Although we may only become consciously aware of our sense of smell when we encounter something pungent, it can greatly influence our quality of life. Smells are processed by our olfactory system, a collection of receptors and nerve cells in the nose and brain.

Odor molecules activate the olfactory system when they bind to receptors in the nostrils. These molecules can have a wide range of chemical and physical properties. However, some odor molecules are mirror images of each other. These variants are known as enantiomers. Some people can naturally smell the difference between enantiomers; others can be taught how to tell them apart. Studying this training process could help us to understand how the olfactory system adapts to new circumstances.

Feng and Zhou trained volunteers to distinguish between odor enantiomers – but only using one nostril. After training, the volunteers were better able to tell the difference between different enantiomers – even for certain scents they had not been trained to discriminate – when sniffing through the trained nostril. However, they got no better at distinguishing between enantiomers when they sniffed them using the other nostril.

Overall, the results reported by Feng and Zhou confirm that human adults can learn how to process the structural features of odorants. However, in contrast to earlier beliefs, they also suggest that one nostril does not readily know what the other learns. This new understanding of how the olfactory system adapts could ultimately help us to develop therapies to restore a lost sense of smell.

Asymmetric carbon atoms, those with four chemically distinct substituents, cause chirality in molecules, that is, the possibility of two nonidentical mirror image forms called enantiomers. Whereas the different enantiomers of a chiral molecule have almost identical physical and chemical properties, they usually have different biological properties and can smell differently to the human nose (Friedman and Miller, 1971; Russell and Hills, 1971). After all, olfactory receptors are proteins that use only one enantiomeric form (the L form) of the amino-acid building blocks (Mombaerts, 1999). The olfactory ability of chiral discrimination has genetic basis (Polak et al., 1989; Saito et al., 2009) but can also be acquired through learning by human adults (Li et al., 2008), which highlights the plasticity of the adult olfactory system. The mechanism underlying such plasticity is not yet fully understood. In vision, the specificity of perceptual learning has long provided important clues to the locus of cortical plasticity (Gilbert, 1994). For instance, learning of texture discrimination is specific for retinal input with little interocular transfer (Karni and Sagi, 1991), and is subserved by dynamic changes of activities in a subregion of the primary visual cortex (V1) corresponding to the trained visual field quadrant (Yotsumoto et al., 2008). In olfaction, an ancient sense that resides in the allocortex, the specificity of perceptual learning has not been systematically characterized. An earlier study tested individuals who could not smell androstenone and found that repeatedly exposing one nostril of these individuals to androstenone improved the androstenone-detection accuracy in both the exposed nostril and the unexposed nostril to the same extent (Mainland et al., 2002). The consensus seems to be that olfactory learning takes place at the level of cortical synthetic processing that shares information from both nostrils, rather than initial analytical processing of chemical features (Mainland et al., 2002; Wilson and Stevenson, 2003).

Unlike the detection of androstenone, which could involve perceptual criterion (Bremner et al., 2003), the discrimination of odor enantiomers ultimately draws upon the enantioselectivity of olfactory receptors. The specificity/generalization of chiral discrimination learning thus offers a unique window into where the plasticity underlying olfactory learning firstly occurs. In two experiments, we trained participants unirhinally for the discrimination between odor enantiomers (procedure illustrated in Figure 1a) and assessed the extent to which the learning effect transferred to the untrained nostril or an untrained enantiomer pair that is structurally distinct from (Experiments 1 and 2) or similar to (Experiment 2) the enantiomer pair used for training.

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Participants' chiral discrimination accuracies at pretest and posttest in Experiment 1.

https://doi.org/10.7554/eLife.41296.005

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The olfactory stimuli in Experiment 1 consisted of two structurally distinct pairs of enantiomers, the enantiomers of α-pinene and those of 2-butanol (Figure 1b). Each served as the training pair for half of the participants and the control pair for the other half. Chiral discrimination was assessed with a triangular unirhinal odor discrimination task. In each trial of the task, participants sampled three bottles (two containing one odorant, the other containing its chiral counterpart), one at a time, by sniffing with one nostril (the other nostril was pinched firmly shut), and then selected the odd one out. At baseline (pretest), discrimination accuracies were at chance (overall accuracy = 0.37 vs. chance = 0.33; t₁₁ = 1.77, p = 0.10; binomial test, p > 0.3) irrespective of the enantiomer pair presented (α-pinene vs. 2-butanol, t₁₁ = 1.07, p = 0.31) or the nostril of presentation (left vs. right, t₁₁ = −0.64, p = 0.54). Training began the day after pretest and comprised sessions spaced 1 day apart (Figure 1a), where participants completed 12 trials of the unirhinal odor discrimination task and received immediate feedback about the correct choice after each trial. Such reinforcement signals have been shown to guide plasticity and induce perceptual learning even in the absence of awareness of the stimulus presentation (Roelfsema et al., 2010). Only the training pair of enantiomers was presented, always to the same nostril (either the left or the right nostril) for a given participant. Training concluded when participants reached a criterion of 9 correct choices out of 12 trials (75%) on two consecutive sessions. A posttest was conducted an hour later, which, like the pretest, involved both the training and the control enantiomer pairs presented to each nostril, without feedback.

Training lasted 12.1 days on average, during which period the participants’ discrimination accuracies showed a continuous improvement from chance level (Figure 1c). It is worth noting that the increments in performance were long-term and referred to learning retained from one daily session to the next. Critically, this improvement did not transfer to the untrained nostril nor to the structurally distinct control enantiomer pair. A direct comparison of the participants’ performances at pretest and posttest (Figure 1d) showed that training doubled the chiral discrimination accuracy for the training pair of enantiomers presented to the trained nostril (from 0.36 to 0.70, t₁₁ = 6.37, p < 0.0001, Cohen’s d = 1.84), but failed to affect the discrimination accuracy for the training pair presented to the untrained nostril (t₁₁ = 0.52, p = 0.61), or the control pair presented to the trained (t₁₁ = 0.60, p = 0.56) or untrained nostril (t₁₁ = 0.84, p = 0.42). Meanwhile, several participants noted at posttest that the trained enantiomers smelled much weaker when presented to the trained as opposed to the untrained nostril.

These results presented a sharp contrast to the internarial transfer of learning of androstenone detection (Mainland et al., 2002), and pointed to plastic changes at a level in the olfactory system where uninarial information is still retained. Considering that olfactory perception begins with systematic mappings of chemical structural features (Khan et al., 2008) yet relies heavily on memory (Wilson and Stevenson, 2003), a natural question that followed was whether such plasticity reflected enhanced differentiation of chirality (molecular configuration) or enhanced differentiation of overall odor quality (odor object) per se (Rabin, 1988) with respect to the trained enantiomers. In the former but not the latter case, we reasoned that learning would generalize, to a certain extent, to an enantiomer pair that is perceptually different from but structurally similar to the trained pair in terms of the substituents attached to the chiral center.

To tease apart the above alternatives, we adopted two structurally similar pairs of enantiomers in Experiment 2, the enantiomers of carvone and those of limonene (Figure 2a). They share an isopropenyl group at the chiral center and a methyl group at the para-position, that is, at the opposite side of the ring structure relative to the chiral carbon atom bearing the isopropenyl group, and differ only by a carbonyl group. We also included the enantiomers of α-pinene (Figure 1b) as a structurally dissimilar control. Carvone, limonene and α-pinene are all found naturally in plants yet smell quite different from one another. Since many volunteers readily distinguished the (+) and (-) forms of carvone and limonene (Laska and Teubner, 1999), we screened participants for the inability to do so and were able to recruit 12 non-discriminators (out of 46 tested, 6 females). As a group, they showed chance-level discrimination accuracies at baseline (pretest, overall accuracy = 0.37 vs. chance = 0.33; t₁₁ = 1.72, p = 0.11; binomial test, p > 0.3) regardless of the enantiomer pair presented (carvone vs. limonene vs. α-pinene, F_{2, 22} = 1.34, p = 0.28) or the nostril of presentation (left vs. right, F_{1, 11} = 0.37, p = 0.55). Half of them were subsequently trained unirhinally to discriminate the enantiomers of carvone, and the other half the enantiomers of limonene, following identical procedures as in Experiment 1.

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Participants' chiral discrimination accuracies and intensity ratings at pretest and posttest in Experiment 2.

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Training lasted for 9.1 days on average and yielded a similar learning curve to that in Experiment 1 (Figure 2b). The improvement in chiral discrimination was again largely confined to the trained nostril. At posttest relative to pretest, the participants showed a substantially enhanced ability to discriminate the training pair of enantiomers presented to the trained (from 0.37 to 0.72, t₁₁ = 10.65, p < 0.0001, Cohen’s d = 3.08), but not the untrained nostril (t₁₁ = 2.11, p = 0.059), with a significant difference between the two nostrils (t₁₁ = 6.05, p < 0.0001, Cohen’s d = 1.75, Figure 2c). In the meantime, they also experienced a reduction in perceived intensity when these enantiomers were presented to the trained (t₁₁ = −4.69, p = 0.001, Cohen’s d = −1.35), but not the untrained (t₁₁ = 0, p > 0.9), nostril, consistent with the participants’ reports in Experiment 1 (Figure 2d).

Importantly, within the trained nostril, a structure-based generalization was evident (Figure 2c): Individuals trained to discriminate the enantiomers of carvone also demonstrated improved discrimination for the enantiomers of limonene (Wilcoxon signed rank test, p = 0.026), but not for the enantiomers of α-pinene (p = 0.32), and vice versa (carvone: p = 0.039; α-pinene: p = 0.60). Overall, discrimination accuracies for the untrained but structurally similar enantiomer pair increased by 25.9% (t₁₁ = 7.00, p < 0.0001, Cohen’s d = 2.02) –– to a lesser extent as compared with the trained pair (35.2%, t₁₁ = −2.59, p = 0.025, Cohen’s d = −0.75) –– when it was presented to the trained nostril, and stayed unchanged when it was presented to the untrained nostril (t₁₁ = 1.17, p = 0.27). Those for the structurally dissimilar enantiomer pair (i.e. the enantiomers of α-pinene) remained unaltered regardless of the nostril of presentation (trained: t₁₁ = 0.58, p = 0.57; untrained: t₁₁ = 0.90, p = 0.39). There was no significant change in perceived intensity for the untrained enantiomers, structurally similar or dissimilar, from pretest to posttest irrespective of which nostril they were presented to (ps > 0.1, Figure 2d). In other words, the acquisition of chiral discrimination was not necessarily accompanied by a change in the perceived saliency (intensity) of the enantiomers’ smells. It was also unlikely that overall perceptual quality contributed to the observed generalization, as the participants rated the racemic mixtures of carvone, limonene and α-pinene as smelling distinctively different from one another (mean similarity rating <20 for any two of the three compounds, on a 100-unit visual analogue scale with 100 denoting extremely similar) and distinguished them with ceiling-level accuracy (>0.9 for any two of them vs. chance = 0.33, triangular binarial odor discrimination test). Taken together, these results suggested that the plasticity underpinning the acquisition of chiral discrimination originates in early olfactory regions that analyze the structural features (i.e. chirality) of uninarial olfactory input. We note that they did not fully exclude the possibility that the participants performed the chiral discriminations based on a learned sub-quality of the odors.

Chirality represents a simple chemical feature of odorants. The specificity to nostril of training and to the structure (rather than overall quality) of the trained compound in the learning of chiral discrimination points to the involvement of early stages in olfactory processing, where uninarial information is retained and structural features are extracted and mapped. In the olfactory system, inputs from the two nostrils remain largely separated up to the primary olfactory cortex including the piriform cortex, the largest recipient of bulbar projections that is heavily implicated in odor object perception (Carmichael et al., 1994; de Olmos et al., 1978; Gottfried, 2010). But in contrast to the chemotopy seen in the olfactory bulb (Khan et al., 2008; but see Soucy et al., 2009), neurons in the piriform cortex exhibit no correspondence between chemical receptive field and position, show minimal cross-habituation between odorants differing by as few as two carbons, and have been proposed to code synthetic odorant identity and odor quality (Kadohisa and Wilson, 2006; Stettler and Axel, 2009; Wilson, 2000). Other primary olfactory regions have not been shown to be heavily engaged in odor discrimination. In addition, axonal projections from the olfactory bulb to the anterior olfactory nucleus pars principalis, the amygdala and the olfactory tubercle exhibit only a crude set of topographical relationships (Friedrich, 2011; Giessel and Datta, 2014; Miyamichi et al., 2011). We therefore infer that an initial locus for plasticity underlying the learning of chiral discrimination likely resides in or upstream of the olfactory bulb. Experience-dependent modulations of olfactory receptor expressions in the epithelium (Tan et al., 2015), odor-induced oscillatory responses in the bulb (Kay et al., 2009) and/or tuning of the receptive fields of mitral-tufted cells (Fletcher and Wilson, 2003; Kato et al., 2012) could in turn be utilized by downstream regions like the posterior piriform to facilitate perceptual discrimination between odor enantiomers (Li et al., 2008). In mice, adult neurogenesis is found to reinforce functional inhibition in the olfactory bulb and critically mediate olfactory perceptual learning (Moreno et al., 2009), but it is highly controversial whether adult neurogenesis exists in human brains (Bergmann et al., 2012; Curtis et al., 2007; Sorrells et al., 2018). The anterior olfactory nucleus pars externa precisely links mirror-symmetric isofunctional mitral-tufted cells between the olfactory bulbs (Grobman et al., 2018; Yan et al., 2008), which in theory could provide bilateral access to unilaterally stored olfactory associations (Kucharski and Hall, 1987). However, as only a subset of mitral-tufted cells are interconnected and the connections are weak (Grobman et al., 2018), the activities of the mirror-symmetric mitral-tufted cells, if any, are likely insufficient to enable discrimination between the trained enantiomers presented to the untrained nostril, resulting in the observed nostril-specific gain in chiral discrimination. The exact neural substrates subserving human olfactory learning await further investigation.

Different theories of perceptual learning have been developed mainly based on studies of visual perceptual learning. They assume that practice improves discrimination by enhancing the signal (Gold et al., 1999; Seitz and Dinse, 2007), filtering/reducing external and internal noise (Dosher and Lu, 1998) or inducing a top-down cascade of weight retuning (Ahissar and Hochstein, 2004). As perceptual training involves exposure to stimuli and consequently adaptation, the impact of adaptation on task performance has been hard to discern (Ahissar and Hochstein, 2004; but see Harris et al., 2012). Participants in the current study consistently showed nostril-specific adaptation to the enantiomers used for training as well as nostril-specific learning of chiral discrimination. In other words, training-induced perceptual gain was not accompanied by an overall enhancement of the perceptual salience (intensity) of the trained odor enantiomers. Neither did it require perceptual adaptation, as learning generalized in a structure-based manner to an untrained enantiomer pair for which the participants reported no change in perceived intensity (Experiment 2). As such, our results seem more consistent with the reverse hierarchy theory (Ahissar and Hochstein, 2004), which asserts that learning is a gradual top-down-guided (e.g., feedback about the correct choice) increase in usability of first high- then lower-level task-relevant information and that this process is subserved by a cascade of top-to-bottom level modifications that enhance task-relevant information (e.g., configurations of the substituents attached to the chiral center). We note that this framework also nicely reconciles our results with the internarial transfer of learning of androstenone detection observed earlier (Mainland et al., 2002). Supposing that learning proceeds as a countercurrent along the olfactory processing hierarchy, the detection of androstenone could be achieved by heightened attention or sensitization to its olfactory quality (amplification of a weak signal which lowers detection threshold) and hence possibly engages a higher generalizing level, whereas the learning of chiral discrimination likely relies more on lower-level inputs and is thus more specific.

On a separate note, a previous study by researchers in Germany tested the olfactory discrimination ability of 20 participants for 10 pairs of enantiomers (Laska and Teubner, 1999) and found that they significantly discriminated the optical isomers of α-pinene, carvone and limonene (mean accuracies ≈ 80% for each enantiomer pair vs. chance = 33%) despite marked individual differences in discrimination performance. In our testing, we also noticed that many individuals readily distinguished the (+) and (-) forms of carvone and limonene and had to screen participants for the inability to do so in Experiment 2. However, we did not encounter any individual who could reliably tell apart the enantiomers of α-pinene without training. It is unclear whether this interesting discrepancy was due to genetic or environmental differences between Chinese and German participants. But it indicates that structural properties of a chiral compound cannot fully predict whether the enantiomers smell differently to a human recipient.

To summarize, the current study suggests that human olfactory perception can partially be a learned trait. Learning can take place at a very early stage of olfactory processing, where the structural features of odorants are analyzed and extracted. Put differently, early analytical processing of the chemical features of unirhinal input is both plastic (experience-dependent) and behaviorally relevant in human adults. Unlike earlier beliefs (Mainland et al., 2002), one nostril does not always know what the other learns.

All data analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1 and 2.

1. 1. Ahissar M
   2. Hochstein S

   (2004) The reverse hierarchy theory of visual perceptual learning

   Trends in Cognitive Sciences 8:457–464.

   https://doi.org/10.1016/j.tics.2004.08.011

   • PubMed
   • Google Scholar
2. 1. Bergmann O
   2. Liebl J
   3. Bernard S
   4. Alkass K
   5. Yeung MS
   6. Steier P
   7. Kutschera W
   8. Johnson L
   9. Landén M
   10. Druid H
   11. Spalding KL
   12. Frisén J

   (2012) The age of olfactory bulb neurons in humans

   Neuron 74:634–639.

   https://doi.org/10.1016/j.neuron.2012.03.030

   • PubMed
   • Google Scholar
3. 1. Bremner EA
   2. Mainland JD
   3. Khan RM
   4. Sobel N

   (2003) The prevalence of androstenone anosmia

   Chemical Senses 28:423–432.

   https://doi.org/10.1093/chemse/28.5.423

   • PubMed
   • Google Scholar
4. 1. Carmichael ST
   2. Clugnet MC
   3. Price JL

   (1994) Central olfactory connections in the macaque monkey

   The Journal of Comparative Neurology 346:403–434.

   https://doi.org/10.1002/cne.903460306

   • PubMed
   • Google Scholar
5. 1. Curtis MA
   2. Kam M
   3. Nannmark U
   4. Anderson MF
   5. Axell MZ
   6. Wikkelso C
   7. Holtås S
   8. van Roon-Mom WM
   9. Björk-Eriksson T
   10. Nordborg C
   11. Frisén J
   12. Dragunow M
   13. Faull RL
   14. Eriksson PS

   (2007) Human neuroblasts migrate to the olfactory bulb via a lateral ventricular extension

   Science 315:1243–1249.

   https://doi.org/10.1126/science.1136281

   • PubMed
   • Google Scholar
6. 1. de Olmos J
   2. Hardy H
   3. Heimer L

   (1978) The afferent connections of the main and the accessory olfactory bulb formations in the rat: an experimental HRP-study

   The Journal of Comparative Neurology 181:213–244.

   https://doi.org/10.1002/cne.901810202

   • PubMed
   • Google Scholar
7. 1. Dosher BA
   2. Lu ZL

   (1998) Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

   PNAS 95:13988–13993.

   https://doi.org/10.1073/pnas.95.23.13988

   • PubMed
   • Google Scholar
8. 1. Fletcher ML
   2. Wilson DA

   (2003) Olfactory bulb mitral-tufted cell plasticity: odorant-specific tuning reflects previous odorant exposure

   The Journal of Neuroscience 23:6946–6955.

   https://doi.org/10.1523/JNEUROSCI.23-17-06946.2003

   • PubMed
   • Google Scholar
9. 1. Friedman L
   2. Miller JG

   (1971) Odor incongruity and chirality

   Science 172:1044–1046.

   https://doi.org/10.1126/science.172.3987.1044

   • PubMed
   • Google Scholar
10. 1. Friedrich RW

    (2011) Olfactory neuroscience: beyond the bulb

    Current Biology 21:R438–R440.

    https://doi.org/10.1016/j.cub.2011.04.036

    • PubMed
    • Google Scholar
11. 1. Giessel AJ
    2. Datta SR

    (2014) Olfactory maps, circuits and computations

    Current Opinion in Neurobiology 24:120–132.

    https://doi.org/10.1016/j.conb.2013.09.010

    • PubMed
    • Google Scholar
12. 1. Gilbert CD

    (1994) Early perceptual learning

    PNAS 91:1195–1197.

    https://doi.org/10.1073/pnas.91.4.1195

    • PubMed
    • Google Scholar
13. 1. Gold J
    2. Bennett PJ
    3. Sekuler AB

    (1999) Signal but not noise changes with perceptual learning

    Nature 402:176–178.

    https://doi.org/10.1038/46027

    • Google Scholar
14. 1. Gottfried JA

    (2010) Central mechanisms of odour object perception

    Nature Reviews Neuroscience 11:628–641.

    https://doi.org/10.1038/nrn2883

    • PubMed
    • Google Scholar
15. 1. Grobman M
    2. Dalal T
    3. Lavian H
    4. Shmuel R
    5. Belelovsky K
    6. Xu F
    7. Korngreen A
    8. Haddad R

    (2018) A Mirror-Symmetric Excitatory Link Coordinates Odor Maps across Olfactory Bulbs and Enables Odor Perceptual Unity

    Neuron 99:800–813.

    https://doi.org/10.1016/j.neuron.2018.07.012

    • PubMed
    • Google Scholar
16. 1. Harris H
    2. Gliksberg M
    3. Sagi D

    (2012) Generalized perceptual learning in the absence of sensory adaptation

    Current Biology 22:1813–1817.

    https://doi.org/10.1016/j.cub.2012.07.059

    • PubMed
    • Google Scholar
17. 1. Kadohisa M
    2. Wilson DA

    (2006) Separate encoding of identity and similarity of complex familiar odors in piriform cortex

    PNAS 103:15206–15211.

    https://doi.org/10.1073/pnas.0604313103

    • PubMed
    • Google Scholar
18. 1. Karni A
    2. Sagi D

    (1991) Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity

    PNAS 88:4966–4970.

    https://doi.org/10.1073/pnas.88.11.4966

    • PubMed
    • Google Scholar
19. 1. Kato HK
    2. Chu MW
    3. Isaacson JS
    4. Komiyama T

    (2012) Dynamic sensory representations in the olfactory bulb: modulation by wakefulness and experience

    Neuron 76:962–975.

    https://doi.org/10.1016/j.neuron.2012.09.037

    • PubMed
    • Google Scholar
20. 1. Kay LM
    2. Beshel J
    3. Brea J
    4. Martin C
    5. Rojas-Líbano D
    6. Kopell N

    (2009) Olfactory oscillations: the what, how and what for

    Trends in Neurosciences 32:207–214.

    https://doi.org/10.1016/j.tins.2008.11.008

    • PubMed
    • Google Scholar
21. 1. Khan AG
    2. Thattai M
    3. Bhalla US

    (2008) Odor representations in the rat olfactory bulb change smoothly with morphing stimuli

    Neuron 57:571–585.

    https://doi.org/10.1016/j.neuron.2008.01.008

    • PubMed
    • Google Scholar
22. 1. Kucharski D
    2. Hall WG

    (1987) New routes to early memories

    Science 238:786–788.

    https://doi.org/10.1126/science.3672125

    • PubMed
    • Google Scholar
23. 1. Laska M
    2. Teubner P

    (1999) Olfactory discrimination ability of human subjects for ten pairs of enantiomers

    Chemical Senses 24:161–170.

    https://doi.org/10.1093/chemse/24.2.161

    • PubMed
    • Google Scholar
24. 1. Li W
    2. Howard JD
    3. Parrish TB
    4. Gottfried JA

    (2008) Aversive learning enhances perceptual and cortical discrimination of indiscriminable odor cues

    Science 319:1842–1845.

    https://doi.org/10.1126/science.1152837

    • PubMed
    • Google Scholar
25. 1. Mainland JD
    2. Bremner EA
    3. Young N
    4. Johnson BN
    5. Khan RM
    6. Bensafi M
    7. Sobel N

    (2002) Olfactory plasticity: one nostril knows what the other learns

    Nature 419:802.

    https://doi.org/10.1038/419802a

    • PubMed
    • Google Scholar
26. 1. Miyamichi K
    2. Amat F
    3. Moussavi F
    4. Wang C
    5. Wickersham I
    6. Wall NR
    7. Taniguchi H
    8. Tasic B
    9. Huang ZJ
    10. He Z
    11. Callaway EM
    12. Horowitz MA
    13. Luo L

    (2011) Cortical representations of olfactory input by trans-synaptic tracing

    Nature 472:191–196.

    https://doi.org/10.1038/nature09714

    • PubMed
    • Google Scholar
27. 1. Mombaerts P

    (1999) Seven-transmembrane proteins as odorant and chemosensory receptors

    Science 286:707–711.

    https://doi.org/10.1126/science.286.5440.707

    • PubMed
    • Google Scholar
28. 1. Moreno MM
    2. Linster C
    3. Escanilla O
    4. Sacquet J
    5. Didier A
    6. Mandairon N

    (2009) Olfactory perceptual learning requires adult neurogenesis

    PNAS 106:17980–17985.

    https://doi.org/10.1073/pnas.0907063106

    • PubMed
    • Google Scholar
29. 1. Polak EH
    2. Fombon AM
    3. Tilquin C
    4. Punter PH

    (1989) Sensory evidence for olfactory receptors with opposite chiral selectivity

    Behavioural Brain Research 31:199–206.

    https://doi.org/10.1016/0166-4328(89)90002-8

    • PubMed
    • Google Scholar
30. 1. Rabin MD

    (1988) Experience facilitates olfactory quality discrimination

    Perception & Psychophysics 44:532–540.

    https://doi.org/10.3758/BF03207487

    • PubMed
    • Google Scholar
31. 1. Roelfsema PR
    2. van Ooyen A
    3. Watanabe T

    (2010) Perceptual learning rules based on reinforcers and attention

    Trends in Cognitive Sciences 14:64–71.

    https://doi.org/10.1016/j.tics.2009.11.005

    • PubMed
    • Google Scholar
32. 1. Russell GF
    2. Hills JI

    (1971) Odor differences between enantiomeric isomers

    Science 172:1043–1044.

    https://doi.org/10.1126/science.172.3987.1043

    • PubMed
    • Google Scholar
33. 1. Saito H
    2. Chi Q
    3. Zhuang H
    4. Matsunami H
    5. Mainland JD

    (2009) Odor coding by a Mammalian receptor repertoire

    Science Signaling 2:ra9.

    https://doi.org/10.1126/scisignal.2000016

    • PubMed
    • Google Scholar
34. 1. Seitz AR
    2. Dinse HR

    (2007) A common framework for perceptual learning

    Current Opinion in Neurobiology 17:148–153.

    https://doi.org/10.1016/j.conb.2007.02.004

    • PubMed
    • Google Scholar
35. 1. Sorrells SF
    2. Paredes MF
    3. Cebrian-Silla A
    4. Sandoval K
    5. Qi D
    6. Kelley KW
    7. James D
    8. Mayer S
    9. Chang J
    10. Auguste KI
    11. Chang EF
    12. Gutierrez AJ
    13. Kriegstein AR
    14. Mathern GW
    15. Oldham MC
    16. Huang EJ
    17. Garcia-Verdugo JM
    18. Yang Z
    19. Alvarez-Buylla A

    (2018) Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults

    Nature 555:377–381.

    https://doi.org/10.1038/nature25975

    • PubMed
    • Google Scholar
36. 1. Soucy ER
    2. Albeanu DF
    3. Fantana AL
    4. Murthy VN
    5. Meister M

    (2009) Precision and diversity in an odor map on the olfactory bulb

    Nature Neuroscience 12:210–220.

    https://doi.org/10.1038/nn.2262

    • PubMed
    • Google Scholar
37. 1. Stettler DD
    2. Axel R

    (2009) Representations of odor in the piriform cortex

    Neuron 63:854–864.

    https://doi.org/10.1016/j.neuron.2009.09.005

    • PubMed
    • Google Scholar
38. 1. Tan L
    2. Li Q
    3. Xie XS

    (2015) Olfactory sensory neurons transiently express multiple olfactory receptors during development

    Molecular Systems Biology 11:844.

    https://doi.org/10.15252/msb.20156639

    • PubMed
    • Google Scholar
39. 1. Wilson DA

    (2000) Comparison of odor receptive field plasticity in the rat olfactory bulb and anterior piriform cortex

    Journal of Neurophysiology 84:3036–3042.

    https://doi.org/10.1152/jn.2000.84.6.3036

    • PubMed
    • Google Scholar
40. 1. Wilson DA
    2. Stevenson RJ

    (2003) The fundamental role of memory in olfactory perception

    Trends in Neurosciences 26:243–247.

    https://doi.org/10.1016/S0166-2236(03)00076-6

    • PubMed
    • Google Scholar
41. 1. Wysocki CJ
    2. Cowart BJ
    3. Radil T

    (2003) Nasal trigeminal chemosensitivity across the adult life span

    Perception & Psychophysics 65:115–122.

    https://doi.org/10.3758/BF03194788

    • PubMed
    • Google Scholar
42. 1. Yan Z
    2. Tan J
    3. Qin C
    4. Lu Y
    5. Ding C
    6. Luo M

    (2008) Precise circuitry links bilaterally symmetric olfactory maps

    Neuron 58:613–624.

    https://doi.org/10.1016/j.neuron.2008.03.012

    • PubMed
    • Google Scholar
43. 1. Yotsumoto Y
    2. Watanabe T
    3. Sasaki Y

    (2008) Different dynamics of performance and brain activation in the time course of perceptual learning

    Neuron 57:827–833.

    https://doi.org/10.1016/j.neuron.2008.02.034

    • PubMed
    • Google Scholar

Author details

1. Guo Feng

   1. State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
   3. Psychological Research and Counseling Center, Southwest Jiaotong University, Chengdu, China

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Writing—original draft

   Competing interests

   No competing interests declared

2. Wen Zhou

   1. State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   zhouw@psych.ac.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6730-2116

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