parlance of programming language translation, the human-computer analogy has been ... Computer Science, George Mason University, Fairfax, VA 22030. 101 ...
COGNITIVE SCIENCE
11, 101-136 (1987)
Plans and Semantics in Human Processing of Language HENRYHAMBURGER George Mason University
STEPHENCRAIN University of Connecticut
The analogy between humans and computers as language processors has previously been exploited primarily with respect to the parsing or analysis phase of processing, as opposed to a synthesis phrase. Here we pursue the analogy on the synthesis side by positing cognitive algorithms that correspond to target language code generated by a compiler. We consider the computational resource demands of these cognitive algorithms and compare them to what i s required to carry out syntactic and semantic processing. It i s argued that cognitive demands are responsible for certain empirical results in developmental psycholinguistics that have previously been attributed to syntactic complexity. The analysis suggests new empirical studies whose results, in turn, provide support for the analysis.
1. INTRODUCTION The analogy between language processing by humans and language processing by programmed computershas been fruitful and deservesto be extended. The purpose of this paper is to discuss the nature of such extension and pursue it, particularly in the non-syntactic portion of the processing. In the parlance of programming language translation, the human-computer analogy has been pursued extensively in the parsing phase of the compiling process but very little in the code generation phase. In parsing, such ideas and constructs as state diagrams, limited lookahead, and the direction of progress in building a parse are widespread in both compiler practice (Ah0et al., 1986) and psycholinguistics (Kimball, 1973; Marcus, 1980; Wanner & Maratsos, 1978). In code generation, however, there is little if any recognized common ground between students of the mind and those of the machine. Symptomatic of the latter situation is the sharp difference in the usage of the word “semantics,” to refer to meanCorrespondence and requests for reprints can be sent to Henry Hamburger, Department of Computer Science, George Mason University, Fairfax, VA 22030. 101
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ing structures in linguistics but to operational effects in computing. Herein, “semantic structures” will be taken in the linguistic sense as, very roughly speaking, interpretable trees of symbols. This paper is an excursion into the less explored non-syntactic portion of the mind-machine analogy for language processing. We ask about the nature of the algorithmic code generated when a human processes a human language, distinguishing such code from (linguistic) semantic structure. More specifically, we ask what might be the primitive operators and the control structures of the cognitive algorithms. We put forward some tentative answers to questions like these and examine some of the consequences for performance in experimental tasks used to track language acquisition in children. We then provide empirical results including children’s perplexity with phrases like “the third biggest ball,’’ despite their ready understanding of “third” and “biggest” separately. For such results, algorithmic considerations give a more compelling account than syntactic or semantic structure. A preview of the supporting argument for this claim appears in summary form in section 1.3. Sections 1.1 and 1.2 concern, respectively, models and experimentation.
1.1. A Three Module Model We are interested in cognitive algorithms and in establishing the specifics of the important role they play in the multi-stage process of language comprehension and response. To specify that role, we lay out an overview of the whole process, including syntax and semantics. We need to specify all three in some detail to permit understanding of how our experimental techniques can separate their various effects. It is reasonable to imagine human language inputs to a processor-human or electronic-undergoing several transductions, possibly overlapping in time. From an input sentence there would arise, possibly in this order: a syntactic structure, a semantic representation, and an algorithm, which would, if carried out, be responsive to the meaning of the sentence. This last entity, the response algorithm, we shall call the “plan” throughout the paper. This is the stage that we emphasize. After the plan is formed, the next step is to execute it, thereby responding to the input. Though not linguistic itself, the plan can play a key role in the experimental evaluation of linguistic performance. In sum, the paper concerns the role of plans in the human processing of human language, in relation to the roles of syntax and semantics. Since plans are central to our concerns, we give a brief example of one. Suppose someone asks you for “the biggest wrench.’’ Although you may respond with intransigence, sarcasm, or even violence, let us assume that you choose to comply, and consider what that requires. You must determine a reference set of wrenches, determine which one is the biggest, and give it to the requester. From the viewpoint of elementary programming with the
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usual nonparallel hardware, the heart of the requisite algorithm is a loop that involves pairwise comparisons of size. As another example, in some computer-based question-answering systemsthe responsive plan for a query might include a database search. Models of psychological process based on notions like mental loops go back at least to the TOTE units of Miller, Galanter, & Pribram (1960). The planning of sentencesthemselves, as opposed to responses, is the concern of Appelt (1985). The role of planning in comprehension by a computer program, as opposed to a person, has a substantial history, beginnins most notably with Wilensky’s PAM (1978). However, it is not the planning process but the plans themselves that receive attention here. Plans that arise in comprehension tasks for people will be our central concern. Unlike the code for computer programs, the code for human plans is not something that we can design, and it is not even open for direct inspection. Therefore the plans written down in this paper have the status of hypotheses about cognition, requiring empirical investigation. The study of plans must keep syntax and semanticsin view, and conversely. Indeed we have argued at some length (Hamburger & Crain, 1984) that the successfulstudy of syntactic development requires attention to the effects of plans. Since for some phrases and sentencesthe associated planning presents a greater cognitive challenge than the syntactic analysis, difficulties with planning can obscure children’s knowledge of syntax. The present study deals not only with plans and syntax, but with semantics as well. Specifically, we examine the important possibility that the complexity we have previously ascribed to plans may just as appropriatelybe attributed to the semanticphase. 1.2. Empirical Studies
Our approach to the subject is a combination of model building and experiments on children’s comprehension of English phrases. The advantage of children as experimental participants and the rationale for our experimental method and material is taken up shortly. The model building consists of specifying what syntax, what semantics, and what plan are appropriate for the phrases to be studied. On the basis of how complex the models of syntax, semantics and plans apparently need to be, and how different they are from each other, we draw some conclusions about how difficult various phases should be. The work with children then provides empirical testing of these conclusions. The results lend support to the general view that plans are important and to specific assertions about what the plans look like and about how their effects arise. Further confirmation of our general position and further support for the specificsof our models come from experimental manipulations that reduce plan complexity without altering syntactic requirements. Still other experiments reveal aspects of children’s information processing strategies directly. For the specifics of experimental design and
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results, as well as further discussion, see section 5. The results reported there form a consistent extension of an empirical program reported in our earlier work (1982, 1984). Children make good subjects for the experiments because their language capabilities are not fully formed. It is therefore possible with children to obtain incorrect responses that indicate how hard some task is. One can devise relatively simple child language experiments in which the various possible responses-both actions and utterances-are grossly different from each other, in ways that relate straightforwardly to theory. Even children’s correct capabilities may be less “automatized” or “compiled” than those of adults, hence more subject to experimental probing. With adults, language processing is so accurate and fast that experimental conclusions frequently must turn on very small differences in reaction time. Other techniques, like inducing garden path effects, do not require reaction times but typically complex sentences for which the specification of plans would be difficult. Another desirable aspect of child studies is that improvements with increasing age indicate the order in which various aspects of language are acquired. Such information guides theories of the acquistion processes, which are of cognitive interest in their own right. We use a variety of empirical methods. One general paradigm used both in this paper and widely in other language acquisition studies is of particular interest because it calls upon all the capabilities under discussion here. This is the “do-what-hay” task, in which a child is to act out a phrase or sentence with toys and other props. Between the language input and the child’s output actions lie all three of the structures whose roles are to be compared: syntactic structures, semantic structures, and plans. It can be seen as an advantage to have all three kinds of structures at our disposal, but we must find ways to manipulate them somewhat independently, in order to sort out their respective effects. Ideally one would seek tasks that allow investigation of these structures one or two at a time, but such tasks are hard to devise. For example, the use of a declarative-as opposed to the imperative of a do-what-I-say-would avoid the requirement that the understander undertake the planning of possible actions. Use of declaratives might, on that account, seem to allow us to observe just syntax and semantics, thereby disentangling them from plans. Unfortunately, to test comprehension of the declarative one would have to follow it up with a question. An example might be the sequence, “Ada is the fourth tallest girl. How many girls are taller than Ada?” The follow-up question is liable to introduce new complexities in syntax, semantics, and, perhaps most importantly, reasoning. On techniques to elicit utterances from children, see Hamburger and Crain (1982). Linguistically, one of our principal concerns here is the superlative adjective, within a noun phrase such as the one mentioned above, “the third big-
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gest ball.” The superlative(here, “biggest”) is of particular interest for our purposes because it interacts semantically in a variety of ways with other elements that may be present in a noun phrase, such as ordinals (e.g., “third”), possessives (“Ann’s”), other adjectives (“green”), and the head noun (“ball”). Equally important, the plans for some of the phrases are particularly complex, whereas, we will claim, the syntactic structures and even the semantic structures are simple, a disparity that makes for relatively clear interpretation of the experimental results.
1.3. Overview and a Highlight Whereas our 1984 paper compared plan effects only to syntactic effects, here we consider also semantic effects. It is not our purpose to deny the importance of semantics, but to blunt any suggestionthat plans might be completely subsumed under semantics. We find that successful comprehension of English superlativenoun phrases by monolingual 4-year-olds is not readily explained in terms of either individual word meanings or the interaction of those meanings in a phrase. Rather, we claim, one must turn to the cognitive primitives and algorithms for a viable explanation of the results. It is a principal objective of this paper to provide an account of such difficulties with phrasal meaning that we observe in appropriately designed experimental tasks. To forestall suspense, we summarize here a key, empirically demonstrable effect of plans that is unexplainable by semantics alone. As noted above, young children (e.g., 4-year-olds) have difficulty with the task of selecting the “third biggest ball” in a display. In contrast, the same children are flawless in processing “the third ball,” a phrase that in the experimental situation conveys the notion of the third lefrmosr ball in the display. Therefore, the two phrases (“the third biggest ball” and “the third ball”) differ semantically only with respect to an ordering primitive: size versus left-toright position. The difference in difficulty cannot be ascribed to semantics, since the two semantic structures are the same and the two primitives are both handled effortlesslyby the children in other circumstances. Ultimately, we will be able to chalk up this difference in performance to a difference of control structure in the respective plans. (Spatial primitives like “leftmost” also play an important role in the study of mental procedures by JohnsonLaird, 1983, Chapter 11). The next section is an overview of certain aspects of the stages of language comprehension, selected with a view to the issues to be raised further on. It reflects some current theory in syntax and semantics, as well as some of our own views, beginning with why such a discussion is necessary. We go into semantics and plans in more detail in sections 3 and 4, respectively, and suggest how the various considerations relate to experimental techniques. Section 5 is a report and discussion of our own most recent experiments.
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2. MODULES AND INTERMEDIATE STRUCTURES This section lays out a framework for the rest of the paper by sketching some specific organizational principles of syntax, semantics, and plans. In doing so, we attend not only to key theoretical positions, but also to empirical results in our current and earlier work and in that of others. Linguists tend to stress the importance of positing syntactic and semantic structure, whereas in computational work the ultimate focus is the creation of a suitable plan for responding to a sentence in specified circumstances. We see each of the three as an important and independent target of empirical investigation. Because people use all three together to comprehend and respond, it is difficult to disentangle the effects of the three. Nevertheless, by paying heed to this complexity when devising experimental tasks, it is possible to make progress. One or more of the modules at a time can be effectively removed from consideration by rendering its job particularly simple or by keeping its job constant or equivalent across inputs. We have used both these strategies. Our point is not merely that three modules exist-many would be willing to grant that at the outset-but that there is a path, albeit a tortuous one, to empirical progress in verifying claims about the specific structure within each of those modules. It is our purpose to point out some pitfalls, to present a coherent overview, and report some empirical results that, together with our earlier work, support our proposals of specific structures in the planning module. Sketching the general nature of these three kinds of structure will provide the basis for showing how our empirical studies, reported and discussed in section 5 , relate to an important line of developmental psycholinguistic work. We continue a tradition of trying to understand children’s rapid achievement of syntactic mastery. To do so, however, we have found it necessary to cast our net beyond syntax, since the observables in empirical studies typically are the product of joint effects by several processing modules, one corresponding to each of the kinds of structure. In our own previous work (1982 and especially 1984) and again in the empirical studies in section 5 of this paper, we attempt to make a proper assignment of responsibility to the various modules for various difficulties that children have with aspects of language processing. We also consider the possibility that dissimilarities among the structures may be a source of difficulty, demanding relatively complex processing even when each structure viewed separately is simple. One of our strategies for devising experiments to sort out the roles of syntax and planning has been to seek ways of undermining the planning demands of a task without altering its syntactic demands. Implementing this strategy requires the kind of background that we explicate in this section and in section 4. A complementary approach is to compare performance on sentences with different syntax but the same meaning and associated plan e.g., de Villiers & de Villiers, 1973; also see Smith & Van Kleeck, 1986). A
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comprehensive statement of the current status of the empirical studies with children, including work not previously published, can be found in Crain (1987). Phenomena reported there concern relative clauses and several other types of noun phrase modification, presupposition, temporal terms, and anaphora. The paper treats some aspects of language that have played an important role in the syntax acquisition literature, as well as some previously unstudied. The formulation of a plan and its execution once it is formed are distinct activities, so that, including syntax and semantics, there are four, not just three, processes to consider. It is convenient, and we believe justified, to speak of the four processes as being carried out by four independent processing modules. Some arguments for the existence of separate modules appear in the relevant subsections of this section. Independent existence of the various modules does not, however, require that they act in a strict sequence. Although it is simplest to speak as though each module completes its work and passes results to the next, one can also envision the passing of partial results, parallel processing, or bidirectionality Our treatment of the various modules is indirect, consisting largely of a specification of what it is that each is to produce. The output from the last of them, plan execution, is just the sequence of observable physical actions by which the child attempts to carry out the particular task, and is of interest for what it reveals about the other modules. The earlier modules produce results that, in contrast, are not observable. These results-syntactic structures, semantic structures, and plans-therefore have the status of cognition-theoretic constructs. Since they are not observed directly, one may wonder whether these intermediate representations actually arise for humans, and indeed absolute behaviorism denies that they do. For computer-based natural-language processors, the corresponding question is whether all these structures should be designed to arise. A clear exposition of how modularity contributes to transportability across subject matter is in Grosz, Appelt, Martin, & Pereira (1985). Other designers have created systems that do not build syntactic structure (e.g., Brown BE Burton, 1975, Schank, 1975). Syntax is also bypassed in Selfridge’s (1982) simulation of an early phase of language acquisition, but the structures acquired are substantially simpler than those we consider. Subsections 2.1 and 2.2 summarize some relevant pieces of existing syntactic and semantic theory, respectively. Our view of plans is introduced in subsection 2.3 Relationships among the various processes are the subject of subsection 2.4.
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2.1. The Syntax Theories of syntax and of its acquisition should be consistent with each other and with child language. This subsection provides a current glimpse of these and their relationship. Syntactic structures encode information that
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can be distilled from word order, word category, inflectional affixes, special morphemes (like “by” and “to” in the English passive and infinitive), etc. It is generally agreed that each syntactic structure is, or at least includes, a labeled, rooted, ordered tree, called a “phrase-marker.” There is also strong evidence for families of phrase types associated with major word categories, often represented using what is called X-bar notation (Jackendoff, 1977). An example of such a family is noun phrase (denoted N” or NP), sub-nounphrase (N’),and noun (A‘),which see use in the accompanying analysis of our example, “the third ball” (Figure 1). N”
Flgure 1. Syntax of “the third boll”
These families of syntactic categories are an important part of both child and adult language. Indeed one proposal (Hornstein & Lightfoot, 1981) is that from the beginning children hypothesize phrase structure rules that introduce intermediate constituents like N’. The argument is one of learnability: If children adopted flat structure (without the intermediate constituent) they would be unable, given the kinds of language data that children actually encounter, to correct themselves so as ultimately to achieve an adult grammar. This view of early structure has been challenged in certain studies that we believe are flawed by their inattention to plans (Matthei, 1982; Roeper, 1972). The cited authors conclude that young children do adopt a flat structure for noun phrases, because the children give what are called intersective interpretations, for example, taking “the second green ball” to mean the ball that is second and is green, rather than the second of.the green balls. However, we have demonstrated that when plan characteristics of the task are simplified, holding syntactic structure constant, children’s failure .rates in comprehension drop dramatically (Hamburger & Crain, 1984). We also showed that most of the errors disappear if pieces of the plan are activated by preparatory exercises related to that plan, again without altering syntax. These results are not anticipated and cannot be explained on the hypothesis that the intersective interpretations arise from syntax. By finding plans rather than syntax at the root of these comprehension difficulties in children, we provide empirical support for the Hornstein and Lightfoot proposal, which explains rapid acquisition of syntax by claiming that certain erroneous syntactic structures just cannot be hypothesized. In other words, this view
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curtails the search space in which the child’s hypothesis mechanism may roam. Arguments for the autonomy of syntax from semantics, that is, for including syntax as an independent level, have been both a key theoretical concern and an important contribution of generative grammar. An important kind of argument for including a particular level of intermediate structure is that without it an account of certain regularities is either impossible or, at best, contorted. A notable example in syntax is the argument that the phrase structure relationship known as c-command is necessary in order to provide a correct account of such important relations as co-reference (Lasnik, 1976). There is evidence that children as well as adults use c-command to determine co-reference, even children younger than those in the present study (Crain & McKee, 1986). Since this structural relation (c-command) is uniquely able to represent the phenomenon (co-reference), it is presumably different from the types of structure at other levels. Such differences will be mentioned later as sources of processing difficulty. The Crain and McKee study provides evidence not only for c-command but also for the view that even in quite young children syntax has primacy over semantics. A typical sentence in their study is “He danced while the lion looked in the mirror .” Children had to determine whether the referent of “he” could be the lion. Children systematically refused to let “he” and “the lion” co-refer, even in the face of circumstances in which that interpretation was semantically appropriate. In other words, the syntactic proscription on such co-reference, which is most naturally stated in terms of c-command, is regarded as inviolable by the minds of children even as young as three years. This result cannot be explained in terms of any combination of semantics and word order, since in the same circumstances the children accepted sentences like “While he danced the lion looked in the mirror.” (The crucial role of syntax in processing is also supported by the fact that peculiar, revolutionary, and false sentencesare understood, for example, “mice chase cats.” This example shows the futility in general of seeking the meaning of a sentence without attention to syntax, say by trying to combine the meanings of its various words in some way that appears to make sense.) Philosophers and other pensive mortals have long been deeply concerned with what constitutes the essence of the human species, with what is and what is not built into our minds biologically. It is therefore appropriate, and thanks to Chomsky (1965) true, that a central issue in linguistic theory is the question of which parts of syntax are innate and which acquired. For the acquired parts, one wants to know what kind of mechanism could possibly do the job of acquisition, given the kinds of language information to which children are exposed. For explication of this view, as well as specific proposals, see Hamburger and Wexler (1975), Wexler and Culicover (1980), Pinker (1984) and Berwick (1985).
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One strategy for thinking about such a mechanism is not to try to specify it in detail but just to posit some apparent preferences that might be implicit in its modus operandi. If children have preferences among syntactic structures, and if we can discern those preferences, we may be able to draw some conclusions about the procedures and initial assumptions that children bring to the task of syntax acquisition. For example, as noted earlier, a preference might exist for shallow, broad phrase-markers in comparison to taller, narrower ones. Recall that such a preference was invoked to explain an apparent tendency by children to supply incorrect interpretations of consecutive adjectives (and also consecutive clauses, Tavakolian, 198 1). Clear proposals like these, accompanied (as they have been) by relevant empirical work, have the potential to illuminate the syntax acquisition process, provided that they take the responsibilities of all the modules into account. 2.2. The Semantics This section leads up to a limitation on the ability of semantics to explain the relative difficulty of phrases like “third biggest ball.” This limitation of semantics provides a reason for turning to plans. En route to this conclusion, we provide a rationale for the particular semantic structure we use, and touch on its relation to syntax and plans. Semantic structures, like syntactic ones, are often taken to be labeled (rooted, ordered) trees. In a compositional semantics, the semantic value associated with each node not at the bottom of the structure is determined by an associated combination rule that acts on the semantic values of the nodes just below it. One way to achieve compositionality of semantics is by means of a correspondence to syntax, with each syntactic rule having an associated semantic mode of combination. There thus arises a homomorphism of syntax to semantics. Though compositionality and correspondence are distinct notions they are often taken together to constitute the Principle of Compositionality, also called Frege’s Principle (Dowty, Wall, & Peters, 1981). The semantic theory of Montague (1970) achieves compositionality by correspondence. We use the word ‘compositionality’ without implying correspondence. In the realm of compilers, the technique known as syntax-directed trunslution (SOT)also specifies a semantics characterized by both compositionality and correspondence to syntax (Knuth, 1968). Although, as noted earlier, ‘semantics’ means somewhat different things in the study of natural and artificial language, there is a suggestive observation to be made here. In creating a representation for the semantic value at a node, Montague permits rearrangement of the contributing semantic constituents. The corresponding capacity in a SDT system makes it non-simple, thereby denying it, in general, the following apparently simplifying processing property. If a SDT system is simple (so that its nonterminals are not rearranged), and if its source language grammar is in the LL(k) class (a subclass of the context-free
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grammars for which top-down, deterministicparsing is always possible with lookahead limited to k tokens), then translation can be carried out by a deterministic pushdown transducer. In effect, simpleness allows one to work on strings rather than trees. It is possible to have hierarchical semantic structures that are compositional but are not isomorphic to syntactic structures, as we do in this paper. For example, the extension of “Evelyn’s older child” is the older member of the set of her children, so that her children apparently constitute a semantic unit. However, if syntactic units must have contiguous constituents, there can be no corresponding syntactic unit, since such a unit would have to comprise the (non-contiguous) elements “Evelyn” and “child”. Compositionality, however, we do adhere to, and there is a particularly good reason to do so. If the semantics is compositional and if, moreover, recursion is permitted among the semantic rules, then it may be possible to give a finite account of how semantic value is assigned to each of the infinitely many possible sentences of a natural language. A semantic phrase type is not just a label but must also have an associated specification of how to determine its semantic value, that is, colloquially speaking, its meaning. If one assumes compositionality, as we do, this means stating how the constituents combine to assign a semantic value to the phrase. Also,to get things started, there must be semantic values associated with individual words. In the widely followed set-theoreticapproach, various kinds of phrases are said to have the capacity to refer to such things as entities, truth values, mappings from entities to truth values, and mappings to and from other mappings. Since sentences clearly have meanings, the assertion that a semantic module exists may not seem to require comment. Still, one might wonder whether the planning process usurps the role of semantics. A clear case for the necessity of a semantics independent of plans is that even when a sentence demands an extensional kind of response, it may have one meaning but many possible correct plans. In the following example, the plan depends on the cognitive (data) structure. Suppose that you and I are asked who was the seventh president of the United States. If I have a memory structurethat stores who each president’s successor was, plus the fact that Washington was first, I can answer the question, but I will be obliged to count through that memory structure. You on the other hand may have been clever enough to store integer-president pairs, making direct access possible. Meaning, as commonly understood, abstracts over such distinctions. For good discussions of this issue as it arose in the debate over “procedural semantics” in computer processing of natural language, see Woods (1978) and Schubert, Goebel, & Cercone (1979). A simplified semantic structure for “third ball” appears in Figure 2. The root node has the label “ c individual entity> ” because extensionally, in actual use, the referent of this phrase will be an individual entity. (For a
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careful intensional account, see Dowty et al., 1981). Again simplifying into an extensional manner of speaking, “#” stands for some particular set that a situation should provide, to be considered conversationally appropriate for the phrase. In the experimental situation the “#” may be thought of as associated with the set of items in the display. The predicate c is-ball > determines a subset of those items at the next level up. The “@” sign refers to the left-to-right ordering convention that is in use. No set is mentioned explicitly in the phrase, but experience in the experimental setting makes it clear to the child that the intended set comprises the objects in the display. The “@” sign is associated with limitations in the ability of semantics to explain certain empirical results. We now briefly describe this limitation on semantics, thereby motivating the next subsection, on plans. First, suppctse that the “@” sign is reinterpreted to stand for a different scale primitive, specifically, size ordering from largest to smallest, instead of left-to-right ordering. The meaning now represented by Figure 2 is that of the phrase, “third biggest ball,” a phrase that one senses is going to be more difficult for children than “the third ball,” as indeed it turns out to be. This difficulty can not be attributed to semantics if, as we have just indicated, the two semantic structures are identical down to the choice of a primitive. When we return to these matters from a planning perspective, a reasonable explanation of the different degrees of difficulty will emerge.
2.3. The Plans Not only are the meanings of phrases important but also the fact that in dowhat-I-say tasks the child is expected to perform actions based on those meanings. Such a performance relies not only on syntax and semantics for comprehension, but also on the ability to generate a plan to carry out the appropriate actions and thereby receive credit for understanding. This sub-
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section posits some primitives and structures for plans. There is also a note on the role of intensionality in distinguishing a plan from its execution. Plans have steps that may call for such things as manipulative actions, like picking something up, and perception-orientedacts, like eye movements and attending to kinesthetic and tactile input concerning the weight of an object. Typically, these steps do not simply take place in sequence, but, rather, are subjected to a control structure similar to the ones found in computer programs, involving selection and iteration (branches and loops) and subroutines. A plan can include cognitive acts that correspond to evaluation of conditions to determine, say, whether to exit from a loop. In summary of plans, they may be composed of: perceptual processing, guidance of sensory organs, information manipulation, procedure control, and manipulative actions. Returning once again to our example, “the third ball,” we have the plan in Figure 3, simplified slightly from our 1984 paper. Plans, like semantics, are of an intensional nature. The existence of intensional semantic structures was argued convincingly by Frege (1892). Their importance in computational systems for natural language is treated by Woods (1978), who draws upon the straw man he calls the “transient process model,” implicitly in use in some systems. In this model the meaning of a definite noun phrase is to bring about the retrieval of its appropriate referent. Woods points out that simply putting the burden of specifying meaning on an unspecified retrieval process reveals nothing. Moreover, the processor will need some kind of intensional representation to guide its 1 sensory-attention-item
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search anyway. Finally, the model is mute on the treatment of indefinite noun phrases, since they need not have referents already in memory. In the do-what-I-say tasks reported below, the child must ultimately deal with the real extensional world, that is, with the items provided in the display. Given a phrase like “the biggest green ball,” the child must, to be considered correct, physically retrieve a particular object. The semantic structure of such a phrase, on the other hand, has nothing to do with any particular physical object and neither does any corresponding plan. Both are intensional and can be formulated in the absence of the display. We encourage this to happen by withholding the display in our experiments until after the presentation of the linguistic material. The particular extension that will be picked out by the semantic structure and plan is determined only after the display has been made available. 2.4. Relationships among the Processes Given the multi-stage process just outlined, with its various intermediate structures, there are many places to look for difficulties when children fail at acting-out tasks designed to study their comprehension. The difficulty may arise from complexity in one of the three intermediate structures or from resource demands by any of the four processes. Complexity in one of the processes should be expected to the extent that there is a mismatch between two successive structures. We noted earlier that such mismatch constitutes a reason for believing that an independent level exists at all. Here we emphasize that for a human processor, especially a child, a mismatch may have an observable consequence: imperfect performance. On the other hand, since children learn to do so well so quickly, there is reason to seek a theory in which the mism-atchesof successive structures are minimal, and indeed some theoreticians have attempted to do so. Montague’s program for similar syntax and semantics, mentioned above, is a case in point. Another attempt to minimize mismatch is of interest, though it is not generally regarded as such. Phrase structure grammar provides a degree of compatibility-absence of mismatch-between syntactic structures and input sentences, in that the word order specified by a phrase-marker is just that of the associated sentence. The task of the first of the four processors, the parser, would presumably be the simpler for having this identity of ordering in its input and output. There are, however serious problems for a purely phrase structural syntax, to wit, the many phenomena that provide the rationale for transformations as a part of syntactic description (see, e.g., Sag, 1982.) One example is the separation of a verb from its object in relative constructions like “the theory that you think I like.” On the other hand, the GPSG (generalized phrase structure grammar) movement makes it clear that the impetus toward this aspect of mismatch minimization is still alive.
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Suppose a grammar directly compatible with sentential word order were somehow wedded to a semantics whose phrases mapped directly onto the syntax. Then one would have to wonder what had happened to all the previously perceived nonlinearities of language. One possibility is that they might have migrated to the specificationof the semantics. Often this specification is made informally in English (or some other natural language) as the meta-language. This is acceptable for purposes of communication, but one runs the risk that certain complexities in the input sentence can wind up as equivalent complexities in the English sentence used to describe (in metalanguage) the relevant aspect of the semantic interpretation. Although with ground literals one has straightforward specificationslike “ ‘Snow is white’ is true just in case snow is white,” in more complex circumstances, problems arise, as will be seen immediately. In the case of multiple quantification, the requisite meta-languagedescription involves substituting, in some systematic manner, each of many realworld entities in each quantified position. Dowty et al. (1981) express the situation this way: It seems that what we need for these cases is some systematic means of “pretending” that each free variable denotes some individual or other, and then later systematically revising our assumption about which individualis denoted by these variables as we reach the appropriate quantifier at the “outer” or “higher” stages of the syntactic formation of the formula. On examination, it seems that the most straightforward way to formalize what is being said in such a description is to express it as a nesting of loops in a very high level computer programming language or an algorithmic language. Briefly put, semantic notation may hide loops. These considerations suggest that some plan-like cognitive structures may be formed even when a comprehender is not charged with carrying out an activity in response to an input sentence. The most straightforward reason to look at plans is, of course, that people do ultimately carry out various kinds of planned activities, even if only mental ones, in response to language output. If there is a mismatch between a sentence and its plan, then somewhere along the line-we may not know exactly where-there is going to be the potential for difficulty. We have previously (1984) used the term “compiling discontinuity” to refer to this kind of mismatch that spans three stages of processing.
3. PROCESSING SEMANTIC STRUCTURE We seek to establish that a role exists for plans independently of semantics. An alternative possibility would be that semantic structure directly guides the response to linguistic input. Even if one grants the face plausibility of an
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independent plan module, it is still important to establish an empirical basis for it and to move toward more specific claims about its nature. In order to speak more clearly about the roles of plans and semantics and the ability of either to explain observed phenomena, we first spell out those portions of each that will be relevant to the phrases and situations under study in the various experiments reported in section 5 or reviewed there. In this section we focus on semantics, in section 4, on plans. One can specify the semantics of the noun phrase at various levels of detail. A relatively coarse version is used in this section to avoid commitment to specifics that are not crucial to the points we wish to make here. It consists only of what might be called the semantic phrasing, that is, a specification of which words combine with which others, semantically. In other words, we will consider just the semantically determined phrasing or constituency relations among words and/or subphrases. Any aspects of meaning that are specific to lexical items or particular phrase types are of concern to this version of the semantics only insofar as they influence constituency. (For a more detailed and more reasonable semantics of noun phrases, see Keenan & Faltz, 1985 and Crain 8c Hamburger, in preparation.) Since the structures in such a semantics are generable from their own phrase structure semantic grammar, one could follow Montague in attempting to devise a viable syntax with directly corresponding constitutency relations. Then the semantic processor, on receiving the syntactic parse of a sentence, would not have to search for structure, but only interpret nodes. However, for reasons noted in section 2.1, we believe such a course is flawed. Moreover, our semantic constituency relations, based on semantic principles, will be inconsistent with the constituency relations one would obtain by syntactic analysis. How might one seek to determine semantic constituency relations in a principled manner? Subsections 3.1 provides relevant principles, which determine, in subsection 3.2, the constituency relationships for the particular phrases to be studied. Also in subsection 3.2 we consider what makes such relationships hard or easy to detect. 3.1. Determining Semantic Constituency Our determination of the semantic structure of noun phrases is based on a straightforward compositional principle: P1. Each substructure is composed at a level that is lower than the level at which it is used.
The idea informally expressed in PI applies to “second biggest green ball” as follows: When an operator like that expressed as “second biggest,” is to operate on a subset, like that expressed as “green balls,” the operator and
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the subset must each be (internally)composed below the level where they are joined. Using parentheses to indicate constituency, the example has the following structure: ((second biggest)(green ball)). Where PI does not apply, it will be assumed that there is no substructure. A related principle that will be useful is P2: (P2)An operator must be composed with appropriate operands.
To use (P2)we need to have some operators and to know what their appropriate operands are. For this purpose we introduce two categories of modifiers: restrictors and selectors. A refinement of this categorization appears in subsection 3.4, where we will also show that ordinals are more complex than what is assumed for the discussion here. Further, we shall make reference to sets rather than types and generally speak in extensional rather than intensional terms, since our particular application is to actual manipulation of items in a specific display. A modifier like “green” that represents a semantic operation of acting on a set to produce a (not necessarily proper) subset of it, we shall call a restrictor. In contrast, other modifiers like “third” that select an element from a set, we shall call selectors. In terms of the set-based semantics mentioned in section 2, selectors and restrictors each require an operand that refers to a set. However, these two kinds of operators produce different kinds of outputs, the former a subset, the latter an element of the set its operand refers to. Therefore (P2)allows a selector to be applied after a restrictor but not before one, since the output produced by a selector is not appropriate input to a restrictor. Returning briefly to the example phrase above, (P2)rules out the phrasing (second(biggest(green ball))), at least on the analysis so far. This is because the selector “biggest” yields an element, not a set, so that the selector “second” receives an inappropriate operand. Note, however, that the reanalysis of ordinals in subsection 3.4 will permit a new interpretation for this phrasing that does not violate (P2). With principles (PI)and (P2),it is possible to determine constituent structures of some phrases. For a three-element phrase there are four candidate structures into which the elements can be assembled. One of these comprises just a ternary branch with no substructure. The other three structures each involve a binary substructure that is composed with the remaining element at the top level. The latter element may be the first, second, or third, yielding three different structures. Examples of English noun phrases of each of the four structural types appear as (1)-(4), respectively. P2 determines the structure of (2) and (3). In each of them the superlative, being a selector, must apply after the other modifier, which is a restrictor in each case. (If the selector were viewed as producing a set necessarily of size one, then subsequent application of operators would be permissible by P2, but redundant, and hence excluded by conversational maxims).
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A A A A
red leather ball
second green ball
Ann’s biggest ball
third biggest ball
To get the structures right one must also distinguish absolute adjectives like “green” from relative adjectives, like “big.” Absolute adjectives, and almost all nouns, specify properties that can be ascertained without regard to context. Relative adjectives, in contrast, are evaluated by comparison to a standard that does vary with context and often depends on the head noun of a noun phrase, as in “big ant,” meaning something that (is an ant and) is big for an ant. Other parts of the noun phrase may also help determine the standard, such as “toy” (but not “red,” or “Ann’s”) in “Ann’s big red toy truck,” whose referent is to be big for a toy truck. Though the relative adjective itself thus has narrow scope, its superlative form has scope over the whole noun phrase, so that “Ann’s biggest truck’ is the biggest only of those trucks that are Ann’s. 3.2. Processing Constituent Structure A semantic structure, unlike a plan, does not specify what to do, in what order, when the display appears in the extensional situation of the experimental task. Therefore a prediction about which semantic structures should cause difficulty hinges on an assumption about how those structures are processed. The possibility that human processors find some kinds of structures easier than others was raised earlier with respect to syntax. Processrelated preferences might also exist among semantic structures. Recall that a semantic structure here is simply a sentence or phrase with an indication of semantically motivated constituency, like “(second (green ball)).” Discontiguous constituents are permitted, but the above notation using parentheses is not adequate for them. In the following discussion, we consider the possibility that semantic parsing is more difficult for some of these structures than for others. In particular, if discontiguity were to cause difficulty, that might arise in two different ways. The syntax might mirror the discontiguity, giving rise to difficulty with syntactic parsing, or the syntax might not mirror semantic constituency, so that potentially difficult structure revisions are needed in going from syntactic to semantic structure. We shall not attempt to distinguish these two possibilities. We now proceed to consider the possibility of preferences among semantic constituency structures. As sources of processing complexity we consider (i) the number of nodes in the hierarchical structure, and (ii) whether a node
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is processed right after it is formed or must be set aside to await the preparation of other nodes with which it is to associate. The number of nodes would be important if forming a node were the main activity. Relatively shallow structures have relatively few nodes, the extreme case being flat structure with only a root and the leaves. Preferences for shallow or for tall structures are available as hypotheses here just as in the syntactic case. The only difference is that here the constituency relationships are determined by semantic, rather than syntactic, criteria, notably the semantic notions of subset-formation and item-selection. Though logically distinct, semantics may impose the same phrasing as syntax, making it hard to distinguish their effects (Crain & Steedman, 1985). Since the ternary structure has no intermediate nodes, it would be the simplest one if construction of nodes were the major processing difficulty. Perhaps, this is the rationale for the dictum that flat structure is easy. However, flat structure imposes memory costs. This is easiest to see in the extreme case of utter flatness, which would force a listener to memorize an entire sentence before commencing structural processing. That nothing of the sort occurs is confirmed in general by informal observation and in detail by careful psycholinguistic experimentation on the online nature of both syntactic and semantic processing during sentence comprehension (Marslen-Wilson& Tyler, 1980). Indeed, it has been argued that memory limitations are the reason why language processing has evolved to include online integration of information (Frazier & Fodor, 1978; Shankweiler & Crain, 1986). Since children’s memory is more limited than adults’, it is not surprising that children are even more dependent on online syntactic parsing strategies, as Crain and Fodor (1984) have shown. Where (tall) structure facilitates semantic integration, the use of such structure should be particularly helpful to children. From the early 1960s onward, it has been widely documented that binary branching structures occur routinely in the syntax of 2-year-olds. (For an early synthesis, see Klima & Bellugi, 1966; Pinker, 1984, discusses how structure emerges, but for an opposing view, see Hill, 1983). More generally, the formation of substructure is a widely occurring linguistic and cognitive device that apparently makes complexity manageable (Simon, 1962). These considerationsabout structure lead us to adopt the assumption that a human semantic processing algorithm is preferred (ceteris paribus) to the extent that its processing is online. Specifically, in the next paragraph and thereafter, we assume that when a semantic process sets aside a node for non-immediate use (say, on a stack), a cost is incurred. Since all of the phrases of concern here will be analyzed as binary, we turn to the branching structures (2), (3), and (4). First note that (3) is the only one of them that composes non-contiguous pieces (specifically, the first and third elements) into a substructure. Such discontiguity could pose difficulties for processing and for the acquisition of an adequate processor
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-either by being inherently awkward, or by being rare, and hence unexpected, or both. To put it the other way around, and in more detail, it seems plausible that the much greater frequency of the contiguous case could lead to the development of a standard procedure for that case at a relatively early stage. This putative greater frequency of contiguous structures might be no accident but rather a result of the existence of simple algorithms that, using a stack for (last-in-first-out) short-term storage, handle only the contiguous case. The existence of such algorithms is a necessary though not sufficient condition for them to be part of the human genetic endowment. On the above account, one could expect noncontiguous structures to be treated incorrectly when first encountered and then later handled, conceivably with some difficulty, as exceptions. Then structure (3) would develop relatively late. The two remaining structures, (2) and (4),differ in that one is left-branching, the other right-branching. (This statement ignores branching into morphemes below the word level). The significance of this distinction for a left-to-right processor is that the right-branching structure, (2), requires that its first item be stored while the other two are being composed with each other. The stored item must also, of course, be retrieved later on, prior to being composed into a top level structure. No such storage and retrieval operations arise to complicate the composition process for the left-branching case. In sum, these considerations suggest that comprehension of (4)is easiest, followed by (2) and then (3). This predicted order contrasts with the one that will emerge below from a consideration of plans.
4. PLANS AND PLANNING What makes a phrase difficult or easy to comprehend from a procedural viewpoint? In the first subsection, we consider three aspects of planning that contribute to an answer to this question. Moving from the broad to the narrow, they are: (i) processing mode, (ii) control structures, and (iii) primitive operators. The second subsection includes displays of specific relevant plans and discussion of them. 4.1. Three Aspects of Planning
The first factor, processing mode, concerns whether the two processes of devising a plan and executing it are segregated in time or may be interleaved so that parts of the execution begin before all of the planning is done. This operating difference also distinguishes compiled versus interpreted versions of computer programming languages, and so we use the words compiling and interpreting to refer to this distinction. Note also that whereas an interpreter executes source code more or less directly, a compiler translates it to executable target-language code. Therefore by introducing explicit (target
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language) plans in this section, we reveal our belief that a child can operate more accurately in compile mode. It is possible to encourage the child to operate in the compile mode by suppressing the display until after the sentence has been presented. One might then expect the child to form a complete plan on the basis of the linguistic input. We have delayed the presentation of the display in the experiments reported below, specifically in the hope of obtaining compile-mode behavior that could then be less ambiguously discussed. Nevertheless it is conceivable for the child to hold off to some extent on planning. An extreme -and implausible-case would be that of a child memorizing the input word sequence and then operating in interpret mode when the display was revealed. Turning next to control structures, we have found that children’s relative success on tasks related to those in this study can be explained in terms of processing loops analogous to the loops that occur in programming languages. To get an idea of what is involved, suppose that compiling is carried out on a phrase like “third thing,” consisting simply of an ordinal and the minimalcontent noun, “thing.” A reasonable plan corresponding to the ordinal is a loop in which the loop variable controls a sequence of visual visits to the successive objects in the display. There must be some way of determining an appropriate starting object and order of succession, for example, by a convention of left-to-right ordering. The loop is exited when the count of relevant objects reaches the number specified by the ordinal. If the noun is more specific than “thing” and/or if there is another modifier, then some of the objects in the display may have to be excluded from the count. In some cases it may be possible to express this restriction as a piece of plan that occurs ahead of the loop, and hence separately from it, whereas for other cases it may be necessary to apply the restriction(s)inside the loop. In the latter case, the compiler must take a piece of code corresponding to the restrictor and insert it between two pieces of code that together specify the loop already needed for the ordinal. For example, if “thing” is replaced by “box” in the phrase above, then a test for box properties must be put into the loop, to determine for each object whether to increment the counter. This breaking apart of the code for the loop into two separate parts, preceding and following the new test, we call a compiling discontinuity. This terminology is appropriate since the discontinuity is present neither in the source code (the natural language phrase) nor in the object code (the plan for it), but only in their relationship. Specifically, as noted, plan fragments arising from the ordinal both precede and follow the plan fragment spawned by the noun. Our previous work (1984) has confirmed, with other constructions, our original hypothesis that compiling discontinuities are difficult for children to handle. An interesting discussion of such matters is Pylyshyn’s (1984) chapter on what he calls the functional architecture.
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A third factor that affects the ease or difficulty of comprehension from a planning viewpoint is the set of primitive operations that are available in the human processor, and how directly useful they are for a particular task. If the primitives are not directly useful, it may still be possible to perform the task, but at the expense of some complexity in control structure. We regard as a primitive the operation of shifting one’s gaze and attention from one item to the next in an ordered display, since doing so requires only the ability to scan in the specified direction and detect when an object enters the center of the field of view. In contrast, the operation of finding the next item according to size ordering we take not to be a primitive operation. To deal with tasks that refer to size ordering, we assume that a subject in the experiments will find it necessary to use the abovementioned gaze-shifting primitive as well as a pair comparison primitive and some control structure, probably including both looping and branching. It is true that if objects of various heights are aligned and bottom-justified, it is possible to detect the tallest, next tallest, and so on, by gradually lowering one’s gaze from above the display. To make this work, however, the individual must, in effect, attend to a portion of the visual field that has the shape of an elongated horizontal window, and keep that window intact while sweeping it downward. We do not believe that the children use such a strategy, but if they did, tallness succession would be a primitive. Possessives, like ordinals, relate closely to simple vision-based operations. In the case of possessives, the child can incorporate the meaning by directing attention to a set restricted by certain spatial limits on the field of view. Thus for “Ann’s biggest cat,” a child would constrain attention to those objects close to Ann, the designated possessor, and then just work on the remainder of the phrase, “biggest cat.” The appropriate limits on the spatial field can be set independently of the selection process for “biggest cat,” hence outside the loop of object succession. The loop variable, then, ranges only over those objects close to the possessor, thereby eliminating the need for a corresponding test inside the loop. Of course, without looking at the display, it is not possible to set the actual physical boundaries required for the field of view. The plan would have to include the intent to fix these values upon presentation of the display, and a method for doing so. 4.2. Some Superlative Plans The interaction between an ordinal and a superlative makes for a tortuous journey from phrase (word sequence) to plan. The effect of the ordinal is to make a selection based upon the rank specified by the numerical value of the ordinal, for example, 3 for “third.” That rank is applied with respect to the ordering specified by the adjective root in the superlative, e.g., bigness in the case of “biggest.” The extensional ordering required in this case is not directly provided by the experimental display. Rather it must be sup-
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plied by the child, perhaps on the basis of some sequence of pairwise comparisons of bigness under the direction of an appropriate control structure. Part of the relevant planning apparatus can be understood by considering first a simple well-known algorithm appropriate for “biggest.” The idea is to have a variable, BZ, always bound to the biggest item encountered so far. To do this, initially bind it to the first item; then, compare it to each other item, reassigning it to the comparison item in case the latter is bigger. This algorithm can be either modified or used as a subroutine in forming an algorithm for “second biggest.” With the subroutine strategy, the foregoing algorithm is applied to the given set, to get the biggest object; this object is removed and the algorithm is applied to the residual set. This procedure involves approximately2n comparisons for a large set of n objects, and the set is scanned twice. To reduce the number of comparisons and scan across the set of objects only once, one can modify the original algorithm to keep track of not only the biggest item encountered so far but also the second biggest so far, B2. The two variables are initially assigned the first two items, in size order. Thereafter, each of the other items is compared to the value of B2. Only if an item is bigger than B2 does it need to be compared also to BZ. After comparison, B2 and BZ are reassigned as appropriate. Computational efficiency and cognitive simplicity are both possible criteria on which to compare these algorithms. If minimizing the number of comparisons were important, the “BZ-B2” approach would have a claim to our attention. For a large randomly ordered display, most items will fail to exceed B2 (especially toward the end) and can therefore be dispensed with after a single comparison, so that the expected number of comparisons is substantially less than 2n. Another potential computational advantage of the technique is that it requires only one scan across the display, in comparison to two for the subroutine method. Such advantages are relevant if the array is large and if it is important to minimize processing time. Cognitive considerations more important than speed favor the use of subroutines. The subroutine approach, by building upon prior knowledge, creates a simpler plan, even though it may take slightly longer to execute. We will see shortly that the subroutine replaces either multiple branching inside a loop or else a loop that is nested within another loop. Either of the latter is, we think, a control structure complexity likely to cause difficulty to a human processor. One could argue that the subroutine itself is a sophisticated control structure that only makes matters worse. An alternative view is that if an individual thoroughly understands a particular subroutine and has used it often, it should be of little more difficulty than a primitive, both for planning and for execution. This notion of piecing together what is known, or “chunking,” dates back to Miller (1956) in information processing psychology and has recently been extensively developed for production system learning by Laird, Rosenblum, & Newel1 (1986).
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Our own position on using the subroutine leads us to propose a new experiment that could further demonstrate the importance of plans in considering psycholinguistic data. We believe that the algorithm that calls on the subroutine is indeed the one that will be used, but that to think of it is a nontrivial cognitive achievement, harder than dealing with the syntax or semantics of the phrase. We therefore anticipate that there will be a stage in a child’s development at which if an experimenter can communicate this algorithm independently of the phrase, the child will subsequently be able to handle the do-what-I-say task for the phrase. This is the kind of result we were able to obtain with the “handling” task and the “first” experiment in our earlier work (1984). The results of the follow-up study for experiment 2 reported in section 5 support this view. The idea that children of this age are capable of devising and using subroutines of about this level of sophistication is consistent with the work of Resnick and others; see Resnick and Neches (1984). The foregoing assertions about specific aspects of plans should become clearer from the following presentations of those plans. A plan for “the biggest thing” is in Figure 4 and there are three elaborations of it in Figure 5 that are plans for “the third biggest thing.” The loop in Figure 4 calls for a sensory-attention variable, sa, to be bound to successive objects in the display, through the effects of the primitive next operator, the visual scanner. The value of sa is whatever the person is looking at. The role of the variable BZ above is played here by ma, a mental attention variable, something the person is trying to remember. Like BZ, ma is bound to the biggest object encountered so far. Remembering this object implies retaining both where it is and how big it is. Such memory might be symbolic or spatial. The person must also remember that the comparisons are based on bigness, so another mental attention variable, 1, is bound to the meaning of “is bigger than” in step 1. The back arrow, , is a generalization of assignment. The algorithm using BZ and B2 described above for “second biggest” is similar to the plans for “third biggest” suggested in Figures 5(a) and 5(b). Each of these is an incomplete plan, showing only some of the modifications that must be made inside the loop of Figure 4. Figure 5(a) shows how to update the three mental attention variables that keep track, respectively, of the third biggest, second biggest, and biggest objects encountered so far. The material in Figure 5(a) replaces the $structure (lines 5 9 ) inside the loop of Figure 4. There are three mental attention variables, mal, ma2, and ma3. Initializing them (not shown) is substantially more complex than the simple initialization in step 3 of Figure 4. The reason we have chosen to give a plan for “third biggest” rather than for “second biggest” is to emphasize the redundancy and ultimate unwieldiness of the approach in 5(a) and thereby motivate the move to 5(b) with its nested loop. There are some numbers to remember in the plan in 5(b), two
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1
1 c [ size , descending ]
; Let
2 sa c initialize (display, leftright)
1be the bigness comparison. ; Look at the first item.
; It is the biggest so far.
3 ma c sa
4 loop
5 6
execution-failure-of sa
7 8 9
if sa
c
.. attempt to ..
; Exit if no more items, that is
exit-tat:
; .. you fail in the
next (display, left-right)
1 ma
then ma
;.. shift attention to next item.
; If this one is bigger than previous biggest t
sa
..
; .. then update what is biggest.
endif
10 endloop
11 return (ma)
; At end, biggest so far is biggest of all. Flgure 4. Plan for "the biggest thing."
of them, as well as the three biggest things so far, in order. The loop of 5(b) is nested within the one in Figure 4. The variable ma1 is a counter that counts up to whatever value is held by ma2, as determined by the particular ordinal, here 3 for "third." There is also an array, maa, of mental attention variables. Initialization of them must now replace the initialization steps in Figure 4. The array element maa[O] is included to simplify the statement of the algorithm. Movement into maa[O] may be regarded as forgetting. It is maa[3], not maa[l], that contains the biggest item encountered so far. This choice also is directed toward keeping the algorithm simple without assuming backward counting. Above we anticipated that a plan like that in 5(b) would compare unfavorably with the one in S(c) on cognitive grounds. It is now clear that the cognitive demands of 5(b) include not only the formation and execution of an explicit nested loop, but also the ability to keep in mind an ordered array of the three biggest items so far. In 5(c), there is implicitly an inner loop, but it is masked by the subroutine. The call to the function, biggest, in line 4, returns the biggest thing (remaining) in the set. That item constitutes a oneelement set which is set-differenced from the set. This discussion of the various kinds of phrases has led to several discrepancies between what is to be expected without and with consideration of plans. We conclude that the interaction of a possessive with a superlative will be easy to handle, despite the discontinuity of the phrase noted in the phrasal discontinuity for structure (3) discussed in section 3. The interaction
1 If sa
2
1 ma3
tben ma3
c sa
3 endif 4
5
ifsa
1 ma2
then ma3
6
c
ma2
ma2 c sa
7 endif 8 if sa
9
1 ma1
tben ma2
10
c ma1
ma1 e sa
I 1 endif
Figure 5(a). Part of one plan far "third biggest thing."
I
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ma2
2 ma1 c initialize (natural-numbers, upward)
3 loop 4
if sa 1 maajmal]
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7
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8
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9
exit-test: ma1
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c
maalmal]
sa
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10 endloop
Flgure S(b). Part of another plan for "third biggest thing."
1
ma2
c
3
2
ma1
c
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3 loop
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4
set c set
5
axit-ted: ma1
6
ma1
c
(biggest)
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7 endloop
Figure S(c). Part of yet another plan for "third biggest thing." This loop uses the plan in Figure 4, (including its loop) as a subroutine.
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of a superlative with other modifiers that place a restriction on the displayed set gives rise to a compiling discontinuity that makes such noun phrases substantially more difficult to handle than those with a possessive and a superlative. This difficulty contrasts with the conclusion drawn from consideration of continuous phrasing in the discussion of (2) in section 3. Finally, the interaction of an ordinal and a superlative leads to a planning requirement for a loop within which there is no primitive operator like visual scanning for getting to the next object. In compensation, one must invoke more complex control structures. This should make such phrases difficult, despite the fact that their semantic structure is not only contiguous but left-branching as well, as noted in the discussion of (4) in section 3. In sum, this discussion of plans and their execution leads to expectations about the order of acquisition that are directly opposite to those suggested in the earlier discussion of the processing of structure. 5. METHOD AND RESULTS
In this section we present three experiments on the acquisition of cognitive compiling. The purpose of these experiments is to provide empirical support for our claim that planning is an important factor in psycholinguistic tasks, independently of syntax and semantics. The psychological reality of planning will be investigated in three ways, each way being instantiated in one of the experiments. In experiment 1 we show that children’s ability to comprehend a phrase is inversely related to the complexity of the associated plan. For this purpose we need sets of phrases that are arguably equal to each other in syntactic and semantic complexity but differ in plan complexity. Next, we address the cognitive difficulty in planning more directly by a within-sentence method, as opposed to the foregoing comparison between sentences. Specifically, in experiment 2 there is a sequence of exercises designed to undermine the planning difficulty of particular phrases. This activity does not provide any extra exposure to the linguistic material, that is, the phrases under study. Nevertheless, we anticipated a reduction in errors on these phrases. The third experiment is an attempt to get a closer look at the cognitive plan in action. To do this, we examine children’s responses in situations which by their nature require a number of observable sequential actions. We believe that the results of the three kinds experiments taken together make a strong case for the importance of cognitive planning in children’s development of language. There follows a set of descriptions of the experiments. Each of them tests for comprehension of noun phrases, including the superlative form of an adjective, usually “biggest.” Each description is accompanied by a sum-
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mary of results on that experiment, with separate analysis by phrase type when this proves advantageous. Forty-five children participated in all, but each in only one experiment. Children worked individually with the experimenter (E) in a room adjoining the classroom. The child was seated at a table, across from E, who was familiar with the child, having attended several play periods. Each testing session began with E spending several minutes with the child, playing with toy figures and games which were placed on the table. Only after the child was freely making conversation did E direct the child to the experimental pretests. 5.1 Pretests
Each child who participated in the experiments first took at least two pretests. The purpose of the pretests was to check each child for mastery of the words and semantic elements that were the building blocks of the phrases and semantic structures under study in the experiments. Different pretests were prerequisite for different experiments, as specified in the descriptions of individual experiments below. Only those children who performed appropriately on the pretests were included in the main experiments. This does not adversely affect the possibility of drawing conclusions, as will be seen.
PRETEST I : Counting, from left to right. Four small circles, all of the same shape and size, appeared in a linear array on a card. The task was to count the circles and state how many there were. If a child did not count from (her) left to right, she was instructed to begin counting with the leftmost one. In the variant of this pretest that was used for experiment 3, the display was a row of objects rather than a picture, specifically, four small boxes. PRETEST 2: Selection by ordinal. Again using the arrays of identical objects from pretest #1, E asked the child to point to the second circle (or box). For experiments 1 and 2, all of the children who passed pretest #1 and pretest #2 were then asked to identify objects denoted by the color terms which were subsequently used in the target phrases of the experimental sentences. None of the children had difficulty with this easy, perfunctorily included task, so we do not number it or refer to it again. For experiment 3, a third pretest was administered to ensure that children were able to use the particular adjectives which they would encounter in that experiment. PRETEST 3: Heavy and light subsets. Six balls appeared in a linear array, three of light Styrofoam and three of solid rubber. All were the same
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size and each one was covered with aluminum foil to make them indistinguishable. The order of light and heavy balls in the array had no simple pattern. The child was instructed to hold a small pan in one hand, and, using only the free hand, to put the heavy balls into the pan. All 14 of the children tested in experiment 3 were able to do this correctly. 5.2. Experiments
EXPERIMENT I: Comparison of phrases. The purpose of this experiment is to test the contrasting predictions of two different accounts of the sources of processing difficulty. One is based on semantic constituent structure, the other on plans. Both accounts assume that the order in which phrases are mastered in children’s linguistic development depends on the ease with which these phrases can be processed. They differ in what they point to as the locus of principal demand on cognitive resources and/or capabilities. The following three phrases are superficially similar. Indeed, 1 and 2 each differ from 3 in only one word position. Nevertheless, the plans associated with these three phases are substantially different. From our earlier considerations of plan complexity, the order in which they appear here is in ascending order of difficulty. Therefore, they should be acquired in this order: 1. John’s biggest ball 2. second green ball 3. second biggest ball
In contrast, considerations of semantic constituent processing led, in section 3, to the prediction that 1, with its crossed branches, would be hardest. Moreover, lack of memory demands for comprehension of left-branching input suggested that 3 would be easiest. Together, these observations on the effects of semantic constituency lead to an ordering just the reverse of the one that plan considerations suggest. Eighteen children participated in this experiment, ranging in age from 3;5 to 5;8, with an average age of 4;5. Each child was presented four target phrases for each of three types, corresponding to the three examples, 1-3, above. Instances of each phrase type differed only in the choice of lexical items, for example, the name of the possessor, the choice of ordinal (second or third), and so forth. Arrays of objects to be selected from were printed on large posterboard cards. These cards were placed next to small toy figures for phrases like “Batman’s biggest box,” to indicate that the objects depicted in one array belonged to one figure, while the objects in another picture belonged to another figure.
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For half of the phrases of the type < Ordinal > < Adj- > est < Noun> (e.g., “second tallest building”) and for all of the phrases of the type < Ordinal > < Adj > < Noun> (e.g., “third green ball”) and < Name> ’s < Adj > -est , there were “biasing” objects present in the array. For instance, one of the objects in the array used with the phrase “second tallest building” was the tallest building and in second position in the array, as demonstrated in Figure 6 with boxes. A child with the intersective misunderstanding is thereby given the opportunity to reveal it. As Matthei found, among kinds of incorrect choices this one is the most frequent when possible. A different kind of “biasing” object appears in Figure 7, in which the object referred to by the phrase “Batman’s biggest box” is not the biggest box of all (Robin has the biggest in his array). For a more detailed discussion of issues involving biasing contexts, see Matthei (1982) and Hamburger and Crain ( 1984).
Results of Experiment I: The pattern of children’s responses supports the predictions of our procedural account, and does not conform to the account based on semantic constituency. Children responded correctly to phrases of the form ’s -est 88% of the time. They gave correct answers only 39% of the time for and were only 17% correct for the phrase type est . These results indicate that any benefit from left-branching semantic structure is outweighed by the procedural complexities. The poor performance of the children on phrases of the form cAdj->est occurred in spite of the fact that half of the presentations for this phrase type were without error-permitting contexts. For the other two phrase types, all contexts were error-permitting. In sum, this first experiment provides clear evidence that plans, not constituent structures (whether syntactic or semantic) are determinates of processing success and failure for young children.
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Figure 6. A display in which the second biggest box is neither the biggest box nor the second (from left to right).
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Figure 7. A display in which Batman‘s biggest box is not the biggest box, although it is, necessarily, Batman’s.
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EXPERIMENT 2: Preparatory task. From a processing viewpoint, forming a large complex structure places considerable demands on resources. If the burdens imposed by aspects of planning can somehow be reduced, this should lead to a correspondingreduction in processing difficulty. To reduce the demands of planning, we have adopted the following strategy: give the child subject the opportunity to pre-plan some part(s) of the procedures which are required for a complex planning structure. If the experimental maneuvers which foster preprocessing result in an appreciable improvement in the child’s performance, it is reasonable to infer that cognitive planning, not syntax or some other module of comprehension, was the cause of the previous performance failures. The plan of concern here is the one that evoked so many errors in experiment 1, associated with phrases like “second tallest building.” This plan requires the interpreter to integrate procedures which allow one to make sequential pairwise comparisons of relative size and, at the same time, increment a counter and test for the satisfaction of the exit criterion. We observed in section 4 that the sequence of pairwise comparisonscould be accomplished by formulating separate plans for each successive comparison, or by creating a subroutine which could be executed successively. We assumed that the subroutine approach was one that a child could use successfully. Therefore, we devised a preparatory task for the present experiment that would enable the child to practice the relevant subroutine. Specifically, with a display of several objects of one type, say boxes, but of different sizes, the child was asked to hand the experimenter the biggest one. Then, once this object was removed from the array, the experimenter asked the child to perform the task again, saying, “NOW,find the biggest box in THIS group.’’ In this way a child can proceed to find the second biggest box without ever hearing the phrase “second biggest box” uttered. Children’s comprehension of the phrase was tested before and after the preparatory task. Thirteen children participated. They ranged in age from 4;6 to 7;0,with an average age of 5;7. Before the preparatory task, each child did pretests #1 and #2 and also was tested on four of the target phrases from experiment 1, two each of the forms and est . Results for the latter confirmed the comparison of difficulty in Experiment 1. Children were again more successful with phrases of the type (50% correct; the figure was 39% in Experiment 1) than with the superlative phrases (8% correct; the figure was 17% earlier). Following these four trials, each child performed the preparatory task of finding the biggest ball in successive arrays. This preparatory task was carried out twice and each time it was followed by a test trial using a card that had already served to test that child’s knowledge of phrases like “second tallest building.”
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Results of Experiment 2: Children gave significantly more correct responses (46%) following the preparatory task ( p c.05). This result suggests that their difficulty with phrases of this sort stems from cognitive coupling. To pursue the matter further, E informally interviewed 9 children who had made at least one error even after the preparatory task. These children were subtly and indirectly confronted with their error, in the following manner. E would ask the child to point first to the biggest ball and then to the second biggest ball. In this situation 6 of the 9 children correctly identified the second biggest ball on two successive trials. EXPERIMENT 3: Sequence of information seeking. In an attempt to get a fine sequential reading of the child’s processing of “biggest heavy ball” we used objects that provide no visual clue to their density. Success in Pretest 3 on “light” and “heavy” was a prerequisite for participation here. We kept one of the child’s hands occupied holding something else, so that the motions of one hand would reveal her/his sequence of information-seeking actions. A secondary objective of this experiment was a comparison of phrase types, as in Experiment 1. The phrase type of principal interest, < Adj- > est , exemplified by “biggest heavy ball,” has no exact counterpart in Experiment 1. The phrase type most similar to it in that experiment was numbered 2 and exemplified by “second green ball.” Here the superlative form of an adjective appears where an ordinal appeared earlier. For this experiment, when E set up arrays of objects in front of the child, the objects were initially hidden from the child’s view by a cardboard box. E told the child that he was going to place objects of various kinds under the box and have the child perform actions with them when the box was removed. In each experiment or pretest, the child was given the experimental sentence before the box was removed. Fourteen children, aged 3;7 and 5;O and with average age 4;4, participated in this experiment. NEW PHRASE: “the biggest heavy ball”. Six balls of various sizes and weights appeared in a linear array. All were covered with aluminum foil and the weight of a ball could not be inferred from its size. There was, however, a simple pattern in the ordering of the array: the first three balls (from left to right) were heavy and the last three were light. On the other hand,-size was not related to position in the array in any simple way. Letting darkness represent heaviness, the positioning of the balls was as shown in Figure 8.
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Figuro 8. A display in which the biggest heavy ball is not the biggest ball. Darkness in this figure represents heaviness.
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The entire array was screened from view while it was being set up and until after E uttered the test phrase. The operative sentence in the instructions was, “Pick up the biggest heavy ball and put it into the pan.” As in pretest #3, the child had one hand engaged in holding the handle of a small pan, so that only one hand was free for the task. This aspect of our procedure makes it possible to observe the sequence in which the child gathers information about weights. Results for New Phrase: Ten of the 14 children gave the correct response. Thirteen of the 14 began by picking up the biggest object, which was located at the right end of the display. This object was one of the light ones, hence not the correct object, a fact they could not have known when selecting it. Incorrect responses: Three of the younger children (aged 3;7,4;1, and 4;2) indicated that the referent should be the big light ball (bL). One of them commented, “but it’s not very heavy.” A fourth child (aged 4;7) picked up, successively, bL, mL, and mH and said, “none of them.” (“H” stands for heavy, “s” for small, etc.)
Correct responses: Among those children who gave the correct response, mH, the order in which they picked up the balls to find out their weights was: bL, mL, mH - 3 children bL, sL, mL, mH - 3 children - 1 child bL, mH all, in some order - 3 children
OTHER PHRASES: Since the other two phrases are treated in Experiment 1, the discussion here is brief. Despite their numbering, we actually performed Experiment 1 later than Experiment 3, in order to confirm statistically the indications of Experiment 3. A difference in technique was the use of toys here versus diagrams in Experiment 1. (This distinction has not proved important and is mentioned here only for the sake of complete reporting). The two experiments agree on the relative difficulty of the phrase types that appear in both. By now it will not seem surprisingthat all but 1 of the 14 children gave the correct response to phrases of the type % -est . More interesting are two spontaneous comments, the first of which we take as a clear indication of the child’s understanding. The child (aged 4;7) said, “It’s his biggest ball, but here’s the biggest ball in the X’s one and the Y’s one” (emphasis by the child). The other spontaneous comment is apparently even more telling because it violates the rules of adult English to make the semantic co-constituents “Xs”and “ball” adjacent within the noun phrase that contains them: “This is his biggest ball. . .right. . .this is the biggest X’s ball.” This seems a
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striking indication of the possible reality and consequences of discontinuity in semantic phrasing. The problems it causes, however, are more readily overcome by a 4-year-old-like the one quoted here-than are the difficulties we ascribe to compiling discontinuity.
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