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Achieving Olfactory Expertise: Training for Transfer in Odor Identification


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Human olfactory function requires the identification of everyday odors. A characteristic feature of olfaction is that most people find it hard to identify and name common odors, and when odors are presented simultaneously in mixtures, performance is even further compromised. Few studies have systematically assessed how training might enhance identification of single odors and mixtures. This study compared how odor identification training with either single odors or binary mixtures affected identification performance, as well as transfer effects to untrained tasks and odors. Twenty-seven healthy participants (22 F; 28.0 ± 4.7 years old) completed identification training of 8 odors using a list of 16 veridical names. The study included 8 training sessions, as well as pretest and posttest evaluations. Results suggest notable effects of learning, as well as transfer to novel tasks and odors. Overall, training with single odors led to slightly better results than the binary mixture condition, suggesting that in novices, odor identification may be facilitated via consolidation of single odor objects, before learning to dissociate binary mixtures. Overall, odor identification may be trained to generate transfer of learning, although transfer effects were observed in both training methods. Our work suggests that odor identification abilities, while often limited, are highly trainable. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]
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Chemical Senses, 2019, Vol XX, 1–7
Original Article
Advance Access publication 23 January 2019
© The Author(s) 2019. Published by Oxford University Press. All rights reserved.
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Original Article
Achieving Olfactory Expertise: Training for
Transfer in Odor Identification
Paulina Morquecho-Campos1, Maria Larsson2, Sanne Boesveldt1 and
JonasK. Olofsson2
1Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlandsand
2Gösta Ekman Laboratory, Department of Psychology, Stockholm University, Stockholm, Sweden
Correspondence to be sent to: Paulina Morquecho-Campos, Division of Human Nutrition and Health, Wageningen University
and Research, Stippeneng 4 (Building 124), 6708 WE, Wageningen, The Netherlands. e-mail:
Editorial Decision 17 January 2019.
Human olfactory function requires the identification of everyday odors. Acharacteristic feature of
olfaction is that most people find it hard to identify and name common odors, and when odors
are presented simultaneously in mixtures, performance is even further compromised. Few studies
have systematically assessed how training might enhance identification of single odors and mix-
tures. This study compared how odor identification training with either single odors or binary mix-
tures affected identification performance, as well as transfer effects to untrained tasks and odors.
Twenty-seven healthy participants (22 F; 28.0± 4.7years old) completed identification training of 8
odors using a list of 16 veridical names. The study included 8 training sessions, as well as pretest
and posttest evaluations. Results suggest notable effects of learning, as well as transfer to novel
tasks and odors. Overall, training with single odors led to slightly better results than the binary
mixture condition, suggesting that in novices, odor identification may be facilitated via consolida-
tion of single odor objects, before learning to dissociate binary mixtures. Overall, odor identifica-
tion may be trained to generate transfer of learning, although transfer effects were observed in
both training methods. Our work suggests that odor identification abilities, while often limited, are
highly trainable.
Key words: memory, mixtures, perceptual learning, smell, transfer effect
Human olfaction was for a long time viewed as underdeveloped
relative to the olfactory capabilities of other animals. However, sys-
tematic comparisons indicate that humans are in fact impressively
accurate at detecting faint odors and at differentiating their percep-
tual qualities (Laska 2017; McGann 2017). Nevertheless, an obvious
shortcoming of human olfaction compared with visual and auditory
perception is the limited ability to identify multiple stimuli presented
at the same time (see, Stevenson and Wilson 2007; Yeshurun and
Sobel 2010, for reviews). Successful identication of odors in a
mixture decreases dramatically with increasing mixture complexity.
Prior research suggests that mixtures of about 3–4 familiar odors
constitute an upper limit, regardless of level of expertise, odor com-
binations, and type of task (Laing and Francis 1989; Laing and
Glemarec 1992; Livermore and Laing 1996, 1998a, 1998b). An
odor mixture tends to be perceived as a “synthetic” stimulus rather
than analytically separate stimuli (Wilson and Stevenson 2003a,
2003b). In an in similar vein, increasingly complex odor mixtures
are perceived as more similar to each other, a phenomenon known as
“olfactory white” (Weiss etal. 2012). The inability to perceive mul-
tiple odorants simultaneously might be a consequence of the organi-
zation of the olfactory system, which includes receptor-level odor
interactions and a lack of topological mapping (Laing and Glemarec
1992; Jinks and Laing 1999; Thomas-Danguin etal. 2014). In this
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study, we focus on the ability to learn the identity of single odors and
binary mixtures, which are difcult but not impossible to identify
(e.g., Livermore and Laing 1996).
Human olfactory ability is most often assessed by multiple-
choice identication of familiar odors (e.g., by asking “Are you
smelling honey, vanilla, chocolate or cinnamon?”; Doty etal. 1984;
Hummel etal. 2007), a task that requires perceptual as well as cross-
modality learning (Wilson and Stevenson 2003b). Repeated expo-
sure to a set of ecologically co-occurring odor molecules leads to
a consolidation of a recognizable “odor object” (e.g., the smell of
honey or chocolate), which is enabled by a unique pattern of neural
activity (Wilson and Stevenson 2003a 2003b; Wilson and Sullivan
2011). However, dissociating and identifying odor components in a
mixture (honey + chocolate) are difcult, and such ability is a key
aspect of expertise (Barkat etal. 2012). Although experts such as
perfumers and oenologists perform better than novices in identifying
odor mixture components, their performance also declines dramati-
cally in mixtures of higher complexity (Livermore and Laing 1996;
Poupon etal. 2018) Furthermore, language may inuence odor per-
ception, as labels affect odor recognition (de Wijk and Cain 1994;
Willander and Larsson 2007) and modify odor hedonic evaluations
(Herz and von Clef 2001; Djordjevic etal. 2008). Olfactory experts
are characterized by extensive and specic vocabularies (Croijmans
and Majid 2016). Our aim was to address how odor identication
training leads to training gains and transfer effects (i.e., benets on
novel odors or tasks). We compared training with single odors versus
binary odor mixtures, as binary odor mixture identication capacity
is characteristic of expert sommeliers (Poupon etal. 2018).
An unresolved issue in olfactory science is how to enhance olfac-
tory performance such that the training gains are generalizable across
novel odors and tasks. Such “transfer of learning” is considered criti-
cal evidence of a learned skill (Cain etal. 1998; Burke and Hutchins
2007; Hager and Hodkinson 2009). Most reported transfer effects
in olfaction have focused on improved discrimination among odors,
rather than odor identication and naming (Rabin 1988; Bende and
Nordin 1997; Wilson et al. 2004; Chollet et al. 2005; Mandairon
etal. 2006).
We outline 2 distinct theoretical accounts that would predict dif-
ferent outcomes in our experiment. The rst, “cognitive” account
suggests that the main limitation in identifying odors is the result of
a cognitive inability to retrieve perceptual components, which makes
it especially difcult to identify odor mixtures. According to this
account, perceiving a binary mixture with 2 familiar components
elicits 2 perceptual odor objects. Although these objects are avail-
able as patterns of neural activity, they are not cognitively accessible
when activated simultaneously. Identication training with binary
mixtures would then enhance the “top-down” ability to access both
odor objects within the mixture percept. This account is motivated
by prior research on odor training, which suggests that cognitive lim-
itations affect odor identication (Cain etal. 1998), but also by the
visual cognitive training literature, where high cognitive demands
are necessary to achieve transfer across sensory materials and tasks
(Schmiedek etal. 2010). In contrast, the “perceptual” account sug-
gests that the main limitation in identifying odor mixture compo-
nents is the result of poorly consolidated single odor objects, leading
to perceptual-level confusion when presented in a mixture. The per-
ceptual account is based on the nding that everyday odors, when
presented in mixtures, often are not available, but blend together to
limit identication (e.g., Barkat etal. 2012). Thus, mixture-based
identication training might not be effective in making them acces-
sible for behavioral recognition. Instead, identication training with
single odors would help consolidate object templates and thereby
enhance identication of single odors and mixtures.
In this study, we trained odor identication with either single
odors or by presentation of binary odor mixtures. After completed
training, potential performance gains in identication of single
and mixture odors were evaluated. Also, potential transfer effects
to untrained tasks and odors were assessed. Two types of transfer
were addressed: 1)transfer to untrained odor identication; whether
training with single odors may lead to improved identication of the
components of binary mixtures, and vice versa; and 2)Transfer to
untrained odors; whether olfactory training may lead to improved
identication of an untrained odor set. Moreover, we also explored
how identication training inuenced olfactory perceptual ratings
and free naming performance.
Materials and methods
Our study was modeled after Livermore and Laing (1996), such that
we expected effect sizes of the same magnitude. This would require
a sample of 28 participants to reach a 5% type-I and 20% type-
II error risk. Atotal of 30 healthy Swedish uent speakers, aged
between 18 and 35years and with no self-reported olfactory prob-
lems, were recruited by internet announcement on a research vol-
unteer website mostly viewed by students in the Stockholm area.
Two participants dropped out after the pretesting session and one
after the 4th training session, citing personal reasons. Thus, 27 par-
ticipants completed training. Participants were randomly allocated
to 1 of 2 training groups, and odor training sets were counterbal-
anced within each group. Participant characteristics are shown in
Table 1. All participants gave their written informed consent before
testing, and they received monetary compensation via a gift certi-
cate at the end of the study. The study was carried out in accordance
with the principles of the Declaration of Helsinki (2013). The study
was performed in the facilities of Gösta Ekman Laboratory at the
Department of Psychology, Stockholm University.
Odor stimuli
Two sets of odors were used. Each set consisted of 8 odors (4 fruity
and 4 non-fruity; see Table 2). In the training sessions, each odor trial
included the identication of one odor from each category (fruity
and non-fruity) to ensure high discriminability, although these cat-
egories were not made explicit to the participants. The odors were
perceived as highly familiar, pleasant, and edible. The concentration
of each odor was adjusted after pilot testing to achieve similar per-
ceptual intensities (mean scores ranged from 4.0 to 5.0 on a 7-point
With exception of the coffee odor, the odors were not diluted
before being used. The coffee odor was made by soaking ground
coffee in mineral oil in a 50/50 mix for 24h, using the resulting oil as
stimulus. The odors were added to cotton pads, which dried for 24h
in opaque glass bottles before use, to stabilize odor intensities. After
the drying period, the cotton pads were placed in new bottles for the
Table 1. Characteristics of 27 participants enrolled in the study
Training group Number of
Age (years;
mean ± SD)
Single-odor 13 27.6± 4.7 12:1
Mixture 14 28.3± 4.8 10:4
2 Chemical Senses, 2019, Vol. XX, No. XX
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testing or training sessions. The bottles were provided with 3-digit
codes, to instruct participants about what bottles to sniff on each
trial. However, these codes were changed every session to prevent
participants from using codes as cues to odor identity. The odor-
ized cotton pads were replaced regularly to avoid contamination and
quality distortions.
The study involved a randomized allocation of participants to the
training conditions (each including 8 training sessions), and all par-
ticipants also completed pre- and posttest sessions. The participa-
tion, involving 10 sessions in total, was completed within 6 weeks.
All sessions were carried out in a well-ventilated olfactory testing
room. In each session, participants identied a set of odors, pre-
sented in a randomized order, using a computerized interface with a
display of 16 odor names. On each trial, participants were instructed
to select only 2 options, 1 per odor, of the 16 odor names.
Pre-test and post-test sessions
Pre-test and post-test sessions comprised 3 tasks with short breaks
in-between each task. Each session lasted for about 45–60min.
Free naming and perceptual evaluation of singleodors
Participants were presented with 16 single odors (Table 2). For each
odor presented, the participant sniffed the open bottle for about 2s
and asked to freely name the odor. They were instructed to provide
as specic names as possible and their responses were recorded by
writing in a computerized display. Free naming accuracy was coded
as 0 when incorrect answers or no responses were given, whereas
veridical names were scored as 1.In addition, participants rated each
odor for intensity, familiarity, meaningfulness, pleasantness, edibility,
and how condent they were regarding the veracity of their assigned
name, on a 7-point scale (1= “Not at all” to 7=“Very much”).
In the posttest session, a simple memory task was also included.
Here, participants were asked whether they had been training with
the specic odor or not. Responses were coded as being correct (1)
or incorrect (0). This procedure served as a “manipulation check”
to assess that participants could distinguish trained from untrained
odors and whether training condition affected memory performance.
The answers were saved using an online interface (www.qualtrics.
Cued identication of singleodors
In this task, participants were requested to identify single odors using
a list of 16 odor names. In total, 16 trails were evaluated by each par-
ticipant. Eight trials consisted of odors from set 1, and 8 trials con-
sisted of odors from set 2 (Table 2), arranged in a randomized order.
Each trial consisted of 2 bottles, each one containing a single odor: 1
fruity and 1 non-fruity odor. The 2 bottles were labeled with the same
3-digit code and followed by a number indicating the order of evalu-
ation (e.g., 846-1 and 846-2). On each trial, participants sniffed the
odor of the rst bottle (e.g., 846-1) and selected an odor label from
the given list. Participants repeated the procedure with the second
bottle (846-2), without snifng the rst one again. Participants were
allowed to select no more than 2 options (1 per odor) and only select
the same label once per trial. An online questionnaire with system
of grading was developed using Questbase (
Correct answers were coded as hits (1), and incorrect answers as
misses (0). Asum score was computed (score range=0–32).
Cued identication of odors in mixtures
In the mixture task, participants received a bottle containing a
mixture of 2 odors; 1 fruity and 1 non-fruity odor. The odors were
applied to separate cotton pads and inserted jointly in the bottle.
Participants were instructed to identify 2 odors per bottle from a list
of 16 veridical names using an online questionnaire. Atotal of 16 tri-
als were performed, 8 trials consisted of odors from set 1, and 8 from
set 2.Participants were allowed to select no more than 2 options
(1 per odor) and only select the same label once per trial. Correct
answers were coded as hits (1), and incorrect answers as misses (0).
Asum score was computed (score range=0–32).
Olfactory training sessions
The olfactory training consisted of 8 sessions. Participants were
randomly allocated to 1 of the 4 following training conditions:
1) single-odor task, odor set 1; 2) single-odor task, odor set 2;
3)mixture task, odor set 1; 4)mixture task, odor set 2.The odors
were presented in a randomized order and each trial was presented
only once in each training session, with a short break in the middle.
Participants were requested to smell the trial and to identify the 2
odors by selecting only 2 labels from a list of 16 odor names (see
earlier). Participants received feedback on the correct answers and
the achieved score right after each trial. To support associative learn-
ing, participants could resample the evaluated odor after receiving
feedback. All responses were coded as correct or incorrect. At the
end of each training session, the participants received feedback on
their total score (range=0–32). Each session lasted about 20min.
Single-odor tasks were performed with 2 bottles sampled sequen-
tially in each trial to balance the number of responses and opportu-
nities for feedback across training groups. The stimulus order within
each trial was randomized to minimize potential effects of order.
Statistical analyses
Data were collapsed across the 2 odor sets for the single-odor and
mixture tasks, respectively. Descriptive data are reported as means
and standard deviation (± SD) unless otherwise specied. Results
Table 2. Odors used in the study
Set Category Odor description Quantity (drops)aSourceb
1 Fruity Pear 2 AB
Blueberry 2 LD
Orange 3 AB
Banana 2 AB
Non-fruity Vanilla 2 LD
Chocolate 2 LD
Cinnamon 2 LD
Hazelnut 3 LD
2 Fruity Apple 2 AB
Strawberry 4 AB
Lemon 2 LD
Peach 2 AB
Non-fruity Coconut 2 LD
Honey 2 AB
Coffee 5 NP
Almond 2 AB
aDrops were added with a plastic transfer pipette 3.5 mL (Sarstedt,
Nümbrecht, Germany), according to the manufacturer of the pipette the drop
size is 30–45 μL.
bAB=AB Stockholms Eter and Essencefabrik; LD=Ljuvliga dofter;
NP=natural product; this natural product was made by soaking ground
coffee in mineral oil in a 50/50 mix for 24h, using the resulting oil as
stimulus. Odors bought at AB and LD were not diluted.
Chemical Senses, 2019, Vol. XX, No. XX 3
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were considered statistically signicant when P < 0.05. Normal
distribution was investigated using visual inspection of probability
plots. All statistical analyses were carried out with the statistical soft-
ware RStudio (RStudio Team 2016) and graphs were made using
GraphPad Prism 5.0 (GraphPad Prism Software).
Odor rating scores were compared by means of tting general-
ized linear mixed-effects models using lme4 package (Bates et al.
2015). Training group (single-odor vs. mixture training group), test
session (pretest vs. posttest), and its interaction were included as
xed factors. Participant and odor were included as random factors.
For the memory task, d’ scores were computed as a “discrimi-
nation index,” to determine sensitivity in recognition. The z-trans-
formed proportions of hits and false alarms were used (z(H) and z(F),
respectively) to calculate the d’ scores by means of d’=z(H) – z(F).
The proportion of hits and false alarms of 1 and 0 were adjusted by
1-1/(2N) and 1/(2N), respectively (Macmillan and Creelman 2005).
Signicant differences between d’ scores were calculated by Mann–
Whitney U test. Data from 2 participants, 1 from each training
group, were excluded due to a technicalerror.
Odor identication performance in the testing and training ses-
sions was assessed by proportion of correct answers. Comparison of
the proportion of correct answers between test sessions and across
training sessions was carried out by tting generalized linear mixed-
effects models using lme4 package (Bates etal. 2015). To evaluate
training outcomes, training group (single-odor vs. mixture training
group), test session (pretest vs. posttest), type of task (single-odor
vs. mixture identication task), and their interactions were included
as xed factors in the model. To study learning rates across training
sessions, training group (single-odor vs. mixture training group), ses-
sion (1st to 8th training session), and their interaction were included
as xed factors in the model. In both models, participant, odor, and
evaluation order were included as random factors. Post hoc testing
with Bonferroni correction was performed when the interaction was
signicantly different by means of lsmeans package (Lenth 2016).
Odor identification performance across training
For both training groups, identication performance increased
notably over the training period (χ2 (7)= 92.32, P<0.01; Figure
1). As expected, single-odor training group generated overall higher
performance than mixture training group (χ2(1)=72.35, P<0.01).
However, post hoc testing showed that identication improved only
until the third training session, and then it remained stable across
remaining sessions. There was no interaction between training group
and training sessions, indicating that the degree of improvement was
equal across the 2 groups (χ2 (7)=8.06, P=0.33).
Training effects on odor perception andnaming
When comparing pretest versus posttest scores, training was asso-
ciated with higher perceived familiarity, meaningfulness, pleasant-
ness, and edibility (all P < 0.01, Table 3a), indicating changes in
perception. Likewise, correct free naming performance increased as
a function of training (χ2(1)=107.7, P<0.01, Table 3b), indicating
transfer to an untrained olfactory task. Subjective condence in odor
naming accuracy increased as well (P < 0.01, Table 3a). Intensity
ratings, however, did not change as a function of training (P > 0.05).
Participants in both training groups reliably recognized which odors
they had trained with (Table 3c; both groups above chance-level;
d’=0). None of the earlier assessments gave statistical interactions
with training group (all P > 0.05), thus both types of training gave
similar results.
Odor identification transfer
As expected, single-odor identication yielded overall higher accu-
racy than mixture identication (χ2(1)= 64.88, P <0.01); and the
performance of both groups improved signicantly due to train-
ing (χ2(1)=52.71, P<0.01). The single-odor training group per-
formed better overall than the mixture training group (χ2(1)=5.15,
P=0.02), and of interest to our hypothesis, the 3-way interaction of
training group × test session × type of task bordered on signicance
(χ2(1)=3.33, P=0.07). We thus decided to conduct follow-up analy-
ses of how training-related improvement and transfer was achieved.
Transfer to untrained odor identicationtask
Results showed a 2-way interaction effect between training group
and test session on the single-odor identication task (χ2(1)=7.30,
P<0.01; Figure 2A). Specically, single-odor training led to a greater
pre-to-post improvement for single-odor identication than did mix-
ture training (training group =χ2(1) = 5.04, P < 0.05). In contrast,
no such interaction effects were shown in mixture identication
(χ2(1)= 0.02, P=0.90; Figure 2B). Regarding improvement in odor
Figure 1. Performance during 8 training sessions according training group; single-odor training group (light gray; ) and mixture training group (black; ).
Results are shown as proportion of correct answers; bars represent standard error.
4 Chemical Senses, 2019, Vol. XX, No. XX
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mixture identication, an overall training effect (posttest vs. pretest
session) was shown (session=χ2(1)=8.08, P<0.01), but it was sim-
ilar for the 2 groups (training group =χ2(1)= 0.72, P = 0.40). This
pattern of results suggests a unique benet from single-odor train-
ing compared with odor mixture training. Furthermore, we analyzed
whether gains on the untrained task differed between the groups; for
single-odor training, the untrained task was mixture identication and
vice versa. Results showed an overall pre-to-post improvement (ses-
sion=χ2(1) =10.37, P< 0.01), indicating transfer to the untrained
task, however, there was no interaction between training group and
test session (χ2(1)=0.64, P=0.42), suggesting the transfer effect was
similar across groups.
Transfer to untrainedodors
We analyzed how our observed training effects (posttest > pretest
performance) transferred to untrained odors. Overall, training
had a positive effect on the identication of trained (χ2(1)=27.44,
P<0.0001) but also on untrained (χ2(1)=9.10, P<0.01) odors, pro-
viding strong evidence of transfer. However, there was an interaction
between training group and test session in trained odors that bor-
dered on signicance (χ2(1)=2.83, P=0.09; Figure 3A), which sug-
gested single-odor training led to a stronger improvement compared
with the mixture training group in evaluating trained odors. On the
other hand, no interaction effect of training group and test session
was shown for untrained odors (χ2(1)=0.09, P=0.77; Figure 3B).
These results indicate there are pronounced transfer effects leading
to improved identication of untrained odors, even though these
effects did not differ between training groups. Follow-up analyses
indicated that the most difcult task, identifying binary mixtures of
untrained odors, did not improve in either group (single-odor train-
ing group χ2(1)=0.02, P=0.90; mixture training group χ2(1)= 0.81,
P=0.37), suggesting a limit to our odor training procedures.
Odor identication is a challenging task, especially when several odors
are presented simultaneously, but this task is critical to disentangle the
complex avor experiences of eating and drinking (Jinks and Laing
1999). Although these limitations are sometimes viewed as resistant
to training, our results showed that odor identication training results
in rapid learning and notable gains. Training effects also transferred to
situations where either the task or the odors were novel. The results are
promising, as they show that odor identication abilities are rapidly
trained and that the training gains are generalizable. However, there was
a limit to the transfer effects, as there was no improvement in the most
difcult condition, namely identifying mixtures of untrainedodors.
A tentative conclusion of this study is that odor mixture identi-
cation may be primarily limited at a “perceptual” level; identication
Table 3. Odor evaluation for each training group and test sessions
Odor evaluation Single-odor training group Mixture training group
a) Odor ratinga
Intensity 4.8± 1.3 4.9± 1.3 4.6± 1.4 4.6± 1.3
Familiarity 5.0± 1.6 5.3± 1.6 5.0± 1.7 5.6± 1.6
Meaningfulness 3.9± 1.8 4.3± 1.9 4.0± 1.8 4.7± 1.7
Pleasantness 4.9± 1.5 5.0± 1.6 4.6± 1.8 5.3± 1.5
Edibility 5.3± 1.9 5.6± 1.8 4.8± 2.1 5.8± 1.5
Subjective condence in odor naming 3.9± 1.9 5.0± 2.1 3.9± 2.0 4.7± 2.0
b) Correct free namingb0.2± 0.4 0.6± 0.5 0.3± 0.5 0.6± 0.5
c) Memory task (d’ score)c 2.9 (0.8) 2.7 (0.5)
aRatings performed on a 7-point scale.
bProportion of correct answers.
cPerformance on memory task, d’ score, data from 2 participants- one from each training group- was not collected. Values are means ± SDa,b, otherwise
median and interquartile rangec.
Figure 2. Performance during pre- (white) and post-test sessions (black) as function of identification task and training group; single-odor identification task (A)
and mixture identification task (B). Results are shown as proportion of correct answers (mean ± SE).
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training with single odors, aimed to consolidate single odor objects
and make them identiable, led to impressive training gains, as well
as transfer to untrained odors. In contrast, the odor mixture training
group was instead aimed to provide a more cognitive benet (dissoci-
ating the two components in the mixture) but lead to slightly weaker
overall results. This conclusion needs to be further explored by future
research, as the results were overall quite similar across the 2 groups.
In general, our results suggest that odor identication and perception
abilities may be trained both with single-odors and mixtures (Cain
etal. 1998). Our ndings are in line with the suggestion that the brain
is capable of processing both analytical and synthetic representations
of the perceived odors (Gottfried 2009). Odor object representa-
tions may become highly exible depending on the task demands
(Gottfried 2009; McGann 2015), and our results t with prior work
showing that odor perception is highly modied by learning (Wilson
and Stevenson 2003a; Wilson 2004; Wilson and Stevenson 2006;
McGann 2015). Future work should focus on the roles of sensory
and cognitive limitations that restrict identication of odors in com-
plex mixtures: 1)lack of identication or recognition of single odors;
2)odor suppression; 3)inhibitory interaction in the olfactory bulb
that interferes in the perception of the 2 single odors; 4)perceptual
odor similarity (Laing and Glemarec 1992; Jinks and Laing 1999;
Wilson and Stevenson 2006; Thomas-Danguin etal. 2014).
One limitation of this study is that the sample size was too
small to detect subtle group differences, and further research, using
high-powered samples, is warranted. Large-scale studies would also
enable a meaningful assessment of how individual differences in
olfactory orientation and expertise are related to the ability to learn.
Our ndings indicated no effect of gender on the obtained ndings,
although it is worth noting that relatively few men participated. It
is well established that women perform better than men in olfactory
identication and episodic odor recognition (Öberg-Blåvarg et al.
2002; Choudhury etal. 2003; Larsson etal. 2004). One interesting
topic for future work is to investigate potential effects of gender on
improvement rate following different training conditions. Moreover,
our odor stimuli were limited to common food odors. Although we
designed the odor set to resemble avor percepts of varying com-
plexity, and thus enhancing ecological validity, almost all of the used
odors were essence oils and it is difcult to rule out that there could
be some overlap among the odor percepts (e.g., pear and apple). In
addition, the use of veridical labels could create certain expectations
and references for the odors (Herz and von Clef 2001; Thomas-
Danguin et al. 2014). For future studies involving odor mixture
training, we suggest an initial familiarization or exposure to single
odors. This might enhance learning and codability of odor objects,
and proper identication and discrimination of the mixture com-
ponents (Rabin 1988; Stevenson etal. 2003; Wilson and Stevenson
2006). Although our participants self-reported a functional sense of
smell and demonstrated it through a considerable high level of per-
formance on the identication of single odors in the pretest session
(proportion of correct answer: 0.63± 0.02), we acknowledge that
self-reported sense of smell provides limited information regarding
objective olfactory function, and these 2 measures are only weakly
correlated (e.g., Landis etal. 2003). We encourage the use of valid
tools such as Snifn’ Sticks’ to determine the inclusion of potential
participants in olfactory studies (Hummel etal. 2007).
Similar to memory tasks such as Snifn’ TOM (Croy et al.
2015), single-odor identication is an easier manner of odor iden-
tication. It has been suggested that odor identication is critical
for odor memory consolidation (Larsson 1997; Kollndorfer et al.
2017). However, our results show both training groups recognized
the mixture odors they had trained with, and thus that recognition
memory effects did not skew our results. Our training results could
potentially be useful in patients with olfactory dysfunction. Mixture
identication, which is an even bigger challenge, could represent a
different way to promote olfactory skills in these patients.
Finally, our odor identication training led to enhanced subjec-
tive odor pleasantness, familiarity, meaningfulness, and edibility.
Training also led to increased ability to name odors when labels
were absent. Recent research has suggested that odor-word associa-
tive learning may increase people’s ability to describe odors, and it is
associated with increased neural activity in areas related to multisen-
sory integration and representation of semantic attributes (superior
frontal gyrus and parietal areas; Fournel etal. 2017). We speculate
that these areas may exert top-down inuences on the olfactory cir-
cuit to shape odor perception as a function of identication training.
In sum, these results showed that odor identication learning is
rather easily achieved and generalizes to untrained odors. Although
the pattern of results was complex, our limited identication capac-
ity might be best addressed by single-odor identication training that
emphasizes odor object consolidation. The ndings also showed that
odor identication training changed the subjective perceptual evalu-
ation of odors and improved free odor naming ability. Our results
may facilitate the development of novel methods to enhance olfac-
tory identication ability. Further work could focus on the corti-
cal systems underlying olfactory training. Activity changes related
to perceptual and cognitive aspects of training (e.g., Fournel etal.
2017) may unravel how olfactory expertise is achieved on behavioral
and cortical levels (Plailly etal. 2012; Sezille etal. 2014; Croijmans
and Majid 2016).
Figure 3. Performance during pre- (white) and post-test sessions (black) as function of odor identification and training group; trained odors (A) and untrained
odors (B). Results are shown as proportion of correct answers (mean ± SE).
6 Chemical Senses, 2019, Vol. XX, No. XX
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This work was supported by funding from the Swedish Foundation for the
Humanities and Social Sciences to M.L. (M14-0375:1); the Marianne and
Marcus Wallenberg Foundation (MMW 2014:0178); and the Knut and Alice
Wallenberg Foundation (KAW 2016:0229) to J.K.O.
We thank the participants for their time, effort, and motivation during the study.
Conflict of interest
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... Second, a design where participants implicitly learn how a new label relates to odors, commonly known to represent "the muted sense" (Olofsson & Gottfried, 2015), enables us to compare category formation patterns when olfactory cues are "unmuted" either with concordantly or discordantly paired linguistic cues. Third, working with a modality where stimulus identification is difficult but trainable (Fournel et al., 2017;Morquecho-Campos, Larsson, Boesveldt, & Olofsson, 2019) helps to avoid rapid ceiling effects. We can work with a modality where initial categorization performance is likely to be near chance level with gradual accuracy increases over learning. ...
... The duration of the training was set to five consecutive days (four days of implicit learning sessions followed by the day of testing) in line with similar studies that tested the effects of verbal labels on learning to categorize non-visual stimuli (e.g., Miller et al. 2018). Following comparable olfactory training designs (e.g., Morquecho-Campos et al., 2019) session lengths were kept between 20 and 30 min. ...
Recent research shows that speakers of most languages find smells difficult to abstract and name. Can verbal labels enhance the human capacity to learn smell categories? Few studies have examined how verbal labeling might affect non-visual cognitive processes, and thus far very little is known about word-assisted odor category learning. To address these gaps, we tested whether different types of training change learning gains in odor categorization. After four intensive days of training to categorize odors that were co-presented with arbitrary verbal labels, people who learned odor categories with odor-label pairs that were more consistent were significantly more accurate than people with the same perceptual experience but who had odor-label pairs that were less consistent. Both groups' accuracy scores improved, but the learning curves differed. The context of consistent linguistic cuing supported an increase in correct responses from the third day of training. However, inconsistent linguistic cuing delayed the start of approximating to target odor categorization until after the fourth day. These results show that associations formed between odors and novel verbal labels facilitate the formation of odor categories. We interpret this as showing a causal link between language and olfactory perceptual processing in supporting categorization.
... Most healthy children and adolescents score at least 10 point in the test out of the total 12 points (65) thus, there is a limited possibility to diagnose participants with remarkably high identification abilities after OT (46). For these, more complex identification tasks might be necessary (46,80). ...
Objective: Mild traumatic brain injury (mTBI) might impair the sense of smell and cognitive functioning. Repeated, systematic exposure to odors, i.e., olfactory training (OT) has been proposed for treatment of olfactory dysfunctions, including post-traumatic smell loss. Additionally, OT has been shown to mitigate cognitive deterioration in older population and enhance selected cognitive functions in adults. We aimed to investigate olfactory and cognitive effects of OT in the pediatric population after mTBI, likely to exhibit cognitive and olfactory deficits. Methods: Our study comprised 159 children after mTBI and healthy controls aged 6-16 years (M = 9.68 ± 2.78 years, 107 males), who performed 6-months-long OT with a set of 4 either high- or low-concentrated odors. Before and after OT we assessed olfactory functions, fluid intelligence, and executive functions. Results: OT with low-concentrated odors increased olfactory sensitivity in children after mTBI. Regardless of health status, children who underwent OT with low-concentrated odors had higher fluid intelligence scores at post-training measurement, whereas scores of children performing OT with high-concentrated odors did not change. Conclusion: Our study suggests that OT with low-concentrated odors might accelerate rehabilitation of olfactory sensitivity in children after mTBI and support cognitive functions in the area of fluid intelligence regardless of head trauma.
... More complex training consisting of three different tasks (detecting target odor in mixtures, classifying odor intensity and quality) was also beneficial for olfactory performance, especially for uncued odor identification (Al Aïn et al., 2019). Finally, training odor identification with a specific set of odors successfully transfers the odor identification ability to untrained odors (Morquecho-Campos et al., 2019). ...
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The sense of smell is interrelated with psychosocial functioning. Olfactory disorders often decrease quality of life but treatment options for people with olfactory loss are limited. Additionally, olfactory loss accompanies and precedes psychiatric and neurodegenerative diseases. Regular, systematic exposure to a set of odors, i.e., olfactory training (OT) has been offered for rehabilitation of the sense of smell in clinical practice. As signals from the olfactory bulb are directly projected to the limbic system it has been also debated whether OT might benefit psychological functioning, i.e., mitigate cognitive deterioration or improve emotional processing. In this review we synthesize key findings on OT utility in the clinical practice and highlight the molecular, cellular, and neuroanatomical changes accompanying olfactory recovery in people with smell loss as well as in experimental animal models. We discuss how OT and its modifications have been used in interventions aiming to support cognitive functions and improve well-being. We delineate main methodological challenges in research on OT and suggest areas requiring further scientific attention.
... It will be argued that much of what has been said in support of (alleged) olfactory objectual representations is explicable in those terms, and therefore, grounds olfactory minimal representation. Shea's (2018) theory of representation is in the lineage of both information-theoretic views of representation (Dretske, 1988) as well as the teleo-semantic tradition (Millikan, 1984). The goal of his framework is to provide a reductive account of the notion of representation that does explanatory work in cognitive science. ...
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There are many accounts of representation in the philosophical literature. However, regarding olfaction, Burge’s (2010) account is widely endorsed. According to his account, perceptual representation is always of an objective reality, that is, perception represents objects as such. Many authors presuppose this account of representation and attempt to show that the olfactory system itself issues in representations of that sort. The present paper argues that this myopia is a mistake and, moreover, that the various arguments in favor of olfactory objects fail. Yet, by taking seriously a minimal notion of representation, adopted from Shea (2018), we can see that the olfactory system is representational after all even if it doesn’t represent objects as such. That is, olfaction issues in minimal representations. Crucially, however, this paper will conclude with an argument to the effect that olfactory object files (objectual representations of olfactory objectual properties) are constructed by interactions between various mental systems. The claim to be defended is that objectual representations of olfactory objects are constructed when minimal olfactory content is embedded in object-files that contain other non-olfactory properties that meet Burge’s criteria for representation. Some extant work on feature-binding, attention, and object-files will be introduced to support the suggestion.
... Moreover, OT improved the olfactory function on both the elderly (Schriever, Lehmann, Prange, & Hummel, 2014) and children (Mahmut et al., 2021) without an olfactory dysfunction. The mechanism by which OT enhances olfactory function may be physiological in nature, evidenced by increased number of responses from the olfactory epithelium (Hummel, Stupka, Haehner, & Poletti, 2018) and olfactory bulb volume after OT (Mahmut, Musch, Han, Abolmaali, & Hummel, 2020) although cognitive processes (e.g., memory) may in combination boost olfactory function (Hummel, Haehner, Hummel, Croy, & Iannilli, 2013;Morquecho-Campos, Larsson, Boesveldt, & Olofsson, 2019). ...
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The standard olfactory training (OT) protocol involves briefly sniffing four different odors, twice per day, for 12 weeks and significantly increases olfactory function. The current study's aim was to explore the effectiveness of enhancing olfactory function using a new OT methodology, specifically, presenting odors over a longer exposure period. Participants were 50 patients with smell loss (25 idiopathic and 25 sinonasal) who wore an odorized nasal clip for 3 hr per day, for 4 weeks. The results showed that only idiopathic patients experienced a significant increase in odor discrimination after OT. Acoustic rhinometric measurements of nasal cavity surface area and volume, CO2 threshold scores, and self-reported olfactory function did not significantly change. While the results provide limited support for the effectiveness of using a nasal clip for OT, research that more fully explores the factors that are associated with enhancing olfactory function (e.g., number of odors and exposure duration) is required to determine its effectiveness more conclusively. Practical Application Although the current findings provide limited support, it remains possible that varying the hours of daily exposure, the duration of treatment and number of odors may make using an odorized nasal clip an effective, novel, and convenient olfactory training method to improving olfactory function.
... Third, the ability to detect single odors in mixtures may be improved by experience (C. Sinding et al., 2015) and training (Barkat et al., 2012;Morquecho-Campos et al., 2019). Fourth, there may be ecological pressures that have acted on the filtering mechanisms of the olfactory system. ...
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Objectives We know little about human olfactory ability in natural settings because current knowledge derives from lab-based studies using non-representative samples of convenience. The primary objective was to use a validated lab tool, the five-item odor identification test, to assess variation in olfactory ability in different environments. Methods Using the five-item test, we conducted two repeated measures experiments that assessed participant ability to correctly identify an odor source in different odor environments. We also examined consistency in odor labelling due to documented potential bias from idiosyncrasies in odor terms. Results We found no variation in olfactory ability due to environment, but this may be due to methodological biases. First, subjective bias results from idiosyncratic differences in participant labelling and researcher coding of answer correctness. Second, better ability to learn odors may provide an advantage to women. Third, reducing positive female learning bias by analyzing consistency in response (regardless of correct odor source identification) fails to assess functional olfactory ability. Functional olfactory ability (naming correct odor source) is significantly better in females, especially in food-rich odor environments. Conclusions Environment was not a significant factor in olfactory ability in this study but that result may be confounded by methodological biases. We not recommend odor identification as a field tool. Functional olfactory ability exhibits a sex-based pattern but consistency in recognizing the same odor does not. Food-rich odors may enhance olfactory ability in females. We discuss evolutionary and ecological implications of superior female functional olfactory ability relative to food foraging activity.
... For example, 1.5 1.10 1.15 1.20 healthy adult humans might improve olfactory performance merely by means of repeated olfactory stimulation (Mainland et al. 2002), and to our knowledge, no analogous effects have been reported in the visual system. Olfactory abilities are highly trainable (Hummel et al. 2009;Damm et al. 2014;Morquecho-Campos et al. 2019). Olfactory experts (e.g. ...
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Human and non-human animal research converge to suggest that the sense of smell, olfaction, has a high level of plasticity and is intimately associated with visual-spatial orientation and memory encoding networks. We investigated whether olfactory memory training would lead to transfer to an untrained visual memory task, as well as untrained olfactory tasks. We devised a memory intervention to compare transfer effects generated by olfactory and non-olfactory (visual) memory training. Adult participants were randomly assigned to daily memory training for about 40 days with either olfactory or visual tasks, that had a similar difficulty level. Results showed that while visual training did not produce transfer to the olfactory memory task, olfactory training produced transfer to the untrained visual memory task. Olfactory training also improved participants' performance on odor discrimination and naming tasks, such that they reached the same performance level as a high-performing group of wine professionals. Our results indicate that the olfactory system is highly responsive to training, and we speculate that the sense of smell may facilitate transfer of learning to other sensory domains. Further research is however needed in order to replicate and extend our findings.
Humans are poorer at identifying smells and communicating about them, compared to other sensory domains. They also cannot easily organize odor sensations in a general conceptual space, where geometric distance could represent how similar or different all odors are. These two generalities are more or less accepted by psychologists, and they are often seen as connected: If there is no conceptual space for odors, then olfactory identification should indeed be poor. We propose here an important revision to this conclusion: We believe that the claim that there is no odor space is true only if by odor space, one means a conceptual space representing all possible odor sensations, in the paradigmatic sense used for instance for color. However, in a less paradigmatic sense, local conceptual spaces representing a given subset of odors do exist. Thus the absence of a global odor space does not warrant the conclusion that there is no olfactory conceptual map at all. Here we show how a localist account provides a new interpretation of experts and cross‐cultural categorization studies: Rather than being exceptions to the poor olfactory identification and communication usually seen elsewhere, experts and cross‐cultural categorization are here taken to corroborate the existence of local conceptual spaces.
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Identifying odors within mixtures is a difficult task: humans are able to recognize only up to 4 odors within a mixture. We wanted to test the effects of olfactory training on this ability. We used 7 odorants to create 35 olfactory stimuli of 1, 2, 3, 4, or 5 odorants. The task consisted of identifying the odorants present within the mixture. We trained novices on this task for 5 days: they came to the laboratory to perform the task once a day before coming back for the final testing. Then, we compared them to sommeliers, thus olfaction experts, and untrained novices. Results showed that sommeliers outperformed the other groups with mixtures of up to 4 odorants but not with mixtures of 5 odorants. The short olfactory training allowed trained participants to perform as well as sommeliers when it came to identifying single odorants but was not enough to improve their performance when stimuli were mixtures of 2 or more odorants. This study supports the idea that the number of odors we can recognize within a mixture is limited but suggests training can improve the performance: a short olfactory training is enough to enhance the ability to identify single odorants, whereas expertise refines identification ability of mixtures of up to 4 odorants.
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Humans are traditionally considered to have a poorly developed sense of smell that is clearly inferior to that of nonhuman animals. This view, however, is mainly based on an interpretation of neuroanatomical and recent genetic findings, and not on physiological or behavioral evidence. An increasing number of studies now suggest that the human sense of smell is much better than previously thought and that olfaction plays a significant role in regulating a wide variety of human behaviors. This chapter, therefore, aims at summarizing the current knowledge about human olfactory capabilities and compares them to those of animals.
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To assess all clinically relevant components of olfactory perception, examinations for olfactory sensitivity, discrimination, and identification are performed. Besides the standard perceptual test battery, episodic olfactory memory might offer additional information about olfactory abilities relative to these standard clinical tests. As both olfactory deficits and memory deficits are early symptoms in neurodegenerative disorders, olfactory memory may be of particular interest. However, to date little is known about episodic olfactory memory performance in patients with decreased olfactory function. This study includes the investigation of olfactory memory performance in fourteen hyposmic patients (8 female, mean age 52.6 years) completing two episodic odor memory tests (Sniffin’ TOM and OMT). To control for a general impairment in memory function, a verbal and a figural memory test were carried out. A regression model with multiple predictors was calculated for both odor memory tests separately. Odor identification was identified as the only significant predictor for both odor memory tasks. From our results we conclude, that currently available olfactory memory tests are highly influenced by odor identification abilities, implying the need for the development and validation of additional tests in this field which could serve as additional olfactory perception variables for clinical assessment.
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People in Western cultures are poor at naming smells and flavors. However, for wine and coffee experts, describing smells and flavors is part of their daily routine. So are experts better than lay people at conveying smells and flavors in language? If smells and flavors are more easily linguistically expressed by experts, or more “codable”, then experts should be better than novices at describing smells and flavors. If experts are indeed better, we can also ask how general this advantage is: do experts show higher codability only for smells and flavors they are expert in (i.e., wine experts for wine and coffee experts for coffee) or is their linguistic dexterity more general? To address these questions, wine experts, coffee experts, and novices were asked to describe the smell and flavor of wines, coffees, everyday odors, and basic tastes. The resulting descriptions were compared on a number of measures. We found expertise endows a modest advantage in smell and flavor naming. Wine experts showed more consistency in how they described wine smells and flavors than coffee experts, and novices; but coffee experts were not more consistent for coffee descriptions. Neither expert group was any more accurate at identifying everyday smells or tastes. Interestingly, both wine and coffee experts tended to use more source-based terms (e.g., vanilla) in descriptions of their own area of expertise whereas novices tended to use more evaluative terms (e.g., nice). However, the overall linguistic strategies for both groups were en par. To conclude, experts only have a limited, domain-specific advantage when communicating about smells and flavors. The ability to communicate about smells and flavors is a matter not only of perceptual training, but specific linguistic training too.
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Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016) provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by R (R Core Team 2015) core packages (as well as a few key contributed ones) that fit linear or mixed models, and provides a simple way of extending it to cover more model classes.
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Historically, the body's sensory systems have been presumed to provide the brain with raw information about the external environment, which the brain must interpret to select a behavioral response. Consequently, studies of the neurobiology of learning and memory have focused on circuitry that interfaces between sensory inputs and behavioral outputs, such as the amygdala and cerebellum. However, evidence is accumulating that some forms of learning can in fact drive stimulus-specific changes very early in sensory systems, including not only primary sensory cortices but also precortical structures and even the peripheral sensory organs themselves. This review synthesizes evidence across sensory modalities to report emerging themes, including the systems' flexibility to emphasize different aspects of a sensory stimulus depending on its predictive features and ability of different forms of learning to produce similar plasticity in sensory structures. Potential functions of this learning-induced neuroplasticity are discussed in relation to the challenges faced by sensory systems in changing environments, and evidence for absolute changes in sensory ability is considered. We also emphasize that this plasticity may serve important nonsensory functions, including balancing metabolic load, regulating attentional focus, and facilitating downstream neuroplasticity.
Semantic description of odors is a cognitively demanding task. Learning to name smells is, however, possible with training. This study set out to examine how improvement in olfactory semantic knowledge following training reorganizes the neural representation of smells. First, 19 nonexpert volunteers were trained for 3 days; they were exposed (i) to odorants presented without verbal labels (perceptual learning) and (ii) to other odorants paired with lexicosemantic labels (associative learning). Second, the same participants were tested in a brain imaging study (fMRI) measuring hemodynamic responses to learned odors presented in both the perceptual and associative learning conditions. The lexicosemantic training enhanced the ability to describe smells semantically. Neurally, this change was associated with enhanced activity in a set of heteromodal areas—including superior frontal gyrus—and parietal areas. These findings demonstrate that odor-name associative learning induces recruitment of brain areas involved in the integration and representation of semantic attributes of sensory events. They also offer new insights into the brain plasticity underlying the acquisition of olfactory expertise in lay people. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
Humans have a good sense of smell In comparison to that of other animals, the human sense of smell is widely considered to be weak and underdeveloped. This is, however, an unproven hypothesis. In a Review, McGann traces the origins of this false belief back to comparative 19th-century neuroanatomical studies by Broca. A modern look at the human olfactory bulb shows that it is rather large compared with those of rats and mice, which are presumed to possess a superior sense of smell. In fact, the number of olfactory bulb neurons across 24 mammalian species is comparatively similar, with humans in the middle of the pack, and our sense of smell is similar to that of other mammals. Science , this issue p. eaam7263
Written by a neurobiologist and a psychologist, this volume presents a new theory of olfactory perception. Drawing on research in neuroscience, physiology, and ethology, Donald A. Wilson and Richard J. Stevenson address the fundamental question of how we navigate through a world of chemical encounters and provide a compelling alternative to the "reception-centric" view of olfaction. The major research challenge in olfaction is determining how the brain discriminates one smell from another. Here, the authors hold that olfaction is generally not a simple physiochemical process, but rather a plastic process that is strongly tied to memory. They find the traditional approach-which involves identifying how particular features of a chemical stimulus are represented in the olfactory system-to be at odds with historical data and with a growing body of neurobiological and psychological evidence that places primary emphasis on synthetic processing and experiential factors. Wilson and Stevenson propose that experience and cortical plasticity not only are important for traditional associative olfactory memory but also play a critical, defining role in odor perception and that current views are insufficient to account for current and past data. The book includes a broad comparative overview of the structure and function of olfactory systems, an exploration into the mechanisms of odor detection and olfactory perception, and a discussion of the implications of the authors' theory. Learning to Smell will serve as an important reference for workers within the field of chemical senses and those interested in sensory processing and perception. © 2006 by The Johns Hopkins University Press. All Rights Reserved.