Learning about categories in the absence ofmemory

Proc. Natl. Acad. Sci. USA Vol. 92, pp. 12470-12474, December 1995

Psychology

Learning about categories in the absence of memory (declarative memory/amnesia/implicit memory/temporal lobe)

LARRY R. SQUIRE*tt AND BARBARA J. KNOWLTONt§ *Veterans Affairs Medical Center, San Diego, CA 92161; and Departments of tPsychiatry and 92093

tNeurosciences, University of California at San Diego, La Jolla, CA

Contributed by Larry R. Squire, September 25, 1995

ABSTRACT A fundamental question about memory and cognition concerns how information is acquired about categories and concepts as the result of encounters with specific instances. We describe a profoundly amnesic patient (E.P.) who cannot learn and remember specific instances-i.e., he has no detectable declarative memory. Yet after inspecting a series of 40 training stimuli, he was normal at classifying novel stimuli according to whether they did or did not belong to the same category as the training stimuli. In contrast, he was unable to recognize a single stimulus after it was presented 40 times in succession. These findings demonstrate that the ability to classify novel items, after experience with other items in the same category, is a separate and parallel memory function of the brain, independent of the limbic and diencephalic structures essential for remembering individual stimulus items (declarative memory). Category-level knowledge can be acquired implicitly by cumulating information from multiple training examples in the absence of detectable conscious memory for the examples themselves. An encounter with a series of objects leads to two different consequences. On the one hand, one learns about the objects in the series such that each object later has an increased likelihood of being recognized. On the other hand, one also accrues information about what all the objects have in common, such that information is acquired about the category that is defined by the objects. As a result, one can later classify new objects accurately as belonging or not belonging to the category. It has generally been supposed that category-level knowledge is acquired in the form of a prototype (the average example) (1-3) or that it arises as an emergent property of memory for the individual objects that are presented (4-9). Accordingly, category-level knowledge about a series of presented items is most easily viewed as derived from and dependent on the same process that supports conscious memory of the individual items. Subjects endorse new items as belonging to a category as a function of the similarity between the new items and what can be remembered about the exam-

ples. A major insight gained about memory in recent years is that memory is not a single faculty but is composed of multiple and separate abilities (10-14). The major distinction is between the capacity for conscious recollections about facts and events (declarative or explicit memory), which depends on limbic and diencephalic brain structures (15, 16) and various nonconscious or implicit memory abilities, which support skill and habit learning, simple forms of conditioning, and the phenomenon of priming (17-19). Recently, amnesic patients, who have impaired declarative memory, were nevertheless found to perform as well as normal subjects at classifying new stimulus items according to whether they belonged to the same category as a set of training items The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

(20, 21). These findings raised the possibility that category learning is independent of and parallel to declarative memory, rather than derivative from it, and that knowledge about categories can be acquired implicitly despite impaired (declarative) memory for the instances that define the category. However, a simple alternative possibility is that, because amnesic patients typically possess some residual capacity for declarative memory, their residual ability to remember a few training examples might be sufficient to support substantial, almost normal classification ability. In other words, the relationship between the strength of item memory and the ability to classify new items might be highly nonlinear (22). We have addressed this issue by studying category learning in a patient who has virtually no declarative memory. METHOD Subjects. E.P. is a 73-yr-old retired laboratory technician with 12 yr of education who developed profound anterograde and retrograde amnesia in November 1992 after herpes simplex encephalitis (Fig. 1). His memory impairment is so severe that after more than 30 testing sessions at his residence during 1995 he does not recognize the examiner and denies having been previously tested. He frequently recounts early memories involving World War II, his childhood in a central California agricultural community, and his teenage hobby as a ham radio operator, but he does not recount more recent memories. In 1995, E.P. obtained a score of 103 on the WAIS-R (Wechsler Adult Intelligence Scale-Revised) and scores of 94, 57, 82, 61, and 56 on the five indices of the Wechsler Memory ScaleRevised (attention-concentration, verbal memory, nonverbal memory, general memory, and delayed memory). These five indices yield means of 100 in the normal population (SD = 15) and minimum scores of 50. On several other tests of anterograde memory, he obtained a zero score. Finally, he exhibited severely impaired remote memory extending across several decades. Aside from his memory impairment, he was impaired at naming line drawings of objects [Boston Naming Test (23); 42 objects correct out of 60, normal score = 57.0]. He also exhibited some behavioral evidence of frontal lobe dysfunction [Wisconsin Card Sorting Test (24); 0 categories sorted, 51% perseverative errors] and the FAS test ofword fluency (26) (18 words produced, 0.10. Whereas the control subjects performed much better on the recognition test than they did on the classification test (95.2% vs. 62.2% correct; d' = 7.23 vs. 0.72; t[3] > 8.0, P < 0.001), E.P. performed worse on the recognition test than on the classifiloo

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FIG. 3. (A) Classification I. Classification of 84 novel dot patterns after studying 40 different training patterns that were distortions of a prototype dot pattern. Control subjects (n = 4, open bars) and E.P. (closed bars) performed similarly, endorsing test items as a function of how closely they resembled the prototype of the training category. In each panel, brackets for the control subjects for each item type (4 prototypes, 20 low distortions, 20 high distortions, and 40 random dot patterns) show the SE of their four scores, each of which was an average from six tests. For E.P., the brackets for each item type show the SE of his performance on six tests. (B) Overall percentage correct scores for classification I (A) and a parallel test of recognition memory (recognition I). E.P. performed as well as control subjects at classification. In contrast, he scored at chance and much worse than controls (49.1% vs. 95.2% correct) when trying to recognize a single dot pattern 5 min after it had been presented 40 times consecutively. The recognition test consisted of 8 repetitions of the study pattern intermixed with 76 random dot patterns. (C) Recognition II. Recognition memory 5 min after 40 presentations of the same prototype dot pattern. The recognition test was structured identically to the classification test (A), and only the instructions differed-i.e., recognition instead of classification. Thus, there were 4 presentations of the prototype target pattern, 20 near targets, 20 far targets, and 40 random patterns. (D) Overall percentage correct scores for recognition II (C). Correct responses consisted of endorsements of the training pattern itself (the 4 targets) and rejections of the other 80 patterns.

Psychology: Squire and Knowlton cation test (61.7% vs. 49% correct, t[5] = 3.6, P < 0.01; d' = 0.60 vs. d' = 13, t[5] = 2.5, P < 0.03. Recognition II. In this task, as in Recognition I, subjects saw the same prototype dot pattern 40 times in succession and then judged 84 patterns according to whether they had been presented previously. The 84 test items consisted of 4 prototypes and 80 other patterns that varied in their resemblance to the training pattern. Control subjects demonstrated a strong effect of item type on their recognition memory judgments (Fig. 3C), F[3,9] = 91.9, P < 0.001, and easily discriminated the 4 target items from the other 80 test items (83.3 ± 3.4% correct; d' = 5.05 ± 0.60; Fig. 3D). In contrast, E.P. could not discriminate the 4 targets from the other 80 test items, demonstrating rio effect of item type on his recognition judgments, F(3,15) = 1.5, P > 0.10 (Fig. 3C). Overall, he scored 48.4 ± 2.3% correct (d' = -0.042, ±0.27), while evenly distributing his "yes" and "no" responses (51.9% vs. 48.1%) (Fig. 3D). Thus, E.P.'s classification performance was better than his recognition memory performance (Fig. 3B, 61.7% correct; Fig. 3D, 48.4% correct; t[5] = 3.35, P < 0.01; d' = 0.60 vs. -0.04, t[5] = 2.02, P = 0.07), even when the two tests were identical. That is, once the study items had been presented, the two tests differed only in the instructions given (classification or recognition). Classification II. The question naturally arises why E.P. could not apply his intact classification ability to the task of recognizing dot patterns as familiar. If E.P. can abstract a prototype from the training items, as appeared to be the case in classification I (Fig. 3A), one would suppose he might also be able in recognition II (Fig. 3C) to acquire the "average" of 40 identical presentations, which could then provide the basis for accurate recognition judgments. Yet, E.P.'s recognition performance was at chance. It was also poorer overall than his classification performance (Fig. 3A vs. 3C; F[1,5] = 7.31, P < 0.05), and his pattern of performance across the four item types tended to differ on these two tests as well, F(3,15) = 2.79, P < 0.08. One possibility is that the different task instructions (classification or recognition) induced subjects to base their judgments on different kinds of knowledge. By this view, E.P. failed recognition because he had not acquired declarative knowledge about the training patterns. He was instructed to recognize but could not do so. The other possibility is that E.P. failed recognition because, instructions aside, he had no basis in either declarative or nondeclarative memory to guide his recognition judgments. He did not have declarative knowledge because he cannot acquire it, and he did not have sufficient nondeclarative knowledge because seeing the same training pattern 40 times in succession is not a good basis for abstracting an average. The abstraction process requires having some information about the range and variance of the features that define the category. In classification II, subjects saw the same dot pattern 40 times in succession and then were instructed to classify 84 new items according to whether they did or did not belong to the training category. This task was the same as recognition II in every respect, except that the instructions differed-i.e., classification instead of recognition. If the difference between E.P.'s poor performance on recognition and his good performance on classification were due simply to instructions, then he should be able to perform well on this task. Alternatively, if E.P. performed poorly on the recognition test because repeated presentations of a single stimulus cannot support the acquisition of nondeclarative category knowledge, then E.P. should perform poorly on classification II. Control subjects easily discriminated among the test items (80.2%, 55.6%, 20.8%, and 8.8% endorsements of the 4 prototypes, 20 low distortions, 20 high distortions, and 40 random patterns; effect of item type on judgments, F[3,9] = 91.8, P < 0.001). By contrast, E.P. performed poorly (endorsement rates for the four item types across his six tests were 50%,

Proc. Natl. Acad. Sci. USA 92 (1995)

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61.7%, 50.8%, and 45.8%), and he exhibited no effect of item type, F(3,15) = 2.0, P > 0.10. Overall, control subjects scored 65.4 ± 1.5% correct (d' = 2.5 ± 0.8), whereas E.P. performed significantly worse (P < 0.01) and no better than chance (55.0 ± 3.3% correct, d' = 0.25 ± 0.17). The steep gradient of control subject performance on this test (contrast with their performance in Fig. 3A; significant interaction of task by item type, F[3,9] = 17.9, P < 0.001) indicates that control subjects endorsed only patterns that very closely resembled the single training pattern, probably because they easily remembered the training pattern. E.P. would not have remembered the training pattern so could not have used this strategy. At the same time, E.P.'s results on this test cannot be interpreted unambiguously. The fact that he scored at chance levels suggests that optimal classification does depend on encountering some variability in the category being learned. Yet, it is also true that his score on this test (55.0%) did not significantly differ from his 61.7% score obtained on classification I (Fig. 3B, t[5] = 1.46, P > 0.10), suggesting that instructions may have some influence on performance.

DISCUSSION E.P. exhibited an intact ability to classify novel dot patterns according to whether they did or did not belong to the same category as the training patterns. His intact ability to acquire category-level information occurred despite a complete failure to recognize previously presented dot patterns as familiar. One might suppose that E.P. performed well on classification, in contrast to recognition, because of differences between the two tests in the amount to be remembered or in the amount of repetition. Thus, in the classification task, E.P. needed only to abstract and retain a single prototype after seeing 40 related dot patterns. To construct a recognition task that would be no more difficult than the classification task, we presented a single dot pattern 40 times in succession, followed by a recognition test in which subjects judged (yes/no) whether a series of dot patterns had or had not been presented previously. In one case (recognition I), the test consisted of 8 presentations of the training pattern intermixed with 76 random dot patterns. In another case (recognition II), the recognition test was structured like a classification test (4 repetitions of the training pattern intermixed with 80 other patterns that varied in their resemblance to the training pattern). Despite the simplicity of these recognition tests-i.e., subjects needed to pick out the dot pattern that had just been presented 40 times consecutively, E.P. performed at chance levels (49.4% on recognition I and 48.4% on recognition II, compared to the control scores of 95.2% and 83.3%, respectively). The key feature of these results is that E.P.'s recognition performance was not only at chance but was significantly poorer than his classification performance. Accordingly, it cannot be the case that intact classification performance in amnesia depends on some residual capacity for recognition memory. In other words, it cannot be that classification and recognition depend on the same underlying memory trace and that a small residue of recognition memory capacity is sufficient to support fully normal classification performance. Control subjects were markedly better at recognition than at classification. E.P. exhibited the opposite pattern and revealed no capacity for recognition at all. Taken together, the results show that category knowledge can develop in the absence of any detectable memory for the training exemplars. The acquisition of category knowledge and the ability to classify novel items based on this knowledge do not require conscious declarative memory of the individual items that define the category. The brain system supporting classification ability must operate in parallel with, and inde-

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We thank J. Zouzounis, B. Kronenberg, and N. Champagne for technical assistance; G. Press, D. Amaral, and L. Stefanacci for their neuroradiological and anatomical expertise; M. Kritchevsky for neurological consultation; and S. Zola for comments. This work was supported by the Medical Research Service of the Department of Veterans Affairs, National Institutes of Mental Health (NIMH) Grant MH24600, the Office of Naval Research, the McKnight Foundation, the Human Frontier Science Program, and an NIMH postdoctoral fellowship (B.J.K.). B.J.K. is now at the Department of Psychology, University of California at Los Angeles.

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