On the Relationship Between Implicit and Explicit Modes in the ...

Journal of Experimental Psychology: Human Learning and Memory 1980, Vol. 6, No. 5, 492-502

On the Relationship Between Implicit and Explicit Modes in the Learning of a Complex Rule Structure Saul M. Kassin Purdue University

Arthur S. Reber Brooklyn College of the City University of New York

Selma Lewis and Gary Cantor Brooklyn College of the City University of New York In a recent article by Reber, it was reported that subjects given neutral instructions to memorize letter strings from a synthetic language learned more about the underlying grammar than did subjects who were instructed to try to discover the rules for letter order. Two experiments explore further the relationship between implicit (unconscious) and explicit (conscious) processes in the acquisition of complex knowledge. In the first, instructional set interacted with the extent to which the rule structure of the letter strings was made salient in the stimulus array. In the second, explicit training had different effects depending on when during learning it was introduced. These results are discussed in terms of the complex, interactive roles that these two modes of apprehension have on acquisition of richly structured stimulus domains. In a recent study (Reber, 1976), data were presented which showed that giving adult subjects instructions to search for the rules that underlie exemplary letter strings from a synthetic language has a generally detrimental impact on their ability to learn about the structure of the language. Performance of subjects given explicit instructions to try "to discover the rules for letter order" was compared with that of subjects who were given no instructions other than to try "to memorize the letter strings." It was found that the explicitly instructed subjects (a) took more trials to reach criterion on a memorization task, (b) performed more poorly on a test of the "well-formedness" of novel letter strings, and (c) showed a pronounced tendency to induce nonrepresentative rules for letter order that were not consistent with the underlying grammar. Simply, the subjects who approached the task from a neutral stance learned more about the synthetic grammar than did those who consciously tried to decipher the rules. This research was supported in part by a grant from the PSC-BHE Research Award Program of the City University of New York. Requests for reprints should be sent to Arthur S. Reber, Department of Psychology, Brooklyn College, Brooklyn, New York 11210.

For several reasons we feel that these findings are important. First, they run counter to the dominant finding in the problemsolving literature where instructions to search for patterns and regularities are beneficial. Second, they reinforce the argument made previously that the intrinsic structure of a highly complex stimulus environment is most effectively acquired in a relatively passive fashion (see Reber, 1967; Reber & Lewis, 1977). Finally, as a consequence of these, they help to substantiate the view currenty espoused by several theorists (often from remarkably diverse perspectives) that the cognitive processes involved in learning of this type are different in kind from those proposed within the more traditional approaches to cognitive theory (see, e.g., Brooks, 1978; Hayek, 1962; Reber & Allen, 1978; Turvey, 1974). This point of view, as we articulate it, maintains that complex structures, such as those underlying language, socialization, perception, and sophisticated games are acquired implicitly and unconsciously; that such knowledge is memorially encoded in the form of abstract representational systems; and that the acquisition process itself contains at its core an induction process

Copyright 1980 by the American Psychological Association. Inc. 0096-I515/80/0605-0492S00.75

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whereby relations among parts of the stimulus environment are mapped in an order that corresponds roughly with the ecological salience of these relations1 (see Reber & Allen, 1978; Reber & Lewis, 1977). Given this perspective, several attempts at replicating our original finding (Reber, 1976) are of some interest. Brooks (1978) presented a successful reproduction of the main result using artificial languages similar to the one in the original study. He used a paired-associate task during learning instead of the memorization procedure,2 but the general finding was the same. Subjects encouraged to search for rules performed significantly more poorly on a discrimination of grammaticality task than subjects given neutral instructions. On the other hand, Mill ward found (as reported in Millward, in press) no difference between explicitly and implicitly instructed subjects. He used the same grammar and memorization procedure as was used in the original study, and so his result is of some importance. Thus, the first question that we need to address here is a simple one. Is the superiority of the implicit-learning condition found by Reber (1976) and Brooks (1978) a reflection of an inherent benefit to be gained from learning such rich and complex systems via a strictly implicit mode, or does the finding of no difference by Millward hint that perhaps some artifact of the particular manner of presentation of the materials is at fault? The first experiment reported here deals with this question and, interestingly, shows another failure to replicate. In fact, we report a reverse effect: Instructions to search for rules will be shown to enhance apprehension of the structure of a synthetic language. However, we shall also identify the variable responsible for the diverse findings and explicate how it fits into the gradually emerging understanding of implicit learning. The variable is a simple one: salience of the patterns of invariance. By salience, we mean the degree to which the critical patterns of letter ordering that make up the language are "obvious." In Reber (1976) it was argued that the explicit instructions were detrimental, in part, because the sa-

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lience of the underlying structure was low, and hence, the subjects' conscious codebreaking strategies were unlikely to be appropriately applied. A typical case was presented there of a subject who engaged in rather sophisticated but futile efforts to discover remote, deterministic dependencies between letters when the actual rules were Markovian. Searching for rules obviously cannot be successful if you cannot find them, and further, such search will interfere with the natural operations of the implicit apprehension processes. Consider, however, what might happen if the underlying structure of the stimulus array were made more "obvious." The likelihood of inducing appropriate rules should increase, and the deleterious effects of the explicit instructions should disappear; in fact, such instructions may produce beneficial effects. Thus, to evaluate this rather compelling possibility, we systematically manipulated both instructional set and structural salience in Experiment 1. The second question is more complicated. It concerns the way in which explicit and implicit modes of learning can be combined to produce the most efficacious method of learning new and complex systems. In 1

As has been argued elsewhere (Reber, 1967; Reber & Allen, 1978), the issue of the complexity of the stimulus environment is most important in the emergence of the processes of implicit learning. However, complexity as a psychological factor is not characterizable in any simple way. The determination of the complexity of any structured field requires consideration of at least three interrelated components: (a) some objective metric for structural richness, (b) the individual observer/learner's set of experiences in dealing with the specific class of situations under consideration, and (c) the observer/learner's abilities to direct and redirect attention to relevant aspects of the display. Psychological complexity can be viewed neither as an aspect of the physical environment nor of the learner alone; it is intimately dependent on both. A forthcoming article (Reber, Note 1) deals at length with this "functionalist" approach to the problem of psychological complexity. 2 The paired-associates task carries with it a number of task demands that modify important characteristics of the cognitive strategies that subjects use in learning and decision making. These issues, however, are not germane to the experiments reported here. For a detailed discussion of the impact of task demands upon the operations of implicit learning, see Reber and Allen (1978).

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Taken together, then, the purpose of these experiments is to take a careful look at several parameters and conditions that affect complex learning. They are part of a continuing series of studies on unconscious cognition, specifically, examinations of the boundary conditions of implicit learning, the circumstances under which tacit knowledge is acquired, and the nature of its interaction with the more often-studied, conscious, explicit knowledge. Figure 1. Schematic representation of the finitestate grammar.

Experiment 1 Method

everyday life, people do not learn about Subjects their environment in strictly implicit or exThe subjects were 16 undergraduates recruited from plicit ways, but rather as some blending of an introduction to psychology course. They all served the two. Acquisition of the syntax of social as part of the course requirement. discourse involves the specific, conscious learning of many well-articulated rules con- Stimulus Materials cerning politeness, deference, and so on, The stimuli consisted of letter strings of Length 3— and these are blended subtly with a rich 8, using the letters P, S, T, V, and X. Grammatical (G) set of unstated, implicitly acquired manners items were those whose letter order patterns conform of behavior that flesh out the fully com- to the finite-state grammar shown in schematic form in Figure 1. This particular grammar is the same one petent social interactor. The master chess used in the original study (Reber, 1976). Although player is a superb mix of well-known and several different finite-state systems have been used consciously represented rules, strategies, in our and others' experiments on implicit learning (see, and heuristics, and a melange of intuitions, e.g., Brooks, 1978; Reber, 1969; Reber & Allen, 1978), unverbalizable patterns of play, and a deep, it seemed wise to keep this aspect of the experiment constant. This grammar generates exactly 43 strings implicit knowledge of the developing form within the specified lengths. For example, the string of a game of which typically only the vague TSSXS is produced by the state sequence 0-1-1-2-0', outlines are conscious. Similar characteri- the string PTVPXVV by the sequence 0-3-3-4-2-3-4-0', zations of all cognitively complex skills can and so forth. Twenty-one of these G strings were used as learning stimuli. They were selected carefully so be made. that all of the structural regularities of the grammar were The empirical issue that we address in the present; for all lengths, T and P were represented as second study concerns the combination permissible initial letters, S and V as permissible terand sequence of implicit and explicit learn- minal letters, and wherever possible, examples of the ing sets. To this end we compare the effects three loops or recursions (S, T, VPX) were given. The nongrammatical (NO) items were produced by of a most explicit learning format, specific introducing a single letter violation into a grammatical instruction in the actual rules used for letter string. For example, PXVPS is an unacceptable string order in a synthetic language, and a most because of the X in the second position. Twenty-five implicit learning format, rapid observation such strings were created by systematically violating of exemplars from the language with no in- these positional restrictions. structions other than to "look at the following stimuli." The variable of interest is the Procedure point in the observation period at which the Learning. The 21 learning stimuli were printed on specific instructions are introduced. The in- index cards and mounted on a large (40 x 60 cm) formation concerning the rule system will be board. The way in which they were mounted defined "salience" variable. Table 1 shows the stimulus shown to have any of a number of effects de- the arrays. In the low-salience format (a), the order of pendent on when in the training session it the items was determined randomly; in the highis introduced. salience format (b), the items were arranged in col-

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Table 1 The Two Stimulus Arrays: (a) Random Display and (b) Structured Display PVV TSXS TSSXXVPS PVPXVPS

TSSSXXVV PTVPXVV TXXVPXVV PTTVV

TSXS TXS TSSSXS

TSSSXXVV TXXVPXVV TXXTTVV TSSXXVV TSXXTVV

(a) TSXXTVPS TXXTVPS PTVPS TXS TSXXTVV (b) TSSXXVPS TSXXTVPS TXXTVPS TXXVPS

umns so that each column represented one underlying stimulus type.3 Subjects were run individually. Each was handed one of the stimulus arrays and told to scan it for 7 min. Subjects operating under the implicit instructional set were told nothing about underlying structure; as in Reber (1976), they were led to believe that they were in a memory experiment and were instructed to "learn and remember as much as you can." Subjects operating under explicit instructions were informed about the existence of a complex set of rules for letter order, and they were instructed that "it would be a great help if you could figure out what these rules are." Counterbalancing instructional set and salience of the stimulus array produced four experimental groups: random/explicit, random/implicit, structured/explicit, and structured/implicit. All subjects were told that they would be "asked some questions" about the array after the observation period was over. Testing. Our usual test of "well-formedness" (see Reber, 1967, 1976) was used to evaluate subjects' knowledge of the structural regularities of the grammar. Each subject was presented with 50 strings of letters, 25 of which were grammatical (the unused 22 G strings plus 3 randomly selected "old" strings from the learning phase) and 25 of which were nongrammatical as described earlier. The full set of 50 was presented twice to each subject to evaluate consistency of responding. The test stimuli were presented individually on slides, and the subjects were told to respond by pressing one of two buttons marked "yes" and "no" depending on whether or not they felt that each test string corresponded to the structure inherent in those that they saw during the learning phase. Note that for the implicitly instructed subjects, this was the first mention of rules for letter order. Subjects were required to provide an estimate of their confidence on each trial on a 5-point scale. Response latencies were also recorded. All subjects were informed at the beginning of the test phase about (a) the 50-50 split between G and NG test stimuli, (b) the presence of "a few" stimuli from the learning phase, and (c) the fact that latencies were being recorded. They were not, however, told about the repetition of test items, and no information about the correctness of their responses was provided.

PVPXTVPS TXXTTTVV PTTTVPS TSSSXS

TSSXXVV PVPXVV PTVPXTVV TXXTVPS

PTTVV PVV PTVPXVV PVPXVV PTVPXTVV

PVPXVPS PTVPS PVPXTVPS PTTTVPS

Results The primary data are the proportions of test items whose grammatical status has been correctly assigned on the "wellformedness" task (P,.) following the various conditions of learning. An analysis of variance on these values revealed a marginal effect of salience of the array, F(l, 15) = 4.42, p = .06, no effect of instructional set, F(l, 15) = 1.41, and a strong interaction between salience and instructions, F(\, 15) = 8.74, p < .01, M5e = 16.87. Thus, instructional set operates in a more complex fashion than was apparent in the original study. It interacts with the salience of the stimulus array such that with a highly structured stimulus field, explicit instructions are beneficial, and with a random array that obscures the underlying patterns for letter order, they are not. These relationships can be seen by scanning the bottom row (Pc) of Table 2. Note that all of these values are significantly above chance (Pc > .6 is needed for an individual subject), indicating that even the poorest performing group showed learning of the rule system. Since each letter string was presented twice during testing, some additional insight into these results can be gathered by 3 A type of grammar, such as the one used here, can also be formally represented as a set of string types. Five such types can account for the strings generated by the schematic structure in Figure 1: (a) T(S)XS, (b) T(S)XX((T)VPX)VV, (c) T(S)XX((T)VPX)VPS, (d) P((T)VPX)VV, and (e) P((T)VPX)VPS, where the parentheses enclose the recursions or "loops" of the grammar.

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Table 2 Test for Use of Nonrepresentative Rules Group Random Pattern

cc

CE EC EE Consistency

P,

Implicit

Explicit

.51 .16 .14 .19 .70 .66

.48 .12 .14 .25 .73 .62

Structured Implicit Explicit .52 .16 .16 .16 .68 .68

.68 .10 .10 .11 .79 .79

Note. C = correct; E = error.

looking at the pattern of correct (C) and erroneous (E) classification responses made to each item. Four possibilities exist: A subject may correctly classify an item on both presentations (CC), make an error on one (EC, CE), or make errors on both (EE). The mean proportions of items reflecting each of these patterns for each group are given in Table 2. The CC pattern contains items whose grammatical status is known to the subject plus those that are guessed correctly twice in a row. The interesting comparisons, however, are between CE, EC, and EE. If subjects' knowledge of grammatical structure is veridical but incomplete, and given that the probability of a correct guess is .5, these three values should all be the same. On the other hand, if subjects are systematically using nonrepresentative rules to make their decisions, an inflated value of EE will emerge, since they will be consistently misclassifying particular letter strings. Thus, by comparing the value of EE with the average of CE and EC, we have a measure of the nonrepresentativeness of the subjects' inductions. An overall chi-square test showed that the EE value was significantly higher than the average of CE and EC, x2(3) = 12.27, p < .001. This effect, however, is captured entirely by the random/explicit condition, x20) = 11.12, p < .001; none of the other groups show an EE rate different from CE and EC. Finally, a variety of other analyses on more fine-grained aspects of the data were carried out and can be summarized as fol-

lows: (a) the length of an item was not a significant factor in any group; (b) string type showed no differential effects across groups, although Type 1 strings were, overall, correctly accepted more often than any of the other four types; (c) no group showed any bias in responding G or NG, and G items were correctly accepted as often on the average as NG items were correctly rejected; (d) the NG items whose violations occurred in either the first or last letter were detected more often than those with violations in an internal letter position (.75 vs. .60), and this pattern was found to be significant for all groups; (e) the old items were slightly more likely to be classified as acceptable than the novel ones in all groups (.71 vs. .68), although the number of old items was too small to make this difference meaningful; and (f) response latencies and confidence ratings were uncorrelated with other measures of subjects' performance. Discussion In any complex learning task, there is a subtle coordination between explicit, conscious processes such as hypothesis testing and overt rule induction and the unconscious, covert processes of implicit apprehension. This experiment is but one of several that have focused on the conditions that affect the balance between them (see also Reber & Allen, 1978). The results implicate the variable of stimulus salience as an important one in determining this balance. In simplest terms, the structured array functions so that the underlying rules for letter order can become relatively obvious, particularly when the subject is given explicit instructions concerning their existence and is encouraged to search for them. Structured arrays without such instructions are not as effective. At the other extreme, the random stimulus display in which the rules are obscured, when combined with instructions that encourage rule search, produces the poorest performance. Moreover, the values of EE indicate that only in this latter condition is there a significant tendency for subjects to induce rules that

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are not truly representative of the underlying grammar. It is instructive to compare the values of CC from the four conditions for, in a sense, these values reflect what is "known" about the rules for letter order. Note that in the structured/explicit condition, the CC value is considerably higher than in the others, and that the other three conditions produce roughly comparable values. The poor performance of the random/explicit group relative to the two implicity instructed groups is thus due, not to a lack of knowledge of the grammar, but rather to the elaboration of inappropriate rules. In essence these subjects' overall level of Pc is low, in part, because they have diminished their opportunity for guessing correctly. To fully appreciate this point, note that the "consistency" measure in Table 2, which is simply the sum of the values of CC and EE, is roughly comparable in all groups save the structured/explicit. Thus, excepting this clearly superior group, all other subjects can be presumed to have the same amount of knowledge about letter ordering—with the random/explicit subjects knowing many false rules. Finally, let us address the issue of Millward's (in press) failure to replicate the original instructional set effect. In that experiment, to increase the number of stimulus items available for learning and testing, Millward (Note 2) increased the length of the strings used during learning up to a maximum of 12 letters. The grammar in Figure 1 can, of course, generate strings of arbitrary length (greater than two), but it does so by the recursive use of the three internal "loops" (S, T, VPX). Thus, increasing the length of the letter strings increases the salience of these recursive elements, thereby making the internal structure of the letter strings much more obvious. These, now salient, components presumably become important cues in directing the conscious rule-induction process encouraged by the explicit instructions. What our experiment accomplished by constructing highly ordered stimulus arrays was thus inadvertently accomplished in Millward's study by the use of longer stimulus items.

Experiment 2 We now turn our attention to a more systematic examination of the interactive functioning of explicit and implicit modes of learning and the optimum combinations for learning. To maximize the impact of each mode, the explicit condition was made maximally explicit with subjects given training with the artificial grammar itself and the implicit maximally implicit with subjects given only rapid observation of a sequence of exemplars from the grammar. The variable of interest here is timing: When during the course of observation of exemplars does explicit information concerning underlying structure have its greatest impact? We entered into this investigation with a distinctly inadequate theoretical basis for making predictions about how this variable would function. To appreciate the problem, consider that any of the following possibilities could easily emerge. Presentation of the structural rules of the grammar at the outset, prior to observation, could enhance learning by directing subjects' attention to the salient aspects of the exemplars. This is akin to the common pedagogic device of giving students a general principle or rule followed by concrete instances. Similarly, presentation of the grammatical structure at the end of the observation period could prove beneficial in that it could formalize the tacit knowledge of grammar induced on the basis of the exemplars. This is also a common pedagogic procedure particularly in "progressive" educational programs where extensive experience with instances is followed with formal codification of principles. Extending these arguments leads to the possibility that the most efficient learning may be produced by a combination of these methods as, for example, by introducing the explicit training phase part way through the presentation of the exemplars. These are not the only foreseeable outcomes. Explicit instruction in the grammar could also be deleterious. For example, initiating learning with an abstract rule system could interfere with or inhibit induction processes that would normally accompany

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observation of exemplars. Studying the transformational grammar of Russian may not be advisable prior to a year in Minsk. Similarly, presentation of the formal structure at the end of the observation period could prove disruptive particularly if the presented structure is discoordinate with the induced forms. Would the knowledge that his billiard skills are manifestations of formal geometric and trigonometric principles necessarily benefit a Willie Mosconi? Worse, both of these negative effects could be manifested in the case of subjects who were given their explicit instructions part way through the implicit learning phase. It is an interesting comment that there are no a priori reasons derivable from existing theory for predicting any of these outcomes. There are, however, some hints that are provided by our previous findings. The studies on instructional set reveal that the critical factor is the degree of coordination between the subject's explicit, conscious code-breaking strategies and the underlying formal structure of the grammar. When the stimulus format cued the relevant structures, explicit instructions were beneficial (see Experiment 1); when the stimulus format obscured these structures, explicit instructions were deleterious (Reber, 1976). It is reasonable, therefore, to expect that such a balance ought to be manifested in this experiment. That is, we might anticipate that the degree of coordination between the tacitly represented rules that the subjects abstract out of the exemplars and the formal, grammatical rules that are presented during the explicit training phase would determine the direction of the data.

Learning Procedures Five different procedures were used. Each was a different combination of explicit and implicit training procedures as follows: Explicit only (Group E). The subjects were presented with the actual schematic diagram of the grammar just as it is shown in Figure 1. They were given an explanation of how it could be used to generate letter strings, and five examples were followed through step-by-step to ensure that the rules for forming strings were understood. The subject was then asked to generate three new strings, one each of lengths four, six, and eight. This procedure took about 10 min, and the diagram remained in front of the subject during this time. The diagram was then taken away and the "well-formedness" test began. Implicit only (Group I). These subjects were initially given neutral instructions. They were told that they were in a simple memory experiment and were requested to attend closely to the stimuli, since they would be asked some questions about them later. They were then shown the 21 grammatical exemplars in a random order. Each was flashed on a screen for 7 sec. The list was presented three times (rerandomizing each time) making a total of 63 observations lasting for just over 7 min. At the end of the observation period, subjects were informed about the existence of rules for letter order and the "well-formedness" test began. Implicit-explicit (Group I-E). These subjects were initially given the same instructions and exposure to exemplars as Group I. After viewing the full set of letter strings, they were given the same training as Group E. Explicit-implicit {Group E-I). The order of training phases was reversed, with the explicit instruction coming at the outset followed by the observation of exemplars. Implicit-explicit-implicit (Group I-E-I). Here the subjects were initially given the Group I instructions and were shown the set of 21 exemplars twice. They were then given explicit training and viewed the set of exemplars a third time. The last three groups should be considered experimental conditions. The first two groups are essentially control groups to assess the effect of each training procedure independently. Both control groups had considerably less exposure to the stimulus materials than the three experimental groups.

Method Subjects

Testing Procedure

The subjects were 75 undergraduates recruited from the same population as those in Experiment 1. They were divided into five groups with each receiving one of the five training procedures outlined later. In all cases, subjects participated individually.

The procedure was the same for all five groups of subjects and was identical to that used in Experiment 1.

Stimulus Materials The same 21 grammatical strings from the first study were used as the exemplars for observation training, and the same 50 test stimuli were employed for the "well-formedness" task.

Results An analysis of variance on the Pc values from the "well-formedness" task snowed a strong overall effect of method of training, F(4, 70) = 5.23, p < .01, M5e = 33.59. A posteriori tests on the group means revealed the following pattern: Group E-I > Group

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I-E = Group I-E-I > Group I = Group E; the two significant comparisons are both at the .01 level. The overall Pc values themselves are presented in Table 3 along with subvalues for the G and NG items. It can be seen that the group differences are largely captured by (a) the ability of all subjects who have had experience with the explicit grammatical system to detect violations of the rules rather well and (b) the heightened awareness of grammaticalness on the part of the subjects in Group E-I. Analysis of the patterns of correct and error responding on individual items produced results generally in keeping with those from the first experiment. These data are given in Table 4. The superiority of Group E-I is reflected in the high CC rate, showing that these subjects clearly had better sense of grammaticalness than any others. Comparing the EE values with the mean of CE and EC revealed an overall effect, X2(4) = 34.5, p < .001. This effect, however, is contained entirely in the high EE values of the two control groups, x2U) = 12.06 and 19.87 for Group E and I, respectively. Both values were significant at the .005 level. Hence, only the single-format training procedures yielded evidence of the use of nonrepresentative rules during decision making. Finally, note that as in Experiment 1, the overall measure of consistency (CC + EE) was roughly comparable for all conditions (excluding the clearly superior Group E-I). The implication is that all subjects "know" the same number of rules, except that for Groups E and I many of these rules are in

Table 4 Test for Use of Nonrepresentative Rules Group Pattern

E-I

I-E

I-E-I

E

I

CC CE EC EE Consistency

.67 .11 .12 .11 .78

.57 .13 .15 .16 .73

.58 .12 .14 .16 .74

.54 .11 .16 .18 .72

.48 .15 .13 .24 .72

Note.Under Pattern, C = correct and E = error; under Group, E = explicit and I = implicit.

fact not reflective of the structure of the underlying grammar. The latency data revealed group differences in this study, unlike the preceding. An analysis of variance showed an overall group effect, F(4, 70) = 5.95, p > .01, MSe = 1.17. A posteriori tests showed that only Group I differed significantly from the others (p < .001 in all cases), with these subjects displaying dramatically shorter response times than any others. We take this result to mean that the purely implicit condition produces decision making on a very general, holistic basis, whereas exposure to the explicit schema for the grammar encourages subjects to attempt a more elemental letter scanning that requires more time. The confidence ratings were unsystematic. There were no group differences and no significant relationships between confidence level and any of the other dependent measures. It is difficult to know just how to handle such a nonresult. We suspect on the basis of several of our other studies, including Experiment 1, that subjects are simply uncomfortable with making decisions based Table 3 on unverbalizable criteria and tend to have Pc Values for Each Group relatively little confidence in their reP,. values sponses. It is worth emphasizing that despite the formal, explicit exposure to the Items schematic of the grammar, none of our subNG Group G Overall jects was able to commit the full system to memory in the time allotted, and all were .75 .76 E-I .79 surprisingly poor at providing concrete justi.75 .70 .67 I-E fications for their decisions during the'' well.71 .67 .75 I-E-I .76 .66 E .57 formedness" test. This finding, of course, I .64 .61 .62 is coordinate with essentially all of the Note. E = explicit; I = implicit; G = grammatical; work with these materials (see especially, Reber & Allen, 1978) as well as with other, NG = nongrammatical.

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complexly structured stimulus domains (see emplars that they were later presented with Reber & Millward, 1968). much in the way that the structured stimulus The various microanalyses of the data array did in the first experiment. In other produced a pattern of results comparable words, the experience of viewing the schewith that from the first experiment. Specifi- matic of the grammar at the outset served cally, (a) item length was unrelated to to establish cognitive "boundaries" for the either ^ or latency; (b) string type was not a tacit induction operations engaged during significant factor; (c) one response bias was the observation period. Thus, even though found—Group E subjects revealed a signifi- they could not know the grammar in a precant tendency to respond "NG" (896 NG cise fashion, they could know what general responses vs. 604 G responses, p < .001); kinds of relationships to look for, and this and (d) initial and terminal letter violations enhanced their ability to induce appropriate were more likely to be detected as NG than rules from the exemplars. internal violations and this pattern emerged The relatively poor performance of the Ifor all groups. E subjects seems best interpreted as a kind of interference effect. Some of these subDiscussion jects and some of those in the I-E-I conOf the various possible combinations of dition said that they wished they had never explicit and implicit training procedures, it seen the diagram of the grammar; they felt seems clear that the optimum mode is that that it disturbed them. They are wrong, of in which the subjects begin with explicit course, as a comparison with Group I reinformation about the structure of the gram- veals, but the sense of contamination that mar and then observe an extended series of they expressed seems very real. The key exemplars generated by it. This finding is factor seems to be the match between the similar to that reported by Danks and Gans implicit representation induced during ob(1975), who found that the learning of the servation and the formal properties of the rule system of a miniature linguistic system set of principles conveyed by the explicit (albeit, a relatively simple one compared training phase. The finite-state grammar diswith that used in our experiments) was opti- played in Figure 1 is a veridical, formal mized by showing subjects the rule matrix rule system that accounts for all of the G early in the training phase. Interestingly, strings used. However, it is not the only they also reported that merely informing a such valid, formal representation, as the subject that there were rules but withholding basic string types in Footnote 3 indicate. concrete information concerning their na- It is, furthermore, but one of a (potentially ture had no facilitative effect and may even infinite) number of possible representations have inhibited performance. and is thus unlikely to be strictly coordinate Danks and Gans (1975) attributed the with the form of the underlying abstract facilitative effect of early knowledge of rule representation which our subjects induced structure to supplying for the subject ap- for themselves. We already have considerpropriate information about the existence able evidence that the typical subject implicand structure of the system. In general, itly codes letter strings of this kind in something of this kind seems to be involved bigram (and occasional trigram) invariance in our experiments as well. Certainly, the patterns based on their cooccurence probasuperior performance of Group E-I cannot bilities (see Reber & Lewis, 1977, for details be attributed to a simple memorization of on these tacit codes and a substantiation of the structure of the grammar during the ex- their veridical status). To a subject with a plicit training phase. Were this the case, structure founded on such a tacit code, the control subjects in Group E would have a finite-state system based on Markovian been able to do much better than they did. principles is not likely to fit terribly Rather, it seems that the explicit presenta- comfortably and will certainly introduce tion of the grammar enabled subjects to considerable conflict during the wellfocus on the important aspects of the ex- formedness task.

IMPLICIT AND EXPLICIT MODES

In general, the better performance of the three experimental groups in comparison with the control groups indicates that a blending of the two modes of learning, interference effects included, is still preferable to the use of only one or the other.4 In fact, this should not be too surprising for this is the way in which knowledge of most complex environments is acquired. Essentially all pedagogic practice involves some form of balanced instruction in both general principles and examples that represent them. It also gives an interesting perspective to an old question, why do examples work? It suggests that examples work to clarify issues only when there already exists some structural knowledge such that the relationships manifested by the example can be focused on and recognized for what they are. The data from these experiments also allow us to pursue an additional point of some theoretical interest. The relevant data come in the form of a nonresult: the failure of the length of a letter string to have an effect on either the probability of a correct classification response or the latency of the response. Elsewhere (Reber & Lewis, 1977) it was argued that there were two coordinated processes through which the memorial representation of a complex structured system was established: a differentiationlike component that mapped relational invariances between elements of the stimulus array and a gestaltlike configurational process that represented structure in a more amorphous, holistic fashion. In Reber and Lewis (1977), the data showed that the manner in which one probes the subjects' memorial system is important in determining which mode of operation is likely to be found. In particular, requiring subjects to work with the microstructure of an artificial language (e.g., by solving anagrams) revealed evidence of the differentiation component, whereas other tasks, such as discriminating well-formed from ill-formed strings, tended to produce behavior that reflected the configurational component. The lack of an effect of the length of a letter string supplies further evidence for the con-

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figurational operation in the test of "wellformedness." Clearly, if subjects take no longer to classify an eight-letter string than they do to classify a four-letter string, one can be rather confident that their decisions are not reached via a piecemeal, letter-byletter analysis. This kind of processing of information seems to be highly analogous to the phenomena studied under the so-called "word superiority effect" (see Baron, 1978, for a review of these). Moreover, during the postexperimental debriefing, most subjects volunteered that, in general, they could sense almost immediately whether a string was acceptable or not. The more explicit letter-by-letter analysis was, in their words, more of an "afterthought" and typically engaged in only with respect to attempts to make concrete use of the training received with the actual grammar. Intuitively, this process seems much like what we do in normal language functioning when we detect an ungrammatical sentence in written text. Something about its incorrectness is almost instantly apparent and does not require a word-by-word or phrase-by-phrase analysis. Retrospectively we can often identify exactly where the source of ungrammaticality lies, but such a conscious, metalinguistic act is remote from consciousness when the initial decision is made. Clear evidence for these processes has recently been reported by Bialystok (in press) in a study of second language learning. Her subjects were presented with test sentences in their second language and were required to judge their grammaticality. She found (and we quote her, for her conclusion could easily have come 4 In Reber and Allen (1978), a simple observation procedure was used that produced relatively high levels of performance on a "well-formedness" task compared with those found here (i.e., Pc = .80). Comparisons between these experiments, however, should be made with caution, since in the Reber and Allen study the subjects were hand-selected undergraduates and graduate students who volunteered for an extensive experiment. We have reason to believe that the differences in performance were not adventitious but rather were due to basic functions of abstraction and cognitive style which show large individual differences, (see Kassin and Reber, 1979, for a report of one such factor.)

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from any of our studies on artificial language learning) that there is an intuitive judgement of overall grammaticality made prior to the analysis of any error which may be present. . . .Once a sentence is judged as incorrect, however, the more detailed information about the error involves the use of explicit knowledge.

Clearly, what we have observed in our synthetic grammar experiments parallels naturally occurring processes in the learning of complex, structured systems. References Notes 1. Reber, A. S. Toward a theory of the cognitive unconscious. Manuscript In preparation, 1980. 2. Millward, R. B. Personal communication, June 1979.

References Baron, J. The word superiority effect: Learning from reading. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 6). Hillsdale, N.J.: Erlbaum, 1978. Bialystok, E. Some evidence for the integrity and interaction of two knowledge sources. In R. Andersen (Ed.), New dimensions in research on the acquisition and use of a second language. Rowley, Mass.: Newbury House, in press. Brooks, L. R. Non-analytic concept formation and memory for instances. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization. Hillsdale, N.J.: Erlbaum, 1978. Danks, J. H., & Cans, D. L. Acquistion and utilization of a rule structure. Journal of Experimental Psy-

chology: Human Learning and Memory, 1975, 1, 201-208. Hayek, F. A. Rules, perception, and intelligibility. Proceedings of the British Academy, 1962, 48, 321-344. Kassin, S. M., & Reber, A. S. Locus of control and the learning of an artificial language. Journal of Research in Personality, 1979,13, 112-118. Millward, R. B. Models of concept formation. In R. E. Snow, P. A. Frederico, & W. E. Montague (Eds.), Aptitude, learning, and instruction: Cognitive process analysis. Hillsdale, N.J.: Erlbaum, in press. Reber, A. S. Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 1967, 5, 855-863. Reber, A. S. Transfer of syntactic structure in synthetic languages. Journal of Experimental Psychology, 1969,57, 115-119. Reber, A. S. Implicit learning of synthetic languages: The role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 1976, 2, 88-94. Reber, A. S., & Allen, R. Analogy and abstraction strategies in synthetic grammar learning: A functional interpretation. Cognition, 1978,6, 189-221. Reber, A. S., & Lewis, S. Toward a theory of implicit learning: The analysis of the form and structure of a body of tacit knowledge. Cognition, 1977,5, 333-361. Reber, A. S., & Millward, R. B. Event observation in probability learning. Journal of Experimental Psychology, 1968,77, 317-327. Turvey, M. T. Constructive theory, perceptual systems, and tacit knowledge. In W. B. Weimer & D. S. Palermo (Eds.), Cognition and the symbolic processes. Hillsdale, N.J.: Erlbaum, 1974.

Received October 1, 1979 Revision received February 18, 1980 •