not any significant differences in response latency. The authors’ results demonstrated color-word interference in a Stroop task with a nonhuman animal. Key words: attention, automaticity, chimpanzee, interference, Stroop effect

THE STROOP EFFECT is one of the bestknown and most robust phenomena in cognitive psychology. Since its original report (Stroop, 1935), the effect has been replicated hundreds of times with dozens of methodological variations (see MacLeod, 1991, for a review). The standard methodology requires participants to name as quickly as possible the color in which words or strings of letters are presented. Although the meaning of the words is irrelevant to the task, participants make more errors or respond more slowly when the meaning of the word is incongruent with the color of the word (e.g., the word “GREEN” in red letters, requiring participants to respond “Red”) than when the meaning of the word is congruent with the color of the word (e.g., the word “RED” in red letters) or unrelated to 217

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it (a string like “XXXXX” in red letters). The Stroop effect reflects interference stemming from competition between conflicting stimulus cues (ink color vs. word meaning), and, in this way, variations on the Stroop color-word task are comparable with many other response-competition paradigms. The reason why the Stroop effect has fascinated so many researchers and generated so many replications is that it reflects a profound failure of selective attention and a clear instance of semantic interference: Despite knowing that word-meaning is irrelevant, the individual is incapable of ignoring this stimulus attribute. It compels attention and influences performance against the constraints of instructions and intentions. Stroop effects are also asymmetrical in that word meaning interferes with color naming, but not vise versa. This means that in Stroop tasks, performance of a psychologically more fundamental operation (naming the color of something) is disrupted by a more psychologically complex operation (knowing the meaning of a color-word, which presumably was learned after and built upon the ability to recognize different colors). Studies with competing response cues are not uncommon in comparative research. For example, Lauwereyns et al. (2000) reported Stroop-like interference effects in monkeys using a manual Go–No-Go task. Monkeys initially were trained to discriminate the color, spatial location, motion, or shape of a stimulus. For three of these four stimulus dimensions, Lauwereyns et al. found interference when irrelevant information appeared and was in conflict with the required response for that trial. This effect emerged both in the response times of the monkeys for the Go response, and in the error rates for both the Go responses and the No-go responses. Although the Lauwereyns et al. study did use color as one of the competing stimulus cues, as in the original Stroop (1935) study, the 2000 study differed from most demonstrations of Stroop effects in that the competition did not reflect interference from the consistently irrelevant semantic processing of meaningful symbols. To date, results from only one study with nonhuman animals (Washburn, 1994) have shown Stroop-like interference from an irrelevant semantic stimulus attribute, which is not surprising because of the difficulty in establishing symbolically meaningful stimuli with nonhuman animals. Washburn tested rhesus monkeys with a numerical analog of the Stroop task. Monkeys were trained to select the larger of two sets of stimuli on a computer screen. Previously, these monkeys had such extensive experience learning the relative quantitative values of Arabic numerals (e.g., Washburn & Rumbaugh, 1991) that the monkeys could reliably select numerals in descending order when given any possible pairing of those numerals. In the numerical Stroop task, the monkeys’ previous experience This research project was supported by National Institute of Child Health and Human Development Grant HD-38051, National Science Foundation and European Science Foundation Grant BCS-0634662, and by the Rumbaugh Fellowship of Georgia State University. Address correspondence to Michael J. Beran, Language Research Center, Georgia State University, 3401 Panthersville Road, Decatur, GA 30034; [email protected] (e-mail).

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with the quantitative meaning of those numerals interfered with performance when the quantitatively larger set of stimuli consisted of numerals representing individually smaller numbers (e.g., comparing the set “777” with the set “333333” where the latter was the correct choice). Monkeys, like humans, responded more slowly on these incongruent trials than they did on congruent trials when the larger set of stimuli consisted of larger Arabic numerals (e.g., “88888” vs. “444”) and in baseline trials in which both stimulus sets consisted of meaningless stimuli (e.g., “CC” vs. “DDDD”). Monkeys also made more errors on the incongruent condition than on the other conditions, providing evidence of a Stroop-like effect for numerical stimuli. Although not a color-word task, as in Stroop’s (1935) study and its many subsequent replications, this numeric Strooplike task did show semantic interference with a more basic psychological judgment (i.e., learning which numeric symbol represents the greater quantity requires the ability first to judge the relative numerousness of arrays). In the present experiment, we had the unique opportunity to assess semantic interference and Stroop-like effects by using color-word stimuli with a nonhuman animal. We tested an adult female chimpanzee named Lana, who had a special experimental history conducive to presenting her with a color-word Stroop task comparable with the one typically used with human participants. Lana was the first chimpanzee that was taught to associate geometric symbols called lexigrams with various real-world referents such as foods, objects, actions, and colors (Rumbaugh, 1977; Rumbaugh & Washburn, 2003). Lana learned arbitrary symbols that she could use to categorize and label the color of random and novel stimuli (e.g., Essock, Gill, & Rumbaugh, 1977). These symbols were acquired through extensive experience in using them repeatedly on a daily basis for many years, and Lana has retained the meaning of those stimuli for many years (Beran, Pate, Richardson, & Rumbaugh, 2000). Thus, color lexigrams may operate for Lana as do color names for humans, to the extent that she may show interference in the classification of stimuli when those stimuli place two properties (actual font color vs. color meaning of the lexigram itself) in conflict. We hypothesized this interference to occur in two specific ways: first, through increased response times to incongruent stimuli; second, as a lower overall performance level in correctly classifying the incongruent stimuli on the basis of their screen color and not the color represented by the specific symbol displayed. If these hypotheses are supported, the results will indicate color-word Stroop effect interference in a nonhuman animal for the first time. We examined the potential Stroop effect in Lana’s performance both through analyses of her response latencies to stimuli in each condition and through her overall performance levels in correctly classifying these stimuli. Although Stroop effects are seldom manifest in the accuracy of responses of most human adults, they have been reported for children (e.g., Gerstadt, Hong, & Diamond, 1994; Prevor & Diamond, 2005), people with attention deficits (e.g., Carter, Krener, Chaderjian, Northcutt, & Wolfe, 1995; Shallice et al., 2002), elderly humans (e.g.,

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McCabe, Robertson, & Smith, 2005; Troyer, Leach, & Strauss, 2006), some clinical populations (e.g., Bannon, Gonsalvez, & Croft, 2002; Lawrence, Houghton, & Douglas, 2004; Nordahl, Carter, & Salo, 2001), and nonhuman primates (Washburn, 1994). Therefore, the use of these two dependent measures (response latency and performance accuracy) allowed us to fully investigate any potential interference that occurs in this task. Any evidence of a Stroop effect using a manual response task would provide an indication of the role of the compatibility of response modality and irrelevant stimulus properties. If compatibility plays a role in the Stroop Effect, manual tasks should show little evidence of the Stroop effect. However, if compatibility plays no role, the Stroop effect should be equally evident with any response mode. To date, several researchers have investigated this aspect of the Stroop effect in humans (e.g., McCain, 1983; Pritchatt, 1968; Treisman & Fearnley, 1969; see MacLeod, 1991, for a review), but the only consensus is that interference is reduced, but not eliminated, when the response modality is manual rather than oral (MacLeod). In the present study, data from a nonverbal organism may also provide additional information about the role of compatibility between stimulus and response. Method Subject The subject in this experiment, Lana, was a 33 year of age female chimpanzee (Pan troglodytes). In addition to serving as the first subject in lexigrambased ape language research (Rumbaugh, 1977; Rumbaugh & Gill, 1976), Lana also had participated in numerous other cognitive studies (e.g., Beran, 2004; Beran & Beran, 2004; Beran & Rumbaugh, 2001; Essock et al., 1977; Hopkins, Morris, Savage-Rumbaugh, & Rumbaugh, 1992; Morris & Hopkins, 1993). She was housed in a building with three other chimpanzees, and she was maintained on a regular diet of fruit, vegetables, and protein supplements throughout the course of the experiment. Lana was taught to associate lexigrams with various real-world referents, and she learned specific lexigrams for labeling the color of different stimuli. Lana is the only chimpanzee at our laboratory that has learned to label items on the basis of their color by using color lexigrams. Apparatus We conducted observations by using a Compaq DeskPro personal computer with attached monitor and Kraft Systems (Vista, CA) joystick. The program that controlled stimulus presentation was written by Michael J. Beran in Microsoft Visual Basic. Lana had extensive experience using a joystick to respond to computer-generated stimuli.


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Design and Procedure Training. Lana initially had to be trained to respond to colored images on the computer screen by matching those images to two new response stimuli. At the start of each trial, a small red square (4.5 mm × 4.5 mm) appeared in the bottom center of the monitor and acted as the cursor, moving on the screen in step with manipulation of the joystick. A dark gray rectangle (6.6 mm × 8.7 mm) appeared in the middle of the screen. The rectangle was the trial initiation stimulus. Lana had to move the cursor into contact with the rectangle to view the sample, which appeared in the center of the screen at the same time when the two response stimuli appeared in the left-center and right-center edges of the monitor. These stimuli were the letters “B” (for blue) and “Y” (for yellow). The “B” stimulus was always located on the left side, and it was the appropriate response for a sample that was colored blue. The “Y” stimulus was always located on the right side, and it was the appropriate response for a sample that was colored yellow. The sample itself was a square (38 mm × 38 mm) that was blue or yellow in color. Lana chose one of the response stimuli by moving the cursor into contact with that stimulus. If she chose the correct stimulus, a melodic tone sounded, and she received a small piece of preferred food from an experimenter seated next to the apparatus. If Lana made an incorrect response, a buzz tone sounded, and she did not receive any food. The intertrial interval was 1 s, after which time the cursor and initiation rectangle reappeared on the screen. The experimenter could not view the screen at any time during the session, and thus could not cue Lana as to the correct response. Lana needed to be very accurate in classifying these stimuli as being either blue or yellow before progressing to the test phase of the experiment. Therefore, she completed 1,000 training trials. Each session consisted of 100 trials. By the end of training, Lana was performing at a very high level (46 of the last 50 trials were correct). We should note the reasons why we used only two color lexigrams. Lana was originally trained on a larger set of color lexigrams, but over the years many of these geometric stimuli were reassigned new referents, primarily because color naming was no longer a priority of the language acquisition project in which researchers observed Lana. Thus, we needed to use only stimuli for which we were certain no new referents had been assigned, and stimuli that we could confidently state Lana still associated with specific colors (e.g., Beran et al., 2000). These criteria provided us with only two such stimuli. However, even in studies with adult humans, sometimes as few as two stimuli are used (e.g., Washburn, Smith, & Taglialatela, 2005), and the effect still is large. Testing. During the test phase, we presented eight different sample stimuli to Lana. First, Lana was given a small block of trials (N = 12 in Session 1, and N = 15 in Session 2) that consisted entirely of the two training stimuli (the blue and

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FIGURE 1. Examples of lexigram stimuli used in this experiment. From left to right: YELLOW, BLUE, and NAME-OF. Originally, the lexigrams were presented in white on a black background. However, in this experiment, the background color varied depending on the condition.

yellow squares). Each subsequent block of eight trials presented, in random order, the following eight stimuli: the yellow square, the blue square, the lexigram “YELLOW” that was colored yellow, the lexigram “YELLOW” that was colored blue, the lexigram “BLUE” that was colored blue, the lexigram “BLUE” that was colored yellow, the lexigram “NAME-OF” that was colored yellow, and the lexigram “NAME-OF” that was colored blue. The “NAME-OF” lexigram operated as part of the original syntax that controlled the computerized apparatus that was used to teach Lana and work with her. “NAME-OF” is a lexigram with which Lana has much experience, but it does not represent a specific color. It is important to note that neither the “YELLOW” lexigram nor the “BLUE” lexigram had any yellow or blue color in them (see Figure 1). This means that in this experiment we presented these stimuli as their original geometric configurations, but in completely novel colors for both the congruent and incongruent conditions. These stimuli produced four types of trials: (a) those with training stimuli, (b) those with congruent stimuli in which the lexigram name matched the actual color of the lexigram, (c) those with incongruent stimuli in which the lexigram name and the actual color did not match, and (d) those with control stimuli (“NAME OF”), where the lexigram was not the name for a color. Across two sessions, Lana completed a total of 270 trials, and we rewarded her for all correct responses with preferred food items. Results Figure 2 shows Lana’s performance during the testing phase as a function of the color of the stimulus (blue or yellow) and the trial type (training, congruent, incongruent, or control). There was no difference in performance as a function of color of the stimulus, χ2(3, N = 270) < 1.00. However, there was a statistically significant difference in performance across conditions, χ2(3, N = 270) = 20.24, p < .001. Performance was significantly poorer than the residuals of the chi square analysis expected for the incongruent stimuli. Indeed, only in the incongruent


Blue 100

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Yellow

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80 70 60 50 40 30 20 10 0 Training

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FIGURE 2. Lana’s performance as a function of the coloration of the stimuli and the type of stimuli presented. The horizontal lines represent the chance level of performance.

condition did performance fail to differ significantly from chance, χ2(1, N = 61) < 1.00. Performance exceeded chance levels in the control condition, χ2(1, N = 61) = 36.21, p < .001; the training condition, χ2(1, N = 87) = 27.60, p < .001; and the congruent condition, χ2(1, N = 61) = 13.79, p < .001. We also examined the mean response times as a function of condition and outcome (Figure 3). We did not include stimulus color in this analysis because there was no indication that performance differed as a function of the stimulus color (Figure 2). Two trials were excluded from this analysis because they had extremely long response times (> 10 s). There was no main effect of condition, F(3, 260) < 1.00 and no main effect for trial outcome, F(1, 260) = 1.25, p = .26. Therefore, Lana showed no evidence of a speed-or-accuracy tradeoff in her responses to the different stimuli in the different conditions. Discussion During the experiment, Lana showed evidence of a Stroop-like effect in the accuracy of her responses. She performed at high levels in correctly classifying stimuli as blue or yellow when we presented the training stimuli, non–colorreferent stimuli, and congruent stimuli. However, she performed at chance levels when we presented incongruent stimuli. Differences in performance could

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Correct

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FIGURE 3. Lana’s mean response times as a function of the condition and the outcome (correct vs. incorrect). Bars indicate standard deviations.

not simply be reflective of differential amounts of experience with the different stimuli in this task because we presented the congruent and noncolor-referent stimuli as often as we did the incongruent stimuli. Rather, these differences indicate that chimpanzees, like children, are relatively lacking in executive (or controlled, effortful) attention compared with human adults (Jones, Rothbart, & Posner, 2003; Rueda, Posner, & Rothbart, 2005). As sometimes is the case for children, (e.g., Gerstadt et al., 1994; Prevor & Diamond, 2005), the effect was in relation to task accuracy (as in the previous study with monkeys by Washburn, 1994). There was no difference in Lana’s response times across stimuli types, and there was no relation between her accuracy and her speed of responding across experimental conditions. This result stands in contrast to most Stroop experiments with humans—where interference is typically found in response latencies—and to previous studies with nonhuman animals (Lauwereyns et al., 2000; Washburn, 1994). For example, in Washburn’s study, humans and monkeys responded significantly more slowly to stimuli that were incongruent (but only the monkeys showed Stroop effects in response accuracy). We do not know why Lana did not show a difference in her response times as a function of the stimulus type, although it should be noted that her response times were quite long relative to


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previous Stroop studies. For example, humans and monkeys in the Washburn study responded in less than 1 s on average, whereas Lana responded in approximately 2 s across all conditions. One possibility is that Lana might have responded at chance levels with incongruent stimuli because of the competing information inherent in the stimulus with regard to its meaning and its perceptual color features. Thus, Lana might not have been sure how she should respond to the incongruent stimuli, and so she responded to the color and to the lexigram’s meaning at random. Because we could not give Lana verbal instructions (or assess her subjective report of why she responded as she did), we cannot be certain that this was a true Stroop effect as exhibited by adult humans. Thus, it is more conservative to report the effect as Stroop-like, much as one would report similar data if collected with preverbal children. This Stroop-like effect would emerge from the conflict of color and meaning in the incongruent stimuli, and it would reflect that this chimpanzee did experience interference between the meaning of the stimulus and its actual color. Thus, the effect is Stroop-like in the sense of reflecting the similarity in conflict on the basis of interference. The Stroop phenomenon is one that occurs even when participants are told that incongruent stimuli will occur. Even when they make mistakes and recognize those mistakes, they are no better at eliminating them on subsequent trials (or at speeding up their responses). That is the hallmark of the automatic component of stimulus processing. In our task, giving Lana a reward for a correct response to incongruent stimuli actually decreased the likelihood of finding an effect on performance because she was being rewarded (or punished) for incorrectly classifying the incongruent stimuli. Therefore, the fact that the effect was still present means that these stimuli were difficult to classify even with transparent reinforcement histories of responses to them. According to the relative speed of processing account for the color-word Stroop effect, interference in responding occurs because participants are faster at reading words than they are at naming colors. Thus, these two processes are in competition for the response output, with incongruent stimuli leading to slower response times and errors because participants have to overcome the tendency to state the word’s meaning (e.g., Klein, 1964; Posner & Snyder, 1975). The strength of association or automaticity account for the Stroop effect (e.g., MacLeod & Dunbar, 1988; Shiffrin & Schneider, 1977; Washburn, 1994) is based on the idea that some stimulus dimensions (such as word meaning) are more highly associated with responding and that these stimuli control behavior in the absence of effortful, inhibitory processing (i.e., attentional allocation). Lana’s Stroop-like effect, like the one reported by Washburn (1994) for rhesus monkeys, seems to support the strength of association view. No evidence indicated that Lana processed symbol meanings more quickly than she did symbol colors—that meaning-naming outraced color-naming for control over behavior. For example, responses were not faster even in the congruent condition, where fast processing of symbol meanings would have aided behavior, than in the other

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conditions. However, the tendency to respond according to symbol meanings is indicated in the comparison of error rates in the congruent condition versus the incongruent condition. Thus, our data further support the suggestion (e.g., Rumbaugh & Washburn, 2003) that lexigrams operate at a symbolic, word-like level for chimpanzees that are reared in environments where they are exposed to these lexigrams and that in such chimpanzees, these lexigrams might automatically evoke representations of their referents. AUTHOR NOTE Michael J. Beran is a research scientist at the Language Research Center of Georgia State University. His research interests include numerical cognition, memory, metacognition, self-control, and other aspects of comparative cognition. David A. Washburn is professor and chair of the Department of Psychology and Director of the Language Research Center at Georgia State University. His research interests focus on attention and executive functions from both a comparative perspective and a human-factors perspective. Duane M. Rumbaugh is professor emeritus in the Department of Psychology, Georgia State University, and lead scientist emeritus at the Great Ape Trust of Iowa. His research interests center on the learning and cognitive skills of primates with particular emphasis on the emergence of intelligent behavior and the promotion of rational behaviorism as a framework to account for learning and behavior. REFERENCES Bannon, S., Gonsalvez, C. J., & Croft, R. J. (2002). Response inhibition deficits in obsessive-compulsive disorder. Psychiatry Research, 110, 165–174. Beran, M. J. (2004). Chimpanzees (Pan troglodytes) respond to nonvisible sets after oneby-one addition and removal of items. Journal of Comparative Psychology, 118, 25–36. Beran, M. J., & Beran, M. M. (2004). Chimpanzees remember the results of one-by-one addition of food items to sets over extended time periods. Psychological Science, 15, 94–99. Beran, M. J., Pate, J. L., Richardson, W. K., & Rumbaugh, D. M. (2000). A chimpanzee’s (Pan troglodytes) long-term retention of lexigrams. Animal Learning and Behavior, 28, 201–207. Beran, M. J., & Rumbaugh, D. M. (2001). “Constructive” enumeration by chimpanzees (Pan troglodytes) on a computerized task. Animal Cognition, 4, 81–89. Carter, C. S., Krener, P., Chaderjian, M., Northcutt, C., & Wolfe, V. (1995). Abnormal processing of irrelevant information in attention deficit hyperactivity disorder. Psychiatry Research, 56, 59–70. Essock, S. M., Gill, T. V., & Rumbaugh, D. M. (1977). Language relevant object- and color-naming tasks. In D. M. Rumbaugh (Ed.), Language learning by a chimpanzee: The LANA project (pp. 193–206). New York: Academic Press. Gerstadt, C. L., Hong, Y. J., & Diamond, A. (1994). The relationship between cognition and action: Performance of children 3 1/2–7 years old on a Stroop-like day-night test. Cognition, 53, 129–153. Hopkins, W. D., Morris, R. D., Savage-Rumbaugh, E. S., & Rumbaugh, D. M. (1992). Hemispheric priming by meaningful and nonmeaningful symbols in language-trained chimpanzees (Pan troglodytes): Further evidence of a left hemisphere advantage. Behavioral Neuroscience, 106, 575–582.


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Manuscript submitted October 15, 2006 Revision accepted for publication December 22, 2006