Discriminative feature integration by individuals - Science Direct

Acta Psychologica 84 (1993) 1-16 North-Holland

Discriminative by individuals

feature integration

D.A. Booth *, R.P.J. Freeman School of Psychology,

University of Birmingham,

Edgbaston,

Birmingham

Bl5 2TT, UK

A tactile pattern on the skin generally comes to be recognised from its function within action. What is perceived tactually and may generate a textural or geometrical sensation is therefore a set of dynamic characteristics that can be extracted by mechanoreceptors and central neural processing from the physics of the interactions of hands, mouth or other parts of the body with common objects and materials. The recognised dynamic (as is true of any sensory modality) may be diagnosable as distinct features that are integrated by the whole system’s operation. If so, this transmission of feature information over a channel or in a dimension can be measured as the strength of the influence of that aspect of the stimulating pattern on the observed output, in units of the acuity of response differences for deviations of the pattern from its familiar configuration of features. When applied at the individual level, such multidimensional discrimination analysis provides powerful diagnoses of pattern-recognition processes.

1. Introduction

Pattern recognition is at the basis of any piece of behaviour, whether it be merely reflexive or it involves a skilled judgement, a step in complex thought or a broad emotional reaction. This Special Issue of Actu Psychol~gica addresses such momentary perceptual integration, for the relatively littlestudied type of environmental information that is transmitted through spatiotemporal patterns of stimulation to mechanoreceptors. This set of papers is also unusual in bringing together short reviews and reports of tactile perception from psychology, neurophysiology and the sensory evaluation of materials, particularly foodstuffs. 1.1. Haptic perception Although a perceptual decision is discrete, nevertheless it normally takes place within a stream of overt activity and problem solving. This is a key aspect of the sense of touch, even though neither unique to this modality nor * Corresponding

author. Fax: (021) 414 4897.

OOOl-6918/93/$06.00 0 1993 - Elsevier Science Publishers B.V. All rights reserved

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essential to it. A tactile pattern is often recognised as a result of active use or exploration of objects and materials; that is, the perception is haptic, dependent on making a grasp at the source of stimulation. Other senses might be considered to have their own equivalent of a haptic aspect. Saccades are crucial to vision, including visual texture (He and Kowler, 1992). Even for audition, we turn the head for certain sorts of sound, and other species cock their ears. For touch, however, literally grasping the stimulus has an importance that requires detailed consideration. Grasp-induced changes in the pattern of tactile stimulation could be useful because some mechanoreceptors adapt to static deformations of the skin. However, a more pervasive factor is that the information about shape and texture conveyed in the activity of mechanoreceptor afferents usually depends on action by the feeler. These movements affect the configuration of the areas of skin touching the object and the forces applied to the points of contact between the skin and the object. It may be that kinaesthetic as well as mechanoreceptive information is sometimes needed to recognise a shape or texture (John et al., 1989). Certainly, a standard dynamics or end-point of movement provides a consistent background against which mechanoreceptor information can be interpreted into judgements about the material or object being touched. In this Issue, Lederman and Klatzky describe a repertoire of exploratory movements which is deployed when the task is to recognise the shape of an object by touch. Similarly, when the texture of food in the mouth is appreciated, movements of the tongue against other oral surfaces or of upper against lower teeth have set the context for effective tactile pattern recognition. Thus, the best judgements of food texture may also be haptic, depending on consistently repeatable pressure and/or speed of closing of the jaw or of wiping of the tongue against the palate (Kokini, 1987). Mechanoreceptors in opposing surfaces at the back of the mouth are connected to neuronal units (Sweazey and Bradley, 1989) in a way that may enable recognition of the size or hardness of the bolus, for example to decide between swallowing it and more mastication. The textures of foods and drinks not only can best be assessed under conditions mimicking normal consumption; the very existence of such percepts depends on the manner in which liquids and solids are normally ‘handled’ by the mouth. In the same way, the sensations of roughness and smoothness on the fingers may well emerge from the frictional requirements for a grip on objects that is firm enough to prevent them slipping out of the hand while gentle enough not to damage them (Johansson and Westling, 1984). Textures of solid foods (Marshall, this Issue) are perhaps epiphenomenal to modulation of jaw-closing reflexes by dental root pressure. Indeed, the sense of touch reminds us that the function of perception is not to provide discriminations or sensations but to guide differential acts and reactions.

D.A. Booth, R.P.J. Freeman

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1.2. Pa ttem recognition

Thus we come back to momentary percepts of size, shape, softness and slipperiness. The main part of this paper below introduces a straightforward but partly novel way to characterise the causal processes involved in recognising patterns of stimulation in any modality. This method is to measure discriminatively the transformation of information momentarily operative when an individual performs a task within familiar variants of a particular situation. A related mathematical approach to standard tasks of categorical recognition is outlined in the subsequent paper (Ennis, 1993). The other ten papers in this Issue illustrate a wide variety of current approaches to the recognition of objects and materials through the sense of touch in particular. They include an application of the individualised quantitative approach, now to be outlined, to tactile pattern recognition in the mouth (Richardson and Booth, this Issue).

2. Causal system analysis Minds are causal systems that, in both universal and idiosyncratic ways, transform environmental information conveyed in patterns of stimulation to the senses into patterns of movement that construct further information in the environment. The science of the mind is therefore concerned with diagnosing from observed input/output relationships what are the information-processing mechanisms that need to be postulated in order to account for those relations. The most direct way in which to make such a diagnosis is to analyse one person’s (or any other cognitive system’s) successful performance of a particular task within a defined situation. Such an investigation would directly engage with the actual causal processes by which that person deals with that situation, unlike studies that analyse data from numbers of people or situations. Of course, to generalise across people or situations, or indeed across tasks, the causal structure of individual performance has to be compared between analyses. Yet this would avoid the assumptions that have to be made in grouping the data before analysis. Indeed, the individualised analyses would test such assumptions to the full. This approach requires the repeated testing of the individual on variants of the situation, when relevant input patterns have been measured or quantitatively controlled. Those inputs and their variations must be observed to be concomitant with variations of output patterns in ways that provide clear dissociations among alternative hypotheses as to the cognitive network implemented by that person in tackling that task. This is simply to deploy within psychonomics the concept of causation as used in any modern quanti-

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tative science. It is to read the mind in much the way that a doctor diagnoses what is happening in someone’s body, an engineer how a machine is working, a physiologist an organ or cell, a macroeconomist an economy, and so on for any sort of causal system. In tactile neurophysiology, as in other areas of single-neurone recording, the practice is to draw conclusions from observations unit by unit of the changes in firing caused by variations in the stimulus. Such individualised and objective approaches have been strangely neglected in psychology and in sensory analysis as practised in consumer product evaluation. Instead, it is the norm in these fields, before attempting any descriptive or inferential analysis, to group the data across people and sometimes across situations too. The subjectival (introspectionist) assumption also remains common in some quarters: a piece of verbal output is assumed to measure the cognitive process to which it grammatically refers, e.g. the magnitude of the private sensation of roughness is assumed to be directly scaled by a numerical score of how ‘rough’ a surface is (Booth, 1987). Contrary to this, as illustrated below, a diagnosis of what there is in awareness that contributes to performance requires a more complex design, involving differences between outputs in their relations to input; the minimal requirement for mapping consciousness is a form of double dissociation (Booth et al., 1987; Booth, 1991). 2.1. Multidimensional signal-difference detection Input-output analysis of individual performance in a distinct task can be achieved by the use of two simple but profound ideas that were first developed by psychologists over a century ago. Indeed, these concepts still give psychology a lead over other system-analytic sciences. One idea is that a mental process is a dimension or scalar continuum. The other is the just noticeable difference (JND), understood as a measure of the causal strength of a mental process or (since the rise of communication theory) the sensitivity of the output from an information-transmitting channel to the signals put into it. The two ideas can be put together to construe perception, emotion and intention as various uses of discriminations among coordinates in a stimulus hyperspace. Preference, affect and sensation are analysed as reactions to the distance of the current situation from a familiar point in a discrimination space that is relevant to the purposes of the task that the person has in hand (literally or metaphorically). Each axis in this space is a distinct feature or pattern of input that influences a particular response or pattern of output. The case of two features suffices to define this theory. These stimulus dimensions are usually plotted in horizontal (x and z> axes, with the response plotted vertically (y axis). A graded response integrating those two

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patterns of input thus lies on a surface. However, the measurements of two (or more) distinct patterns controlling a response can be expressed in a single formula. Hence, this surface (as also a response integrating three or more patterns) can be collapsed to a line. This yields a psychophysical function on the conventional x-y plot, but with levels of a complex stimulus on the x axis.

In other words, all the features of a situation that are salient in one type of response can be included in a single input calculation. Conversely, an apparently simple feature of the situation might be shown to be a combination of sub-features that normally are confounded in the environment and might be treated as mutually redundant or might be integrated by the perceiver: demonstrating the subdynamics requires the normal source of that influence to be broken up into distinct patterns that are separately varied and shown each to affect the feature-specific recognition response. 2.2. A discrimination metric Thus, a plot with one stimulus dimension can be used to illustrate the application of the concept of the JND to multidimensional recognition, as follows. The evidence that the stimulus as represented in this plot is indeed controlling the response is the linearity and precision of the JND-scaled function. Limited only by measurement error, the better predictive of response levels is the linear regression from calculated JND levels of the stimulus, the more realistic is that formula for the stimulus. This differential acuity of recognition is the strength of control of this response by this stimulus for this person in that situation. It is partly represented by the variance accounted for by the regression (a traditional measure of causal strength in psychometric scaling). It is also partly represented by the slope of the regression line (a traditional measure of causal strength in psychophysics and some other parts of experimental psychology, e.g. serial search). A simple measure of such suprathreshold sensitivity combines these regression-line parameters. This is the difference in stimulus levels at which the distributions of response levels overlap by 50%, a definition of difference threshold (DT) identical to that of the traditional JND. In signal-detection theory, this DT corresponds to a bias-free sensitivity (d’) value of about unity. When ratios of the measure of stimulus level used (i.e. a semilogarithmic plot) produce a linear psychophysical function and the distributions of the untransformed responses at different stimulus levels are normal and do not vary in standard deviation over the range tested, then this DT is a constant ratio - the Weber-Fechner fraction that the JND is of the stimulus level at which it is estimated. These conditions do not have to be assumed but can be tested for on any set of data (and thus far in our work, there has been no

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evidence of departure from them). Thus, instead of the laborious procedures traditionally used to produce JND scales (such as repeating the Method of Comparisons at many different stimulus levels), a few ratings made under conditions of minimum bias (Poulton, 1988) provide an estimate of the Weber ratio (Booth, 1988). The formula follows from the concept of a JND under conditions of Weber-ratio constancy (Conner et al., 19881, namely: DT = antilog (2 X 0.675 X s/m> - 1, where 0.675 is the z-score for 25% (the 50% overlap coming half from each response distribution), s is the standard deviation of the response residuals and m is the slope of the regression line of stimuli onto responses. In some instances, the most nearly linear psychophysical function is provided by differences in stimulus level, rather than ratios. This is to be expected with descriptive stimuli such as the declared sweetness or calorie content of a foodstuff. It is an empirical matter for each particular stimulus and response whether levels are equally discriminable as ratios, differences or some other function. Indeed, if both ratios and differences can be excluded by the observations in favour of a determinate formula for, say, a relationship that changes over the tested range, then that provides the psychophysical ‘law’ in those circumstances. Also, if response variance was shown to change systematically over the tested stimulus range under unbiased conditions, then the response transformation that restored constancy of variance would identify the ‘law’ in that case. Unlike power functions that are free to give any exponent, these are not curve-fitting manoeuvres but tests among formulae having fiied parameters for the causal effect of an input pattern on an output pattern. Putting these two old concepts together then, JNDs (i.e. DTs) provide a metric for the dimensions of recognition performance. The JND scale may prove not to be exactly the correct metric but it has enabled us to make rapid progress so far in identifying patterns within and between modalities that control a wide variety of responses, from the most specific description of an aspect of a situation to the most global response to it such as choice between objects. 2.3. Discrimination from learned norms The situation or object that is recognised in performing a cognitive task is the combination of stimulus levels that can be considered to be the origin of the multidimensional discrimination space. The difference in overall response between that to this familiar norm and that to the test situation or object decreases in proportion to the distance in DT units of the origin from the tested point (i.e. to its coordinates). That is, the strength of the response is predicted by the regression:

Response + ( cd:)1’2,


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where di is the distance along the ith dimension from the origin, i.e. the levels in the familiar object or situation. For example, where the recognised configuration is the most preferred or most motivating object or goal in that situation, then the disposition to choose the object or to act with that intention declines in proportion to that distance (Booth et al., 1983). Shepard (1957) made an analogous point about overall similarity; however, judgements of ‘(dis)similarity’ between arbitrary stimuli may not follow these principles (especially if data are grouped), since there is no saying what space(s) the two stimuli occupy for each person tested or what constructs of similarity and what response-scaling transformations are being used without individualised analysis of the discriminative tasks being performed in such experiments. 2.4. Feature integration into overall response The integral of two or more distinct feature patterns may constitute a recognisable superfeature or combination of patterns of its own. For example, if two or more textures are consistently experienced in a food, that combination may become that food’s distinctive texture, e.g. the thickness and smoothness of cream (Richardson and Booth, this Issue). Then, as the strengths or sizes of the features increase or decrease in proportion, so the strength or size of the superfeature will grow or decline. That is, in DT-scaled dimension&, size estimates of an object having two features, x and z, should be proportional to the line x = z, the diagonal through the origin. If the assessor can judge purely how much there is of the distinctive complex texture (say) in a test sample, then this strength rating should be proportional to the projection of the tested levels of x and z onto the x = z line (Fig. 1). That is, for n integrated features, by extension of Pythagoras’s Theorem: Off-strength

or off-size response + IZ- ‘/* t

di,

i=l

(2)

where n is the number of feature patterns being integrated in the response and di is the discriminal distance between test and norm in the ith feature. An assessor may also be able to respond according to the discriminal distance of the test combination from the normal proportions among features (Fig. l), perhaps as a lack of realism or quality in the test sample:

Off-quality response + [ &df

- n-‘(

idi)*]‘;‘.

(3)

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z-axis

x-axis Fig. 1. A discrimination-scaled stimulus space for two patterns (X and z), onto which are projected the response lines for the intensity of each pattern (analytical descriptions, along the axes) and of a familiar combination at the origin (along the diagonal, Eq. (2)), the pure quality of those analytical or integral patterns (perpendiculars to the intensity lines, Eq. (3)) and the overall realism or preference (Eq. (I)). The level of each type of response is determined by the distance that the test situation is along that response’s line.

Determining the structure of an overall learned response to a situation is, however, only the most basic use of individualised multidimensional discrimination analysis. Any other response function can be mapped into the stimulus space, such as quantitative description of sensed aspects of materials or general-knowledge features of objects or situations. Moreover, successful modelling of such potentially more analytical responses can be very useful in guiding hypotheses as to the basis of the more integrated, overall response function (Booth, 1990). The most obvious instance is where the person seems to be able to describe a stimulus dimension, i.e. analyses out conceptually what may be one of the discrete patterns of input. This presumably is at least a naming skill and may usually be a perceptual skill also, acquired by experience with situations varying saliently in that dimension only. Thus people may be able to attend to the visual features that comprise a recognisable woodworking tool or letter of the alphabet. In some cases, the smell of a substance seems


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Response Strength

Feature Strength (dS)

Fig. 2. Response to variations in strength of a uni- or multidimensional feature in a constant multidimensional context at a moderate distance from the familiar situation (in discriminationscaled stimulus space). The contextual ‘defect’ reduces the recognition response hut this effect decreases the further that the varied feature is away from its most familiar level, in a determinate manner in accord with Pythagoras’s Theorem.

to be largely composed of odour notes typical of other substances (Cain, 1980; Kendal-Reed and Booth, 1992). Complex textures may also be amenable to descriptive analysis. The food industry has depended on this in efforts to understand intra-oral textures such as the thick feeling of a gel or syrup (Stanley and Taylor, this Issue). The descriptive analysis of creamy texture as both smooth and thick (Kokini, 1985) guided our work on the physical bases of creaminess (Richardson and Booth, this Issue). 2.5. Context effects The stimulus dimension (or set of dimensions) that is being varied (S) during testing lies within a context (C) of other relevant dimensions which may be constant. If this test context is at some multidimensional distance (dC) from the normal or familiar context, it is to that degree defective. Even when the varied dimensions are each exactly at the familiar level, the test situation will still have that defect (Fig. 2; see Booth and Shepherd, 1988). The overall response (R) to the test should then be predicted by a two-dimensional integration of varied and contextual elements: R = ( dS2 + dC2f2,

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where dS is the distance of the test stimulus from the familiar situation. This determinate equation for an individual’s response to imperfect test stimuli obviates the need for the unpredictive data-fitting by hierarchical regression to which group response surface methodology resorts (e.g. Moskowitz, 1983). By putting stimulus and context on the same quantitative footing, this approach provides a way out of the supposedly unidimensional artificiality of traditional psychophysics (Lockhead, 1992). People indeed can discriminate with ease the multidimensional distances of tests from familiar situations. For example, frequent eaters of a food know what composition they like or do not like, at least as precisely as they can describe its taste, smell and feel (Conner and Booth, 1992). They are recognising how close the test item is in the salient dimensions to their favourite version. By the same token, people readily integrate material features in a foodstuff that act on different senses, such as taste, odour and colour. Similarly, they interact such material characteristics with semantic attributes like fat contents (Richardson et al., 1993), the name of a constituent or perceived risk to the heart or waistline (Freeman and Booth, 1993). Table 1 Models of preconscious, analytical and conceptual processing predicting (%r’) individuals’ preference ratings for each of 3 sessions’ samples of a familiarly flavoured drink containing independently varied levels of sucrose and citric acid Assessor

Preconscious

code

2D

1D

2D

Analytical 1D

Conceptual 2D

1D

KML

27 67 25

27 61 25

0

18 3

7 0 33

20 30 6

19 28 6

JHS

0 0 8

1 0 7

0 69 61

17 37 10

12 53 33

3 5 16

RR1

30 0 1.5

29 0 0

40 10 7

0 1 2

58 87 72

60 92 81

KRS

0 34 71

0 40 77

37 53 I

40 5 8

56 34 38

56 34 52

Preconscious: Distances based on acuities of preference ratings for tastants. Analytical: Based on DTs from psychophysical functions of sweetness on sucrose and sourness on citric acid. Conceptual: Acuities of sweetness and sourness for preference. 2D, 1D: Two-dimensional processing modelled as the square root of the sum of the squared DT-scaled distances, as hypothesised; one-dimensional processing modelled by adding distances.

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The usual assumption by psychophysicists that rated intensities of flavour or texture characteristics reflect low levels of sensory processing is therefore open to question. Preferences for familiar foods require high-level sensory integration in primates (Rolls, 1993), close to forebrain control of ingestive and expulsive movements of the mouth. Descriptive analysis may involve rationalisation of the initial overall reaction in terms of beliefs as to the composition of the food (Booth et al., 1987). Indeed, individualised multidimensional discrimination analysis can distinguish performance using a conceptual rationale from use of described physical percepts and also from non-analytical (preconscious) perception. If the data yield a psychophysical function of the physical stimuli on an integral response and also one treating their analytical descriptions as stimuli, the individual has had options in that session among analytical, preconscious and conceptual processing of the integration. A predominant level of processing is diagnosed if an integration using discrimination distances based one of the three types of input/output function is more predictive than the integrations based on the other two discriminations (Booth and Freeman, 1993). In Table 1, three assessors fairly consistently performed the task in one mode but the fourth seemed to be acquiring an increasingly pre-attentive strategy over the three sessions.

3. Illustrations

of individuals’

recognition

That example and the others we give to conclude this general account of personal causal analysis come from descriptive analyses of gustatory mixtures. One reason for doing that here is that the fallacy of direct perception is most seductive for the sense of taste. Some psychologists and physiologists of taste seem to sustain the old assumption that descriptions of taste quality and intensity are direct read-outs from neural activity in sensory pathways. Even if specific receptor types send activity along labelled lines to anatomically distinct locations at the first afferent relay, natural tastes pose substantial problems of pattern recognition, providing a testbed for an experimental approach before trying to crack tougher nuts like tactile perception. Sweet, salty, sour and bitter sensations are innately recognisable, or so it seems from the different facial expressions of newborn babies when solutions of sugar, NaCl, acids or alkaloids are put on the tongue. Nevertheless, we still have to learn the names for the gustatory patterns induced by these sources. Furthermore, we learn to recognise the complex patterns in common mixtures of tastants. Such integral responses were obtained to a mixture of sucrose and citric acid in a drink containing realistic orange fruit colouring and aroma. Assessors usually had a lower DT than for the other models for the hypothesised formula of drink liking for the realism of its orangey taste (Eq. (1)) and of the

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Table 2 Predictions (%r’) of an individual’s ratings of overall realism, strength and balance of the tastes of test mixtures of sucrose and citric acid in an orange drink from the hypothesised discrimination distances given respectively by Eqs. (11, (2) and (3) at three levels of processing, for data from Session 3 (KRS) Response

Process level a

Minimum distance (Eq. (1))

Distance along diagonal (Eq. (2))

Distance perpendicular from diagonal (Eq. (3))

Hedonic realism

P A C

77

7 38

13 8 0

8 4 40

Taste strength

P A C

9 3 4

13 9 40

21 0 9

Taste balance

P A C

63 0 1

0 0 17

4 1 31

a P: Preconscious distances (from psychophysical functions of tastants’ levels onto choice ratings). A: Analytical processing (tastant sweetness or sourness psychophysics). C: Conceptual attribution (distances based on sweetness or sourness ratings onto choice ratings).

overall strength (2) and quality (3) of the orange taste. An assessor’s results for one session, for example (Table 2), showed this pattern at the level of ascribing choice to taste concepts; however, minimum two-dimensional distance strongly controlled preference at the preconscious level, where it also dominated taste mixture balance ratings. It should be noted that this illustrates how hedonic responses can provide objective sensory data so long as the strength of preference for a material is treated as resulting from its perceptual distance from ideal (Booth et al., 1983; Conner and Booth, 1992; cf. Drewnowski, this Issue; Tyle, this Issue). 3.1. Common channels Models (1) to (3) apply insofar as the tastants (or other sources of stimulation) produce entirely distinct patterns of receptor activation. If, however, two tastants act to some extent on the same type of receptor or over the same afferent pathway (or, in general, separate sources of stimulation produce the same pattern, whatever different patterns they also produce), then they will operate in part through a single channel. This is readily identified as an effect mediated additively on the same dimension, rather than by a multidimensional space of orthogonal channels in accord with Pythagoras’s Theorem.

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Table 3 Predescriptive afferent interaction between sugar and acid (MTE session 3): unidimensional discrimination models (%r*) of the sweetness and sourness of mixtures of sucrose, fructose, citric acid and malic acid in an orange drink Response

Sweetness Sourness

Sweetness distances

Sourness distances

Sugars

Sugars and acids

Acids

Acids and sugars

47 30

57 15

5 67

20 43

In psychometric scaling, a parallel distinction has been made in terms of city-block and Euclidean metrics, i.e. different orders of Minkowski space. On our approach, though, the issue is simply a difference or an identity of dimensions, patterns or channels. This cuts through some of the issues of integrality versus analyticity of judgement and independence of stimulus, concept or response dimensions. For example, Attneave’s (1950) classic finding that length and tilt of a parallelogram combined additively showed only that those judges were extracting some common feature between length and tilt; the obvious candidate is the projection of the two long sides onto a line parallel to them. A gustatory example of common patterns from separate features is provided by an experiment in which two sugars, sucrose and fructose, and two acids, citric and malic, were mixed in the orange drink (Table 3; Freeman and Booth, 1993): there was an indication that one or both of these acids acted on a receptor for sugars, in the 10% increase in sweetness variance accounted for when acid-sweetness distances were added to sugar-sweetness distances. Hence, if tartaric acid acts on some of the same receptors as glucose, for example, we do not need complex and hard-to-control tests for cross-adaptation (Bartoshuk, 1987); the acid-sugar mixture will act on fruity taste through addition of discrimination distances of acid and sugar on one of the two separate dimensions that arise from actions of sucrose and tartaric acid on different receptors. Moreover, the acuity (.DT) for a moeity provides a measure of receptor-ligand match. Hence this approach is potentially much more powerful than conventional structure-activity studies.

4. Prospects for multidimensional

analysis

It is a mistake therefore to regard taste recognition as a cognitively trivial problem. Colour perception relies on only three types of receptor and yet both the laboratory neurophysiology and the psychology of real environments continue to pose major challenges. Tactile pattern recognition is likely to

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require even more demanding coordination of cognitive measurement to ecological physics and receptor physiology. There are two or three times as many histologically distinct types of mechanoreceptor, with probably many more functional distinctions based on location within the skin as well as on cellular structure. Moreover, spatial and temporal summation generate multireceptor function. Such large numbers of channels make progress difficult by any method. However, a well-judged choice of phenomena for discriminative dimensional analysis may be one of the most productive next steps. Insofar as the tactile recognition channels are functionally discrete, then this orthogonality of dimensions provides a hierarchy of levels of perceptual integration (and hence of causal analysis) between multimodal perceptual tasks and isolated use of a distinct receptor type. As with visual shape or movement recognition (which depends on spatiotemporal integration across receptors of one type), it is crucial to identify the environmental physics that is the source of the stimulation pattern actually used, whether describably or preconsciously, to compose the recognisable face or walk. The texture of familiar fat-in-water emulsions such as dairy creams, for example, might as a first step be analysed into thickness and smoothness and then each of these component features might resolve into alternative or combined receptor patterns generated by different emulsion characteristics (Richardson and Booth, this Issue). Recent progress in individualised cognitive analysis of odour recognition encourages pursuit of this strategy. Spatiotemporal patterns seem not to be important in smell, unlike touch. On the other hand, there are probably hundreds of olfactory receptor types, each best stimulated by a different electronic configuration that may recur in many volatile compounds. Many natural smells are composed of dozens or even hundreds of contributing compounds, Yet in some cases a mixture of a modest number of appropriate compounds provides a pattern of olfactory stimulation very similar to the real mixture and there is reason to hope that this could be widely true. The use of physically simplified models of nonetheless familiar shapes and textures is productive also in tactual perception (Lederman and Klatzky, this Issue; Marshall, this Issue). Furthermore, in at least a few cases, the compounds that simulate a complex mixture each have a smell themselves like a different familiar substance (Kendal-Reed and Booth, 1992). This provides an intermediate level of analysis. No less important, it gives a richer variety of cases from which to triangulate the structural requirements for ligands to particular olfactory receptor types. The same strategy for the sense of touch would be to elucidate the characteristics of materials that are both adequate stimuli to mechanoreceptor afferents and central units (e.g. Lederman, 1974; Phillips and Johnson, 1981; LaMotte and Srinivasan, 1991) and also reliable and distinctive standards for specific descriptors in the textural vocabulary devel-

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oped by experienced sensory analysts (e.g., Green, this Issue; Marshall, this Issue). These hopes for psychological analysis may or may not apply, however, to purely neurophysiological analyses. The JND-scaling approach remains to be tested on single-neurone activity when not a strictly additive effect of converging pathways. The basic assumption that the whole information-processing system is well-adapted to a familiar task does not necessarily extend to particular components of the system’s physical machinery. Nevertheless, a genuine ‘grandmother’ neurone, if it existed, would be expected to integrate grandma’s features in the same way that the grandchild does whose brain cell it is.

References Attneave, F., 1950. Dimensions of similarity. American Journal of Psychology 63, 546-554. Bartoshuk, L.M., 1987. ‘Is sweetness unitary? An evaluation of evidence for multiple sweets’. In: J. Dobbing (Ed.), Sweetness (pp. 33-47). London: Springer-Verlag. Booth, D.A., 1987. ‘Objective measurement of determinants of food acceptance: Sensory, physiological and psychosocial’. In: J. Solms, D.A. Booth, R.M. Pangborn and 0. Raunhardt (eds.1, Food acceptance and nutrition (pp. l-27). London: Academic Press. Booth, D.A., 1988. Estimating JNDs from ratings. Chemical Senses 13, 671 (Abstract). Booth, D.A., 1990. ‘Designing products for individual customers’. In: R.L. McBride and H.J.H. MacFie (eds.1, Psychological bases of sensory evaluation (pp. 163-193). London: Elsevier Applied Science. Booth, D.A., 1991. ‘Learned ingestive motivation and the pleasures of the palate’. In: R.C. Bolles (Ed.), The hedonics of taste (pp. 29-58): Hillsdale, NJ: Erlbaum. Booth, D.A., M.T. Conner and S. Marie, 1987. ‘Sweetness and food selection: Measurement of sweeteners’ effects on acceptance’. In: J. Dobbing (Ed.), Sweetness (pp. 143-160). London: Springer-Verlag. Booth, D.A. and R.P.J. Freeman, 1993. ‘Sub-, per- and conceiving: Feature-discrimination channels in individuals’ integral and analytical recognition’. Brain, Behaviour & Cognition Society/Experimental Psychology Society, Toronto, July 1993. Booth, D.A. and R. Shepherd, 1988. Sensory influences on food acceptance - The neglected approach to nutrition promotion. BNF Nutrition Bulletin 13, 39-54. Booth, D.A., A.L. Thompson and B. Shahedian, 1983. A robust, brief measure of an individual’s most preferred level of salt in an ordinary foodstuff. Appetite 4, 301-312. Cain, W.S., 1980. ‘Chemosensation and cognition’. ECRO IV/ISOT VII (pp. 347-357). London: IRL Press. Conner, M.T. and D.A. Booth, 1992. Combining measurement of food taste and consumer preference in the individual: Reliability, precision and stability data. Journal of Food Quality 15, l-17. Conner, M.T., A.V. Haddon, ES. Pickering and D.A. Booth, 1988. Sweet tooth demonstrated: Individual differences in preference for both sweet foods and foods highly sweetened. Journal of Applied Psychology 73, 275-280. Drewnowski, A., 1993. Individual differences in sensory preferences for fat in model sweet dairy products. Acta Psychologica 84, 103-110 (this Issue). Ennis, D.M. 1993. A single multidimensional model for discrimination, identification and preferential choice. Acta Psychoiogica 84, 17-27 (this Issue).

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Freeman, R.P.J. and D.A. Booth, 1993. Individuals’ recognition of food taste patterns and integration of taste and calorie labelling. Appetite 20, 148 (Abstract). Green, B.G., 1993. Oral astringency: A tactile component of flavor. Acta Psychologica 84, 119-12.5 (this Issue). He, P. and E. Kowler, 1992. The role of saccades in the perception of texture patterns. Vision Research 32, 2151-2163. Johansson, R.S., and G. Westling, 1984. Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip. Experimental Brain Research 66, 141-154. John, K.T., A.W. Goodwin and I. Darian-Smith, 1989. Tactile discrimination of thickness. Experimental Brain Research 78, 62-68. Kendal-Reed, M. and D.A. Booth, 1992. Human odor perception by multidimensional discrimination from remembered patterns. Chemical Senses 17, 649 (Abstract 1421. Kokini, J.L., 1985. Fluid and semi-solid food texture and texture-taste interactions. Food Technology 39(111, 87-94 Kokini, J.L., 1987. The physical basis of liquid food texture and texture-taste interactions. Journal of Food Engineering 6,51-58. LaMotte, R.H. and M.A. Srinivasan, 1991. ‘Surface microgeometry: Neural encoding and perception’. In: 0. Franzen and J. Westman (eds.), Information processing in the somatosensory system (pp. 49-58). London: Macmillan. Lederman, S.J., 1974. Tactile roughness of grooved surfaces: The touching process and effects of macro- and microsurface structure. Perception and Psychophysics 16, 385-395. Lederrnan, S.J. and R.L. Klatzky, 1993. Extracting object properties through haptic exploration. Acta Psychologica 84, 29-40 (this Issue). Lockhead, G.R., 1992. Psychophysical scaling: Judgments of attributes or objects? Behavioral and Brain Sciences 15, 543-601. Marshall, R.J., 1993. Physicochemical properties accounting for cheese texture. Acta Psychologica 84, 69-77 (this Issue). Moskowitz, H.R., 1983. Product testing and sensory evaluation of foods. Westport, CT Food & Nutrition Press. Phillips, J.R. and R.O. Johnson, 1981. Tactile spatial resolution: II. Neural representation of bars, edges, and gratings in monkey afferents. Journal of Neurophysiology 46, 1192-1203. Poulton, E.C., 1988. Bias in quantifying judgments. New York: Academic Press. Richardson, N.J., D.A. Booth and N.L. Stanley, 1993. Effect of homogenisation and fat content on oral perception of low and high viscosity model creams. Journal of Sensory Studies 8, 133-143. Richardson, N.J. and D.A. Booth, 1993. Multiple physical patterns in judgments of the creamy texture of milks and creams. Acta Psychologica 84, 93-101 (this Issue). Rolls, E.T., 1993. ‘Central neural systems controlling ingestion in primates’. In: D.A. Booth (Ed.), Neurophysiology of ingestion. Oxford: Pergamon. Shepard, R.N., 1957. Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space. Psychometrika 22, 325-345. Stanley, N.L and L.J. Taylor, 1993. Rheological basis of oral characteristics of fluid and semi-solid foods: A review. Acta Psychologica 84, 79-92 (this Issue). Sweazey, R.D. and R.M. Bradley, 1989. Responses of neurons in the lamb nucleus tractus solitarius to stimulation of the caudal oral cavity and epiglottis with different stimulus modalities. Brain Research 480, 133-150. Tyle, P., 1993. Effect of size, shape and hardness of particles in suspension on oral texture and palatability. Acta Psychologica 84, 111-118 (this Issue).