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Nostril-specific and structure-based olfactory learning of chiral discrimination in human adults

  1. Guo Feng
  2. Wen Zhou  Is a corresponding author
  1. Institute of Psychology, Chinese Academy of Sciences, China
  2. University of Chinese Academy of Sciences, China
  3. Southwest Jiaotong University, China
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Cite this article as: eLife 2019;8:e41296 doi: 10.7554/eLife.41296

Abstract

Practice makes perfect. In human olfaction, such plasticity is generally assumed to occur at the level of cortical synthetic processing that shares information from both nostrils. Here we present findings that challenge this view. In two experiments, we trained human adults unirhinally for the discrimination between odor enantiomers over a course of about 10 to 11 days. Results showed that training-induced perceptual gain was restricted to the trained nostril yet partially generalized to untrained odor enantiomers in a structure- rather than quality- based manner. In other words, learning enhanced the differentiation of chirality (molecular configuration) as opposed to overall odor quality (odor object) per se. These findings argue that, unlike earlier beliefs, one nostril does not readily know what the other learns. Moreover, the initial analytical processing of the structural features of uninarial olfactory input remains plastic in human adults.

https://doi.org/10.7554/eLife.41296.001

eLife digest

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.

https://doi.org/10.7554/eLife.41296.002

Introduction

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.

Figure 1 with 1 supplement see all
Nostril-specific olfactory learning of chiral discrimination.

(a) Schematic illustration of the experimental procedure, which comprised of three phases: pretest, training and posttest. During the training phase, participants were trained unirhinally for chiral discrimination with a pair of odor enantiomers. (b) Chemical structures of the enantiomers of α-pinene and 2-butanol. Each enantiomer pair served as the training pair for half of the participants in Experiment 1 and the control pair for the other half. (c) Improvements in chiral discrimination over the course of training (green curve). Data points were linearly interpolated and averaged across participants. Shaded area represents SEMs. Dots mark the mean discrimination accuracies at pretest and posttest for the training pair of enantiomers presented to the trained nostril. (d) Chiral discrimination accuracies at pretest and posttest for the training pair and the control pair of enantiomers presented to the trained vs. untrained nostril. Dashed lines: chance level (0.33); error bars: SEMs; ***p < 0.001.

https://doi.org/10.7554/eLife.41296.003

Results

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, F2, 22 = 1.34, p = 0.28) or the nostril of presentation (left vs. right, F1, 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.

Figure 2 with 1 supplement see all
Structure-based generalization of chiral discrimination learning.

(a) Chemical structures of the enantiomers of carvone and limonene, differing only by a carbonyl group. Each enantiomer pair was used for training for half of the participants in Experiment 2. (b) Improvements in chiral discrimination over the course of training (green curve). Data points were linearly interpolated and averaged across participants. Shaded area represents SEMs. Dots mark the mean discrimination accuracies at pretest and posttest for the training pair of enantiomers presented to the trained nostril. (c) Chiral discrimination accuracies at pretest and posttest for the training pair (carvone or limonene) and the non-training pairs of enantiomers, one structurally similar to the training pair (limonene or carvone) and one structurally distinct (α-pinene), presented to the trained vs. untrained nostril. (d) Intensity ratings at pretest and posttest for the three pairs of enantiomers (ratings averaged between the two enantiomers in each pair) presented to the trained vs. untrained nostril. Dashed lines: chance level (0.33); error bars: SEMs; *p < 0.05; ***p < 0.001.

https://doi.org/10.7554/eLife.41296.006

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.

Discussion

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.

Materials and methods

Participants

A total of 24 healthy non-smokers completed the training for chiral discrimination, which lasted for 10 to 11 days on average: 12 (6 males, mean age ±SD = 24.8 ± 2.1 years) in Experiment 1 and another 12 (6 males, 21.3 ± 2.0 years; screened from 46 volunteers) in Experiment 2. The sample size was motivated by those used in previous studies (Karni and Sagi, 1991; Mainland et al., 2002). All participants reported to have a normal sense of smell and no respiratory allergy or upper respiratory infection at the time of testing. Written informed consent and consent to publish was obtained from participants in accordance with ethical standards of the Declaration of Helsinki (1964). The study was approved by the Institutional Review Board at Institute of Psychology, Chinese Academy of Sciences.

Olfactory stimuli

The olfactory stimuli were presented in identical 280 ml narrow-mouthed glass bottles. They consisted of the enantiomers of α-pinene (1% v/v in propylene glycol) and 2-butanol (1% v/v in propylene glycol) in Experiment 1 (Figure 1b), and the enantiomers of carvone (0.5% v/v in propylene glycol), limonene (0.5% v/v in propylene glycol) and α-pinene (1% v/v in propylene glycol), as well as their racemic mixtures (0.5%, 0.5% and 1% v/v in propylene glycol, respectively), in Experiment 2 (Figure 2a). Each bottle contained 10 ml clear liquid and was connected with either one (for those containing an odor enantiomer) or two (for those containing a racemic mixture) Teflon nosepieces. Participants were instructed to sample the olfactory stimuli by inhaling through the nosepieces and exhaling through the mouth.

At the concentrations used, the odor enantiomers were clearly detectable yet did not elicit a significant trigeminal response, as assessed in an independent panel of 20 participants (10 males, 22.6 ± 1.7 years), who failed to localize the side of uninarial stimulation for all of them in a lateralization test (Wysocki et al., 2003) (32 trials per participant, mean accuracy for each enantiomer <0.52 vs. chance = 0.5, ps >0.7).

Procedure

Experiment 1 comprised three phases: pretest, training and posttest (Figure 1a). At pretest (day 0) and posttest (day N), the participants performed 36 trials of a triangular unirhinal odor discrimination task. In each trial of the task, they were blindfolded and were instructed to close either the left or the right nostril with the index finger. The open nostril was subsequently presented with three bottles, two containing one odorant, the other containing its chiral counterpart, one at a time in random order, and the participants were asked to select the odd one out. Specifically, the nosepieces connected with the bottles were positioned into the open nostril by the experimenter, one at a time, without the participants holding or touching the bottles. There were 9 trials per enantiomer pair per nostril, with a break of at least 30 s in between two trials. The order of the enantiomer pairs was counterbalanced across participants (i.e., half of the participants first performed the task with the enantiomers of α-pinene, followed by the enantiomers of 2-butanol; the other half did the reverse). For each participant, the nostril of presentation (open nostril) was alternated in consecutive trials. No feedback was provided.

Training began the day after pretest (day 1) and consisted of sessions spaced 1 day apart (day 1 to day N). In each session, the participants, blindfolded, completed 12 trials of the triangular unirhinal chiral discrimination task and received immediate feedback about the correct choice. Half of the participants were randomly assigned to be trained with the enantiomers of α-pinene and the other half the enantiomers of 2-butanol. The training pair of enantiomers was always presented to the left nostril for 5 of the participants (randomly assigned, 2 trained with the enantiomers of α-pinene) and to the right nostril for the other 7 (4 trained with the enantiomers of α-pinene). We limited the number of trials in each session to reduce olfactory fatigue, as participants in our pilot testing reported that they could only smell a faint odor after about 12 trials, which were at least 36 repetitive exposures to the (+) and (-) forms of the same chiral compound presented in the same nostril. Training concluded when the participants reached a criterion of 9/12 correct choices on two consecutive sessions (day N-1 and day N). The posttest was conducted an hour later (day N). The same bottles used for the trained nostril were also used for the untrained nostril. During the course of the experiment, the odor solution in each bottle was replaced with a freshly prepared solution of the same compound every two days.

Experiment 2 followed similar procedures to those described above, except for the followings: Participants were screened for the inability to distinguish the (+) and (-) forms of carvone and limonene (accuracy <0.56 for each enantiomer pair vs. chance = 0.33). At pretest (day 0) and posttest (day N), each participant completed 54 trials of the triangular unirhinal odor discrimination task (9 trials per enantiomer pair per nostril, 3 pairs of enantiomers). Prior to the odor discrimination task, they rated the intensity of each odor enantiomer, presented monorhinally, on a 7-point Likert scale, with 7 signifying very strong. In the training phase (day 1 to day N), half of the participants were trained with the enantiomers of carvone and the other half the enantiomers of limonene. The training pair of enantiomers was always presented to the left nostril for half of the participants (3 trained with the enantiomers of carvone) and to the right nostril for the other half (3 trained with the enantiomers of carvone). After the posttest, 10 of the participants sampled the racemic mixtures of carvone, limonene and α-pinene, presented birhinally, and provided ratings for the perceived similarities between every two of them on a 100-unit visual analogue scale, with 100 representing extremely similar. They also completed a triangular odor discrimination task using these three racemic mixtures. In each trial of the task, they were blindfolded and were presented birhinally with three bottles, two containing one racemic mixture, the other containing a different racemic mixture, one at a time in random order, and were asked to select the odd one out. There were 12 trials, 4 trials for every pairwise combination, with a break of at least 30 s in between two trials. No feedback was provided.

Analysis

For each of Experiments 1 and 2, the participants’ chiral discrimination accuracies at pretest were analyzed in a repeated measures ANOVA, using enantiomer pair (Experiment 1: α-pinene vs. 2-butanol; Experiment 2: carvone vs. limonene vs. α-pinene) and nostril of presentation (left vs. right) as the within-subject factors. The overall accuracies were subsequently compared against chance (1/3) in a one-sample t test and a binomial test (averaged number of correct responses in 36 or 54 trials vs. chance). We were primarily interested in whether training-induced perceptual gain would transfer to an untrained pair of odor enantiomers or to the untrained nostril. To this end, we performed a series of paired-samples t tests to compare the chiral discrimination accuracies between posttest and pretest for each pair of odor enantiomers presented to the trained nostril as well as to the untrained nostril.

In Experiment 2, as the participants showed significantly improved chiral discrimination for the untrained enantiomer pair that was structurally similar to the trained pair when it was presented to the trained nostril, we performed follow-up Wilcoxon signed rank tests (non-parametric, suitable for small sample sizes) to specifically examine whether those trained with the enantiomers of carvone (n = 6) also demonstrated improved discrimination (posttest vs. pretest) for the enantiomers of limonene and α-pinene, and whether those trained with the enantiomers of limonene (n = 6) also demonstrated improved discrimination for the enantiomers of carvone and α-pinene. In addition, we performed paired-samples t tests to compare the increments in chiral discrimination accuracy (1) between the trained enantiomer pair and the untrained but structurally similar pair presented to the trained nostril and (2) between the trained enantiomer pair presented to the trained nostril and that presented to the untrained nostril. We also compared in a series of paired-samples t tests the participants’ intensity ratings between posttest and pretest for each pair of odor enantiomers (ratings averaged between the two enantiomers in each pair) presented to the trained or the untrained nostril. All statistical tests other than the binomial tests were two-tailed.

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Decision letter

  1. Naoshige Uchida
    Reviewing Editor; Harvard University, United States
  2. Andrew J King
    Senior Editor; University of Oxford, United Kingdom

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Nostril-specific and structure-based olfactory learning of chiral discrimination in human adults" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Andrew King as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Rafi Haddad (Reviewer #1); Joel Mainland (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This study addressed the question of whether perceptual learning occurs at a peripheral level or in the central nervous system in an olfactory discrimination task. The novelty of this study is twofold: (1) the authors demonstrate the nostril specificity of odor discrimination ability, and (2) the authors provide insights into the specific aspects of stimuli that the subjects learn. That is, perceptual leaning generalizes to other odorant pairs that share common structural configuration. Together, the data support the idea that a certain type of learning to discriminate two odors can happen in the periphery.

Essential revisions:

Although the findings are interesting, both reviewers raised the concern that the authors have made misleading interpretations of their findings. Below the two reviewers each point out this problem in a slightly different ways. We would like the authors to revise their manuscript to more carefully draw their conclusions more in line with the data.

Reviewer #1:

General assessment:

The results presented in this manuscript are interesting and important. The experiments are straight forward (but see list of issues below). Overall I like the study. My main reservation is related to positioning and interpreting the result.

Main:

The authors position the results as if it contrast previous studies in the way odor information is shared across nostrils: "contrary to earlier beliefs, one nostril does not readily know what the other learns". However, the Mainland study is based on a detection task and the current study is a discrimination task. This alone can explain the different results.

In my opinion, the interesting part here is that odor discrimination tasks may involve plasticity that is neither transferred to, nor accessible from the untrained side. This is in line with results in other sensory systems (e.g. Karni and Sagi). How this result can be reconciled with the Mainland result should be discussed in the Discussion.

In my opinion, it is also important to discuss the results in light of what we know from rodent experiments on bilateral odor processing. The Kucharski and Hall (1987) studies showed that odor association can be unilateral but it could be accessed bilaterally. The failure to discriminate enantiomers from the untrained nostril reported in this study could be explained by assuming that discrimination is achieved by learning to pay attention to weakly responding M/T neurons that respond differently to the two enantiomers. Since only a subset of M/T cells are inter-connected and these connections are weak (Grobman et al., 2018) the weakly activated M/T cells cannot activate their mirror-symmetric M/T cells resulting in a unilateral odor plasticity. Other explanations that involve plasticity in the piriform cortex can be suggested. In any case, the manuscript should discuss their result in light of these important previous results.

Technical important issues:

1) It is now customary to present in the graphs all data points and not only the average and standard deviation (e.g., Figure 1C-D). This provides a better understanding of the data variability. Please show all data points and show the results for each of the two odor pairs.

2) The Materials and methods sections must be extended and improved as essential parts are not well explained.

a) It is not clear to me how exactly the subjects performed the task (how the bottle looked and handled, did they used new bottles/odors every day or the same bottles for the entire experiment)?

b) Was the same bottle used for the posttest with the trained nostrils also used with the untrained nostril?

c) What statistical tests were used? Sometimes it is t-test (I guess from the reported t value) and sometimes Wilcoxon signed test. Please state which test is used and provide justification for the selection of each test.

d) In this regard: Was the analysis of comparing discrimination success rates done against the expected 33% or against the mean of the pretest group? It may give different results, for example the result shown in the right panel of Figure 2C might suggest that there is a small but significant improvement in the untrained nostril (P = 0.059 which is probably < 0.05 if you compare to 33% expected chance level). While this is not a dramatic change from the paper statement it might show that some information is transferred.

3) Why do the posttest results look significantly lower than the average of the last day in both experiments (Figures 1C and 2B)?

Reviewer #2:

This manuscript describes a straightforward experiment that addresses an important issue: is learned odor discrimination achieved at the level of the olfactory mucosa or in the CNS? The described experiments convincingly demonstrate that learning to discriminate two odors can happen in the periphery.

The only major concern is that the authors have an overly broad interpretation of their findings at two points.

First, the current experiments do not definitively show that the nostril is learning molecular features rather than odor quality. Just because the racemic mixtures can be discriminated and are rated as dissimilar doesn't mean that the subjects aren't learning a sub-quality of the odors that allows them to perform the discrimination.

Second, the statement that "human olfactory perception is a learned trait" is a very broad conclusion from these results. This manuscript does not rule out the possibility that some of olfactory perception is innate.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Nostril-specific and structure-based olfactory learning of chiral discrimination in human adults" for further consideration by eLife. Your revised article has been favourably evaluated by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Andrew King as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Rafi Haddad (Reviewer #1); Joel Mainland (Reviewer #2).

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance. Specifically, both reviewers thought that you have addressed their previous concerns very well, and that the manuscript warrants publication at eLife. As you will see below, however, one of the reviewers still raised a concern that the claim about generalization based on molecular features remains inconclusive. We think that this can be addressed by revising the text. We would therefore like to invite you to resubmit a revised version of the manuscript.

Essential revisions:

The current experiments still do not definitively show that the nostril is generalizing based on molecular features rather than odor quality. To illustrate this, thegoodscentscompany.com describes (-) carvone as "sweet spearmint herbal minty" and (+)-limonene as "citrus orange fresh sweet".

An alternative explanation to the authors' molecular feature theory is that they are training subjects to discriminate sweet from non-sweet. This presumably doesn't directly relate to structure as the "sweet" odor is a (+) enantiomer for one odor and (-) enantiomer for another. Instead, it is a sub-quality that emerges from the whole set of receptors being activated. Neither enantiomer of pinene is "sweet", so the learning doesn't generalize. This is not the strongest argument, but the current experiments do not rule it out. I suggest the authors soften their language and suggest that there may be alternative explanations. The statement that the "learned sub-quality had to be a direct derivative of the enantioselectivity", for example, is incorrect.

https://doi.org/10.7554/eLife.41296.011

Author response

Reviewer #1:

Main:

The authors position the results as if it contrast previous studies in the way odor information is shared across nostrils: "contrary to earlier beliefs, one nostril does not readily know what the other learns". However, the Mainland study is based on a detection task and the current study is a discrimination task. This alone can explain the different results.

In my opinion, the interesting part here is that odor discrimination tasks may involve plasticity that is neither transferred to, nor accessible from the untrained side. This is in line with results in other sensory systems (e.g. Karni and Sagi). How this result can be reconciled with the Mainland result should be discussed in the Discussion.

The reverse hierarchy theory (Ahissar and Hochstein, 2004) nicely reconciles our results with the internarial transfer of learning of androstenone detection observed in the Mainland et al. study (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) 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. These are now incorporated in the second paragraph of the Discussion section.

In my opinion, it is also important to discuss the results in light of what we know from rodent experiments on bilateral odor processing. The Kucharski and Hall (1987) studies showed that odor association can be unilateral but it could be accessed bilaterally. The failure to discriminate enantiomers from the untrained nostril reported in this study could be explained by assuming that discrimination is achieved by learning to pay attention to weakly responding M/T neurons that respond differently to the two enantiomers. Since only a subset of M/T cells are inter-connected and these connections are weak (Grobman et al., 2018) the weakly activated M/T cells cannot activate their mirror-symmetric M/T cells resulting in a unilateral odor plasticity. Other explanations that involve plasticity in the piriform cortex can be suggested. In any case, the manuscript should discuss their result in light of these important previous results.

We thank the reviewer for directing us to the helpful references and for the explanation regarding why both bilateral access to unilaterally stored olfactory associations and unilateral odor plasticity (as observed in the current study) are possible. They are now incorporated in the first paragraph of the Discussion section.

Technical important issues:

1) It is now customary to present in the graphs all data points and not only the average and standard deviation (e.g., Figure 1C-D). This provides a better understanding of the data variability. Please show all data points and show the results for each of the two odor pairs.

We have plotted all data points for all enantiomer pairs in Figure 1—figure supplement 1 and Figure 2—figure supplement 1.

2) The Materials and methods sections must be extended and improved as essential parts are not well explained.

a) It is not clear to me how exactly the subjects performed the task (how the bottle looked and handled, did they used new bottles/odors every day or the same bottles for the entire experiment)?

The odor enantiomers were presented in identical 280ml narrow-mouthed glass bottles. Each bottle contained 10 ml clear liquid and was connected with a Teflon nosepiece. The participants were blindfolded during pretest, training and posttest and were instructed not to grasp or touch the bottles. In each trial of the triangular unirhinal odor discrimination task, the experimenter presented the bottles to the participant’s open nostril (positioning the nosepieces into the open nostril), one at a time. The odor solution in each bottle was replaced with a freshly prepared solution of the same compound every two days. These details are now clarified in the Materials and methods section of the revised manuscript.

b) Was the same bottle used for the posttest with the trained nostrils also used with the untrained nostril?

Yes. We have made it explicit in the Materials and methods subsection “Procedure”.

c) What statistical tests were used? Sometimes it is t-test (I guess from the reported t value) and sometimes Wilcoxon signed test. Please state which test is used and provide justification for the selection of each test.

We have now described the statistical analyses in detail in the Materials and methods subsection “Analysis”.

d) In this regard: Was the analysis of comparing discrimination success rates done against the expected 33% or against the mean of the pretest group? It may give different results, for example the result shown in the right panel of Figure 2C might suggest that there is a small but significant improvement in the untrained nostril (P = 0.059 which is probably < 0.05 if you compare to 33% expected chance level). While this is not a dramatic change from the paper statement it might show that some information is transferred.

The comparison was between posttest and pretest since we were primarily interested in whether training-induced perceptual gain would transfer to an untrained pair of odor enantiomers or to the untrained nostril. We have made this explicit in the Materials and methods subsection “Analysis”.

3) Why do the posttest results look significantly lower than the average of the last day in both experiments (Figures 1C and 2B)?

As can be seen from Figure 1—figure supplement 1 and Figure 2—figure supplement 1 (left panels, trained nostril), this pattern was mainly driven by a minority of the participants whose posttest discrimination accuracies for the trained pair of odor enantiomers fell below 60% (2 in Experiment 1 and 3 in Experiment 2). As the posttest was conducted an hour after the completion of the last training session, the participants could be fatigued. It was also possible that the lack of feedback at the posttest hampered their performance.

Reviewer #2:

[…] The only major concern is that the authors have an overly broad interpretation of their findings at two points.

First, the current experiments do not definitively show that the nostril is learning molecular features rather than odor quality. Just because the racemic mixtures can be discriminated and are rated as dissimilar doesn't mean that the subjects aren't learning a sub-quality of the odors that allows them to perform the discrimination.

The discrimination of odor enantiomers ultimately draws upon the enantioselectivity of olfactory receptors. If what was learned was a sub-quality that enabled the discrimination between the (+) and (-) forms of both carvone and limonene but not those of α-pinene, this sub-quality had to be a direct derivative of the enantioselectivity of the olfactory receptors shared by carvone and limonene. In this sense, the learning of chiral configurations and that of odor sub-qualities contingent on chiral configurations are flip sides of the same coin.

In the last paragraph of the Results section, we have clarified that our results do not exclude the possibility that the participants performed the chiral discriminations based on a learned sub-quality of the odors. Rather, they argue that this learned sub-quality had to be a direct derivative of the enantioselectivity of the olfactory receptors shared by carvone and limonene.

Second, the statement that "human olfactory perception is a learned trait" is a very broad conclusion from these results. This manuscript does not rule out the possibility that some of olfactory perception is innate.

We fully agree and have changed the statement to “human olfactory perception can partially be a learned trait”.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Specifically, both reviewers thought that you have addressed their previous concerns very well, and that the manuscript warrants publication at eLife. As you will see below, however, one of the reviewers still raised a concern that the claim about generalization based on molecular features remains inconclusive. We think that this can be addressed by revising the text. We would therefore like to invite you to resubmit a revised version of the manuscript.

The current experiments still do not definitively show that the nostril is generalizing based on molecular features rather than odor quality. To illustrate this, thegoodscentscompany.com describes (-) carvone as "sweet spearmint herbal minty" and (+)-limonene as "citrus orange fresh sweet".

An alternative explanation to the authors' molecular feature theory is that they are training subjects to discriminate sweet from non-sweet. This presumably doesn't directly relate to structure as the "sweet" odor is a (+) enantiomer for one odor and (-) enantiomer for another. Instead, it is a sub-quality that emerges from the whole set of receptors being activated. Neither enantiomer of pinene is "sweet", so the learning doesn't generalize. This is not the strongest argument, but the current experiments do not rule it out. I suggest the authors soften their language and suggest that there may be alternative explanations. The statement that the "learned sub-quality had to be a direct derivative of the enantioselectivity", for example, is incorrect.

We have followed the reviewer’s suggestion and removed the statement that the “learned sub-quality had to be a direct derivative of the enantioselectivity of the olfactory receptors shared by carvone and limonene”. We have also acknowledged that our data “did not fully exclude the possibility that the participants performed the chiral discriminations based on a learned sub-quality of the odors”. As an aside, we would like to note that in terms of absolute configuration, both (-) carvone and (+) limonene have a rectus configuration.

https://doi.org/10.7554/eLife.41296.012

Article and author information

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

Funding

Chinese Academy of Sciences (Key Research Program of Frontier Sciences, QYZDB-SSW-SMC055)

  • Wen Zhou

Chinese Academy of Sciences (Strategic Priority Research Program, XDB32010200)

  • Wen Zhou

National Natural Science Foundation of China (31422023)

  • Wen Zhou

National Natural Science Foundation of China (31830037)

  • Wen Zhou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Zhonghua Lu for comments and suggestions. This work was supported by the Key Research Program of Frontier Sciences (QYZDB-SSW-SMC055) and the Strategic Priority Research Program (XDB32010200) of the Chinese Academy of Sciences, the National Natural Science Foundation of China (31830037 and 31422023) and Beijing Municipal Science and Technology Commission.

Ethics

Human subjects: Written informed consent and consent to publish was obtained from participants in accordance with ethical standards of the Declaration of Helsinki (1964). The study was approved by the Institutional Review Board at Institute of Psychology, Chinese Academy of Sciences (H15004).

Senior Editor

  1. Andrew J King, University of Oxford, United Kingdom

Reviewing Editor

  1. Naoshige Uchida, Harvard University, United States

Publication history

  1. Received: August 21, 2018
  2. Accepted: December 23, 2018
  3. Version of Record published: January 17, 2019 (version 1)

Copyright

© 2019, Feng and Zhou

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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