Olfactory perceptual learning: the critical role of memory in odor discrimination

Neuroscience and Biobehavioral Reviews 27 (2003) 307–328 www.elsevier.com/locate/neubiorev

Review

Olfactory perceptual learning: the critical role of memory in odor discrimination Donald A. Wilsona,*, Richard J. Stevensonb a

Department of Zoology, University of Oklahoma, Norman, OK 73019, USA Department of Psychology, Macquarie University, Sydney, NSW, Australia

b

Received 30 October 2002; revised 15 March 2003; accepted 8 April 2003

Abstract The major problem in olfactory neuroscience is to determine how the brain discriminates one odorant from another. The traditional approach involves identifying how particular features of a chemical stimulus are represented in the olfactory system. However, this perspective is at odds with a growing body of evidence, from both neurobiology and psychology, which places primary emphasis on synthetic processing and experiential factors—perceptual learning—rather than on the structural features of the stimulus as critical for odor discrimination. In the present review of both psychological and sensory physiological data, we argue that the initial odorant feature extraction/analytical processing is not behaviorally/consciously accessible, but rather is a first necessary stage for subsequent cortical synthetic processing which in turn drives olfactory behavior. Cortical synthetic coding reflects an experience-dependent process that allows synthesis of novel co-occurring features, similar to processes used for visual object coding. Thus, we propose that experience and cortical plasticity are not only important for traditional associative olfactory memory (e.g. fear conditioning, maze learning, and delayed-match-tosample paradigms), but also play a critical, defining role in odor discrimination. q 2003 Elsevier Ltd. All rights reserved. Keywords: Olfactory memory; Perceptual learning; Piriform cortex; Olfactory bulb; Olfactory coding; Odor discrimination; Olfactory psychophysics; Synthetic coding

Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The case for analytical processing in olfactory perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Olfactory sensory physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Olfactory psychophysics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Is olfactory perception synthetic? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. The perception of odor mixtures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Exposure, expertise, and the perception of odor mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Perceptual learning and odor perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Experimental demonstrations of olfactory perceptual learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Exposure, expertise, and olfactory perceptual learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. The role of learning and memory in odor perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Neurobiology of olfactory perceptual learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Experience-induced receptor plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Experience-induced olfactory bulb plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Experience-induced piriform cortical plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Summary and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

* Corresponding author. Tel.: þ 1-405-325-0527; fax: þ1-405-325-2699. E-mail addresses: [email protected] (D.A. Wilson), [email protected]. edu.au (R.J. Stevenson). 0149-7634/03/$ - see front matter q 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0149-7634(03)00050-2

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1. Introduction The major problem in olfactory neuroscience is to determine how the brain discriminates one odorant from another. The traditional approach involves identifying how particular features of a chemical stimulus are represented in the nervous system and considerable progress has been made in this regard. Current work in mammals suggests that specific features of odorant molecules are recognized by a large family of receptor proteins [16,35]. A spatial representation of odorant features is created through precise receptor projections to olfactory bulb glomeruli [42,156]. This spatial representation is enhanced by convergence and lateral synaptic interactions within the olfactory bulb, resulting in olfactory bulb output neurons (mitral cells) with feature detecting odorant receptive fields [97]. Precise temporal patterning of spike trains across multiple output neurons is hypothesized to further accentuate unique, odorspecific patterns of neural activity [76]. However, a complete understanding of odor discrimination must account for two distinct characteristics of olfactory behavior. First, despite the highly analytical nature of peripheral odorant coding, humans and animals are very poor at analyzing complex odorant mixtures, i.e. identifying components within a mixture of four or more odorants [72, 141]. Second, however, humans and animals are fairly good at simple, olfactory figure-ground problems, i.e. identifying an odorant against an odorous background or analyzing binary mixtures [72,132,141]. In fact, it could be argued that odorants are never experienced in the absence of some background stimulus, and thus, that all odor discrimination tasks involve some aspect of figure-ground determination. A clue as to the underlying bases of these seemingly opposite abilities (strong synthetic processing of complex mixtures yet with a strong analytic figure-ground discrimination) comes from the fact that both phenomena are significantly improved by prior experience with the target odorant [17, 112,132,143,162]. The data reviewed here suggest that exposure to odorants is required for the olfactory system to learn that particular combinations of features, extracted by the olfactory periphery, frequently co-occur. Hebbian-like associative synaptic plasticity within the piriform cortex and possibly olfactory bulb records these combinations as unique, odor objects, similar to visual perceptual association or facial recognition cells in visual inferotemporal cortex. Once this initial perceptual learning has occurred, some analytic processing of odorant mixtures and figure-ground discrimination can occur. Without initial perceptual learning, or with more complex mixtures, individual odorants may not be identified from the jumble of simultaneously activated features. In the present review, we argue that, contrary to what is stated or implied in most current views of olfaction [76,97, 182], the initial odorant feature extraction/analytical processing is not behaviorally/consciously accessible, but

rather is a first necessary stage for subsequent cortical synthetic processing which in turn drives olfactory behavior [41,177]. For example, neurons in piriform cortex serve as sites of convergence of peripherally extracted odorant features, and thus encode complex feature ensembles rather than the simple features encoded by mitral cells [175]. However, given the broad range of odorants encountered over a lifetime and the deleterious effect of memory damage on olfactory discrimination, it is unlikely that cortical synthetic coding is due entirely to innate hard-wiring and simple anatomical convergence. Rather, new data suggests that cortical synthetic coding reflects an experiencedependent process that allows synthesis of novel cooccurring features through Hebbian synaptic plasticity [174,176], similar to processes used by inferotemporal cortex visual object responsive neurons [95,119,157]. Cortical synthetic processing has the adaptive advantage of allowing identification of, and discrimination between, a broad range of complex odorants containing novel combinations of features, as well as recognition of partially degraded inputs. As described below, standard combinatorial feature detection models of olfaction, without some form of a strong synthetic memory component should be limited in both of these regards. However, the reliance on synthetic coding early in the olfactory pathway may limit analytic processing of stimulus mixtures compared to more thoroughly investigated thalamocortical systems that utilize multiple levels of processing. Thus, we propose that experience and its neural corollary (cortical plasticity) play a critical, defining role in odor discrimination at both the neural and behavioral level. This proposal is supported by a sizeable body of psychological data, as well as more recent sensory physiological analyses of piriform cortex function. In this paper we review the current understanding of peripheral odor coding, then in this context, we review the behavioral literature. Finally, we review new work on olfactory perceptual learning and its potential cortical mechanisms. This review is not about olfactory memory in the traditional sense of associative or explicit learning, e.g. remembering that one odorant signals a reward while another does not (see Refs. [14,31,32,138] for reviews of associative olfactory memory). Rather, it focuses on a rapid form of perceptual learning that we believe is necessary for discriminating those odorants in the first place, and places memory and synthetic processing at the core of basic olfactory function.

2. The case for analytical processing in olfactory perception Historically, the study of odor discrimination has been driven by a focus on olfactory receptors, with substantially less attention paid to central processing. While initial emphasis on inputs is a rational approach to understanding information processing by a system, in the case of olfaction

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it can lead to an undue emphasis on analytical odor processing. Thus, a significant theoretical [3,96,158] and experimental [45,74,135] effort has been made to identify both ideal odorant receptor ligands and their presumed perceptual counterpart, primary odors (Amoore [3] provides an excellent historical review of odorant classification approaches). Recently, potential ideal ligands have been identified for some odorant receptors [5,44], and appear to be consistent with an analytical, feature-detection role for the olfactory periphery. Furthermore specific anosmia’s exist for a small set of odors which may be indicative of a one receptor-one perception relationship for a very limited set of biologically meaningful molecules [2,19,180]. Unfortunately, this approach has frequently resulted in an apparent conceptual merging of receptor mechanisms with mechanisms of perception. While characteristics of odorant recognition and ligand binding by olfactory receptor proteins may place restrictions on ultimate perceptual abilities (e.g. discrimination of entanomers), receptor function need not be identical to perceptual function. In fact, we propose here that the analytical functions of peripheral olfactory encoding are not expressed at a behavioral level in olfactory perception (see below). Thus, olfactory perception-our ability to discriminate one odor from another-while utilizing the initial analytical stages of peripheral odor coding, is dependent on higherorder synthetic processing, largely occurring in cortical areas, and dependent on memory.

3. Olfactory sensory physiology Since the seminal findings of Buck and Axel in 1991 [16] of a large family of perhaps 1000 olfactory receptor protein encoding genes in the mouse, a general picture of a highly analytical and combinatorial peripheral odor coding has emerged. In this view, odorants are functionally fragmented into molecular features, each of which serves as a ligand for one of the many odorant receptor proteins. Through both spatial convergence and temporal coherence of first and second order neurons, these features are then reassembled by the olfactory bulb and cortex into recognizable odor perceptions. This overview of peripheral odor coding will focus on mammalian systems, although relevant correspondence with other systems will be noted. Olfactory receptor neurons are believed to express perhaps a single olfactory receptor protein [91,115,133]. Factors controlling receptor gene selection and expression are unknown, although appear to be at least partially under the control of sensory experience [27,38]. Receptor proteins are expressed in a mosaic across the olfactory epithelium, with neurons expressing a particular receptor protein are often surrounded by neurons expressing different receptor proteins [91,133]. There is a zonal expression pattern in mice however, with specific receptor proteins primarily

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expressed only by neurons within one of four zones situated roughly parallel to nasal airflow [115]. The differential spatial expression patterns of olfactory receptor proteins across the olfactory epithelium results in a spatial pattern of relative responsiveness to odorants [13,71, 86,89,131]. Thus, the magnitude of an electroolfactogram (EOG) response of a population of receptor neurons is dependent on not only the nature and concentration of the odorant, but also where the recording electrode is placed [89,131]. The rodent olfactory epithelium displays spatial patterns of sensitivity to odorants varying in the nature and location of functional groups and in carbon chain length [131], just as do individual olfactory receptor neurons [13, 67,91,129,135]. Thus, olfactory receptor neurons appear to have receptive fields not for odorant molecules as a whole, but rather for molecular features of the odorant [97]. (In contrast, the accessory olfactory system, which is involved in detecting evolutionarily relatively stable pheromonal odorants, may have receptors that recognize more complete odorant molecules [78]. Similarly, receptor responses to biologically significant chemical stimuli may also be driven by more complex stimulus patterns and/or interactions [25, 48,162,165]) In the main olfactory system, it is unclear exactly what constitutes an odorant feature or how complex features may be, although evidence exists for physiochemical features composed of functional groups and/or carbon chain length [67,91,129,135]. One of the most direct determinations of what constitutes an effective molecular feature or ligand for an odorant receptor protein, has been performed by Araneda et al. [5] for the mouse I7 receptor. This receptor appears to require both an aldehyde functional group and a specific length carbon chain (between 7 and 11) for maximal evoked activity. Substituting a different functional group or changing the carbon chain length reduces or eliminates responsiveness. As long as these components were present however, other modifications of the molecule were relatively well tolerated. Similar results have been obtained for the mouse olfactory receptor 912-23 [44]. Thus, the term odorant feature will be used in this review to imply some physiochemical component of the odorant molecule, of currently undetermined complexity. Receptor neuron axons terminate on second order neurons, mitral cells, via synapses located in olfactory bulb glomeruli (Fig. 1). Glomeruli are spherical collections of dense neuropil including receptor neuron to mitral cell synapses [134]. Despite the broad distribution across the olfactory epithelium of receptor neurons expressing a specific receptor protein [115], all receptor neurons expressing the same receptor protein send axons converging on only a few glomeruli [116,163]. A single glomerulus, and thus its associated mitral cells, receives input from a homogeneous population of receptors all expressing the same receptor protein [9,116,163]. By examining the response of a glomerulus to odorants, for example with metabolic imaging or intrinsic optical

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Fig. 1. (Top) Schematic representation of the functional anatomy of the vertebrate primary olfactory pathway. Odorants are hypothesized to be composed of multiple features (B, C, D, E, F) that are each recognized by phenotypically different olfactory receptor neurons (ORN). Contrast between features is enhanced through convergence and lateral interactions in the main olfactory bulb (MOB). Features are bound into odor objects through temporal synchrony and anatomic convergence within the anterior piriform cortex (aPCX) and potentially other cortical structures. (Bottom) Representation of the primary neuroanatomical structures implicated in rodent and human olfaction.

imaging, the receptive fields of receptor neurons impinging on that glomerulus can be assayed [63 – 65,124]. In this way, it has been determined that receptor neurons with receptive fields for similar odorant features converge onto neighboring glomeruli. Thus, a single glomerulus maximally responsive to aldehydes of intermediate carbon chain length will be near to glomeruli maximally responsive to aldehydes with shorter and longer chain lengths [101,124,160]. Odorant features, therefore, are encoded with a spatial component (and temporal components addressed below), with different odorants evoking unique spatial patterns of

glomerular, and thus mitral cell, activity. In the few cases where it has been examined (both in invertebrates), odorant mixtures may activate additional or novel glomeruli compared to those activated by the components, yet are still represented by multiple glomeruli [62,165]. The advantage of spatial coding of odorant features is that similar features can be made more distinct through lateral inhibition. Circuits underlying lateral, feedforward and feedback inhibition are extensive in the olfactory bulb, with the primary mediators being juxtaglomerular neurons near the olfactory nerve input in the glomerular layer and the more numerous granule cells interacting with mitral cell lateral dendrites via reciprocal dendrodendritic synapses [134]. Together, these inhibitory interneurons shape mitral cell odorant receptive fields into center-surround domains. Mitral cells express excitatory receptive fields which are very similar to receptor neurons, with excitation evoked by odorants containing specific functional groups and/or carbon chain lengths. However, in addition to the excitatory responses, odorant features slightly different from the maximally effective ones often evoke synaptic inhibition, dramatically increasing discrimination of similar features by mitral cells [70, 88,184]. Importantly, inhibition in the olfactory bulb is modulated by both behavioral state and past experience [179,185], suggesting that the ability of mitral/tufted cells to discriminate odorant features could be affected by previous exposure (see below). In addition to a spatial component to odor coding in the olfactory bulb, there may be a strong temporal component. It has been known for decades that the olfactory bulb circuit responds to odorants in a strongly oscillatory manner, as determined by local field potential recordings [1,40]. Local field potentials reflect currents mediated by neural elements within the olfactory bulb. As might be expected, therefore, more recently it has been demonstrated that mitral cells fire in phase with these oscillations, with individual neurons often firing at very specific time phases of the population oscillation in an odorant-specific manner (invertebrates: [76], vertebrates: [49,69]). The oscillations of both population field potentials and single-units are in part controlled by granule cell mediated GABAergic inhibition [8,77,104, 113,152]. It has been hypothesized that these odor-evoked population oscillations reflect or facilitate synchrony in firing of mitral cells in spatially diverse regions of the olfactory bulb [69,76]. Such synchrony may allow temporal binding of activity representing multiple odorant features evoked by a given odorant stimulus. In support of this hypothesis, it has been demonstrated in invertebrates that GABA receptor antagonists, which disrupt normal odorevoked oscillations and therefore mitral cell synchrony, also impair behavioral odorant discrimination [152]. Thus, at the level of the olfactory epithelium and olfactory bulb, odorants are broken down into constituent features which are then encoded combinatorially through spatially diverse, yet temporally synchronous patterns of

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spike trains. Left unaddressed regarding feature detection, however, is whether, or how well, individual features extracted by the periphery and enhanced within the olfactory bulb can be identified at the behavioral level. If information about individual features is available in olfactory bulb output spike trains, then it might be supposed that analytical processing of odorants and odorant mixtures would be highly advanced. Behavioral work, however suggests rather poor analytic capabilities, which is also a prediction of the new model proposed below. How does mitral cell firing synchrony or temporal patterning produce odorant feature binding? One possibility is that tight temporal control of mitral cell output facilitates temporal summation by third order neurons in the olfactory cortex. Mitral cell axons terminate on pyramidal cell apical dendrites in piriform cortex, where they form glutamatergic, excitatory synapses. Mitral cells receiving input from a phenotypically specific group of receptor neurons terminate in small clusters within the anterior piriform cortex, and more broadly in the posterior piriform cortex [18,188]. Given the large number of receptor types and relative size of these mitral cell terminal clusters, it is assumed that there is extensive overlap in termination. Thus, single piriform pyramidal cells may receive input from mitral cells conveying processed information from many different olfactory receptor types. Piriform cortical pyramidal cells, in turn, make extensive associational connections throughout the piriform cortex, back to the olfactory bulb and even to other cortical structures [52]. The relatively diffuse afferent input combined with a broad, extensive intracortical association fiber system creates a highly combinatorial network, ideal for synthetic processing of complex feature ensembles [52]. The diffuse, combinatorial network array also results in a relatively weak spatial odorant representation in cortex [21,57], in strong contrast to the spatial patterning in olfactory bulb. Initially, therefore, the piriform cortex may function largely as a coincidence detector, with not single odorant features, but the appropriate combination of features required to maximally excite the cortical neuron. Neurons in the anterior piriform cortex display receptive fields for novel odors that are superficially similar to both olfactory receptor neurons and mitral cells, in that responses vary along dimensions such as carbon chain length [173]. One significant difference between piriform cortical and mitral cell odor responses is that cortical responses tend to be briefer and habituate (adapt) much faster than mitral cell responses [28,92,170]. This appears to be due in part to a rapidly induced short-term synaptic depression of mitral cell synapses onto cortical neurons [171]. A second, and related significant difference between piriform cortical receptive fields and mitral cell receptive fields is that mitral cells cannot discriminate between odorants within their receptive fields while cortical neurons can. Specifically, habituation to one

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odorant within the receptive field of a mitral cell produces generalized depression of responses across the receptive field (high cross-habituation). In contrast, habituation to one odorant within the receptive field of an anterior piriform cortex neuron produces highly selective depression, with minimal cross habituation to other odorants [172,173]. These results support the view of feature detecting mitral cells (reduce responsiveness to the feature and responses to all odorants decrease), while piriform cortical neurons have more synthetic receptive fields, treating each odorant as a unique, relatively independent stimulus [52,175]. The rapid and odorspecific nature of cortical habituation leads to specific, novel predictions of how the olfactory system moves from a highly analytical sense to a more synthetic sense, able to recognize odorants against backgrounds, yet unable to identify components of complex mixtures. The model described below further explores this issue. The reliance of the peripheral olfactory system on feature detection rather than receptors that recognize complete molecules greatly expands the range of odorants that the system can discriminate. The strong evidence for first and second order neuron feature detection suggests that olfaction is a highly analytic sense, at least peripherally. The use of temporal synchrony in mitral cell activity and anatomical convergence of mitral cell output then allows for synthesis of features into odorant objects. Unfortunately, this model, as described and/or implied in the current literature does not fit well with the fact that odor mixtures are generally irreducible at a behavioral level and further, should have great difficulty with odorant figure-ground problem mentioned above. That is, if a given odorant activates a combination of receptors, for example A, B, C and D, the olfactory bulb mechanisms discussed above allow recognition of that odor. A problem arises, however, if odorant ABCD is placed against a background of CDEF (or almost any other background). How does the system extract one pattern when it is embedded in a background pattern? Most odors are mixtures of chemicals, and furthermore generally experienced against some background odorants. Simple feature extraction and temporal synthesis of all cooccurring features cannot account for the ability to solve this olfactory figure-ground problem, even though humans and animals can generally recognize a familiar odorant against a background. Finally, it is unclear how this model accounts for the robust ability of the olfactory system to deal with partially degraded inputs (i.e. loss of features; [87,136,137]) and the susceptibility of odor discrimination to memory disorders [30,90]. The next section reviews what is known about behavioral olfactory abilities, and then is followed by a re-analysis of olfactory sensory physiology and presentation of a new view of olfactory discrimination, highly dependent on rapid perceptual learning.

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4. Olfactory psychophysics 4.1. Is olfactory perception synthetic? The early stages of olfactory information processing, reviewed above, strongly imply an analytical sense, in which the stimulus is reduced into its constituent physiochemical features. A totally different picture of olfactory information processing has emerged from studies of human and animal odor perception. In this case perception appears to be primarily synthetic, that is it results in an irreducible perceptual experience. Support for this conclusion is based upon two types of finding. First, the apparent difficulty that humans and animals have in identifying the component parts of odor mixtures. Second, the limited effects of experience, training or type of odor on this ability. We start here by reviewing the evidence for synthetic processing and the limitations that this imposes on olfactory perceptual learning. The olfactory perceptual learning literature is then reviewed and a psychological model of these processes is suggested. 4.2. The perception of odor mixtures If the perceptual experience of an odor is irreducible, then it should not be possible to identify the components of an odor mixture. In humans, this question has been explored in a number of different ways. First, Laing and Francis [72] had participants smell a set of seven labeled odorants, each composed of just one chemical. Participants were asked to familiarize themselves with each odor and its name. Next, either single odorant or mixtures made up of two, three, four or five components, were presented to participants in quasirandom order. In each case participants were asked to identify the odorant/s present during that trial. Identification fell from 55% correct with one odorant, to 12% for two, 6% for three, 3% for four and zero for five component mixtures. Although these findings are highly suggestive, it is possible that poor performance was due to some aspect of the procedure, such as adaptation or task difficulty. In a further experiment Laing and Glenmarec [73] had participants complete broadly the same type of task, but this time they were asked to identify whether one particular component, sniffed prior to the test session, was present or absent in each of the test phase mixtures. This selective attention procedure did not significantly increase performance beyond that reported by Laing and Francis [72]. This again suggests that participants found it increasingly difficult to identify the components of mixtures containing more than two or three components. This limitation on the number of odorants identified in a mixture could also arise from participants inability to retain a clear perceptual representation of the to-be-identified target during each selective attention trial. To investigate this possibility Jinks and Laing [60], selected for each participant, their three most familiar odors. Familiarity was

chosen as a likely basis for ensuring a better representation, as familiarity with an odor is known to increase participants ability to discriminate it from other odors-a topic returned to later in this review. Using this more refined procedure, participants were able to correctly identify, at above chance level, the occurrence of the target in a mixture consisting of up to 12 components. Nonetheless, and consistent with their earlier findings, participants were not able to identify the target when there were more than this number of odorants, suggesting an absolute ceiling on this ability. A further possible constraint on participants ability to identify the components of odor mixtures are the odorants themselves. For example, if all the odorants were ‘good blenders’ that is they readily formed a mixture which was hard to discriminate into its parts, this could also be a major contributor to participants poor performance. To examine this possibility, Livermore and Laing [85] randomly allocated participants to either a ‘good blenders’ or ‘poor blenders’ condition, with the odors selected for each condition based upon advice by an expert panel of perfumers and flavorists. The results suggested that odor type had little bearing upon participants absolute ability to identify a mixture’s components. Although ‘poor blenders’ were more easily identified when mixtures contained just two or three components, performance was equally poor for all mixtures containing four or more odorants. Experiments on animal’s ability to identify the components of odor mixtures present a far greater experimental challenge. In addition, the scope of this work has been quite limited, in that few studies have gone beyond simple binary mixtures. The typical procedure adopted in all of the animal experiments is to examine generalisation of learning from either an element (e.g. pure odor A) to a mixture (odor A mixed with odor B) or the reverse. This presents marked difficulties in interpretation, because the type of task that the experimenter chooses can affect whether animals treat the odor mixture as a unitary stimulus (synthetic) or as a stimulus composed of specific features (analytic). Consequently the failure to find generalisation or not is highly dependent upon the type of experimental task. This confound is especially problematic because, as noted above, animal studies rarely use more than binary mixtures. If all types of study, regardless of task, found that generalisation failed to occur beyond three or more component mixtures this would substantially support the evidence obtained in humans. One of the few studies to explore multi-component mixtures in animals was conducted by Staubli et al. [141]. Rats were trained to respond to one combination of odorants, ABC þ (with each letter representing a different odorant), which predicted food in one arm of a radial maze, whilst another combination, ABD-, consistently predicted its absence from another arm of the maze. Having learnt this relationship to criterion, animals were then presented with a new problem; odor C 2 now predicted the absence of food and odor D þ its presence. The degree to which the rats are

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slowed in learning this new problem reflects the degree to which they generalize from the single odor, back to the mixture on which they were originally trained. Consistent with the findings from human participants, rats learnt the new problem as fast as they had learnt the original, suggesting little generalisation from the mixture to the element. Some evidence of generalisation was, however, obtained when the initial training combinations were composed of two (i.e. AB þ vs. AC 2 ) odors, but not when four odor (i.e. ABCD þ vs. ABCE 2 ) mixtures were used. Staubli et al. [141] study is consistent with the human findings reviewed above. However, there are several studies in rats which show ready generalisation between binary mixtures and their components. For example, Linster and Smith [81] demonstrated that rats trained to dig for food under a scented cup, generalized from a trained binary odor mixture to its components and from a trained component to a binary mixture. Although performance was considerably reduced in the generalisation condition, it was still significantly above a neverpresented control odor. Rats can, therefore, under appropriate conditions treat binary stimuli analytically. Nonetheless, this finding in itself is still consistent with human studies, where the components of binary mixtures can also be identified. Three other types of finding also have a bearing upon the question of whether the processing of mixtures is synthetic or analytic. All derive solely from the human literature. The first comes from a study conducted by Livermore and Laing [84], in which they examined whether limitations on identifying odors in a mixture would be further constrained if the odorants themselves were each perceptually unique but highly complex mixtures of chemicals (e.g. chocolate, cheese, lavender, etc). Participants were presented with single odorants (e.g. chocolate) and mixtures (e.g. chocolate and lavender), with mixtures containing up to eight components. Participants task, as before, was to identify which odorant/s were present in each mixture. Consistent with their previous findings, participants correctly identified the single odorants on 50% of occasions, two component mixtures on 15% occasions, three on 5%, four on 3% and zero for higher order mixtures. These findings suggest that each complex mixture of odorants (e.g. chocolate) is treated as a unique odor object, with apparently the same properties as a single chemical odorant. This strongly suggests that odorants are treated as irreducible entities. A second finding which has emerged from a number of human studies, concerns the perceived complexity of odor mixtures. If human participants were to treat odor mixtures in an analytic manner, then multi-component mixtures should be perceived as increasingly complex, as their number of components increased. However, in three studies [72,73,98] where complexity was defined as the total number of odors identified as being present in a stimulus

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regardless of accuracy, both showed that complexity only increased from the single to the three-component odor mixtures. Beyond this point participants treated all higher order mixtures as equally complex, with participants reporting an average of around three components. Similar results have been obtained in an earlier study by Jellinek and Koster [59], who found no relationship between the perceived complexity of an odor, as estimated on a rating scale and its number of chemical components. Together, these findings again suggest that the analytic ability of the olfactory system is highly constrained and that beyond fairly simple mixtures of pure chemicals (or simple mixtures of complex odor objects), mixtures themselves are treated synthetically. A third and related issue concerns the perceptual characteristics of odor mixtures. Jinks and Laing [61] asked participants to evaluate each single odorant or mixture using the 146 item Dravnieks scale. This scale is composed of a series of odor object names (e.g. mothballs). Participants task is to estimate the degree to which a target odor or mixture smells like that particular item. Two interesting findings emerged from this study. First, the total number of qualities identified did not change as the mixtures grew more complex, a finding consistent with the limited changes in complexity reported in their other experiments [72,73]. Second, although no new characteristics were identified in more complex mixtures, participants tended to shift towards more general terms to describe the mixtures (e.g. describing an odor as ‘chemical-like’ rather than as ‘minty’). This is consistent with recent unpublished findings from our laboratory, which show that unfamiliar odors tend to be characterized by more generic level descriptors. That complex mixtures should be described in this way is consistent with them being treated like novel unfamiliar odors, rather than as mixtures of familiar odorants. The evidence reviewed above, drawing upon both human and animal participants, suggests that although the olfactory system can process olfactory information analytically when the stimuli are binary mixtures of familiar odors, as mixture complexity increases, participants tend to process the stimulus as a discrete irreducible entity. This is suggested by; (1) the apparent ceiling to identifying components in odor mixtures, which varies relatively little when using different experimental techniques or odorants; and (2) by other findings which suggest that the perceptual characteristics of more complex mixtures of odors are highly similar (in terms of complexity, number of characteristics etc.) regardless of the actual number of chemicals that are present in the mixture. These results, though not so well developed in animals, suggest that when a mixture is perceived, especially the type of odorant typically encountered in the environment, that is one composed of many hundreds of chemicals (e.g. coffee or wine; [56]), it is treated synthetically as an odor object.

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4.3. Exposure, expertise, and the perception of odor mixtures If odor mixtures are processed synthetically, as suggested by the findings reviewed above and if this reflects a fundamental perceptual limitation of olfactory information processing, then experiential factors should have a relatively small impact upon participants absolute ability to identify the components of an odor mixture. However, although the absolute ceiling may be impenetrable, it is plausible that participants’ experience could influence their propensity to identify components of odor mixtures beneath this limit. These two issues are explored in this section. The only study to examine whether perceptual expertise affects the absolute ability to identify components in an odor mixture was conducted by Livermore and Laing [83]. Using a procedure akin to those described above, they had a group of expert perfumers and flavorists and ordinary untrained participants identify components of simple and complex odor mixtures (i.e. from 1 to 5 components). If there is an absolute perceptual limit to the ability to identify components in an odor mixture, then there should be no difference in the maximum number of components that experts and untrained participants can identify. Consistent with this prediction, experts, although slightly better at correctly identifying the components of binary and ternary mixtures, did not significantly differ from untrained participants for four or five component mixtures. In the latter case performance was uniformly poor (see Fig. 2). That is the same absolute limit appeared irrespective of expertise.

Fig. 2. Data adapted from Livermore and Laing, [83] on the ability of olfactory experts and recently trained controls to identify components within an odor mixture (odor analysis). The data demonstrate two important points: (1) experts and non-experts have the same identification ceiling, i.e. analysis breaks down as mixture complexity increases beyond 3–4 components; (2) experts perform better than non-experts below the ceiling, i.e. evidence of a form of olfactory perceptual learning.

In all of the studies examining the ability of human participants to identify odors in mixtures, the odorants have always been familiar to participants. This has been explicitly incorporated into the design, so that most experiments involve a pretraining phase in which participants learn the name for the odor components prior to the test phase of the study. Before turning to consider the effect of familiarity, it is worth reflecting on how the olfactory system might treat a combination of two unfamiliar odors presented simultaneously in a mixture. If identification depends upon the participant having some form of accurate perceptual representation of the target odorant, how could participants identify whether a stimulus was composed of two novel odorants? In an important series of studies which we return to later in this review, Rabin [112] investigated the effect that familiarity had on identifying the components of binary odor mixtures. In one experiment he had participants select from a large battery of odors those that they found most and least familiar. These stimuli were then used to test whether participants’ ability to identify these components were affected by the relative novelty or familiarity of the stimuli. Participants were presented with a target stimulus, followed (or preceded) by an odor mixture, which either did or did not contain the target. Targets could be familiar or unfamiliar and the mixture could also contain either familiar or unfamiliar stimuli. Rabin [112] found that the ability to identify a component in a mixture was influenced separately by both the familiarity of the target, and by the familiarity of the components in the mixture. Thus an unfamiliar target mixed with an unfamiliar contaminant was only correctly identified on 58% of occasions, rising to 87% correct when both the target and contaminant were familiar. Although there are no current theories of olfactory perception which can account for this finding, for our purposes here it is sufficient to note that when the components of an odor mixture are unfamiliar, participants appear to have far greater difficulty in identifying the components, even though that component was presented either just before or just after the mixture. This too is consistent with the olfactory system treating a mixture synthetically when its elements are unfamiliar. The experimental findings reviewed above suggest first, that expertise has no impact on the absolute number of odors which can be identified in a mixture and second, that routine exposure to odors appears to affect how binary mixtures are perceived. Both of these findings are entirely consistent with a synthetic view of olfactory information processing but are not readily accommodated within an analytic perspective for two reasons. First, an analytic perspective would suggest that experience should facilitate the identification of features in a complex whole, just as expert radiographers get better at identifying disease features in X-ray photographs [100]. Clearly this does not appear to be the case with olfaction. Second, a system based purely upon featural analysis should

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not be affected by whether the stimulus is familiar or novel, as the appropriate features should still be available for detection irrespective of experience. Again, this appears not to be the case. In conclusion, psychological studies of odor mixture perception suggest that people and animals process odor stimuli as discrete irreducible entities, rather than analytically, as a collection of reducible physiochemical features.

5. Perceptual learning and odor perception If odor perception were primarily analytic and dictated solely by the physiochemical features of odorants, learning would not be expected to have any significant role. Yet the preceding decade has seen a small but growing body of research which suggests that learning is in fact the key to understanding how the brain processes and discriminates odorants. In this section we review the role of learning in odor perception, from human and animal studies in the laboratory and of studies of ‘experts’ who have acquired perceptual knowledge either through incidental exposure or through deliberate programs of training. We then suggest a psychological model of odor perception based upon learning and memory, and examine whether this model better accounts for the findings reviewed here. 5.1. Experimental demonstrations of olfactory perceptual learning Our initial interest in the role of learning in odor perception arose from the observation that many human participants describe certain odors, such as vanilla, caramel and strawberry, for example, as smelling ‘sweet’. Before considering the validity of this observation it is important, first, to recognize that the perceptual experience of sweetness is normally associated with stimulating taste receptors on the surface of the tongue. That is taste and smell represent two discrete perceptual systems [109]. The perception of sweetness encountered when smelling odors such as vanilla, could of course result from participants using the word ‘sweet’ as a metaphor for liking. This is especially so as sweet tastes are typically reported as pleasant. However, several pieces of evidence suggest this is not the case. First, it has been known for some time by the food industry, that the addition of tasteless but sweet smelling odorants to foods, can enhance peoples judgment of how sweet that particular food tastes (e.g. an international flavor house manufactures a tasteless but sweet smelling odorant marketed as Sweetness enhancer). We, amongst others [39], have investigated this more formally and found that the degree to which a tasteless odorant smells sweet when sniffed, is positively related to the degree to which it will enhance the perceived sweetness of a sucrose solution to which it is added as a flavorant [151]. This suggests that participants cannot readily discriminate

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the sweet quality resulting from the odor from the sweet quality resulting from the odorless tastant sucrose. Sweet tastes, such as sucrose, are also known to affect perception of sour tastes such as citric acid [149]. We reasoned that sweet odors might also have the same effect. When the sweet smelling but tasteless odorant caramel was added to sour tasting citric acid, participants reported that citric acid tasted less sour [151]. Although these two findings rely upon self-report of olfactory and taste sensations, a third and more recent finding looked at the effect of tastes on threshold detection for the sweet smelling odorant benzaldehyde [24]. They observed that participants could detect benzaldehyde at a significantly lower concentration when it was presented against a background of sweet tasting saccharin, rather than against a background of meaty tasting monosodium glutamate. Together, these findings suggest that the quality of sweetness experienced when participants smell an odor such as benzaldehyde or vanilla, bears a close resemblance to that produced by sweet tastes on the tongue. Odors appear to acquire the characteristic of ‘sweetness’ (for review see Ref. [146]). In a series of experiments we have demonstrated that pairing the sweet taste of sucrose with an unfamiliar but tasteless odorant such as lychee or water chestnut, results in that odorant being judged as smelling sweeter, post-conditioning, whilst pairings with water have no effect [145]. These perceptual changes do not appear to result from the demand characteristics of the experiment, as in our earlier studies the procedure was carefully masked and participants were typically unable to describe either the experimental contingencies or the purpose of the study. These changes in sweetness, produced by the conditioning procedure, can also occur very rapidly, often within two or three exposures to the mixture [111]. In addition, odorants which have been paired with sucrose have been found to be more successful in enhancing the sweetness of sucrose solutions post-conditioning [111]. Finally, not only have we been able to obtain evidence of changes in perceived sweetness following pairings of an odor and sucrose, but we have also been able to demonstrate: (1) acquired odor sourness, following pairings with citric acid [150]; and (2) acquired odor bitterness, following pairings with bitter tasting sucrose-octaacetate (Boakes, unpublished data). The perceptual changes reported by participants in these experiments do not typically co-occur with changes in liking for these odors. This has been established by simply asking participants to rate both the perceptual and hedonic qualities of odorants prior to and after conditioning. However, the likely cooccurrence of perceptual and hedonic changes following pairings with sweet tastes is a far more significant problem when examining the effects of odor – taste pairings in rats. In this case changes appear to be primarily hedonic [128]. Nevertheless, evidence has emerged from a related procedure which has examined pairings of salt with odors

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[127]. In these experiments rats received one odor paired with saline and another odor presented in plain water. Rats were then injected with furocemide to induce a salt craving. Rats showed a marked preference for odorous water, where the odor was originally paired with salt. Their key experiment examined the effect of specific brain lesions on this form of learning. They reasoned that if the animals acquired the sensory aspects of ‘salty’ then lesions to the insular cortex, believed to be responsible for the qualitative experience of taste, should prevent learning occurring. Another group of rats received lesions to their prefrontal cortex as this is believed to be important in the acquisition of the hedonic properties of the tastant [118]. Interestingly, they found that although lesions to the insular and prefrontal cortex did not affect the animals preference for saline solution following furocemide, both lesions abolished preference for the salt paired odor. This suggests that the rats acquired both the hedonic and the taste quality of salt. It could be argued that the finding of acquired taste properties by odors represents some particular quirk of the olfactory system, rather than a more general propensity for humans and animals to learn relationships about chemosensory events which occur simultaneously. A more recent body of evidence argues against this point. We have investigated whether presenting pairs of odors mixed together can have a similar effect on perception of odor quality, as presenting an odor and taste mixture can. These experiments have all adopted a similar design in which human participants are repeatedly asked to sniff two odor mixtures (AX and BY or AY and BX). Following these pairings, we have observed that the odor elements appear to take on the qualities of their mixture partner, thus for a participant exposed to an AX mixture, A gets to smell more X-like and X gets to smell more A-like, in comparison to the equally exposed but unpaired stimuli, A and Y [142,143]. These effects appear to occur most strongly when the odor elements of a mixture differ somewhat in familiarity. One problem with measuring odor quality (e.g. how cherry-like does this smell) is that participants conception of a particular odor quality is subject to a great deal of individual variation. To get round this problem most of our experiments have also asked participants to judge how similar each of the elements are to each other (e.g. following AX, BY pairings is A more like X than Y). A consistent finding, using a wide variety of different odors, has been that odors experienced as a mixture are judged to smell more alike, than odors which have been equally exposed, but never paired together as a mixture [20,142,144,148]. In a further series of studies we have examined whether a more objective measure of this effect would also detect conditioning. To investigate this we used an oddity or triangle test of discrimination, in which participants are presented with three stimuli (e.g. A vs. A vs. X) after which they have to pick the odd one out. Following an identical training procedure to our previous experiments (e.g. AX, BY), triangle tests revealed poorer discrimination between

elements previously mixed together (A vs. X, B vs. Y)-mean correct trials, 77%-than between unmixed pairs (A vs. Y, B vs. X)—mean correct trials, 87% [144]. More recent experiments, in which only one odor mixture is experienced (e.g. AX), followed by triangle tests involving comparisons of two pairs of stimuli A vs. X and B vs. Y, have shown that the elements of the pre-exposed mixture are more difficult to tell apart (mean correct, 77%) than the non-exposed control stimuli (mean correct, 89%; Stevenson and Case, unpublished data). Our view of these effects, odor-taste learning and odorodor learning, has been that the mixtures are encoded into odor memory. These encodings then appear to affect subsequent perception of the target odor. This perspective has been reinforced by an important series of experiments conducted during the 1980s by Rabin [112]. Rabin ([112], Experiment 1) exposed a group of participants to a set of seven unfamiliar odors. In a subsequent same-different discrimination test their performance-equivalent to about 88% correct, was significantly better than that of the two non-exposed control groups, at 81% correct. Similar findings have also been obtained, but using far fewer than the twelve exposures employed by Rabin [112]. Jehl et al. [58] gave participants either zero, 1, 2 or 3 exposures to sets of unfamiliar odors. This was followed by a same-different test, which revealed that discrimination between members of the set increased with exposure, reflecting in the main a decreased false alarm rate. For participants undergoing three exposures, d0 was around 4.0, whilst the no exposure group recorded a d0 of 1.6. Both of these experiments directly relate to Rabin’s [112] Experiment 2, which we discussed earlier, where participants were asked to identify whether or not a particular target was present or absent in an odor mixture. As for the experiments reported here, Rabin’s second study also showed that experience with an odor can significantly enhance participants ability to discriminate it (see above). Although the animal literature has yet to directly address these issues of learning and odor perception, there is some limited evidence of a similar type of exposure effect, to those observed by Rabin [112] and Jehl et al. [58] in humans. In a further experiment by Staubli et al. [141], they reported that animals initially trained to criterion on X þ and Y 2 , were slower to acquire a new discrimination between ABC þ and ABX 2 than animals initially taught to discriminate between E þ and F 2 . This suggests that prior exposure (reinforced in this case) apparently had the effect of retarding learning when the role of the X element was changed on the subsequent conditioning day. This suggests that animals could detect the presence of X in the ABX mixture more readily than animals not exposed to X on the previous day. Thus pre-exposure (albeit reinforced) appeared to enhance discrimination. A second study involved using a habituation/cross-habituation paradigm to examine discrimination of odorants within an homologous series of ethyl esters [37]. Esters could not be behaviorally

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discriminated if they varied in length by a single carbon, unless the animal had undergone an aversive conditioning procedure 24 h previously with that odor. Thus, again preexposure enhanced odor discrimination. 5.2. Exposure, expertise, and olfactory perceptual learning In the human and animal experiments examined so far, we have clear evidence that either unreinforced or reinforced exposure to odors can affect participants’ experience of odors and relatedly, an odorant’s discriminability. These laboratory based examples are not the only place where evidence of perceptual learning can be obtained. If experience does play a significant role in odor perception, then we should expect to find that either passive experience of odors (e.g. by regular wine drinkers) or specific expertise (e.g. by wine experts) would also result in improvements in these groups ability to discriminate relevant odors when compared to perceptual novices. Although there are many examples of olfactory expertise, such as perfumers and flavorists, dairy and food product judges and expert sensory evaluation panels, most attention has so far focussed on wine tasting, with one study on beer. Consequently, this forms the majority of our review of olfactory expertise and perceptual learning. Relatively few experimenters have directly examined whether expertise with wine improves its discriminability. Melcher and Schooler [93] selected three groups of participants; those that never drank wine, those that drank wine frequently, but had no expertise (hereafter regulars) and wine experts, who both drank wine often and had considerable knowledge about it. All three groups were asked to sample one wine. Following a brief interval, participants were presented with four wines, one of which had been presented earlier-the target. Participants were asked to sample each wine and rate their certainty as to whether that the wine had occurred before. The novice group performed at chance level. However, although the wine experts were slightly better than the regulars, though not significantly so, both of these groups were significantly better than novices and both could readily identify the target at above chance level. Thus perceptual experience of wine enhanced discrimination, apparently irrespective of wine knowledge. Two further studies have examined whether mere exposure to wine (or some other form of training) can enhance discrimination in novices. Both Walk [166] and Owen and Machamer [105] found that exposure alone improved wine discrimination, with most of this effect manifesting through enhanced ability to spot when two wines were the same. In a related study using beer, Peron and Allen [107], gave novice participants various forms of training to enhance their ability to discriminate beer. The only form of training which produced a significant improvement in discrimination was mere exposure. These three findings again suggest that exposure alone can be

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sufficient to improve discrimination of stimuli whose principle sensory characteristic is olfactory. Two further studies are also of interest, but to a lesser degree as they did not include a control group who regularly drank wine. Thus these two studies, unlike Melcher and Schooler’s [93], cannot readily separate the effects of perceptual experience from conceptual knowledge (e.g. better ability to verbally describe olfactory/taste stimuli). This is a point to which we will return below. In Solomon’s [139] study, he compared the ability of expert wine drinkers and novices to discriminate a set of wines using the triangle or oddity test described earlier. Novice participants performed at chance level and were significantly worse than experts at this discrimination task. In the second study, Bende and Nordin [10] also compared novices with experts, but this time tested whether experts perceptual (or conceptual) abilities extend beyond wine. Participants were asked to detect when various citral – eugenol mixtures had a detectable citral note. Experts were significantly better at this task than novices. A final issue which needs to be examined here is the effect that conceptual, especially verbal knowledge may have on odor discrimination tasks in human participants. Rabin [112], extended his exposure study (Experiment 1) by having a further group of participants learn a label for each unfamiliar odor. This group effectively combines both the effects of mere exposure and label learning. Participants in this group performed even better at the discrimination task (94% correct, compared to 88% in the exposure group and 81% in the controls), suggesting that being able to accurately label olfactory sensation can further enhance discrimination. Whether this effect results from the label directly affecting perception or, as is probably more likely, its ability to enhance participants memory over the interstimulus interval, remains to be determined (see for example [4] for the detrimental effects of a verbal suppression task on odor memory). Effective use of labels can also affect wine discrimination. Although the studies cited above clearly indicate that exposure alone can enhance performance, Melcher and Schooler [93] also ran a further between groups manipulation. Half of all participants were asked to write a verbal description of the wine, whilst the other half (reported earlier) solved puzzles during the interval between sampling the target and the test phase. Interestingly, producing a description of the target had no effect on the performance of the experts, but significantly reduced performance in the regulars to chance level. These results are intriguing for two reasons. First, they suggest that deliberate use of a verbal strategy did not enhance performance in the expert group, suggesting that verbal knowledge may not have assisted performance on this task (contrast with Rabin’s findings). Second, the drop in performance in the regulars suggests that their poor use of labels impaired performance. That is the experts could readily change between verbal and

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perceptual descriptions, whilst the regulars apparently relied solely upon their perceptual representation. In this section, so far, we have examined four general types of finding. First, that pairings of tastes with odors can change participants perception of the odor. Second, that pairings of odors into mixtures, can change their elements perceptual qualities, their similarity to each other and reduce their discriminability. Third, that exposure to a set of unfamiliar odors can enhance their discriminability from each other. Fourth, that incidental exposure to odors, such as those encountered with wine drinking, can result in enhanced ability to discriminate wines. Although these perceptual effects could result from changes at the receptor level (up or down-regulation), it would appear, especially in light of evidence presented later on the effects of memory impairment on odor perception, that these changes primarily reflect central rather than peripheral processes. In sum, they suggest that learning and memory play an important role in odor perception. 5.3. The role of learning and memory in odor perception So far we have reviewed two apparently unrelated sets of behavioral findings. The first, focussed on whether odor mixtures were perceived as discrete irreducible entities or as separate parts. The finding of a ceiling in the ability of participants to identify the parts of odor mixtures led us to conclude that participants treat mixtures with three or more components, synthetically. We then examined the role of learning in odor perception and concluded that both the perception of odor quality and relatedly, discrimination, can be strongly affected by past experience. Both conclusions are important because they are in direct contradiction to theories which base odor perception primarily upon the physiochemical features of an odorant. In this section we offer an alternative psychological model which can encompass both the physiochemical approach and, more importantly, the two sets of findings reviewed above. In a recent article we advanced a theory of odor perception based upon learning and memory [147]. We proposed that odor perception involves the following type of processes: (1) When an odor is smelled, its neural representation (the input) is automatically matched to all previous encodings stored in a discrete odor memory system; (2) When the input matches an encoding, this activates it proportional to the degree of match and the overall pattern of activation across encodings in the store represents the participants perception of the odor; (3) When smelling an unfamiliar odor, whose input may match many encodings slightly, but none completely, participants experience a representation which is easily confused with that produced by other unfamiliar odors. This model is readily able to deal with the two key issues raised by the behavioral data reviewed above. First, the model is consistent with the synthetic processing findings, because its unit of analysis is the ‘odor’ rather than

the ‘feature’. Second, it is readily able to accommodate the perceptual learning findings. For example, Rabin [112] and Jehl et al. [58] have convincingly shown that preexposure to an unfamiliar odor enhances its discriminability. In terms of the model this reflects the effect of encoding the odor’s neural pattern on its subsequent perception. Thus when the odorant is smelled again, it comes to activate primarily its own encoding, different to that activated by the other familiar odors. The finding that pairing odors can reduce the discriminability of the components can also be accounted for by the same process. The mixture is encoded in odor memory. Later, when one of its elements is smelled alone, its input pattern should be sufficiently similar to the mixture’s encoding to activate it. Thus what is experienced when the A component of an AX mixture is smelled, is not what is actually there, but what it reminds participants of. As X will also activate AX in the same way, A and X thus share a common engram and are consequently judged more alike and less discriminable. With the odor-taste findings a simple modification to the model is required, namely to allow odor memory to store other information than just input patterns. (We note in passing that an alternate version of the model would simply have an association between the encoding and taste; there is insufficient evidence as yet to distinguish these alternatives). On this basis an encoding which includes the odor and taste is stored following exposure. Later, when the odor is smelled alone, it recovers the whole previous experience of odor and taste. Thus a fragment of an original input can still result in the original whole being experienced. It is interesting to note that similar findings to this have been observed in rats. In a series of experiments, Slotnick, Bell, Panhuber and Laing [136], conditioned rats to respond to a particular odor. They then lesioned the vast majority of the olfactory bulb, including the area known to be most metabolically active when rats smelled the target odor. Remarkably these animals were still able to respond appropriately to the target odor even though the majority of the stimulus information was missing. Presumably, as with odors and tastes or odor components of previously encountered mixtures, a fragmentary experience can still act to recover the whole. Before examining how this type of model might be represented at the neurophysiological level, one further implication is worth noting. Given the hypothesized role of perceptual learning in odor perception, damage to odor memory should result in poor or even absent ability to discriminate between odors based upon their quality, with no loss in ability to discriminate between them based upon their intensity (a process not presumed to rely solely on memory). This type of finding, preserved sensitivity and poor quality perception, represents the most common form of olfactory neuropsychological problem in humans ([169], for review see Ref. [147]). For example, HM, who received bilateral surgical resection of the medial temporal lobe (including the piriform cortex) for chronic epilepsy, has

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been tested for both odor sensitivity and qualitative discrimination. Eichenbaum, Morton, Potter and Corkin [30], reported that he showed broadly normal olfactory sensitivity on a range of tests, but performed at chance level when identifying whether two odors of similar intensity but differing quality, were the same or different. These deficits appeared to be limited to olfaction, in that HM could discriminate objects visually and by touch and identify them. Similar findings have been observed in Alzheimer’s disease and in other conditions known to affect explicit memory [90]. In the past, these type of observations have been regarded as rather interesting oddities. The type of account we are suggesting allows them to be readily integrated with other findings. In summary, the model we are suggesting stresses the role of past experience in how odors are perceived and discriminated. In the final section of the review we examine a parallel neurophysiological framework to the psychological account proposed here and then briefly examine some predictions which can be derived from this perspective.

6. Neurobiology of olfactory perceptual learning The research reviewed in the previous section demonstrates that (1) behavioral expression of analytic processing of complex odorant mixtures is severely limited beyond 2 – 3 components; (2) behavioral expression of synthetic processing is quite advanced for odorant mixtures, even extending to cross-modal synthesis of odors and tastes; (3) both of these behavioral abilities, as well as simple odor discrimination, are enhanced by prior experience with the odorants involved. These data suggest that olfactory perceptual learning is a robust phenomenon, and further that perceptual learning is a critical component of basic olfactory function. Recent work in several labs has begun to unravel how experience affects odor coding at the neural level and how those changes may be translated into modified olfactory behavior. It is proposed here that olfactory recognition and discrimination involve processes similar to those involved in facial recognition and perceptual organization of visual objects [95,119,157]. Fig. 3 shows a classic example of visual perceptual organization. The visual scene is composed of multiple features, that without prior experience, may appear to be a random jumble. However, with prior experience (in this case with dogs) it is possible to recognize the visual object ‘dog’ against the background pattern. The characteristics of previous experience that facilitate this visual organization and object recognition include seeing the unique spatial pattern of features that constitute a ‘dog’ under a variety of conditions, and seeing those spatial features move in a synchronous, spatially coherent pattern [34,168]. It has been hypothesized that this spatial and temporal coherence of multiple visual features allows Hebbian synaptic plasticity in higher order visual cortices

Fig. 3. On any given inhalation, multiple odorants composed of multiple features will activate olfactory receptors and their central targets. In order to discriminate between odors or detect an odor against a background, perceptual organization (synthesis) of features belonging to the target odor (‘dog’) must occur, similar to what is needed for the visual organization task shown here. In both vision and olfaction, prior experience with the target stimulus enhances recognition of the target stimulus (perceptual learning). This grouping of ‘dog’ odor components, however, will greatly impair identification of individual components within the ‘dog’ odor mixture. Original photo by R.C. James.

to bind those features into a recognizable visual object by creating synthetic receptive fields in single neurons [95,119, 157]. Learning that a particular feature combination is different from other feature combinations enhances object discrimination, i.e. perceptual learning. We propose that olfactory recognition and discrimination follow a very similar process, although of course without the strong spatial component in the stimulus. Discriminating one odor from another (or from background) first requires appropriate perceptual grouping of odorant features, some of which are from the target odorant, and some of which are from the background or distracter odorants. The olfactory system must be able to not only detect and discriminate individual features, but also appropriately group those features acquired during a given inhalation into odor objects and background. Given the behavioral capabilities described above, it is not sufficient to bind all features detected during an inhalation into a single odorant object, yet significant synthesis must occur since even single molecules are composed of many features. We further propose that olfactory perceptual organization, i.e. synthesis, of multiple odorant features, occurs through a perceptual learning process largely mediated by long-term changes in the piriform cortex. Temporally bound odorant features become linked through Hebbian synaptic

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plasticity within piriform cortical association fiber circuits such that subsequent exposure to even degraded or partial inputs evokes a representation of the entire odorant [7,52]. Through exposure to odorants, thus, multiple features come to be grouped or synthesized into odor objects. The data described below suggests this synthesis can occur very rapidly. Once this synthesis occurs, the odor object becomes a perceptually irreducible whole, with identification of component features eliminated, yet with resistance to partial degradation of input. Thus, if the features contributing to ‘dog’ odor are experienced alone or against a variety of backgrounds, a unique representation of ‘dog’ odor will be formed within the piriform cortex. Once this representation, or template, has been formed, the odor can be more easily recognized against background odors or discriminated from dissimilar odors. However, if the odor has only been experienced in the context of a constant background or distracter odors (i.e. always in a similar mixture), or occurs in the presence of an overwhelming number of other features (complex mixtures) identification of the target odor within the mixture will be hindered due to the cortical synthesis of all the features together. Olfactory perceptual organization and perceptual learning could be mediated by changes throughout the olfactory system, including, but not limited to olfactory receptors, the olfactory bulb and the piriform cortex (Fig. 1). Experienceinduced changes have been described for odor response patterns of each of these system components. Relevant forms of plasticity would have to occur relatively quickly and persist for at least days in order to account for the characteristics of behavioral perceptual learning described above. 6.1. Experience-induced receptor plasticity Experience-induced changes in receptor protein gene expression or receptor activated second messenger cascades have been described in both vertebrate [27] and invertebrate [38] receptor neurons. Changes in receptor gene expression can also be induced experimentally through for example, transfection techniques [187]. These changes in receptor function are accompanied by changes in odor-evoked receptor responses [103,187] and in behavioral responsiveness to the experienced odor [103]. Perceptual learning often occurs fairly rapidly however [47,159], thus at least initially changes in receptor gene expression appear unlikely to be a major contributor. On the other hand, long-term exposure to odorants could modify expression of specific receptor proteins leading to modification in how effectively particular molecular features are subsequently detected, for example, perhaps accounting for acquired sensitivity to androstenone or other odorants in humans [181] and animals [167,183,186]. Thus, prolonged odorant exposure could produce changes in receptor expression and/or sensitivity which

could modify odorant feature detection. However, this would likely have little direct impact on odor feature synthesis and, combined with the prolonged time course, seems unrelated to rapid perceptual learning and synthesis of complex mixtures described above. 6.2. Experience-induced olfactory bulb plasticity A similar experience-dependent modification in odorant feature encoding may occur in olfactory bulb circuitry. Associative olfactory conditioning, or exposure to behaviorally relevant odors has been shown to modify odorevoked local field potential oscillations in the olfactory bulb [15,22,41,164] that are believed to primarily reflect mitral cell-inhibitory granule cell synaptic interactions [29,41,113]. Furthermore, associative odor conditioning modifies mitral cell odor response patterns to the learned odor [14,178,179]. In invertebrates, synchrony in spike trains between different olfactory lobe output neurons may also be modified by context or experience [23,43]. As noted above, if different mitral cells convey information about different molecular features, then variations in mitral cell output synchrony could have a significant impact on coding by target cortical neurons receiving convergent inputs. In fact, pharmacological disruption of olfactory lobe output neuron spike train temporal patterns disrupts behavioral odor discrimination in invertebrates [152], although perhaps not in vertebrates [104]. Similar ensemble recordings have not yet been performed during learning in vertebrate models. Finally, neurogenesis and survival of local populations of olfactory bulb interneurons can be modulated by olfactory experience [102, 108,117]. Incorporation of new neurons into odorant feature-specific circuits could further enhance discrimination of those features from other similar features. As with the reported changes in receptor response patterns, these changes in bulb circuit function require long-term training. However, there are several changes in olfactory bulb odor coding that can occur more rapidly. For example, olfactory bulb glomerular activity shifts in a reproducible way over the course of several seconds of odor stimulation [140]. The number of odor activated glomeruli and/or spatial extent of glomerular layer activation decreases and becomes more focused after several seconds of stimulation. In invertebrates, synchrony between olfactory lobe output neuron spike trains varies dynamically over the course of repeated odor pulses, with a reduction in extraneous, non-synchronous spikes over time [23,43,75, 106]. However, it is not clear if there are long-term consequences of these dynamic events. For example, it is not known if the focusing of glomerular activity or fine tuning of spike train synchrony is maintained when the odor is repeated several minutes or hours later, or if the process begins anew on each exposure. A long-term or persisting change would be required to account for olfactory perceptual learning as described here.

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These experience-induced changes in odor response of olfactory bulb circuits could have at least two consequences for olfactory perceptual organization and perceptual learning. First, the changes in receptor expression, inhibitory circuitry, focusing of glomerular layer activity and enhanced synchrony of mitral cells could increase discrimination of molecular features. Precise identification of features should enhance discrimination of feature patterns. However, enhanced discrimination of odorant features alone is not sufficient to account for the experience-induced changes in discrimination of odorants as complex objects which occurs at the behavioral level. As discussed above, experience does not enhance the ability to analyze complex mixtures at the behavioral level, thus enhanced discrimination of odorant features must contribute to some other process. On the other hand, enhanced temporal synchrony of spike trains from mitral cells conveying information about different features could enhance organization of those features into odor objects either directly though temporal binding [69,76] or through enhanced temporal summation by cortical target neurons receiving convergent input. Again, however, in order to account for behavioral olfactory perceptual learning there must be a long-term trace of these dynamics, such that on repeat stimulation some hours or days later, the circuit immediately responds in the ‘finetuned’ manner, and does not have to re-learn (re-synthesize) the odor. This is still largely an unexplored issue. In summary, olfactory experience can modify both detection and discrimination of odorant features through changes in the olfactory epithelium and olfactory bulb, i.e. enhance identification of odorant features. However, in contrast to these changes in peripheral neural coding, experience does not enhance the behavioral ability to analyze or recognize components of complex odorant mixtures. This suggests a disconnect between functions of peripheral odor coding and olfactory behavior. We propose that the olfactory system must begin with feature identification (mediated by experience-dependent receptor and olfactory bulb coding), but it is the synthesis of these features into odorant objects that drives odor discrimination behavior. As discussed here, olfactory bulb circuitry may begin this process of synthesis. Olfactory bulb glomerular activation and output neuron spike train temporal synchrony may be focused or enhanced by experience—processes which could facilitate odorant feature synthesis [69,76]. However, we propose that the piriform cortex, with its combinatorial circuitry and rapid plasticity play a critical role in olfactory perceptual learning, odorant synthesis and discrimination. 6.3. Experience-induced piriform cortical plasticity As described in Section 2, individual mitral cells project to the anterior piriform cortex in small patches, with overlap between mitral cells conveying information from different

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olfactory receptor types [188]. Experience-dependent changes in mitral cell synchrony, discussed above, could enhance temporal summation by piriform cortical target neurons receiving convergent input and produce long-term changes in cortical circuits through Hebbian synaptic plasticity. It is this cortical plasticity that we believe underlies the major components of olfactory perceptual organization, synthetic coding, and rapid perceptual learning. Both the mitral cell output synapses and cortical association fiber synapses express activity-dependent plasticity [53,66,68,121,153]. Thus, repeated co-occurrence of synaptic activity evoked by specific combinations of odorant features could result in a functional synthesis of those features such that subsequent exposure to a partially degraded signal could still evoke a ‘complete’ odor sensation and recognition [7]. In addition, the reported rapid habituation of piriform cortex responses [11,12,170] could allow filtering of background odors, enhancing the ability of the cortex to synthesize just those features that compose the odor object and contributing to figure-ground organization. It should be emphasized that simple anatomical convergence alone of heterogeneous mitral cells in the piriform cortex is not sufficient to account for either anterior piriform cortex sensory physiology nor behavioral psychophysics. On the contrary, odor experience rapidly leads to synthetic receptive fields in anterior piriform cortex [176], as well as resulting in synthetic processing of stimulus mixtures at the behavioral level [37]. This suggests that even basic odor discrimination is memory-dependent, a prediction supported by both physiological and behavioral data (see below). Synaptic plasticity in the piriform cortex has been extensively investigated both in vivo and in vitro [53,66,68, 82,121,126,153], and includes an NMDA-receptor dependent mechanism apparently similar to hippocampal associative long-term potentiation [68], as well as strong modulation by ACh from the horizontal limb of the diagonal band of Broca [53] and norepinephrine from the locus coeruleus [54]. Thus, the functional anatomy of the piriform cortex would appear to support synthetic coding of multiple odorant features [50]. Recent single-unit recordings in both awake [130] and anesthetized [172,173,176] rats further support the view of synthetic processing in the piriform cortex. For example, Schoenbaum and Eichenbaum [130] have shown that piriform cortex neurons in freely moving rats respond not only to odor stimuli, but also to contextual cues or task-demands associated with those odors. Furthermore, our recent work clearly shows significant differences in how mitral cells and piriform cortical neurons respond to odors [173]. These differences can be most parsimoniously interpreted with mitral cells having feature-detecting receptive fields and piriform cortical neurons having synthetic, complex receptive fields. Both piriform cortex anatomy and piriform receptive fields are strikingly similar to the higher order visual cortical

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area called inferotemporal cortex (IT; [52]). The proposed role of the IT cortex in vision is object recognition [95,119, 157]. Thus, while cells in primary visual cortex and more peripheral areas have simple receptive fields for visual features (e.g. spots, contrast edges, angles), cells in IT have complex receptive fields for ‘objects’, such as faces or hands. Complex receptive fields in IT are also highly plastic. Repeated presentation of optimal stimuli causes a rapid decrement in single-unit responses [79], similar to that found in piriform cortex [170]. In addition, exposure to the optimal stimulus from multiple viewing angles allows the complex receptive field to become ‘view-invariant’. An example of a cell with a view-invariant receptive field would be a cell that responds similarly to a face regardless of whether the face is presented as a front-view, a profile or any intermediate view. These view-invariant receptive fields can emerge after a brief exposure (as little as 5 s) to the rotating stimulus (perceptual learning; [159]). Thus, the functional anatomy of the visual IT cortex allows multiple visual features, extracted and enhanced by more peripheral structures, to be synthesized into perceptual wholes through rapid associative synaptic plasticity. It is hypothesized here that the piriform cortex serves a similar role in olfaction. Another similarity between the visual system and the olfactory system is a strong cortical feedback to its afferent structures, i.e. the olfactory bulb in the case of the piriform cortex. Experience-induced changes within the piriform cortex could, thus contribute to the shifts in mitral/tufted cell receptive fields to familiar odors described above, similar the the role of cortico-thalamic projections in other sensory systems [46,99]. Recent single-unit recording experiments support this view of a synthetic, highly plastic piriform cortex. Current views of odorant-receptor interactions and odor coding assume that even simple molecules have several ‘features’ that are extracted and used by the olfactory system for odorant identification [5,182]. For example, even short chain single molecules activate several olfactory bulb glomeruli, and it is assumed that each glomerulus or pair of glomeruli represent single molecular features [64]. Thus, when a mitral cell shows cross-habituation between odorants within its receptive field, a simple explanation is that the receptive field reflects responsiveness to a particular molecular feature and all odorants containing that feature should excite that cell to a certain extent. Habituation to the feature then, should (and does) reduce responsiveness to all odors [173]. Thus, our observed lack of cross-habituation between odors within anterior piriform cortical neuron receptive fields could be explained if the cortical neurons are responding to molecules as a whole (synthetic processing of feature ensembles), rather than being simple feature detectors. Rather than relying on the assumption that simple molecules contain multiple features, a more direct test of the synthetic coding hypothesis was performed using odorant mixtures [61], where ‘features’ can be

experimentally identified and controlled. Using binary (1:1) mixtures of odorants, we now have evidence that while anterior piriform cortex neurons discriminate between mixtures and their components [172,176], mitral cells do not [176]. That is, habituation (50 s exposure) to a binary mixture produces cross-habituation between the mixture and its components in mitral cells, but not in anterior piriform cortex neurons. These data clearly fit with the hypothesis of feature extracting simple receptive fields in mitral cells and synthetic, complex receptive fields in anterior piriform cortex neurons outlined above. Given the diversity of odorants and odorant mixtures possible in the environment, it is unlikely that synthetic receptive fields in the piriform cortex are innate. Just as view-invariant complex receptive fields in IT cortex require experience for their formation [119,157], we hypothesize that complex receptive fields in piriform cortex are synthesized during olfactory experience. More specifically, we hypothesize that a relatively short period of simple exposure (# 50 s) is sufficient to create feature ensembles within the anterior piriform cortex. For example, after a total of 50 s of exposure to a mixture novel to the animal (e.g. isoamyl acetate þ peppermint), anterior piriform cortex neurons showed little cross-habituation to the components (e.g. peppermint alone). If synthetic coding accounts for the lack of cross-habituation (enhanced odor discrimination), and the feature ensemble is being synthesized/organized during the 50 s exposure, then it might be predicted that a shorter exposure to the mixture might actually produce greater cross-habituation to the components (poorer odor discrimination). Our recent data suggest this precise outcome [176]. A 10 s mixture exposure produced somewhat less self-habituation in cortical neurons than a 50 sec exposure (response magnitude 35 ^ 8% of baseline at 10 s and 31 ^ 8% at 50 s), yet produced greater crosshabituation to the components (Fig. 4). Synaptic plasticity and learning-induced changes in the piriform cortex [53,125], as well as behavioral olfactory memory [26,114,120,125] have been shown to be modulated by acetylcholine. Thus, if the apparent emergence of complex, synthetic receptive fields in piriform cortical neurons is due to odor-evoked cortical synaptic plasticity, it should be impaired by blockade of cortical cholinergic receptors. We examined the effect of both systemic and cortical application of the muscarinic antagonist scopolamine on habituation of anterior piriform cortex unit odor responses, in vivo. We found that while scopolamine did not appear to affect odor responses or odor habituation per se, it had a dramatic effect on cross-habituation/odor-discrimination of novel odors [174]. Either systemic or cortical application of scopolamine prior to odor habituation to novel odorants significantly enhanced cortical cross-habituation to similar odorants. In fact, scopolamine-treated cortical neurons showed similar levels of cross-habituation as normal mitral cells [174]. These results suggest that scopolamine-treated piriform cortical neurons functioned

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[80,104]. However, an injection of scopolamine during the pre-exposure period impairs this perceptual learning (no drug is present during the subsequent behavioral test; [37]). Thus, acetylcholine modulates piriform cortex synaptic plasticity [53,125], experience-dependent effects on piriform cortex single-unit receptive fields [174] and behavioral perceptual learning [37].

7. Summary and future directions

Fig. 4. The ability of aPCX neurons to discriminate binary mixtures of dissimilar odorants from their components is dependent on the duration of exposure to the mixture. The bottom panel displays an intracellularly recorded aPCX neuron response to a 50 s odorant presentation. The response rapidly changes over the first 10 –15 s, and is then largely suppressed for the remainder of the stimulus. Tests of cross-habituation between the binary mixture and its components (top panel) after 10 s of exposure to the mixture reveals similar amounts of self- and crosshabituation, i.e. failure to discriminate. Tests of cross-habituation after 50 s of exposure to the mixture (in different cells and animals) reveals significantly less cross-habituation, i.e. the cell has learned to discriminate between the mixture and its components. Mitral/tufted (M/T) cells however, fail to discriminate between the mixture and its components, consistent with the feature detecting role hypothesized for these odors. From Wilson, [176].

similar to feature-detecting mitral cells and were unable to build synthetic representations of odor objects. Importantly, scopolamine can also impair behavioral olfactory perceptual learning. Rats do not express a behavioral discrimination of ethyl esters differing by a single carbon if the odorants are novel to the animal [37]. The animals can make this discrimination with prior exposure to the odorants 24 h before testing (perceptual learning). Similar effects of pre-exposure on fine odor discriminations have been reported for other odorants in rats

In this review we outline evidence that simple analytical feature extraction and binding of features co-occurring during an inhalation is not sufficient to account for behavioral olfactory discrimination. The psychophysical data demonstrate that while individuals are effective at recognizing an odorant against a background, mixtures are primarily treated synthetically at the behavioral level with minimal analytic ability. The fact that both analytical and synthetic processing of odors is enhanced by prior experience with those odors provides a critical clue as to the underlying mechanisms of odor discrimination. Namely, we review evidence suggesting that perceptual organization of odorant features, which is necessary for odor objects to be distinguished from odor backgrounds and simple mixture analysis, can occur only if (1) the features within the complex stimulus are accurately extracted, discriminated and synchronized by peripheral olfactory circuits and (2) previously experienced patterns of features have been stored in piriform cortex through Hebbian synaptic plasticity. If complex odor mixtures are novel (i.e. resulting in impaired feature extraction and/or lack of stored cortical representations) or too complex (i.e. resulting in inability to differentiate multiple cortical representations) analytical processing of odors will not be possible and odors will be treated either as a collective jumble of features or as a single synthesized odor object. This view of olfactory discrimination and the critical role of perceptual learning leads to several testable hypotheses at both the behavioral and physiological levels. The most apparent is that odor discrimination at both the behavioral and single unit levels should increase with experience with the target odorants. Based on results from perceptual learning in other sensory systems, this enhancement should show only minimal transfer to similar odorants and no transfer to molecularly dissimilar odorants. A particularly interesting test might be to look for transfer or generalization between odorants that are molecularly similar but perceptually dissimilar [110]. A second prediction is that familiarity with individual components of an odor mixture may enhance analytical identification of those components in simple mixtures or in figure-ground problems, again at both the behavioral and cortical single-unit levels [132]. As discussed above, there is evidence supporting both of these predictions, however, further work should be done.

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Another issue raised by the proposed model is that, given the apparent critical role of synaptic plasticity and perceptual learning in normal odor discrimination, it may be necessary to re-evaluate past work on the pharmacology of olfactory memory to isolate the potential contribution of sensory/perceptual deficits. That is, we predict that some apparent deficits in odor memory/recognition, may represent impairment in odor discrimination. Pharmacological manipulations could leave detection thresholds intact, but prevent the cortex from learning new odorant objects, thus decreasing odor discrimination and appearing as an impairment in an odor memory task as described above. Two interesting behavioral predictions emerge from the type of learning and memory based model advanced here. First, we would suggest that at birth, neonates should only poorly discriminate odors based upon quality and that their ability to do so should progressively improve until early adulthood. Evidence exists for odor discrimination of dissimilar odorants in both human infants [33,123] and infant rats [36] within hours or days of birth. In addition, olfactory bulb single-unit responses and glomerular spatial patterns of odor-evoked activity are present near birth in the rat [51,89]. However, no developmental study to our knowledge has carefully controlled for odor intensity as a factor in behavioral odor quality discriminations, and only a single study utilized molecularly similar odorants [123] which could provide a more sensitive measure of the effects of both ontogeny and early experience on odor perception. There is strong evidence that early experience can modify odor preferences in both humans [94,155] and animals [55,154,178], though studies of the effects of early experience on odor discrimination per se appear lacking. A second behavioral prediction based on this model is that we might expect considerable differences between cultures which differ in olfactory experience. Although considerable attention has been given to studying whether hedonic reactions to smells vary between cultures ([122]), only two studies, to our knowledge, have explored whether the qualities perceived by members of different cultures are dissimilar. In the first study [6], German and Japanese participants were asked to describe the smell of a range of odors which fell into one of three classes; ‘German’ odors (e.g. aniseed), ‘Japanese’ odors (e.g. dried fish) and international odors (e.g. coffee). As expected, hedonic ratings were more positive for a group’s own culturally specific odors. However, there were interesting differences in the qualitative reports. For example, German participants tended to describe dried fish as smelling like ‘excrement’ and soya bean as ‘cheesy smelly feet’, whilst few Japanese thought so. On the other hand, Japanese participants regarded aniseed as smelling like ‘disinfectant’ and Indian ink as ‘medicinal’, unlike German participants. In the second study [161], Japanese and Nepalese participants were asked to arrange 20 Japanese food odors in such a way that similar odors were placed near each other and dissimilar

ones further apart. Whilst Japanese participants closely grouped fish-related odors, Nepalese participants characterized them in a completely different way, suggesting that they had no common quality. Interestingly, Nepalese participants rarely encounter fish in their native diet. Although these two studies suggest that cultural differences in exposure to odors can affect perception of odor quality, key tests using discrimination between groups likely to differ considerably in their odor history remain to be carried out. This is especially unfortunate given the current trend for exposure to similar food and scented products. In conclusion, the existing body of data that we have reviewed here is strongly supportive of a learning and memory based model of odor perception. Moreover, the implicit view of odor perception, in which the odor’s physiochemical features primarily define perception cannot readily account for these findings. In sum, we would argue that odor perception reflects participant’s olfactory experience. The anatomy and physiology of the olfactory system, and particularly piriform cortex, produces rapid synthesis of co-occurring inputs, making learning and memory a fundamental feature of odorant discrimination and perception.

Acknowledgements DAW was supported by grants from National Institutes of Health (NIDCD), National Science Foundation and the Oklahoma Center for the Advancement of Science and Technology. RJS was supported by grants from the Australian Research Council and Macquarie University. The authors would like to thank Coral McCallister for figure preparation.

References [1] Adrian ED. The electrical activity of the olfactory bulb. EEG Clin Neurophysiol 1950;2:377–88. [2] Amoore JE. Specific anosmia: a clue to the olfactory code. Nature 1967;214:1095–8. [3] Amoore JE. Molecular basis of odor. Springfield, IL: Charles C. Thomas; 1970. [4] Annett JM, Leslie JC. Effects of visual and verbal interference on olfactory memory: the role of task complexity. Br J Psychol 1996;87: 447 –57. [5] Araneda RC, Kini AD, Firestein S. The molecular receptive range of an odorant receptor. Nat Neurosci 2000;3:1248–55. [6] Ayabe-Kanamura S, Schicker I, Laska M, Hudson R, Distel H, Kobayakawa T, Saito S. Differences in perception of everyday odors: a Japanese–German cross cultural study. Chem Senses 1998; 23:31–8. [7] Barkai E, Bergman RE, Horwitz G, Hasselmo ME. Modulation of associative memory function in a biophysical simulation of rat piriform cortex. J Neurophysiol 1994;72:659–77. [8] Bazhenov M, Stopfer M, Rabinovich M, Abarbanel HDI, Sejnowski TJ, Laurent G. Model of cellular and network mechanisms for

D.A. Wilson, R.J. Stevenson / Neuroscience and Biobehavioral Reviews 27 (2003) 307–328

[9]

[10] [11] [12]

[13] [14] [15]

[16]

[17]

[18]

[19]

[20] [21]

[22]

[23]

[24]

[25] [26]

[27]

[28]

[29] [30] [31] [32]

odor-evoked temporal patterning in the locust antennal lobe. Neuron 2001;30:569 –81. Belluscio L, Lodovichi C, Feinstein P, Mombaerts P, Katz LC. Odorant receptors instruct functional circuitry in the mouse olfactory bulb. Nature 2002;419:296–300. Bende M, Nordin S. Perceptual learning in olfaction: professional wine tasters versus controls. Physiol Behav 1997;62:1065–70. Best AR, Wilson DA. Synaptic correlates of cortical adaptation. Society for Neurosciences Abstracts; 2002 Bouret S, Sara SJ. Locus coeruleus activation modulates firing rate and temporal organization of odour-induced single-cell responses in rat piriform cortex. Eur J Neurosci 2002;16:2371–82. Bozza TC, Kauer JS. Odorant response properties of convergent olfactory receptor neurons. J Neurosci 1998;18:4560 –9. Brennan PA, Keverne EB. Neural mechanisms of mammalian olfactory learning. Prog Neurobiol 1997;51:457 –81. Bressler SL. Changes in electrical activity of rabbit olfactory bulb and cortex to conditioned odor stimulation. Behav Neurosci 1988; 102:740–7. Buck L, Axel R. A novel multigene family may encode odorant receptors: a molecular bases for odor recognition. Cell 1991;65: 175–87. Bult JHF, Schifferstein HNJ, Roozen JP, Voragen AGJ, Kroeze JHA. The influence of olfactory concept on the probability of detecting sub- and peri-threshold components in a mixture of odorants. Chem Senses 2001;26:459–69. Buonviso N, Revial MF, Jourdan F. The projections of mitral cells from small local regions of the olfactory bulb: an anterograde tracing study using PHA-L (Phaseolus vulgaris Leuco-agglutinin). Eur J Neurosci 1991;3:493–500. Cain WS, Schmidt R. Sensory detection of glutaraldehyde in drinking water—emergense of sensitivity and specific anosmia. Chem Senses 2002;27:425–33. Case TI, Stevenson RJ, Boakes RA. Olfactory perceptual learning: acquired equivalence and acquired distinctiveness. Submitted. Cattarelli M, Astic L, Kauer JS. Metabolic mapping of 2DG uptake in the rat piriform cortex using computerized image processing. Brain Res 1988;442:180 –4. Chabaud P, Ravel N, Wilson DA, Mouly AM, Vigouroux M, Farget V, Gervais R. Exposure to behaviourally relevant odour reveals differential characteristics in rat central olfactory pathways as studied through oscillatory activities. Chem Senses 2000;25: 561–73. Christensen TA, Pawlowski VM, Lei H, Hildebrand JG. Multi-unit recordings reveal context-dependent modulation of synchrony in odor-specific neural ensembles. Nat Neurosci 2000;3:927 –31. Dalton P, Doolittle N, Nagata H, Breslin PAS. The merging of the senses: integration of subthreshold taste and smell. Nat Neurosci 2000;3:431– 2. Derby CD, Ache BW. Quality coding of a complex odorant in an invertebrate. J Neurophysiol 1984;51:906–24. DeRosa E, Hasselmo ME. Muscarinic cholinergic neuromodulation reduces proactive interference between stored odor memories during associative learning in rats. Behav Neurosci 2000;114:32–41. Dittman AH, Quinn TP, Nevitt GA, Hacker B, Storm DR. Sensitization of olfactory guanylyl cyclase to a specific imprinted odorant in Coho salmon. Neuron 1997;19:381–9. Duchamp-Viret P, Palouzier-Paulignan B, Duchamp A. Odor coding properties of frog olfactory cortical neurons. Neuroscience 1996;74: 885–95. Eeckman FH, Freeman WJ. Correlations between unit firing and EEG in the rat olfactory system. Brain Res 1990;528:238 –44. Eichenbaum H, Morton TH, Potter H, Corkin S. Selective olfactory deficits in case H.M. Brain 1983;106:459 –72. Eichenbaum H. Using olfaction to study memory. Ann N Y Acad Sci 1998;855:657 –69. Engen T. Odor sensation and memory. New York: Praeger; 1991.

325

[33] Engen T, Lipsitt LP. Decrement and recovery of responses to olfactory stimuli in the human neonate. J Comp Physiol Psychol 1965;59:312–6. [34] Fahle M. Figure-ground discrimination from temporal information. Proc R Soc Lond B 1993;254:199–203. [35] Firestein S. How the olfactory system makes sense of scents. Nature 2001;413:211–8. [36] Fletcher ML, Wilson DA. Ontogeny of odor discrimination: a method to assess novel odor discrimination in neonatal rats. Physiol Behav 2001;74:589–93. [37] Fletcher ML, Wilson DA. Experience modifies olfactory acuity: acetylcholine-dependent learning decreases behavioral generalization between similar odorants. J Neurosci 2002;22:RC201(1-5). [38] Fox AN, Pitts RJ, Robertson HM, Carlson JR, Zwiebel LJ. Candidate odorant receptors from malaria vector mosquito Anopheles gambiae and evidence of down-regulation in response to blood feeding. Proc Natl Acad Sci USA 2001;98:14693–7. [39] Frank RA, Byram J. Taste-smell interactions are tastant and odorant dependent. Chem Sens 1988;13:445–55. [40] Freeman WJ. Spatial properties of an EEG event in the olfactory bulb and cortex. EEG Clin Neurophysiol 1978;44:586 –605. [41] Freeman WJ. Neurodynamics: an exploration in mesoscopic brain dynamics. New York: Springer; 2000. [42] Friedrich RW, Korsching SI. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging. Neuron 1997;18:737 –52. [43] Friedrich RW, Stopfer M. Recent dynamics in olfactory population coding. Curr Opin Neurobiol 2001;11:468–74. [44] Gaillard I, Rouquier S, Pin JP, Mollard P, Richard S, Barnabe C, Demaille J, Giorgi D. A single olfactory receptor specifically binds a set of odorant molecules. Eur J Neurosci 2002;15:409–18. [45] Gesteland RC, Lettvin JY, Pitts WH. Chemical transmission in the nose of the frog. J Physiol 1965;181:525–59. [46] Ghazanfar AA, Krupa DJ, Nicolelis MAL. Role of cortical feedback in the receptive field structure and nonlinear response properties of somatosensory thalamic neurons. Exp Brain Res 2001;141:88–100. [47] Gilbert CD, Sigman M, Crist RE. The neural basis of perceptual learning. Neuron 2001;31:681–97. [48] Girardot MN, Derby CD. Neural coding of quality of complex olfactory stimuli in lobsters. J Neurophysiol 1988;60:303–24. [49] Giraudet P, Berthommier F, Chaput M. Mitral cell temporal response patterns evoked by odor mixtures in the rat olfactory bulb. J Neurophysiol 2002;88:829–38. [50] Granger R, Lynch G. Higher olfactory processes: Perceptual learning and memory. Curr Opin Neurobiol 1991;1:209–14. [51] Guthrie KM, Gall C. Anatomic mapping of neuronal odor responses in the developing rat olfactory bulb. J Comp Neurol 2003;455: 56–71. [52] Haberly LB. Parallel-distributed processing in olfactory cortex: new insights from morphological and physiological analysis of neuronal circuitry. Chem Senses 2001;26:551–76. [53] Hasselmo ME, Barkai E. Cholinergic modulation of activitydependent synaptic plasticity in the piriform cortex and associative memory function in a network biophysical simulation. J Neurosci 1995;15:6592–604. [54] Hasselmo ME, Linster C, Patil M, Ma D, Cekic M. Noradrenergic suppression of synaptic transmission may influence cortical signalto-noise ratio. J Neurophysiol 1997;77:3326– 39. [55] Hudson R. Olfactory imprinting. Curr Opin Neurobiol 1993;3: 548– 52. [56] Hudson R. From molecule to mind: the role of experience in shaping olfactory function. J Comp Physiol 1999;185A:297–304. [57] Illig KR, Haberly LB. Odor-evoked activity is spatially distributed in piriform cortex. J Comp Neurol 2003;457:361– 73. [58] Jehl C, Royet JP, Holley A. Odor discrimination and recognition memory as a function of familiarization. Percept Psychophys 1995; 57:1002–11.

326

D.A. Wilson, R.J. Stevenson / Neuroscience and Biobehavioral Reviews 27 (2003) 307–328

[59] Jellinek JS, Koster EP. Perceived fragrance complexity and its relation to familiarity and pleasantness. J Soc Cosmet Chem 1979; 30:253–62. [60] Jinks A, Laing DG. A limit in the processing of components in odour mixtures. Percept 1999;28:395–404. [61] Jinks A, Laing DG. The analysis of odor mixtures by humans: evidence for a configurational process. Physiol Behav 2002;72:51–63. [62] Joerges J, Kuttner A, Galizia CG, Menzel R. Representations of odours and odour mixtures visualized in the honeybee brain. Nature 1997:285–8. [63] Johnson BA, Woo CC, Leon M. Spatial coding of odorant features in the glomerular layer of the rat olfactory bulb. J Comp Neurol 1998; 393:457–71. [64] Johnson BA, Leon M. Odorant molecular length: one aspect of the olfactory code. J Comp Neurol 2000;426:330 –8. [65] Jourdan F, Duveau L, Astic L, Holley A. Spatial distribution of [14C]2-deoxyglucose uptake in the olfactory bulbs of rats stimulated with two different odours. Brain Res 1980;188:139–54. [66] Jung MW, Larson J, Lynch G. Long-term potentiation of monosynaptic EPSPs in rat piriform cortex in vitro. Synapse 1990; 6:279–83. [67] Kaluza JF, Breer H. Responsiveness of olfactory neurons to distinct aliphatic aldehydes. J Exp Biol 2000;203:927–33. [68] Kanter ED, Haberly LB. NMDA-dependent induction of long-term potentiation in afferent and association fiber systems of piriform cortex in vitro. Brain Res 1990;525:175 –9. [69] Kashiwadani H, Sasaki YF, Uchida N, Mori K. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J Neurophysiol 1999; 82:1786–92. [70] Katoh K, Koshimoto H, Tani A, Mori K. Coding of odor molecules by mitral/tufted cells in rabbit olfactory bulb. II. Aromatic compounds. J Neurophysiol 1993;70:2161–75. [71] Kent PF, Mozell MM. The recording of odorant-induced mucosal activity patterns with a voltage-sensitive dye. J Neurophysiol 1992; 68:1804–19. [72] Laing DG, Francis GW. The capacity of humans to identify odors in mixtures. Physiol Behav 1989;46:809–14. [73] Laing DG, Glenmarec A. Selective attention and the perceptual analysis of odor mixtures. Physiol Behav 1992;52: 1047–53. [74] Laing DG, Legha PK, Jinks AL, Hutchinson I. Relationship between molecular structure, concentration and odor qualities of oxygenated aliphatic molecules. Chem Senses 2003;28:57 –69. [75] Laurent GJ, Bazhenov M, Stopfer M, Sejnowski TJ. Fast odor learning and reliability of odor responses in the locust antennal lobe. Soc Neurosci Abstracts 2002;. [76] Laurent G, Stopfer M, Friedrich RW, Rabinovich MI, Volkovskii A, Abarbanel HDI. Odor encoding as an active, dynamical process: experiments, computation, and theory. Ann Rev Neurosci 2001;24: 263–97. [77] Lei H, Christensen TA, Hildebrand JG. Local inhibition modulates odor-evoked synchronization of glomerulus-specific output neurons. Nat Neurosci 2002;5:557–65. [78] Leinders-Zufall T, Lane AP, Puche AC, Ma W, Novotny MV, Shipley MT, Zufall F. Ultrasensitive pheromone detection by mammalian vomeronasal neurons. Nature 2000;405:792 –6. [79] Li L, Miller EK, Desimone R. The representation of stimulus familiarity in anterior inferior temporal cortex. J Neurophysiol 1993; 69:1918–29. [80] Linster C, Johnson BA, Morse A, Yue E, Leon M. Spontaneous versus reinforced olfactory discriminations. J Neurosci 2002;22: 6842– 5. [81] Linster C, Smith BH. Generalization between binary odor mixtures and their components in the rat. Physiol Behav 1999;66:701–7. [82] Litaudon P, Mouly AM, Sullivan R, Gervais R, Cattarelli M. Learning-induced changes in rat piriform cortex activity mapped

[83]

[84]

[85]

[86]

[87]

[88] [89]

[90]

[91] [92]

[93]

[94] [95]

[96] [97]

[98] [99]

[100]

[101]

[102] [103]

[104]

[105]

using multisite recording with voltage sensitive dye. Eur J Neurosci 1997;9:1593–602. Livermore A, Laing DG. Influence of training and experience on the perception of multicomponent odor mixtures. J Exp Psychol Hum Percept Perform 1996;22:267–77. Livermore A, Laing DG. The influence of chemical complexity on the perception of multicomponent odor mixtures. Percept Psychophys 1998;60:650– 61. Livermore A, Laing DG. The influence of odor type on the discrimination and identification of odorants in multicomponent odor mixtures. Physiol Behav 1998;65:311 –20. Lowe G, Gold GH. The spatial distributions of odorant sensitivity and odorant-induced currents in salamander olfactory receptor cells. J Physiol 1991;442:147–68. Lu XCM, Slotnick BM. Olfaction in rats with extensive lesions of the olfactory bulb: implications for odor coding. Neuroscience 1998; 84:849–66. Luo M, Katz LC. Response correlation maps of neurons in the mammalian olfactory bulb. Neuron 2001;32:1165–79. Mackay-Sim A, Kesteven S. Topographic patterns of responsiveness to odorants in the rat olfactory epithelium. J Neurophysiol 1994;71: 150 –60. Mair RG, Gesteland RC. Response properties of mitral cells in the olfactory bulb of the neonatal rat. Neuroscience 1982;7: 3117–25. Mair R, Harrison LM, Flint DL. The neuropsychology of odor memory. In: Schab FR, Crowder RG, editors. Memory for odors. New Jersey: LEA; 1995. p. 39. Malnic B, Hirono J, Sato T, Buck LB. Combinatorial receptor codes for odors. Cell 1999;96:713–23. McCollum J, Larson J, Otto T, Schottler F, Granger R, Lynch G. Short-latency single-unit processing in olfactory cortex. J Cogn Neurosci 1991;3:293–9. Melcher JM, Schooler JW. The misrememberance of wines past: verbal and perceptual expertise differentially mediate verbal overshadowing of taste memory. J Mem Lang 1996;35: 231 –45. Mennella JA, Beauchamp GK. Infants exploration of scented toys; effects of prior experiences. Chem Senses 1998;23:11–17. Miyashita Y, Hayashi T. Neural representation of visual objects: encoding and top-down activation. Curr Opin Neurobiol 2000;10: 187 –94. Moncrieff RW. The chemical senses. London: Hill; 1951. Mori K, Nagao H, Yoshihara Y. The olfactory bulb: coding and processing of odor molecule information. Science 1999;286: 711 –5. Moskowitz HR, Barbe CD. Profiling of odor components and their mixtures. Sen Proc 1977;1:212–26. Murphy PC, Duckett SG, Sillito AM. Feedback connections to the lateral geniculate nucleus and cortical response properties. Science 1999;286:1552–4. Myles-Worsley M, Johnston WA, Simons MA. The influence of expertise on X-ray image processing. J Exp Psychol Learn Mem Cog 1988;14:553–7. Nagao H, Yamaguchi M, Takahash Y, Mori K. Grouping and representation of odorant receptors in domains of the olfactory bulb sensory map. Microscopy Res Tech 2002;58:168 –75. Najbauer J, Leon M. Olfactory experience modulates apoptosis in the developing olfactory bulb. Brain Res 1995;674:245–51. Nevitt GA, Dittman AH, Quinn TP, Moody JrWJ. Evidence for a peripheral olfactory memory in imprinted salmon. Proc Natl Acad Sci USA 1994;91:4288–92. Nusser Z, Kay LM, Laurent G, Homanics GE, Mody I. Disruption to GABA-A receptors on GABAergic interneurons leads to increased oscillatory power in the olfactory bulb network. J Neurophysiol 2001;86:2823–33. Owen DH, Machamer PK. Bias-free improvement in wine discrimination. Percept 1979;8:199 –209.

D.A. Wilson, R.J. Stevenson / Neuroscience and Biobehavioral Reviews 27 (2003) 307–328 [106] Perez-Orive J, Mazor O, Turner GC, Cassenaer S, Wilson RI, Laurent G. Oscillations and sparsening of odor representations in the mushroom body. Science 2002;297:359 –65. [107] Peron RM, Allen GL. Attempts to train novices for beer flavor discrimination: a matter of taste. J Gen Psychol 1989;115:403–18. [108] Petreanu L, Alvarez-Buylla A. Maturation and death of adult-born olfactory bulb granule neurons: role of olfaction. J Neurosci 2002; 22:6106–13. [109] Pierce J, Halpern B. Orthonasal and retronasal odorant identification based upon vapor phase input from common substances. Chem Sens 1996;21:529 –43. [110] Pierce Jr. JD, Zeng XN, Aronov EV, Preti G, Wysocki CJ. Crossadaptation of sweaty-smelling 3-methyl-2-hexenoic acid by a structurally-similar, pleasant-smelling odorant. Chem Senses 1995; 20:401–11. [111] Prescott J. Flavour as a psychological construct: implications for perceiving and measuring the sensory qualities of foods. Food Qual Pref 1999;10:1–8. [112] Rabin MD. Experience facilitates olfactory quality discrimination. Percept Psychophys 1988;44:532 –40. [113] Rall W, Shepherd GM. Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in the olfactory bulb. J Neurophysiol 1968;31:884 –915. [114] Ravel N, Elaagouby A, Gervais R. Scopolamine injection into the olfactory bulb impairs short-term olfactory memory in rats. Behav Neurosci 1994;108:317– 24. [115] Ressler KJ, Sullivan SL, Buck LB. A zonal organization of odorant receptor gene expression in the olfactory epithelium. Cell 1993;73: 597–609. [116] Ressler KJ, Sullivan SL, Buck LB. Information coding in the olfactory system: evidence for a stereotyped and highly organized epitope map in the olfactory bulb. Cell 1994;79:1245 –55. [117] Rochefort C, Gheusi G, Vincent JD, Lledo PM. Enriched odor exposure increases the number of newborn neurons in the adult olfactory bulb and improves odor memory. J Neurosci 2002;22: 2679–89. [118] Rolls ET, Baylis LL. Gustatory, olfactory and visual convergence within the primate orbitofrontal cortex. J Neurosci 1994;14: 5437–52. [119] Rolls ET. Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition. Neuron 2000; 27:205–18. [120] Roman FS, Simonetto I, Soumireu-Mourat B. Learning and memory of odor-reward association: selective impairment following horizontal diagonal band lesions. Behav Neurosci 1993;107: 72–81. [121] Roman F, Staubli U, Lynch G. Evidence for synaptic potentiation in a cortical network during learning. Brain Res 1987;418:221 –6. [122] Rozin P. The use of characteristic flavourings in human culinary practice. In: Apt M, editor. Flavour: its chemical behavioural and commercial aspects. Colorado: Westview Press; 1978. p. 101–27. [123] Rovee CK. Olfactory cross-adaptation and facilitation in human neonates. J Exp Child Psychol 1972;13:368–81. [124] Rubin BD, Katz LC. Optical imaging of odorant representations in the mammalian olfactory bulb. Neuron 1999;23:499–511. [125] Saar D, Grossman F, Barkai E. Long-lasting cholinergic modulation underlies rule learning in rats. J Neurosci 2001;21:1385–92. [126] Saar D, Grossman Y, Barkai E. Learning-induced enhancement of postsynaptic potentials in pyramidal neurons. J Neurophysiol 2002; 87:2358–63. [127] Sakai N, Imada S. Bilateral lesion of the insular cortex or of the prefrontal cortex block the association between taste and odor in the rat. Neurobiol Learn Mem 2003;80:24 –31. [128] Sakai N, Yamamoto T. Effects of excitotoxic lesions on tastemediated odor learning in the rat. Neurobiol Learn Mem 2001;75: 128–39.

327

[129] Sato T, Hirono J, Tonoike M, Takebayashi M. Tuning specificities to aliphatic odorants in mouse olfactory receptor neurons and their local distribution. J Neurophysiol 1994;72:2980–9. [130] Schoenbaum G, Eichenbaum H. Information coding in the rodent prefrontal cortex. I. Single-neuron activity in orbitofrontal cortex compared with that in pyriform cortex. J Neurophysiol 1995;74: 733– 50. [131] Scott JW, Brierley T, Schmidt FH. Chemical determinants of the rat electro-olfactogram. J Neurosci 2000;20:4721– 31. [132] Sekiguchi T, Suzuki H, Yamada A, Kimura T. Aversive conditioning to a compound odor stimulus and its components in a terrestrial mollusc. Zoo Sci 1999;16:879 –83. [133] Serizawa S, Ishii T, Nakatani H, Tsuboi A, Nagawa F, Asano M, Sudo K, Sakagami J, Sakano H, Ijiri T, Matsuda Y, Suzuki M, Yamamori T, Iwakura Y, Sakano H. Mutually exclusive expression of odorant receptor transgenes. Nat Neurosci 2000;3: 687– 93. [134] Shepherd GM, Greer CA. Olfactory bulb. In: Shepherd GM, editor. The synaptic organization of the brain, 4th ed. New York: Oxford University Press; 1998. p. 159–203. [135] Sicard G. Olfactory discrimination of structurally related molecules: receptor cell responses to camphoraceous odorants. Brain Res 1985; 326:203–12. [136] Slotnick BM, Bell GA, Panhuber H, Laing DG. Detection and discrimination of propionic acid after removal of its 2-DG identified major focus in the olfactory bulb: a psychophysical analysis. Brain Res 1997;762:89–96. [137] Slotnick BM, Graham S, Laing DG, Bell GA. Detection of propionic acid vapor by rats with lesions of olfactory bulb areas associated with high 2-DG uptake. Brain Res 1987;417:343–6. [138] Slotnick B. Animal cognition and the rat olfactory system. Trends Cogn Sci 2001;5:216– 22. [139] Solomon GEA. Psychology of novice and expert wine talk. Am J Psychol 1990;103:495–517. [140] Spors H, Grinvald A. Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb. Neuron 2002; 34:301– 15. [141] Staubli U, Fraser D, Faraday R, Lynch G. Olfaction and the data memory system in rats. Behav Neurosci 1987;101:757–65. [142] Stevenson RJ. The acquisition of odour quality. Q J Exp Psychol 2001;54A:561 –78. [143] Stevenson RJ. Associative learning and odor quality perception: how sniffing an odor mixture can alter the smell of its parts. Learn Motiv 2001;32:154–77. [144] Stevenson RJ. Perceptual learning with odors: implications for psychological accounts of odor quality perception. Psychonom Bull Rev 2001;8:708–12. [145] Stevenson RJ, Boakes RA, Prescott J. Changes in odor sweetness resulting from implicit learning of a simultaneous odor-sweetness association: an example of learned synesthesia. Learn Motiv 1998; 29:113–32. [146] Stevenson RJ, Boakes RA. Sweet and sour smells: learned synesthesia between the senses of taste and smell. In: Spence C, Calvert G, Stein B, editors. Handbook of multisensory processing. Boston: MIT Press; 2003. in press. [147] Stevenson RJ, Boakes RA. A mnemonic theory of odor perception. Psychol Rev 2003;110:340– 364. [148] Stevenson RJ, Case TI, Boakes RA. Smelling what was there: acquired olfactory percepts are resistant to further modification. Learning and Motivation 2003;34:185– 202. [149] Stevenson RJ, Prescott J. Judgments of chemosensory mixtures in memory. Acta Psychol 1997;95:195–214. [150] Stevenson RJ, Prescott J, Boakes RA. The acquisition of taste properties by odors. Learn Motiv 1995;26:433–55. [151] Stevenson RJ, Prescott J, Boakes RA. Confusing tastes and smells: how odours can influence the perception of sweet and sour tastes. Chem Sens 1999;24:627–35.

328

D.A. Wilson, R.J. Stevenson / Neuroscience and Biobehavioral Reviews 27 (2003) 307–328

[152] Stopfer M, Bhagavan S, Smith BH, Laurent G. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 1997;390:70 –4. [153] Stripling JS, Patneau DK, Gramlich CA. Selective long-term potentiation in the pyriform cortex. Brain Res 1988;441:281–91. [154] Sullivan RM, Landers M, Yeaman B, Wilson DA. Good memories of bad events in infancy. Nature 2000;407:38–9. [155] Sullivan RM, Taborsky-Barba S, Mendoza R, Itano A, Leon M, Cotman CW, Payne TF, Lott I. Olfactory classical conditioning in neonates. Pediatrics 1991;87:511–8. [156] Sullivan SL, Ressler KJ, Buck LB. Spatial patterning and information coding in the olfactory system. Curr Opin Genet Dev 1995;5:516–23. [157] Tanaka K. Mechanisms of visual object recognition studied in monkeys. Spatial Vision 2000;13:147–63. [158] Timmermans J. Un nouvel etat mesomorphe, les cristaux organiques plastiques. J Chim Phys 1938;35:331–44. [159] Tovee MJ, Rolls ET, Ramachandran VS. Rapid visual learning in neurones of the primate temporal visual cortex. NeuroReport 1996;7: 2757– 60. [160] Uchida N, Takahashi YK, Tanifuji M, Mori K. Odor maps in the mammalian olfactory bulb: domain organization and odorant structural features. Nat Neurosci 2000;3:1035–43. [161] Ueno Y. Cross-cultural study of odor perception in Sherpa and Japanese people. Chem Senses 1993;18:352–3. [162] Valentincic T, Kralj J, Stenovec M, Koce A, Caprio J. The behavioral detection of binary mixtures of amino acids and their individual components by catfish. J Exp Biol 2000;203:3307–17. [163] Vassar R, Chao SK, Sitcheran R, Nunez JM, Vosshall LB, Axel R. Topographic organization of sensory projections to the olfactory bulb. Cell 1994;79:981–91. [164] Viana Di Prisco G, Freeman WJ. Odor-related bulbar EEG spatial pattern analysis during appetitive conditioning in rabbits. Behav Neurosci 1985;99:964 –78. [165] Vickers NJ, Christensen TA, Hildebrand JG. Combinatorial odor discrimination in the brain: attractive and antagonist odor blends are represented in distinct combinations of uniquely identifiable glomeruli. J Comp Neurol 1998;400:35–56. [166] Walk RD. Perceptual learning and the discrimination of wines. Psychonom Sci 1966;5:57–8. [167] Wang HW, Wysocki CJ, Gold GH. Induction of olfactory receptor sensitivity in mice. Science 1993;260:998–1000. [168] Watt R. Understanding vision. London: Academic Press; 1991. [169] White TL. Olfactory memory: the long and short of it. Chem Sens 1998;23:433–41. [170] Wilson DA. Habituation of odor responses in the rat anterior piriform cortex. J Neurophysiol 1998;79:1425–40.

[171] Wilson DA. Synaptic correlates of odor habituation in the rat anterior piriform cortex. J Neurophysiol 1998;80:998–1001. [172] Wilson DA. Odor specificity of habituation in the rat anterior piriform cortex. J Neurophysiol 2000;83:139–45. [173] Wilson DA. A comparison of odor receptive field plasticity in the rat olfactory bulb and anterior piriform cortex. J Neurophysiol 2000;84: 3036–42. [174] Wilson DA. Scopolamine enhances generalization between odor representations in rat olfactory cortex. Learn Mem 2001;8:279–85. [175] Wilson DA. Receptive fields in the rat piriform cortex. Chem Senses 2001;26:577–84. [176] Wilson DA. Rapid, experience-induced enhancement in odorant discrimination by anterier piriform cortex neurons. Journal of Neurophysiology 2003; in press. [177] Wilson DA, Stevenson RJ. The fundamental role of memory in olfactory perception. Trends Neurosci 2003; in press. [178] Wilson DA, Sullivan RM. Neurobiology of associative learning in the neonate: Early olfactory learning. Behav Neural Biol 1994;61: 1– 18. [179] Wilson DA, Sullivan RM, Leon M. Single-unit analysis of postnatal olfactory learning: modified olfactory bulb output response patterns to learned attractive odors. J Neurosci 1987;7:3154–62. [180] Wysocki CJ, Beauchamp GK. Ability to smell androstenone is genetically determined. Proc Natl Acad Sci USA 1984;81: 4899–902. [181] Wysocki CJ, Dorries KM, Beauchamp GK. Ability to perceive androstenone can be acquired by ostensibly anosmic people. Proc Natl Acad Sci USA 1989;86:7976–8. [182] Xu F, Greer CA, Shepherd GM. Odor maps in the olfactory bulb. J Comp Neurol 2000;422:489 –95. [183] Yee KK, Wysocki CJ. Odorant exposure increases olfactory sensitivity: olfactory epithelium is implicated. Physiol Behav 2001;72:705–11. [184] Yokoi M, Mori K, Nakanishi S. Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb. Proc Natl Acad Sci USA 1995;92:3371–5. [185] Young TA, Wilson DA. Frequency-dependent modulation of inhibition in the rat olfactory bulb. Neurosci Lett 1999;276: 65 –7. [186] Youngentob SL, Kent PF. Enhancement of odorant-induced mucosal activity patterns in rats trained on an odorant identification task. Brain Res 1995;670:82–8. [187] Zhao H, Ivic L, Otaki JM, Hashimoto M, Mikoshiba K, Firestein S. Functional expression of a mammalian odorant receptor. Science 1998;279:237–42. [188] Zou Z, Horowitz LF, Montmayeur JP, Snapper S, Buck LB. Genetic tracing reveals a stereotyped sensory map in the olfactory cortex. Nature 2001;414:173–9.