Animal Learning & Behavior 2000.28 (3),211-246 An elemental model ofassociative learning: I. Latent inhibition and perceptual learning 1. P.L. McLAREN and N.J. MACKINTOSH University ojCambridge, Cambridge, England This paper presents a brief, informal outline followed by a formal statement of an elemental asso­ ciative learning model first described by McLaren, Kaye, and Mackintosh (1989). The model assumes representation of stimuli by sets of elements (i.e., microfeatures) and a set of associative algorithms that incorporate the following: real-time simulation of learning; an error-correcting learning rule; weight decay that distinguishes between transient and permanent associations; and modulation of as­ sociative learning that gives high salience to and, hence, promotes rapid learning with novel, unpre­ dieted stimuli and reduces the salience for a stimulus as its error term declines. The model is applied in outline fashion to some of the basie phenomena of simple conditioning and, in greater detail, to the phenomena of latent inhibition and perceptual learning. A detailed account of generalization and dis­ crimination will be provided in a later paper. In this paper, we attempt to show what associative, el­ might be represented. Since those stimuli are typically emental models have to otTerthe learning theorist by con­ lights, tones, buzzers, flavors, and so forth, the question sidering one particular instantiation ofthis type ofmodel, has not seemed to require any very complicated answer. which was originally outlined some years ago (McLaren, The most common, perhaps unreflective, suggestion has Kaye, & Mackintosh, 1989). We begin with a briefintro­ been to take what we shall term an elemental stance, duction to the model, emphasizing the representational which sees stimuli as sets ofelements and generalization assumptions contained in it and giving an overview ofthe between one stimulus and another coming about as a con­ novel mechanisms it contains for salience modulation sequence oftheir sharing common elements. Early state­ and trace decay. This introduction is followed by a more ments ofthis position were provided by Thorndike (1911) formal presentation ofthe model, and we then move on to and Hull (1943), the idea was developed and formalized consider the experimental evidence addressed by the model in statistical learning theory (Atkinson & Estes, 1963; and, in particular, how it copes with data that have come Bush & Mosteller, 1951), and is incorporated without to light in the last decade. In this paper, we focus mainly much comment into such standard modern models as on latent inhibition and perceptual learning, leaving to a Rescorla and Wagner (1972) and Wagner (1981). It was later paper a more detailed discussion ofdiscrimination left to those who were more specifically interested in dis­ and generalization. Our conclusion is that, in general, the crimination learning and choice to consider other possi­ model does sufficiently well to establish just how much bilities. Spence (1952) allowed that although animals can be done with a simple, associative analysis, but there would normally represent discriminative stimuli in an el­ are areas where the model is either inadequate and re­ emental fashion, they were capable, ifnecessary, ofrep­ quires further development or may well be just plain resenting them as compounds (a black door on the left or wrong. In any case, we believe that the representational as­ a white door on the right)---;'ii,point.~fview developed by sumptions and associative mechanisms considered here Medin (1975). Gulliksen and Wolfl'e'(1938) developed a are likely to have some significant role to play in any fu­ fully fledged configural analysis ofdiscrimination learn­ ture theory ofassociative learning. ing, according to which animals trained on a simultaneous black-white discrimination learned to make one response AN OUTLINE OF THE THEORY to the configuration ofblack on the left and white on the right and another to the configuration ofblack on the right Representation ofStimuli and white on the left. Pearce (1987, 1994) has recently done Conditioning theorists have not often taken much trou­ much to revive interest in a configural approach to condi­ ble to specify how the stimuli they use in their experiments tioning and discrimination learning. The most explicit of the elemental theories of condi­ tioning was stimulus-sampling theory (Atkinson & Estes, Correspondence concerning this article should be addressed to 1963; Estes, 1959), which conceptualized all stimuli as I. P.L. Mcl.aren, Department of Experimental Psychology, University sets of elements, the elements themselves being simple ofCambridge, Downing Street, Cambridge CB2 3EB, England (e-mail: primitives (corresponding, perhaps, to what would today [email protected]). be termed the microfeatures of a stimulus). The central -Editor's Note: This article was invited by the editors. postulate of the theory was that only a subset ofthe ele- 211 Copyright 2000 Psychonomic Society, Inc. 212 McLAREN AND MACKINTOSH lated to have a tuning curve, responding most strongly to Pattern of one particular value on the dimension and less strongly to Activation for neighboring values. Thus, variation along a stimulus di­ One Stimulus mension, such as wavelength, will, for the most part, be represented by different sets of units corresponding to ~ different values on the dimension, rather than the activa­ tion level ofan individual unit's being the primary indi­ cator ofvalue on the dimension (Thompson, 1965). Fig­ ure 1 represents this schematically. Each unit's tuning curve is such that it responds most strongly to a certain value on the dimension, and this response drops offwith distance from this optimal value. Note that many units will be active when any stimulus on that dimension is present: Value on Dimension The coding ofposition on the dimension is thus in terms ofa pattern ofactivation. Where we are dealing with vari­ 00000000000000000 ations in intensity, we assume that increases in intensity Units by Ordinal Position on Dimension are represented not only by increases in the activity of units already active, but also by the recruitment ofaddi­ Figure 1. Representation of a stimulus dimension in an ele­ tional neighboring units. Thus, for both kinds ofdimen­ mental system. A given point on the dimension is coded by a pat­ tern of activation over units tuned to different points on that sion, the coding of different values on the dimension is dimension. The pattern of activation indicated is taken to be ap­ achieved partly by differences in which units are activated proximately Gaussian and centered on the stimulus value on the and partly by differences in their level ofactivation. dimension. See the text for more details. 3. Although not random, sampling is selective: Not all the elements ofa given stimulus will actually be sampled during presentation, and hence, not all oftheir correspond­ ments potentially activated by the presentation ofa given ing units will be activated on a given trial. stimulus are actually sampled, and hence active, on any Points 1 and 2 are reasonably straightforward and do one occasion on which the stimulus is presented. This sam­ not, perhaps, require further explanation at this stage. pling postulate allowed the theory to combine the principle But Point 3 does. This sampling assumption allows the ofall-or-none, one-trial learning with the observed real­ theory to predict a gradual improvement in performance ity that conditioning typically proceeds rather slowly. over successive trials, superimposed on significant vari­ Although the elements actually sampled on one trial will ability from one trial to the next. Speed ofconditioning all be fully conditioned on that trial, since they constitute to a particular conditioned stimulus (CS) can be directly only a random subset ofthe total, there will be significant related both to the absolute number ofelements sampled variability in responding from one trial to the next, and and to the proportion ofthe total number ofpossible ele­ conditioning will not be complete until virtually all the ments actually sampled on anyone trial. Neither ofthese elements have been sampled and thus conditioned. predictions requires that sampling be purely random, We take stimulus-sampling theory as our point ofde­ however, and it seems more plausible to suggest that the parture, but neither one-trial learning nor random sam­ sampling process should depend on the nature of the pling of elements are assumed in our theorizing. And stimulus and on any constraints on the organism's ability we also part company with early versions of stimulus­ to inspect or attend to it. For simple stimuli, such as tones sampling theory that, in true stimulus-response tradition, and colored lights, it is reasonable to suppose that there allowed associations to be formed only between stimuli will be relatively little variability in the sampling from one and responses. The critical assumptions we borrow are the instant to the next and that a high proportion ofelements following. will be sampled. For more complex stimuli, such as a vi­ 1. The representation of a stimulus consists of a pat­ sual shape or pattern, a photograph ofa scene, or the ex­ tern of graded activation distributed over a set of units perimental context or operant chamber, whose defining corresponding to the elements ofthe stimulus, rather than characteristics are multiple and distributed over space, there being a one-to-one correspondence