cortex and the putamen. The ROI-based statistical analysis of the sucrose vs. artificial saliva stimulation showed a statistical activation only in the central thalamic area and a statistical decreased activation in two thalamic areas, the fusiform gyrus, the hippocampus, the olfactory bulb, the prepyriform area, the nucleus accumbens, and a tendency toward lower activation in nine others brain structures such as, for instance, the entorhinal cortex, the dorsal anterior cingulate cortex, the para-hippocampal cortex, the nucleus accumbens, the substantia nigra, and the globus pallidus (Figure 3B).

## Brain Responses to Quinine Stimulation

Compared with artificial saliva stimulation, the quinine stimulation promoted also activations, but in different subparts, in the prepyriform area, the dorsal posterior cingulate cortex, the dorsal lateral prefrontal cortex, a larger activation in the fusiform gyrus, the caudate nucleus, the nucleus accumbens, and an additional activation in the para-hippocampal cortex and the orbitofrontal cortex (Figure 2A).

The ROI-based statistical analysis supports in part the global brain changes and showed an activation of the fusiform gyrus, the nucleus accumbens, the para-hippocampal cortex, the ventral anterior thalamic nucleus, a tendency toward activation of seven other brain structures, i.e., the orbitofrontal cortex, the putamen, and the caudate nucleus (Figure 2D).

The quinine vs. artificial saliva brain maps corroborated these observations (Figure 3A). Compared to artificial saliva, quinine promoted a larger activation in the caudate nucleus, a decreased activation in the ventral part of the anterior prefrontal cortex, the putamen, and both activation and decreased activation in the insular cortex, the prepyriform area, the dorsal lateral prefrontal cortex, the dorsal posterior cingulate cortex, and the hippocampus. The ROI-based statistical analysis of the quinine vs. artificial saliva stimulation only showed a tendency toward activation in four brain structures, i.e., the lateral dorsal thalamic nucleus, the anterior entorhinal cortex, the geniculate nuclei, and the fusiform gyrus; a statistical decreased activation in the prepyriform area, the lateral dorsal thalamic nucleus, the hippocampus, the claustrum, and a tendency toward decreased activation in eight others brain structures, i.e., the globus pallidus, the olfactory bulb, the para-hippocampal cortex, the insular cortex, and the nucleus of the solitary tract (Figure 3C).

## Comparison of Brain Responses to Sucrose vs. Quinine Stimulation

The quinine vs. sucrose brain map allowed discriminating the impact of each stimulation. Quinine promoted a higher activation in the dorsal lateral prefrontal cortex, the ventral part of the anterior prefrontal cortex, the prepyriform area, the para-hippocampal cortex, the fusiform gyrus, the hippocampus, the caudate nucleus, the posterior region of the putamen, the nucleus accumbens, and in the amygdala (Figure 3A, in red). Sucrose promoted a higher activation in the primary somatosensory cortex, the dorsal anterior cingulate cortex, the dorsal part of anterior prefrontal cortex, and in the putamen (Figure 3A, in

blue). Note that each stimulation showed higher activation in different subparts of the insular cortex, the anterior prefrontal cortex (larger activation for sucrose stimulation), the putamen (larger activation for sucrose stimulation), and the caudate nucleus (larger activation for quinine stimulation).

The ROI-based statistical analysis of the quinine vs. sucrose stimulation validated in part the global brain changes and showed a higher activation from quinine in the fusiform gyrus, the caudate nucleus, the geniculate nuclei, the nucleus accumbens, and the anterior entorhinal cortex, a tendency toward higher activation of five other brain structures including the dorsal anterior cingulate cortex, the orbitofrontal cortex, the substantia nigra, the lateral dorsal thalamic nucleus, and the olfactory bulb, whereas we could only detect a higher statistical activation with sucrose stimulation in the globus pallidus and the claustrum (Figure 3D).

## DISCUSSION

For the first time, this proof-of-concept study describes in the minipig model the brain responses to contrasted gustatory stimulations with fMRI. After validation of our fMRI paradigm with visual stimulation, we were able to detect the brain responses elicited by artificial saliva, sucrose, and quinine oral gustatory stimulations. The statistical analysis also allowed investigating the specificity of quinine stimulation, as an aversive stimulation paradigm, vs. sucrose stimulation, as a pleasant stimulation paradigm.

As a prerequisite, we aimed to validate our experimental setup, image processing, and statistical analysis approaches with a visual stimulation paradigm, as investigated in a previous study (Gizewski et al., 2007). We were able to detect statistically significant brain responses in the contralateral visual cortex but also a trend toward activation in the ipsilateral visual cortex, which is in accordance with previous studies related to visual stimulation in pigs (Fang et al., 2006) and in minipigs (Gizewski et al., 2007). In this proof-of-concept study, the brain responses of five animals were sufficient to detect the brain activation elicited by visual stimulation (uncorrected statistic at a *p*-value level of 0.05). Note that correction for multiple comparisons with satisfactory *p*-values threshold in fMRI paradigms has been achieved in pigs only with deep brain stimulation (Min et al., 2012; Knight et al., 2013; Paek et al., 2015; Gibson et al., 2016; Settell et al., 2017), which is a highly invasive treatment deeply modifying brain activity. The ROI-based statistic without correction for multiple ROI comparison is a non-standard approach regarding to usual human fMRI-based statistical analyses, but was used to provide first evidence is this exploratory study. Considering the limited number of animals and the fact that they were anesthetized during the imaging procedure, detection of a specific BOLD signal in accordance with our hypotheses was already a satisfactory achievement. Of course, further studies should consider increasing the number of animals or stimulations per animal to reach a better statistical power and improving the anesthesia and/or stimulation paradigms to reduce inter-individual variability.
Even though artificial saliva is usually considered as a neutral stimulus (Hellekant et al., 1997, Clouard et al., 2014b), our results showed that it elicited a broad spectrum of brain responses in olfactogustatory brain areas, limbic and corticostriatal areas, meaning that it is not a trivial stimulation. Our approach and the global brain maps of sucrose vs. artificial saliva stimulation obtained here are comparable to those described in pigs by Clouard et al. (2014b), who investigated the impact of oral sucrose 5% stimulation on brain blood flow. Interestingly, we could also detect decreased activation in the caudate nucleus, the nucleus accumbens and the amygdala, which was reported in obese humans with sucrose stimulation compared with non-obese humans (Green et al., 2011). The global brain map of quinine vs. artificial saliva stimulation can be compared to the study of Zald et al. (2002), in which bitter stimulation promoted an increased cerebral blood flow in the dorsal cingulate cortex, the orbitofrontal cortex and the nucleus accumbens. Overall, our data are in line with a recent meta-analysis searching common pattern of activity related to basic taste stimulation, i.e., activation in the insular cortex, the thalamic region, the hippocampus, the putamen, and the cingulate cortex (Yeung et al., 2017).

Based on behavioral data available in animals (McCutcheon et al., 2012; Clouard et al., 2014a; Roura et al., 2016; Roura and Fu, 2017) and humans (Veldhuizen et al., 2006; Rudenga et al., 2010; Dalenberg et al., 2015; Field et al., 2015), the quinine vs. sucrose stimulation paradigm is now used as a common paradigm to decipher the central integration of aversive vs. pleasant stimulations using neuroimaging approaches (Zald et al., 2002; Rudenga et al., 2010; Szalay et al., 2012; van den Bosch et al., 2014; Dalenberg et al., 2015; Field et al., 2015).

The amygdala, which has direct connections from lingual nerves (King et al., 2014), is involved in pleasant and unpleasant stimuli (O'Doherty et al., 2001) and has been reported to be more activated with quinine compared to water stimulation (Zald et al., 2002). However, increased activation in amygdala with sucrose compared to quinine stimulation was also reported in humans between sucrose likers and quinine dis-likers (van den Bosch et al., 2014). In a previous study in the human, the orbitofrontal cortex, which is involved in hedonic taste valence, was found modulated by the pleasantness of the gustatory stimulation (Small et al., 2003). The anterior cingulate cortex, which is involved in taste pleasantness (Grabenhorst and Rolls, 2008), was more activated with sucrose stimulation than with quinine stimulation, which suggests a higher activation in this brain region with pleasant vs. unpleasant stimulation (Small et al., 2003; Green et al., 2015). Overall, sucrose stimulation promoted less activation in the brain than quinine stimulation, as already described in the human (Zald et al., 2002; Szalay et al., 2012). As major actors of the reward system, the nucleus accumbens and the caudate nucleus (Liu et al., 2011) were found activated with sucrose stimulation (Smith, 2004; Haase et al., 2009; Jacobson et al., 2010), but other authors showed that sucrose might promote inhibition in the nucleus accumbens in rats, whereas quinine promoted activation (Roitman et al., 2005). In our study, quinine was more effective than sucrose to induce activation in both these brain structures, but a concentration of 5% sucrose (i.e., 0.15 M) might have not been

sufficient to promote a prominent response in the reward-related brain regions compared to the concentration used in the aforementioned studies (i.e., 0.64 M). Even though the limited number of animals and the use of uncorrected statistics are important limitations in our study, we managed to describe brain responses differences between quinine and sucrose stimulations in brain structures already highlighted in human studies as aforementioned. It is necessary to keep in mind that discrepancies with previous studies might be related to physiological differences in the olfaction of pigs compared to humans (Roura et al., 2016; Roura and Fu, 2017), to differences related to the tastants' concentration (Yeung et al., 2016, 2018) and/or novelty (Clouard et al., 2012a; Kishi et al., 2017), or to the hunger/satiety status during imaging (Haase et al., 2009). Finally, it is important to remind that our animals were anesthetized, which might modulate or attenuate brain responsiveness (Hendrich et al., 2001; Willis et al., 2001).

## CONCLUSION

To date, among all fMRI studies performed in pigs or minipigs, this pilot study provides for the first time some preliminary evidences on brain responses to gustatory stimulation with pleasant and aversive compounds in the minipig model. Even though our study used a limited number of animals with uncorrected statistics, our overall approach can be of high interest for preclinical studies investigating the sensory integration from gustatory stimulation such as in young vs. elderly investigations (Green et al., 2011) or in pathologies such as metabolic syndrome (Green et al., 2015) or obesity (Szalay et al., 2012).

## AUTHOR CONTRIBUTIONS

NC, MF, ER, and DV-L contributed in the experimental design. NC, EB, RJ, SQ, PM, and HS-J contributed in the technical development. NC, PM, and RJ performed the experiments. NC and PM analyzed the data. NC, HS-J, MF, ER, and DV-L wrote the manuscript.

## FUNDING

This study was funded by the AlimH Division (Nutrition, Chemical Safety and Consumer Behaviour) of INRA. A financial contribution was also provided by the University of Queensland.

## ACKNOWLEDGMENTS

We thank the staff from the animal facility UEPR (Unité Expérimentale Porcs de Rennes, St. Gilles, France) of INRA for their technical support: Serge Dubois, Bruno Fontaine, Renan Delaunay, and Alain Chauvin. We also acknowledge the PRISM (Plateforme de Recherche en Imagerie et Spectroscopie Multimodales, Rennes, France) core facility for its technical support.
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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Coquery, Meurice, Janvier, Bobillier, Quellec, Fu, Roura, Saint-Jalmes and Val-Laillet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# More Than Meets the Eye: The Impact of Materialism on Information Selection During Luxury Choices

Catherine Audrin¹,²,³*, Tobias Brosch¹,², David Sander¹,² and Julien Chanal²

¹ Swiss Center for Affective Sciences, Geneva, Switzerland, ² Department of Psychology, University of Geneva, Geneva, Switzerland, ³ Research Support Centre, University for Teacher Education, Lausanne, Switzerland

Visual attention is an important condition for consumer decision-making. However, not much is known on individuals’ determinants of this visual attention. Using eye tracking, this study investigated how psychological values (i.e., materialism) modulate visual attention to specific sources of information (i.e., product, brand and additional information) in the context of luxury consumption. Participants were asked to perform a forced-choice experiment, where products were randomly assigned with luxury and non-luxury brands (Experiment 1) and product information (Experiment 2). Experiment 1 revealed that materialism was related to relatively higher attention to luxury as opposed to non-luxury and higher choice proportion of products displayed with a luxury brand. Experiment 2 showed that when providing additional product information (e.g., regarding the material) in addition to the brand, all participants chose luxury products more often. Interestingly, choices seemed to be driven by enhanced attention to brand for participants with high levels of materialism when choosing luxury products. In contrast, choices were driven by text for participants with low levels of materialism for non-luxury products. This suggests that individuals with high levels of materialism may prefer luxury products for different reasons than individuals with low levels of materialism: while the first focus on the symbolic dimension conveyed by the brand (Experiment 1), the latter pay attention to the actual product characteristics (Experiment 2). Taken together, our results suggest that materialism as a psychological value has an impact on visual attention and information selection during decision-making in the context of luxury consumption.

**Keywords:** eye-tracker, information processing, materialism, luxury, choice

**OPEN ACCESS**

**Edited by:**
Gérard Coureau,
INSERM U1028 Centre de Recherche
en Neurosciences de Lyon, France

**Reviewed by:**
Claudio Lucchiari,
Università degli Studi di Milano, Italy
Claus-Christian Carbon,
University of Bamberg, Germany

**\*Correspondence:**
Catherine Audrin
catherine.audrin@unige.ch

**Received:** 17 April 2018
**Accepted:** 20 July 2018
**Published:** 24 August 2018

**Citation:**
Audrin C, Brosch T, Sander D and Chanal J (2018) More Than Meets the Eye: The Impact of Materialism on Information Selection During Luxury Choices. *Front. Behav. Neurosci.* 12:172. doi: 10.3389/fnbeh.2018.00172

## INTRODUCTION

When you scroll down on your computer on a retailer website, what information do you attend to when trying to make your decision? Visual attention is defined as the degree to which people visually focus on a stimulus within their range of exposure (Solomon et al., 2010), and is an important precondition for product choice. Attentional mechanisms allow people to select a subset of information, while suppressing the non-selected information for further processing (Wedel and Pieters, 2008). This selection of information is a crucial step in purchase decisions (Milosavljevic and Cerf, 2008), suggesting that it may be helpful to measure visual attention and information acquisition using techniques such as eye-tracker to better understand the process leading to these kinds of decisions.
Several studies have been performed in the context of purchase decisions to investigate the role of visual attention processes. As Orquin and Mueller Loose (2013) illustrates in his review, visual attention is strongly related to eye movements. Recording eye-movement data allows to study the process of information acquisition, which is performed by eye fixations and saccades (Shi et al., 2012). Eye-tracking data thus does not only record the time spent looking at a product, but also the position and duration of each eye fixation (Chandon et al., 2009). Interestingly, previous research revealed a discrepancy between self-reported and eye-tracking measures (Graham and Jeffery, 2011), suggesting that people are usually not aware of their eye fixations (Chandon et al., 2009). Studies using eye-tracking measures have pointed out that during choice, a pre-decisional gaze bias occurs toward the preferred option (Chae and Lee, 2013). This bias, referred to as the gaze cascade (Shimojo et al., 2003), consists in a shift of attention toward the preferred choice alternative (Krajbich and Rangel, 2011; Willemsen et al., 2011). The preferred option is thus observed during a greater amount of time (Glaholt and Reingold, 2011; Glöckner and Herbold, 2011). The attentional Diffusion-Drift Model (aDDM, Krajbich and Rangel, 2011) suggests that gaze fixation is the mechanisms by which decision makers retrieve information about each option. According to this model, spending time looking at an option means that we accumulate evidence in favor of the fixated alternative (Krajbich and Rangel, 2011).

Information selection is achieved through two processes: bottom-up and top-down processes (Wedel and Pieters, 2008). Bottom-up processes correspond to a rapid and automatic way to capture attention (Milosavljevic et al., 2012). They refer to factors such as visual saliency (Glaholt and Reingold, 2011; Atalay et al., 2012; Milosavljevic et al., 2012; Janiszewski et al., 2013). In contrast, top-down processes refer to a voluntary attentional capture which requires personal and active search (Wedel and Pieters, 2008). This voluntary focus may be driven by the task, by people's previous knowledge, social identity (Xiao and Van Bavel, 2012), interests or goals (Milosavljevic and Cerf, 2008; Glöckner and Herbold, 2011). As an example of the impact of people's previous knowledge, brand usage (or familiarity with a brand) has been shown to diminish search costs for the consumer, leading them to greater effectiveness in terms of decision making (Chandon et al., 2009). Further evidence suggest that top-down processes drive attention toward the pieces of information that are relevant or critical for the ongoing decision (Ares et al., 2013; Orquin and Mueller Loose, 2013). For instance, consumers who are evaluating which product they intend to buy spend more time on text information (Rayner et al., 2001; Cisek et al., 2014), whereas consumers assessing product advertisements spend more time on pictorial information (Rayner et al., 2008). Xiao et al. (2016) suggest that social identity may tune visual attention. In their model, visual inputs are embedded with social values. As a consequence, people's social identity strongly influences their perception. Interestingly, these top-down influences may act on early attentional stages of visual perception (Brosch and Van Bavel, 2012; Xiao et al., 2016). More generally, people's motives, experiences and concerns may have an impact on their visual attention to a stimulus (Pool et al., 2016). As an example, research

has revealed that participants' pro-environmental orientation lead them to have a greater propensity to attend to climate change images (Sollberger et al., 2017). Another study suggested that attention paid to nutrition labels was influenced by participants' goals (Graham and Jeffery, 2011): focusing on health goals enhanced participants' attention and intensity of processing (Visschers et al., 2010), leading to an increase of choices of healthy products (van Herpen and Trijp, 2011). Thus, literature suggest that visual attention may be influenced by top-down processes such as personal characteristics, their goals and social identity (see e.g., Pool et al., 2016).

In this study, we were interested in how materialism as a psychological value may impact visual attention to specific sources of information in the context of luxury consumption. Materialism is defined as an extensive concern for material objects and worldly possessions (Belk, 1985), and leads people to have high commitment toward the acquisition and consumption of possessions (Rindfleisch et al., 1997; Dittmar, 2005). As a value, materialism has an impact on people's goals, and is considered to be a tendency to favor extrinsic aspirations (i.e., wealth, popularity, attractiveness, and conformity) over intrinsic aspirations (i.e., self-acceptance, affiliation, community feeling, safety, spirituality, hedonism, and health). Materialistic individuals, i.e., people with high levels of materialism, are more likely to look for prestigious products reflecting a high social status (Fournier and Richins, 1991; Wang and Wallendorf, 2006) than non-materialists. More specifically, materialism is a critical dimension of luxury consumers' values (e.g., Fournier and Richins, 1991) as it enhances interest for luxury brands (Gil et al., 2012), preference for luxury goods (Wong and Ahuvia, 1998; Prendergast and Wong, 2003) and brand label consideration (Audrin et al., 2017a,b).

Brand labels are extrinsic cues (Bredahl, 2004). Extrinsic cues are any piece of information about the product that is not directly part of the product itself (Zeithaml, 1988), such as its price or the label displayed on it. Intrinsic cues refer to the physical composition of the product. Taking the example of a handbag, intrinsic cues would include the color, the texture or the material of the product (Zeithaml, 1988). Literature has shown the importance of extrinsic cues in consumer decision-making. For example, consumers' expectancies about a brand impact experienced pleasantness when consuming the product (e.g., Allison and Uhl, 1964; McClure et al., 2004). When participants were drinking Coke and Pepsi without knowing what they are drinking, experienced pleasantness was similar for both drinks. However, when drinks were labeled with a brand (Coke or Pepsi), participants reported increased preferences toward Coke (McClure et al., 2004). Thus, consumers heavily rely on extrinsic cues to build their preferences (Kuusela et al., 1998; Kardes et al., 2001; Veale and Quester, 2009; Baer et al., 2017).

Here, we report two eye-tracker studies designed to assess how materialism impacts visual attention to extrinsic (i.e., pictorial) information (Experiment 1) as well as both extrinsic (i.e., pictorial) and intrinsic (i.e., text) information (Experiment 2) that was provided for a set of products in a forced-choice task. In the first experiment, we randomly presented products with either luxurious or non-luxurious brand labels
(i.e., pictorial information) to participants with high and low levels of materialism. Regarding Experiment 1, our hypotheses were that (1) all participants would choose more often products at which they look longer, (2) people scoring higher on materialism would look longer at products presented with a luxurious brand label and (3) people scoring higher on materialism would choose more often products presented with a luxurious brand. In Experiment 2 participants with high and low level of materialism were randomly presented with products in either a luxurious or a non-luxurious condition. The conditions were determined by both pictorial (i.e., brand label) and text (i.e., country of origin and material) information. Regarding Experiment 2, we hypothesized that (1) all participants would more often choose products at which they looked longer. Based on previous evidence suggesting that when providing with information about brand and quality, both participants with high and low level of materialism evaluate more positively luxury (Audrin et al., 2017a), we hypothesize that (2) all participants would show higher visual attention and choose the luxurious condition more often than the non-luxurious condition. Finally, as previous evidence reveals that materialism is related to high importance to brand labels and that people scoring low on materialism look for more information, we hypothesize that (3) pictorial information would be more determinant in the choices of participants scoring high on materialism, whereas textual information would be more determinant in the choices of participants scoring low on materialism.

## PRE-TEST STUDY

We first attempted to identify luxurious and non-luxurious brands adapted to our sample. One hundred and sixty-five female students in the first year of a psychology degree at the University of Geneva were asked to name as many luxury brands as they could. Then, they were presented with 106 brands of ready-to-wear products and asked whether they knew them. These brands were selected from advertisements seen in the newspapers or on television. In addition, we selected brands mentioned in the GenY Prestige Brand Ranking (L2 Think Tank and Stren, 2010), which ranks the top luxurious brands for women. For each brand, a label was presented and participants were asked to click on “1” if they knew the brand and “0” if they did not. Based on these tests, we computed scores for each brand to select the most well-known luxurious and non-luxurious brands. Chanel®, Gucci®, Dior®, and Louis Vuitton® appeared to be the most well-known luxurious brands and H&M®, Zebra®, GAP®, and Forever21®, the most well-known non-luxurious brands. Knowledge of the brands was moreover tested on participants of the eye-tracker experiments: on average, participants knew 93.67% of the brands (SD = 8.25).

# EXPERIMENT 1

## Methods

### Participants and Procedures

To test the effect of luxurious vs. non-luxurious brands and its moderation by materialism on choice, 70 female psychology

students at the University of Geneva were recruited for Experiment 1 (mean age = 22 ± 3 years). Our stimuli were products designed to be worn by women, we consequently only invited females to participate. Participants were first asked to complete an online full version of the Aspiration Index (Grouzet et al., 2005) in order to measure their materialism. After several weeks, all participants who completed the Aspiration Index were invited to the lab to participate to the eyetracker experiment in exchange for course credits. When coming to the laboratory, participants were invited to sit in front of a computer in a cubicle. We presented a set of 46 images of ready-to-wear products from the following categories to the participants: belts, handbags, purses and scarves. The selected products came from brands most-likely unknown to our sample (i.e., brands unknown as assessed in the pre-test study above), which ensured that the products or their brand could not be recognized by our participants. Each product was presented with 1 out of 8 brands. Half of the products were randomly presented with one out of four luxurious brand, while the other half was presented with one out of four non-luxurious brands. The luxurious and non-luxurious brands were randomized between participants so that each product was seen with each of the brands across the whole sample (see **Figure 1**). The study was performed according to the rules and regulations of the University of Geneva and the declaration of Helsinki. All participants gave their written informed consent to take part in the study, which was officially approved by the ethics committee of the University of Geneva.

## Data Acquisition

### Materialism

To assess participants’ level of materialism, we used the full version of the Aspiration Index (Grouzet et al., 2005), which refers to Kasser (1999)’s conceptualization of materialism as a balance between extrinsic and intrinsic aspirations. In the Aspiration Index (Grouzet et al., 2005), people assessed importance of 57 aspirations on a scale ranging from 1 (“not important at all”) to 9 (“extremely important”). These goals refer to 11 aspirations, which can be separated into two main dimensions, i.e., intrinsic and extrinsic dimensions. Extrinsic

<figure><img src="image_31.png" /><figcaption>**FIGURE 1** | Schematic description of the task in Experiment 1 and 2.</figcaption></figure>
dimensions refer to the importance given to one's own image (e.g., "I hope for the future that my image will be one other's find appealing"), popularity (e.g., "I will be admired by many people"), financial success (e.g., "I will have expensive possessions"), and conformism (e.g., "I will live up to the expectations of my society"). Intrinsic dimensions are the importance given to his own health (e.g., "I will feel energetic and full of life"), affiliation (e.g., "There will always be someone around to take care of me"), spirituality (e.g., "I will find personal answers to universal spiritual questions such as: Is there a supreme spiritual being? Is there life after death? What is the meaning of life?"), community (e.g., "I will assist people who need it, asking nothing in return"), hedonism (e.g., "I will have a lot of excitement in my life"), safety (e.g., "I will have few threats to my personal safety"), and self-acceptance (e.g., "I will feel free"). Materialism scores were computed as the relative importance of extrinsic (mean of the extrinsic dimensions' scores) vs. intrinsic aspirations (mean of the intrinsic dimensions' scores) (Kasser, 1999; Grouzet et al., 2005). The more people considered extrinsic aspirations important compared to intrinsic aspirations, the more they were materialists. Our sample had an average of -1.589 score on materialism (SD = 1.272).

## Eye-Tracking

Participants seated 60 cm away from a 43 cm-wide screen and were asked to keep their head in the same position during the experiment. Movement of the eyes were recorded at a 60-Hz frequency using infrared cameras of the Tobii eye-tracker located at the bottom of the screen. The eye-tracker was calibrated before each session. This phase consisted in the presentation of moving dots that participants were asked to follow without anticipating the dots' movement.

After this calibration, participants were presented with two ready-to-wear products side by side. For each trial, one product was presented with a luxurious brand and the other with a non-luxurious brand. The side of the luxurious/non-luxurious condition was randomized. Participants were asked to select the product they would prefer to get if they could obtain it. There was no time constraint. Each participant had to make 46 choices, where each of the 46 products was presented twice (each time with the same brand label, every time against another product). After each choice, a fixation cross was displayed in the center of the screen to wait for the next pair of products to be presented. The order of presentation was randomized between participants.

## Data Analyses

Data analyses were performed with R (R Development Core Team, 2008), lmerTest (Kuznetsova et al., 2017), and lme4 packages (Bates et al., 2015). (Generalized) Linear Mixed Model ((G)LMM) analyses were performed on eyetracker and behavioral data. We used participants and stimuli as random effects (i.e., random intercepts) to control for variance due to participants and items, respectively (Judd et al., 2012). For the fixed effects, we assigned the coding -1/+1 as advised by Judd et al. (2012), which allowed us to interpret these effects as main effects. We visually inspected residual plots to detect deviations from homoscedasticity or normality. Descriptive analyses (mean

and s.e.) are reported below. Concerning the eye-tracker data, analyses were performed on the total fixation time. Due to its non-normal distribution, fixation time was log-transformed, but for the ease of interpretation, means were back-transformed for Table 1. Our model contained Condition (luxurious vs. non-luxurious condition), Materialism and Information (product vs. brand) as fixed effects. Concerning the behavioral data, we performed analyses on participants' choices (Table 2). For each choice, we predicted the probability that the product on the left would be chosen. Selecting the product on the left or on the right as a dependent variable is equivalent, as the position (right or left) of the luxurious and non-luxurious brands was randomized. To analyse this dichotomous variable, we performed a multilevel logit model. The fixed effects were Condition (luxurious vs. non-luxurious condition), Materialism (materialist vs. non-materialist) and the Time (time spent before making the choice in milliseconds). Descriptive statistics for the choice variable are reported in Table 2.

## Results

### Total Fixation Time

Results for the total fixation time showed a main effect of Information [$b$ = 0.787, $IC_{95\%}$ = [0.763; 0.812], $t_{(8,125)}$ = 62.992, $p$ < 0.001; see Table 3], revealing that participants spent more time looking at the product picture than at the brand. The Condition × Materialism interaction effect was significant [$b$ = -0.013, $IC_{95\%}$ = [-0.026; -0.001]; $t_{(8,089)}$ = -2.047, $p$ = 0.041], supporting our hypothesis that materialism had an impact on the time spent on luxury vs. non-luxurious condition. Interestingly, results revealed that the more participants are materialistic, the less they look at non-luxury. There was no further significant effect.

### Choice

Results showed a main effect of Condition ($b$ = 0.232, $IC_{95\%}$ = [0.112; 0.354], $z$ = 3.771, $p$ < 0.001; Table 4), where products associated with luxurious information were more often chosen than products associated with non-luxurious information. The

<table><caption>TABLE 1 | Descriptive statistics for the Total Fixation Time variable (mean and s.e.).</caption><thead><tr><th>Materialism</th><th>Condition</th><th>Information</th><th>Time (ms)</th><th>s.e.</th></tr></thead><tbody><tr><td>-1 s.d.</td><td>Luxury</td><td>Image</td><td>958.337</td><td>84.227</td></tr><tr><td>-1 s.d.</td><td>Luxury</td><td>Brand</td><td>176.542</td><td>11.632</td></tr><tr><td>-1 s.d.</td><td>Non-luxury</td><td>Image</td><td>957.088</td><td>80.370</td></tr><tr><td>-1 s.d.</td><td>Non-luxury</td><td>Brand</td><td>182.390</td><td>11.626</td></tr><tr><td>mean</td><td>Luxury</td><td>Image</td><td>944.758</td><td>56.846</td></tr><tr><td>mean</td><td>Luxury</td><td>Brand</td><td>179.456</td><td>7.850</td></tr><tr><td>mean</td><td>Non-luxury</td><td>Image</td><td>929.435</td><td>54.242</td></tr><tr><td>mean</td><td>Non-luxury</td><td>Brand</td><td>184.375</td><td>7.847</td></tr><tr><td>+ 1 s.d.</td><td>Luxury</td><td>Image</td><td>932.756</td><td>79.051</td></tr><tr><td>+ 1 s.d.</td><td>Luxury</td><td>Brand</td><td>182.032</td><td>10.917</td></tr><tr><td>+ 1 s.d.</td><td>Non-luxury</td><td>Image</td><td>904.994</td><td>75.431</td></tr><tr><td>+ 1 s.d.</td><td>Non-luxury</td><td>Brand</td><td>186.128</td><td>10.912</td></tr></tbody></table>
<table><caption>**TABLE 2** | Descriptive analyses for the Choice variable (mean and s.e.).</caption><thead><tr><th>Materialism</th><th>Condition</th><th>Proportion of choice</th><th>s.e.</th></tr></thead><tbody><tr><td>-1 s.d.</td><td>Luxury</td><td>50.8%</td><td>0.023</td></tr><tr><td>-1 s.d.</td><td>Non-luxury</td><td>49.12%</td><td>0.023</td></tr><tr><td>mean</td><td>Luxury</td><td>51.58%</td><td>0.016</td></tr><tr><td>mean</td><td>Non-luxury</td><td>48.41%</td><td>0.017</td></tr><tr><td>+1 s.d.</td><td>Luxury</td><td>52.29%</td><td>0.023</td></tr><tr><td>+1 s.d.</td><td>Non-luxury</td><td>47.70%</td><td>0.023</td></tr></tbody></table>

<table><caption>**TABLE 3** | Results for the Total Fixation Time variable, ***p < 0.001; *p < 0.05.</caption><thead><tr><th>Fixed Effects</th><th>b</th><th>SE</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.579</td><td>0.002</td><td>0.001***</td></tr><tr><td>Condition</td><td>0.017</td><td>0.012</td><td>0.137</td></tr><tr><td>Information</td><td>0.787</td><td>0.013</td><td>0.001***</td></tr><tr><td>Materialism</td><td>0.018</td><td>0.038</td><td>0.641</td></tr><tr><td>Condition × Information</td><td>0.005</td><td>0.012</td><td>0.629</td></tr><tr><td>Condition × Materialism</td><td>-0.013</td><td>0.006</td><td>0.041*</td></tr><tr><td>Information × Materialism</td><td>-0.001</td><td>0.003</td><td>0.261</td></tr><tr><td>Condition × Information × Materialism</td><td>-0.001</td><td>0.008</td><td>0.801</td></tr></tbody></table><table><thead><tr><th>Random effects</th><th>σ<sup>2</sup></th><th>SE</th></tr></thead><tbody><tr><td><strong>Participants</strong></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.153</td><td>0.391</td></tr><tr><td><strong>Stimuli</strong></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.003</td><td>0.609</td></tr></tbody></table>

marginal effect of Time ($b = 0.051$, $IC_{95\%} = [-0.000; 0.103]$, $z = 1.941$, $p = 0.052$) supported the hypothesis that the more people looked at the product, the more they chose it. Finally, the Condition × Materialism interaction was significant ($b = 0.086$, $IC_{95\%} = [0.023; 0.148]$, $z = 2.708$, $p = 0.006$), supporting the hypothesis that the impact of materialism depended on the product: the more participants were materialistic, the more they would choose luxury products over non-luxury products ($b = 0.12$, $z = 2.562$, $p < 0.001$).

## Discussion

The results presented above revealed that the longer participants looked at a product, the higher the probability that this product was chosen. While the result is marginally significant in our study, it is congruent with previous evidence (Glaholt and Reingold, 2011; Atalay et al., 2012; Milosavljevic et al., 2012) suggesting that higher attention to a product is driven by enhanced interest (Fink et al., 1996), thus leading to enhanced preference. Our results reveal for the first time that this is also true in the context in luxury consumption.

Our results further suggest that despite their longer attention to the product itself compared to the extrinsic cues, participants still integrated extrinsic cues (i.e., brand label) into their choices. The more participants were materialistic, the less they looked at products displayed with a non-luxurious brand. These results

<table><caption>**TABLE 4** | Results for the Choice variable, ***p < 0.001; *p < 0.05.</caption><thead><tr><th>Fixed Effects</th><th>b</th><th>SE</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>-0.318</td><td>0.128</td><td>0.013*</td></tr><tr><td>Materialism</td><td>-0.037</td><td>0.036</td><td>0.304</td></tr><tr><td>Condition</td><td>0.232</td><td>0.062</td><td>0.001***</td></tr><tr><td>Time</td><td>0.051</td><td>0.026</td><td>0.052</td></tr><tr><td>Condition × Materialism</td><td>0.086</td><td>0.032</td><td>0.006***</td></tr><tr><th>Random Effects</th><th>σ<sup>2</sup></th><th>SE</th><th></th></tr><tr><td><strong>Participants</strong></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.041</td><td>0.202</td><td></td></tr><tr><td><strong>Stimuli</strong></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.398</td><td>0.631</td><td></td></tr></tbody></table>

revealed that the mere association of a brand with a product can lead participants to look at it differently, and to choose it differently, depending on their materialism. This is in line with previous evidence revealing the importance of external cues in consumer choices (e.g., McClure et al., 2004), but we revealed here that this can also be observed in the context of luxury consumption.

Finally, our results reveal that psychological values such as materialism modulate the impact of extrinsic cues (e.g., Sörqvist et al., 2015). While the effect was not strong in our results regarding the pattern of visual attention, results on the choice variable revealed that the brand had a different impact on participants' choice, depending on their level of materialism. These results show further evidence that the brand was a strong vector of the luxury dimension of a product for materialistic people (Audrin et al., 2017a,b). More generally, this result echoes previous results revealing the strong link between materialism and luxury (Gil et al., 2012).

In the next experiment, we studied how providing textual information about the product in addition to the brand label modulates participants' visual attention and subsequent choices. We hypothesize that, in contrast to Experiment 1, where only brand information was provided, providing supplementary information about the product quality will lead both participants with high and low levels of materialism to prefer luxury.

## EXPERIMENT 2

Products are embedded with multiple cues (Miyazaki et al., 2005) such as quality information and brands information. When facing multiple congruent cues, people usually take them into account in an additive way (Anderson, 1981; Miyazaki et al., 2005). However, research suggest that individual characteristics may impact the way multiple cues are integrated in the process of decision-making. For instance, Ahluwalia et al. (2000) revealed that depending on their brand commitment, consumers gave different diagnostic weights for positive and negative information.

In this experiment, we wanted to assess how materialism as a value impacts information integration during choice. To this
end, we provided text information in addition to the brand such as the country of origin and the material of the products. Our hypothesis was that when provided with supplementary intrinsic information, both participants with high and low materialism will prefer luxury. We suggest however that this may be driven by different reasons: materialistic participants can be expected to predominantly consider the symbolic aspect embedded in luxury, as previous research has shown that people high in materialism focused on the brand in the context of luxury consumption (Gil et al., 2012; Audrin et al., 2017a). Thus, we hypothesize that, as in Experiment 1, people with high level of materialism would primarily focus on the brand. In contrast, we hypothesize that consumers with low levels of materialism would primarily look for more objective information about the product as provided by the textual information, as literature suggests that they like luxury preferably for the quality it guarantees (Audrin et al., 2017a). Thus, we suggest that people low in materialism will mostly look at intrinsic textual information about the product. Taken together, we hypothesized that (1) all participants would show higher visual attention and choose luxurious condition more often than non-luxurious condition (Model 1), (2) all participants would choose more often products at which they looked longer (Model 2) and finally (3) pictorial information would be more determinant in the choices of people with high level of materialism, whereas textual information would be more determinant in people's with low level of materialism choices (Model 2).

# Methods

## Participants and Procedure

Among 195 female students in psychology at the University of Geneva, 60 were recruited based on their materialism scores on the Aspiration Index (Grouzet et al., 2005). Four of them were removed from the final sample because the eye-tracker was not able to detect their eyes, resulting in a sample of 56 participants (*mean age*: 22 ± 4 years). Our final sample consisted of 24 people with high levels of materialism (*mean* = 0.29 ± 0.42) and 32 people with low levels of materialism individuals (*mean* = -2.55 ± 0.52).

When coming to the laboratory, participants were invited to sit in front of a computer in a cubicle. The same set of 46 images of ready-to-wear products were presented as in Experiment 1. Half of the products were randomly assigned to in the luxury condition, while the other half was presented in the non-luxury condition. The luxury and non-luxury conditions were randomized between participants so that each product was seen with each of the brands across the whole sample. The eye-tracker was calibrated before each session. The task was similar to the one described in Experiment 1. In order to test the interaction between Materialism, Condition and Information, we manipulated the text information provided with each product. Each product was presented with a brand, as well as with information about the product's price, country of origin, and material. This information was gathered from the websites of the brands in question, and adjusted according to the presented product. Half of the products were presented in the luxurious condition (i.e., country of origin, price, material), while the other

half was presented in the non-luxurious condition. The number of words was the same in the text associated with luxurious and non-luxurious brands (3 words for the country where the product was made and 2 words for the composition; see **Figure 3** for a prototypical example). The study was performed according to the rules and regulations of the University of Geneva and the declaration of Helsinki. All participants gave their written informed consent to take part in the study.

# Data Acquisition

## Materialism

One hundred and ninety-five participants were presented with the Aspiration Index, and we computed their materialism scores as the relative importance of extrinsic aspirations vs. the importance of intrinsic aspirations. Based on these materialism scores for all participants, we selected the upper (high level of materialism) and lower (low level of materialism) quartile of the initial sample to participate to the experiment.

# Eye-Tracking Data

When coming to the laboratory, participants were seated 60 cm away from a 43 cm-wide screen and were asked to keep their head in the same position during the experiment. As for Experiment 1, movement of the eyes were recorded at a 60-Hz frequency using infrared cameras of the Tobii eye-tracker, located at the bottom of the screen.

# Data Analyses

To answer our first hypothesis (i.e., that all participants will show higher visual attention and choose luxurious condition more often than non-luxurious condition), we performed a linear mixed model on the total fixation time (Model 1). We entered participants and stimuli as random effects. As fixed effects, we introduced Information (i.e., picture vs. text information), Condition (luxurious vs. non-luxurious condition), and Materialism (high vs. low materialism) and their interaction. Descriptive statistics are reported in **Table 5**.

In order to assess our second (i.e., all participants will choose more often products at which they looked longer) and third hypotheses (pictorial information will be more determinant in participants high on materialism, whereas textual information will be more determinant in the choices of participants with low levels of materialism, respectively), a generalized linear mixed model was performed on the choices made by participants (Model 2). For each choice, we predicted the probability that the product on the left would be chosen. The fixed effects were Condition (luxurious vs. non-luxurious condition) and Materialism (high vs. low on materialism). We further introduced two variables referring to the time spent on the information variable. The first variable accounted for the relative time spent on the picture (i.e., product and brand) on the left as opposed to the total time spent on the pictorial information before making the choice (Picture Time). The second variable accounted for the relative time spent on the text information of the product on the left as opposed to the total time spent on the text information before making the choice (Text Time). Participants and stimuli were introduced as random
<table><caption>**TABLE 5** | Descriptive statistics (mean and s.e.) for the Total fixation time variable.</caption><thead><tr><th>Materialism</th><th>Information</th><th>Condition</th><th>Time (ms)</th><th>s.e.</th></tr></thead><tbody><tr><td>High</td><td>Text</td><td>Luxury</td><td>632.693</td><td>50.735</td></tr><tr><td>High</td><td>Picture</td><td>Luxury</td><td>1136.966</td><td>52.348</td></tr><tr><td>High</td><td>Text</td><td>Non-luxury</td><td>524.014</td><td>53.117</td></tr><tr><td>High</td><td>Picture</td><td>Non-luxury</td><td>1095.82</td><td>46.878</td></tr><tr><td>Low</td><td>Text</td><td>Luxury</td><td>691.535</td><td>41.558</td></tr><tr><td>Low</td><td>Picture</td><td>Luxury</td><td>1189.684</td><td>38.569</td></tr><tr><td>Low</td><td>Text</td><td>Non-luxury</td><td>623.427</td><td>47.714</td></tr><tr><td>Low</td><td>Picture</td><td>Non-luxury</td><td>1190.373</td><td>47.756</td></tr></tbody></table>

<table><caption>**TABLE 6** | Descriptive analyses (mean and standard error (s.e.)) for the Choice variable.</caption><thead><tr><th>Materialism</th><th>Condition</th><th>Proportion of choice</th><th>s.e.</th></tr></thead><tbody><tr><td>High</td><td>Luxury</td><td>55.8%</td><td>0.034</td></tr><tr><td>High</td><td>Non-luxury</td><td>44.1%</td><td>0.034</td></tr><tr><td>Low</td><td>Luxury</td><td>55.5%</td><td>0.030</td></tr><tr><td>Low</td><td>Non-luxury</td><td>44.4%</td><td>0.030</td></tr></tbody></table>

effects (random intercepts). Descriptive statistics are reported on **Table 6**.

## Results

### Total Fixation Time

Results of Model 1 showed a main effect of Information [$b = -0.146, IC_{95\%} = [-0.155; -0.138], t_{(7,048)} = -34.366, p < 0.001$], revealing that participants spent more time looking at the pictorial elements than at the text information. Results further revealed a main effect of Condition [$b = 0.016, IC_{95\%} = [0.007; 0.023], t_{(6,994)} = 3.913, p < 0.001$; **Table 7**], supporting our hypothesis that all participants looked more at luxurious products than at non-luxurious products. The Condition × Information interaction was significant [$b = -9.25 * 10^{-3}, IC_{95\%} = [-0.017; -0.001]; t_{(6,994)} = 2.305, p = 0.023$], showing that the difference between the time spent looking at luxurious and non-luxurious products was more important when participants focused on text information ($t = -3.688, p < 0.001$) than when they looked at the picture ($t = -1.495, p > 0.05$). The Information × Materialism interaction was marginally significant [$b = -8.069 * 10^{-3}, IC_{95\%} = [-0.016; 0.003], t_{(6,997)} = 2.305, p = 0.058$, see **Figure 2**], suggesting that the impact of materialism on time spent looking at picture vs. text was different. Specifically, this suggests that for both groups, the time spent looking at the picture was longer than the time spent looking at the text, but that this difference was slightly more important for participants with high levels of materialism. Results revealed no further significant effect.

### Choice

Results from Model 2 (i.e., with Condition, Materialism and Time) showed a main effect of Condition ($b = 0.258, IC_{95\%}$

<table><caption>**TABLE 7** | Results for the Total fixation time variable, ***$p < 0.001$; *$p < 0.05$.</caption><thead><tr><th>Fixed effects</th><th>b</th><th>SE</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>2.810</td><td>0.024</td><td>0.001***</td></tr><tr><td>Condition</td><td>0.016</td><td>0.004</td><td>0.001***</td></tr><tr><td>Information</td><td>-0.147</td><td>0.002</td><td>0.001***</td></tr><tr><td>Materialism</td><td>-0.001</td><td>0.023</td><td>0.795</td></tr><tr><td>Condition × Information</td><td>-0.009</td><td>0.004</td><td>0.023*</td></tr><tr><td>Condition × Materialism</td><td>0.005</td><td>0.004</td><td>0.253</td></tr><tr><td>Information × Materialism</td><td>-0.008</td><td>0.004</td><td>0.059.</td></tr><tr><td>Condition × Information × Materialism</td><td>0.002</td><td>0.004</td><td>0.485</td></tr><tr><th>Random effects</th><th>σ<sup>2</sup></th><th>SE</th><th></th></tr><tr><td>Participants</td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.03</td><td>0.17</td><td></td></tr><tr><td>Stimuli</td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>0.001</td><td>0.041</td><td></td></tr></tbody></table>

[$b = [0.169; 0.349], z = 5.640, p < 0.001$; **Table 8**], where luxury products were chosen more often than non-luxurious products. The main effects of Picture Time ($b = 3.547, IC_{95\%} = [2.936; 4.153], z = 11.429, p < 0.001$) and of Text Time ($b = 1.079, IC_{95\%} = [0.780; 1.388], z = 6.957, p < 0.001$; see **Table 8**) revealed that, as hypothesized, the more participants looked at the text and pictorial information, the more they chose it. The interaction between the Picture Time and Text Time was significant ($b = -1.904, IC_{95\%} = [-3.656; -0.059], z = -2.033, p = 0.042$), indicating that the impact of the time spent looking at the picture on the choice weakened when the time spent looking at the text increased. Moreover, the interaction effect between Condition, Materialism and Picture Time was marginally significant ($b = 0.582, IC_{95\%} = [-0.024; 1, 469], z = 1.915, p = 0.055$; **Figure 3**). This interaction revealed that when participants with low levels of materialism looked longer at non-luxurious picture, they chose them more often ($z_{low-materialism} = 5.325, p < 0.001$). On the other hand, participants with high levels of materialism who looked longer at the picture of luxurious condition chose them more often ($z_{highmaterialism} = 5.794, p < 0.001$).

Finally, the interaction effect between Condition, Materialism and Text Time was significant ($b = -0.386; IC_{95\%} = [-0.660; -0.09], z = -2.702, p = 0.006$; **Figure 4**). This interaction revealed that when participants who scored high on materialism looked longer at text information of non-luxurious products, they would choose these products more often ($z = 1.882, p = 0.059$). In contrast, when participants scoring low on materialism looked longer at the text information of luxurious products, they would choose these products more often ($z = 5.233, p < 0.001$).

## Discussion

Supporting our hypothesis, results from Experiment 2 showed that when providing multiple congruent sources of information about a product, all participants chose luxury products more often. Our results highlight that individual characteristics have
<figure><img src="image_32.png" /><figcaption>**FIGURE 2** | Time spent looking (mean ± SEM) as a function of the Information (text vs. picture) for participants with high (gray) and low (black) levels of materialism (black). *p* < 0.05.</figcaption></figure>

an impact on how these multiple sources of information are taken into consideration when making a choice (Ahluwalia et al., 2000). Eye-tracking data revealed different patterns of visual attention for the groups: the more participants with high levels of materialism paid attention to the *picture* of the product (i.e., brand and picture), the more often they chose luxury products. On the contrary, the more participants with low levels of materialism looked at the *text* information, the more often they chose luxury products. These results suggest that individual values and goals lead to different patterns of visual attention and information integration during decision-making and product choice. Participants scoring high on materialism considered specifically the symbolic aspect embedded in luxury (i.e., they mostly focus on the brand): the more they looked at pictorial information, the more they chose luxury products. Participants scoring low on materialism looked for textual information about the product: the longer they looked at text information, the more they chose luxury products. This suggests that the preference for luxury may be related to specific dimension of luxury (Audrin et al., 2017a) depending on peoples' values. People with high levels of materialism favor symbolic dimensions by focusing on pictorial brand-related element (Audrin et al., 2017a,b). By contrast, participants with low levels of materialism pay relatively more attention to factual product information (Audrin et al., 2017a).

GENERAL DISCUSSION

In this work, we investigated how materialism modulates visual attention to specific sources of information in the context of luxury consumption. Using eye-tracking, we tested how visual attention allocated to pictorial brand information (Experiments 1, 2) and textual quality information (Experiment 2) was related to product choices for participants with high and low levels of materialism.

Experiment 1 tested the importance of pictorial brand-related information. Results revealed that higher levels of materialism were related to higher visual attention to the luxurious condition (i.e., product and brand). Increased visual attention furthermore enhanced the probability of choosing luxury products. Experiment 2 tested the importance of both pictorial and textual information, revealing that when adding supplementary textual information about the products, all participants paid increased attention and chose the luxurious condition more often. Differences in materialism may have lead participants to choose luxurious products for different reasons: the longer participants with higher levels of materialism looked at pictorial information, the more often they choose luxury products. In contrast, the longer participants with low levels of materialism looked at textual information, the more they choose luxury products.

Our results provide congruent evidence for the gaze cascade effect suggested by Shimojo et al. (2003). As suggested by (Shimojo et al., 2003), eye movements and eye gaze reveal a shift of attention toward one alternative (Krajbich and Rangel, 2011), and are involved in preference formation. This preference formation may further indicate interest toward the observed alternative (Shimojo et al., 2003). Our results are congruent with this literature, as the more participants looked at a product or its feature, the more frequently they chose it. Moreover, our results reveal that materialism, as a psychological value, may have an impact on visual attention. This is congruent with previous evidence revealing the importance of top-down processes, notably the importance of people's motives, experiences and concerns, on visual attention (Pool et al., 2016).

Results from a retail context (Chandon et al., 2009) suggest that gaze may be an indicator of *attention*. On the other hand, Shimojo et al. (2003) suggest that this may be an indicator of *interest*. Finally, Fink et al. (1996) suggest that *interest* may lead to higher *attention* toward an option. Our results seem to provide congruent results with Fink et al.'s work: while previous
<table><caption>**TABLE 8** | Results for the Choice variable (Model 2), ***p < 0.001; **p < 0.01; *p < 0.05.</caption><thead><tr><th>Fixed effects</th><th>b</th><th>SE</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>-0.197</td><td>0.104</td><td>0.057</td></tr><tr><td>Materialism</td><td>-0.09</td><td>0.056</td><td>0.099</td></tr><tr><td>Condition</td><td>0.258</td><td>0.046</td><td>0.001***</td></tr><tr><td>Picture time</td><td>3.547</td><td>0.31</td><td>0.001***</td></tr><tr><td>Text time</td><td>1.079</td><td>0.155</td><td>0.001***</td></tr><tr><td>Condition × Materialism</td><td>-0.047</td><td>0.047</td><td>0.317</td></tr><tr><td>Materialism × Picture Time</td><td>0.081</td><td>0.305</td><td>0.791</td></tr><tr><td>Condition × Picture Time</td><td>-0.100</td><td>0.304</td><td>0.743</td></tr><tr><td>Materialism × Text Time</td><td>-0.107</td><td>0.149</td><td>0.476</td></tr><tr><td>Condition × Text Time</td><td>-0.027</td><td>0.143</td><td>0.853</td></tr><tr><td>Picture Time × Text Time</td><td>-1.904</td><td>0.937</td><td>0.042*</td></tr><tr><td>Materialism × Condition × Picture Time</td><td>0.582</td><td>0.304</td><td>0.055.</td></tr><tr><td>Materialism × Condition × Text Time</td><td>-0.386</td><td>0.143</td><td>0.006**</td></tr><tr><td>Condition × Picture Time × Text Time</td><td>1.239</td><td>0.938</td><td>0.186</td></tr><tr><td>Materialism × Picture Time × Text Time</td><td>0.282</td><td>0.934</td><td>0.763</td></tr><tr><td>Condition × Materialism × Text Time × Picture Time</td><td>0.144</td><td>0.935</td><td>0.878</td></tr></tbody></table><table><thead><tr><th>Random effects</th><th>σ<sup>2</sup></th><th>SE</th></tr></thead><tbody><tr><td>Participants</td><td>0.05</td><td>0.241</td></tr><tr><td>Intercept</td><td></td><td></td></tr><tr><td>Stimuli</td><td>0.14</td><td>0.377</td></tr><tr><td>Intercept</td><td></td><td></td></tr></tbody></table>

research has suggested that materialism was related to higher interest toward luxury (Gil et al., 2012), our result reveal that this interest was related to higher attention to luxury. Future research should however specifically test the relation between materialism, interest and attention toward luxury.

As pointed out in the literature, when making a decision, individuals use attribute information as a way to estimate their probable satisfaction with the choices they are facing. However, products are often embedded with multiple attributes, which makes the integration of all pieces of information too difficult and too costly in terms of cognitive processes. People often focus on one single source of information, in order to make their decision easier (Johnson et al., 2012). This strategy allows consumers to reduce the cognitive effort, and to specifically focus on the attribute they perceive as the most important (Johnson et al., 2012). Our results suggest that this strategy was also used in our experiments, and further highlights that psychological values modulated the selection of the most important attribute.

Our findings support the suggestion that materialism, measured as a psychological value, might lead to a propensity to attend to specific aspects of luxury. As the previous literature reveals, materialistic people focus on the conspicuous aspects provided by luxury possessions (Gil et al., 2012; Audrin et al., 2017a), further suggesting that they give importance to the brand

as it can be displayed overtly. Conversely, people with low levels of materialism may consume luxurious products because of their quality (Audrin et al., 2017a).

Taken together, our results provide new insights to the field of research on consumer decision-making in the context of luxury consumption. To the best of our knowledge, our study is the first to manipulate displayed brands and text information of ready-to-wear products in an eye-tracking setting. Our results provide evidence for the importance of the brand in consumers' preferences establishment. Notably, our results show how strong the impact of brand information may be to (materialistic) consumers, as the brands were randomly presented with products, but still lead to a preference for the respective products. This result, consistent with previous research (e.g., Audrin et al., 2017a,b), points out that values are important factors to be taken into account when studying consumer decision-making. While the behavioral (choice variable) results revealed strong evidence in favor of our hypotheses, evidence was weaker regarding the eye-tracking data, which gave only moderate support to our hypotheses. This kind of discrepancy between self-reported and eye-tracking measures has been previously shown in the literature (Graham and Jeffery, 2011). However, it suggests that these results should be taken cautiously, and that the experiments reported here should be replicated in order to show more compelling evidence regarding the impact of personal values on visual attention in the context of luxury consumption. The measure of fixation duration should also be taken cautiously. Indeed, Krasich et al. (2018) revealed that mind wandering (i.e., the tendency to zone out, when the attention shifts away from the on-going task toward unrelated thoughts such as grocery shopping or upcoming vacations) was related to longer fixation durations, thus suggesting that longer fixation time is not a necessarily a proof of enhanced attention or interest.

Two additional limitations of our study are related to our sample. First, our participants were exclusively students, who may not be very familiar with luxury consumption, as luxury products are high-priced and thus not affordable to students. It would be interesting to evaluate the extent to which our results may apply to actual luxury consumers. Second, we focused our research on female participants. This was related to the products chosen, which were exclusively designed for women. An interesting follow-up would be to evaluate whether the results observed in our study are also true for men. Indeed, as Stokburger-Sauer and Teichmann (2013) mentioned, female participants have more positive attitudes toward luxury brands. Thus, assessing how men are sensitive to brands vs. textual information may provide further explanations on what people look for when consuming luxury.

## CONCLUSION

Our results point out how psychological values (i.e., materialism) modulate visual attention to specific sources of information (i.e., pictorial brand vs. text quality information). Eye-tracking data
<figure><img src="image_33.png" /></figure>

**FIGURE 3** | Proportion of choices of the object as a function of relative time spent looking at the picture in luxury (black plain line and circles) and non-luxury condition (gray dashed lines and triangles). Left panel depicts people with high levels of materialism and right panel depicts participants with low levels of materialism.

<figure><img src="image_34.png" /></figure>

**FIGURE 4** | Proportion of choices of the object as a function of relative time spent looking at the text in luxury (black plain line and circles) and non-luxury condition (gray dashed lines and triangles). Left panel depicts people with high levels of materialism and right panel depicts participants with low levels of materialism.

revealed that levels of materialism modulate the importance allocated to specific sources of information in the process of luxury decision-making: when only brand information was

available, materialism was related to enhanced preference for luxury. However, when supplementary information about product quality was available, all participants tended to
choose luxury more often. Interestingly, while people with high levels of materialism focused on the product picture and the brand, participants with low levels of materialism focused more on the text information about product quality when choosing luxury products. To summarize, our results suggest that materialism as a psychological value may impact how visual attention is directed toward specific sources of information in the context of luxury consumption.

## DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

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## AUTHOR CONTRIBUTIONS

CA, TB, DS, and JC contributed conception of the study. CA designed the study, organized the database, performed the statistical analysis, wrote the first draft of the manuscript. TB, DS, and JC contributed to manuscript revision, read and approved the submitted version.

## ACKNOWLEDGMENTS

We thank Géraldine Coppin for her valuable insights into this work and for helping improve the manuscript. This research was supported by the National Center of Competence in Research (NCCR) for the Affective Sciences, financed by a grant from the Swiss National Science Foundation (51NF40-104897), hosted by the University of Geneva.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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# Current Status on the Functional Characterization of Chemosensory Receptors of *Cydia pomonella* (Lepidoptera: Tortricidae)

Alberto Maria Cattaneo*

Division of Chemical Ecology, Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden

*Cydia pomonella* (Lepidoptera: Tortricidae) is a major pest of apple, pear and walnuts. For its control, alternative strategies targeting the olfactory system, like mating disruption, have been combined with insecticide applications. The efficacy of these strategies headed the direction of efforts for the functional characterization of codling moth chemosensory receptors to implement further control methods based on chemical sensing. With the advent of transcriptomic analysis, partial and full-length coding sequences of chemosensory receptors have been identified in antennal transcriptomes of *C. pomonella*. Extension of partial coding sequences to full-length by polymerase chain reaction (PCR)-based techniques and heterologous expression in empty neurons of *Drosophila melanogaster* and in Human Embryonic Kidney cells allowed functional studies to investigate receptor activation and ligand binding modalities (deorphanization). Among different classes of antennal receptors, several odorant receptors of *C. pomonella* (CpomORs) have been characterized as binding kairomones (CpomOR3), pheromones (CpomOR6a) and compounds emitted by non-host plants (CpomOR19). Physiological and pharmacological studies of these receptors demonstrated their ionotropic properties, by forming functional channels with the co-receptor subunit of CpomOrco. Further investigations reported a novel insect transient receptor potential (TRPA5) expressed in antennae and other body parts of *C. pomonella* as a complex pattern of ribonucleic acid (RNA) splice-forms, with a possible involvement in sensing chemical stimuli and temperature. Investigation on chemosensory mechanisms in the codling moth has practical outcomes for the development of control strategies and it inspired novel trends to control this pest by integrating alternative methods to interfere with insect chemosensory communication.

**Keywords:** *Cydia pomonella*, chemosensory receptors, functional characterization, *Drosophila* empty neuron system, human embryonic kidney (HEK293T) cells

**OPEN ACCESS**

**Edited by:**
Gérard Manière,
Université de Bourgogne, France

**Reviewed by:**
Ryuichi Okada,
Kobe University, Japan
Nai-Yong Liu,
Southwest Forestry University, China

**\*Correspondence:**
Alberto Maria Cattaneo
albertomaria.cattaneo@slu.se

**Received:** 28 June 2018
**Accepted:** 06 August 2018
**Published:** 27 August 2018

**Citation:**
Cattaneo AM (2018) Current Status on the Functional Characterization of Chemosensory Receptors of Cydia pomonella (Lepidoptera: Tortricidae). *Front. Behav. Neurosci.* 12:189. doi: 10.3389/fnbeh.2018.00189

## INTRODUCTION

The codling moth *Cydia pomonella* (Lepidoptera: Tortricidae) is a major pest insect of commercial crops such as apple, pear and walnuts of Palearctic and Nearctic regions (Witzgall et al., 2008).

Integrated with insecticides, alternative methods are commonly used to control this insect (Starà et al., 2008; Odendaal et al., 2015; Arnault et al., 2016; Iraqui and Hmimina, 2016). Among these methods, mating disruption, which targets the olfactory system of *C. pomonella*
males through the use of female sex pheromones, demonstrated efficient results to limit crop infestation (Hathaway et al., 1974; Ridgway et al., 1990; Light et al., 2001; Light, 2016). Furthermore, odors emitted by host-plants (kairomones), are combined with pheromones to enhance male attraction for the codling moth (Knight and Light, 2001; Light et al., 2001; Witzgall et al., 2001, 2005; Yang et al., 2004).

In insects, odors such as pheromones and kairomones are detected by olfactory sensory neurons (OSNs) that innervate specialized sensilla on their antennae (Buck and Axel, 1991; Chess et al., 1992; Vosshall et al., 2000; Carlson, 2001; Kurtovic et al., 2007). On the dendritic membrane of OSNs, odorants and pheromones mostly bind a class of seven-transmembrane proteins known as odorant receptors (ORs; Clyne et al., 1999). Deciphering mechanisms of receptor/ligand interactions and understanding pharmacological, kinetic properties and activation modalities of OR proteins, unveil promising aspects to improve strategies for the control of pest insects (Jones et al., 2011; Pask et al., 2011, 2013; Röllecke et al., 2013; Bobkov et al., 2014). Identification of ligands for specific ORs (deorphanization) among odors emitted from females and plant-hosts of the codling moth facilitates our understanding of the neurobiological and behavioral aspects at the base of the chemical ecology of *C. pomonella*. This contributes to possible application of novel ligands for semiochemical-based control strategies.

This mini-review reports the state of the art of current findings on the functional characterization of codling moth ORs as well as findings of a novel transient receptor potential (TRP) channel expressed in the olfactory system of *C. pomonella*. This contribution introduces ongoing studies on the molecular aspects of chemical sensing of the codling moth and their possible application to current control strategies targeting the olfactory system of the insect.

## IDENTIFICATION OF CHEMOSENSORY RECEPTORS OF *Cydia pomonella*

By means of a polymerase chain reaction (PCR)-based technique, the 3' end of gene transcripts encoding putative members of *C. pomonella* ORs (*CpomORs*) have been initially identified from total ribonucleic acid (RNA) samples extracted from antennae (Garczynski et al., 2012). In this study, a similar method described by Buck and Axel (1991) was used to design degenerate forward primers based on polypeptide sequence alignments of the C-terminus of 12 members of the pheromone receptor subfamily of *Bombyx mori* (Lepidoptera: Bombycidae) and *Heliothis virescens* (Lepidoptera: Noctuidae). Forward primers were used to amplify partial 3'-ends starting from retro-transcribed 3'-cDNA templates generated by SMART™ kits (Clontech, Mountain View, CA, USA). Among amplified 3'-ends, the first set of *CpomORs* were identified. This method represented the first effort in the isolation of *CpomORs* from antennal RNA samples, leading optimization of further RACE-PCR approaches to amplify the full-length coding sequences of other chemosensory genes of this insect, aimed to address their phylogenetic and functional characterization.

With the advent of transcriptomic analysis, a wider investigation was conducted by the use of 454-next generation sequencings (NGS) of antennal RNA-samples (Bengtsson et al., 2012). For the first time, a wide asset of assembled fragments of gene coding sequences was identified, revealing 14 candidate ionotropic receptors (IRs), one candidate gustatory receptor (GR) and 43 candidate ORs. Among these, five ORs were members of the putative pheromone receptors (PRs) subfamily: a monophyletic clade in Lepidopteran insect OR phylogeny, with receptors that predominantly respond to odors emitted by females (Jacquin-Joly and Merlin, 2004; Ihara et al., 2013; Leal, 2013). Among the five candidate PRs reported by Bengtsson et al. (2012), two PRs represented some of the same ORs identified in the previous work of Garczynski et al. (2012). With the aim to complement these studies, using Illumina-based RNA-sequencing, assembly of a transcriptome from male, female and larval olfactory tissues of the codling moth, a more complete list of chemosensory receptors of *C. pomonella* was updated to 21 IRs, 20 GRs and 58 putative ORs, among which, 11 represented members of the PR-clade (Walker et al., 2016; **Table 1**). Identification of IRs and GRs in antennal transcriptomes of the codling moth was in accordance with the reported findings of their functional importance in insect chemosensation (Clyne et al., 2000; Robertson et al., 2003; Benton et al., 2009; Montell, 2009; Ai et al., 2010; Silbering et al., 2011; Rytz et al., 2013; Missbach et al., 2014; Sanchez-Alcaniz et al., 2018). Despite their importance, most of the efforts to functionally characterize chemosensory receptors of the codling moth targeted ORs, with particular focus on members of the PR-subfamily.

## FUNCTIONAL CHARACTERIZATION OF *CpomOR3*

CpomOR3 represents the first OR of the codling moth that was isolated, heterologously expressed and functionally characterized (Bengtsson et al., 2014). Expression of this receptor was conducted in *Drosophila melanogaster* ab3A (Dobritsa et al., 2003; Gonzalez et al., 2016) and aT1 (Kurtovic et al., 2007; Montagné et al., 2012) empty neurons, to screen a panel of ligands among pheromones, synergists and antagonists known for their activation of the olfactory system of *C. pomonella*. Activation of CpomOR3 was demonstrated for the plant volatile ethyl-E,Z,2,4-decadienoate, commonly known as pear ester (Jennings et al., 1964; Berger and Drawert, 1984; Light et al., 2001; Yang et al., 2004; Knight et al., 2005; Willner et al., 2013). Phylogenetic analysis demonstrated CpomOR3 to be a PR-candidate. Activation of a putative PR to the synergist pear ester was in accordance with neurological effects identified when this compound was tested with the primary sex pheromone component of the codling moth (codlemone) on AL-glomeruli of the insect (Trona et al., 2010, 2013). CpomOR3 response to pear ester gave further support to the role of this compound as a kairomone, already known to enhance male attraction in orchards when combined with female pheromones (Light et al., 2001; Yang et al., 2004). These findings suggest a possible role of
<table><caption>**TABLE 1** | Updated list of *Cydia pomonella* odorant receptors (CpomORs) in comparison among results from Garczynski et al. (2012); Bengtsson et al. (2012) and Walker et al. (2016), based on their techniques (brackets).</caption><thead><tr><th>Walker et al. (2016)<br>(Illumina)</th><th>Bengtsson et al. (2012)<br>(454)</th><th>Garczynski et al. (2012)<br>(RACE-PCR)</th><th>Status</th><th>Clade</th></tr></thead><tbody><tr><td>CpomORco</td><td>CpomOR2</td><td>-</td><td>Complete<sup>a</sup></td><td>Co-receptor</td></tr><tr><td>CpomOR1</td><td>CpomOR4</td><td>CpomOR11</td><td>Complete<sup>a,b</sup></td><td>PR<sup>M</sup></td></tr><tr><td>CpomOR2a</td><td>CpomOR5</td><td>CpomOR1a<br>CpomOR11a</td><td>Incomplete<sup>b</sup></td><td>PR</td></tr><tr><td>CpomOR2b</td><td></td><td></td><td></td><td></td></tr><tr><td>CpomOR2c</td><td></td><td></td><td></td><td></td></tr><tr><td>CpomOR3</td><td>CpomOR3</td><td>-</td><td>Deorphanized<sup>a,b;1,2</sup></td><td>PR</td></tr><tr><td>CpomOR4</td><td>CpomOR6</td><td>CpomOR4</td><td>Complete</td><td>PR</td></tr><tr><td>CpomOR5</td><td>-</td><td>-</td><td>Complete<sup>b</sup></td><td>PR<sup>M</sup></td></tr><tr><td>CpomOR6a</td><td>CpomOR1</td><td>-</td><td>Deorphanized<sup>a,b;2</sup></td><td>PR<sup>M</sup></td></tr><tr><td>CpomOR6b</td><td>-</td><td>-</td><td>Complete<sup>b</sup></td><td></td></tr><tr><td>CpomOR7</td><td>-</td><td>-</td><td>Complete</td><td>PR<sup>M</sup></td></tr><tr><td>CpomOR8</td><td>-</td><td>-</td><td>Complete</td><td>PR</td></tr><tr><td>CpomOR9</td><td>-</td><td>-</td><td>Incomplete</td><td>PR</td></tr><tr><td>CpomOR10</td><td>CpomOR28</td><td>-</td><td>Complete<sup>b</sup></td><td>OR</td></tr><tr><td>CpomOR11</td><td>CpomOR11</td><td>-</td><td>Incomplete</td><td>OR<sup>l</sup></td></tr><tr><td>CpomOR12</td><td>-</td><td>-</td><td>Incomplete</td><td>OR</td></tr><tr><td>CpomOR13</td><td>CpomOR8</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR14</td><td>CpomOR14</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR15</td><td>CpomOR20</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR16</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR18</td><td>CpomOR10</td><td>-</td><td>Complete</td><td>OR<sup>l</sup></td></tr><tr><td>CpomOR19</td><td>CpomOR19</td><td>-</td><td>Deorphanized<sup>b;3</sup></td><td>OR</td></tr><tr><td>CpomOR20</td><td>CpomOR18</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR21</td><td>-</td><td>-</td><td>Incomplete</td><td>PR<sup>F</sup></td></tr><tr><td>CpomOR22</td><td>CpomOR15</td><td>-</td><td>Complete<sup>b</sup></td><td>PR<sup>F</sup></td></tr><tr><td>CpomOR25</td><td>CpomOR21</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR26</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR27</td><td>CpomOR27</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR28</td><td>CpomOR26</td><td>-</td><td>Complete<sup>b</sup></td><td>OR</td></tr><tr><td>CpomOR29</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR30</td><td>CpomOR30</td><td>-</td><td>Complete</td><td>OR<sup>F</sup></td></tr><tr><td>CpomOR31</td><td>-</td><td>-</td><td>Complete</td><td>OR<sup>M</sup></td></tr><tr><td>CpomOR32</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR35</td><td>CpomOR35</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR37</td><td>CpomOR36</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR38</td><td>-</td><td>-</td><td>Incomplete</td><td>OR</td></tr><tr><td>CpomOR39</td><td>CpomOR38</td><td>-</td><td>Complete<sup>b</sup></td><td>OR</td></tr><tr><td>CpomOR40</td><td>CpomOR33</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR41</td><td>-</td><td>-</td><td>Complete</td><td>OR<sup>F</sup></td></tr><tr><td>CpomOR42</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR44</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR46</td><td>CpomOR16</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR47</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR49</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR53</td><td>CpomOR9</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR54</td><td>CpomOR7<br>CpomOR41</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR56</td><td>CpomOR37</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR57</td><td>CpomOR31</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR58</td><td>CpomOR34</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR59</td><td>CpomOR12</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR60</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR61</td><td>CpomOR17</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR62</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR63</td><td>CpomOR23</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR64</td><td>CpomOR24</td><td>-</td><td>Complete</td><td>OR<sup>l</sup></td></tr><tr><td>CpomOR65</td><td>CpomOR22</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR66</td><td>CpomOR32</td><td>-</td><td>Incomplete</td><td>OR</td></tr><tr><td>CpomOR67</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>CpomOR68</td><td>-</td><td>-</td><td>Complete</td><td>OR</td></tr></tbody></table>

(Continued)
<table><caption>**TABLE 1** | (Continued)</caption><thead><tr><th>Walker et al. (2016)<br>(Illumina)</th><th>Bengtsson et al. (2012)<br>(454)</th><th>Garczynski et al. (2012)<br>(RACE-PCR)</th><th>Status</th><th>Clade</th></tr></thead><tbody><tr><td>CpomOR71</td><td>-</td><td>-</td><td>Complete<sup>b</sup></td><td>OR<sup>L</sup></td></tr><tr><td>CpomOR72</td><td>CpomOR40</td><td>-</td><td>Complete</td><td>OR</td></tr><tr><td>Not found</td><td>CpomOR13</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Not coding</td><td>CpomOR43</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Not coding</td><td>CpomOR44</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

<sup>a</sup>Receptors with male-bias expression; <sup>b</sup>Receptors with female-bias expression; <sup>c</sup>Receptors with larval-bias expression (Walker et al., 2016). <sup>a</sup>Receptors heterologously expressed in Human Embryonic Kidney (HEK293T); <sup>b</sup>Receptors heterologously expressed in Drosophila empty neurons (Figure 1). Deorphanized receptors are indicated with numbers based on published data: <sup>1</sup>Bengtsson et al. (2014); <sup>2</sup>Cattaneo et al. (2017b); <sup>3</sup>Gonzalez et al. (2015). Accession to the updated dataset of CpomORs is available in Walker et al. (2016).

kairomones in the mate-choice behavior and in the reproductive isolation of tortricids (Trona et al., 2013; Bengtsson et al., 2014).

To better elucidate mechanisms involving pear ester sensing for CpomOR3, functional characterization experiments based on *Drosophila* empty neurons have been implemented through the heterologous expression of this receptor in Human Embryonic Kidney (HEK293T) cells (Cattaneo et al., 2017b). The use of an *in vitro* method represented an alternative to the common approaches for the functional characterization of insect ORs based on *Drosophila* empty neurons. The choice of this method was supported by successful attempts on the expression of PR-candidates from moths belonging to Bombycidae (Grosse-Wilde et al., 2006), Noctuidae (Grosse-Wilde et al., 2007), Saturniidae (Forstner et al., 2009) and Tortricidae (Steinwender et al., 2015). Comparison of heterologous expression between *Drosophila* OSNs and HEK293T is shown in Figure 1.

In search of other possible ligands for CpomOR3, screening of a compound library on HEK293T cells validated activation of the receptor to both pear ester and the analogous methyl-(E, Z)-2, 4-decadienoate. Sensing of an analogous methyl-ester for the codling moth was reported for the first time by demonstrating larval attraction from emissions of ripe Bartlett pear (Knight and Light, 2001), although origins of methyl ester as a plant-emitted odorant are still debated. Indeed, aside from emission by Bartlett pear, methyl ester was found in the head, thoraxes and fecal pellets of the bark beetle *Pityogenes chalcographus* (Coleoptera: Curculionidae; Birgersson et al., 1990). In addition, methyl ester was also found in emissions from stink bugs of the genus *Euschistus* (Heteroptera: Pentatomidae; Aldrich et al., 1991; Tognon et al., 2016).

A remaining question is if interaction of the analogous methyl ester with the same receptor of ethyl-(E,Z)-2,4-decadienoate may result in a similar effect in the antennal lobe as an evidence of its synergism with codlemone (Trona et al., 2010, 2013).

## FUNCTIONAL CHARACTERIZATION OF CpomOR6a

Heterologous expression of codling moth receptors in HEK293T cells also deorphanized the PR candidate CpomOR6a as responsive to (E, E)-8-12-dodecadien-1-yl acetate (Codelmone acetate; Cattaneo et al., 2017b). Combining heterologous expression in *Drosophila* aT1, (E,Z)- and (Z,Z)-geometric isomers of codlemone acetate were also identified as partial

ligands of the receptor. Together with these ligands, CpomOR6a sensed (E)-10-dodecadien-1-yl acetate and, although with less specificity, (Z,E)-8-12-dodecadien-1-yl acetate.

Codlemone acetates are main pheromone components emitted by female moths closely related to *C. pomonella* (Frerot et al., 1979; Roelofs and Brown, 1982; Davis et al., 1984; Witzgall et al., 1996; Chambers et al., 2011). Although receptors of codlemone acetates of these species have not been isolated and deorphanized yet, overall sequence similarities and relatively high expression in *C. nigricana* and *Hedya nubiferana*, suggested the gene locus *OR6* to express a conserved receptor between *C. pomonella* and these tortricid species (Gonzalez et al., 2017). While speculative, a possible explanation of the existence of the codlemone acetate receptor in *C. pomonella* may be as a remnant of the former ancestor of the insect. However, conserving a receptor dedicated to detect other species may be important for reproductive isolation of the codling moth. Otherwise, since moths emitting codlemone acetates share the same host range with *C. pomonella*, detection of codlemone acetates may facilitate host finding for the codling moth. The evolution of a receptor specialized for the detection of a main pheromone compound, like codlemone, may likely represent a step towards allopatric speciation of *C. pomonella*.

Among candidate PRs of the codling moth (Table 1), the most likely sensor for codlemone is CpomOR1 given its abundant expression in OSNs of male moths (Bengtsson et al., 2014; Walker et al., 2016). Although heterologous expression methods in HEK cells and *Drosophila* empty neurons were unable to demonstrate CpomOR1 responsiveness to codlemone (Cattaneo et al., 2017b), future deorphanization attempts will unveil if this prediction holds true. Another remaining question is whether the transcript variant CpomOR6b has the same response spectrum as CpomOR6a and what might be its relevance, especially considering the lack of knowledge on alternative splicing in lepidopteran PRs (Garczynski and Leal, 2015).

## FUNCTIONAL CHARACTERIZATION OF CpomOR19

Although CpomOR19 is not a PR-candidate in *C. pomonella*, testing heterologous expression of this receptor in *Drosophila* ab3As is part of documented deorphanizations of chemosensory receptors of the codling moth (Gonzalez et al., 2015).
<figure><img src="image_36.png" /></figure>

**FIGURE 1** | Illustration of heterologous expression methods adopted to functionally characterize odorant receptors (ORs) of *Cydia pomonella*. (A) Functional expression in empty neurons of *D. melanogaster*, between ab3A (1) and aT1 (2) neurons. Spike trains example are provided below; red bars indicate stimulation period. Spike scales between ab3A/B and aT1 are shown on the top-right. Recording electrodes are shown as white triangles. (B) Functional expression in Human Embryonic Kidney (HEK293T) cells: (1) transfection of HEK293T cells with plasmids carrying coding sequences of Orco (blue) and ORx (yellow): activation of the Orco+ORx cation channel by ligand-binding is represented; calcium-influx in response to Orco+ORx activation is indicated with a blue arrow. (2) Incubation of HEK cells with a Ca<sup>++</sup>-sensitive dye: fluorescent response elicited by calcium-influx through the cation channel is represented. Perfusion system is shown as a white rectangle. Ligands are represented as red circles. Orco+ORx is represented as blue/yellow bars. Images of HEK293T cells at the bottom are part of our published data (Cattaneo et al., 2017b). Licence to the use of the material reported in the aforementioned publication is available at the following link: http://creativecommons.org/licenses/by/4.0/.

CpomOR19 is responsive to 1-indanone; several analogs of this compound (2-methyl-1-indanone, 2-ethyl-1-indanone and 3-methyl-1-indanone) elicit responses of different sensitivities by this receptor. These compounds are renowned for their “non-host” origins (Klein et al., 1990; Anderson et al., 1993; Nagle et al., 2000; Okpekon et al., 2009; Rukachaisirikul et al., 2013). CpomOR19 binding to indanes represented the first deorphanization of a receptor of the codling moth to compounds emitted by non-hosts. Interestingly, different sensitivities between 1-indanone and its analogs for the CpomOR19 binding is consistent with observations reported between pear and methy esters on CpomOR3, where one carbon of the alkyl group may determine different binding affinity, perhaps due to differences in the polarity of the compounds (Cattaneo et al., 2017b).

By use of the same method, activation of the ortholog
SlitOR19 of the African cotton-leaf worm *Spodoptera littoralis*
(Lepidoptera: Noctuidae) demonstrated conservation in binding
1-indanone and analogous. When compared, ab3A expressing
CpomOR19 and SlitOR19 showed increased response to indanes,

when substituted with alkyl groups at position two and three of
the five-membered ring. On the contrary, indanes provided with
methyl substituents on the benzene ring largely did not activate
these receptors. Furthermore, indanes provided with alcohols,
hydrocarbons and amine groups also did not activate any of
the two receptors, which suggested a conserved function for
CpomOR19 and SlitOR19 orthologs, despite the phylogenetical
and ecological distance of their respective moths. A recent report
on *Spodoptera* ORs provides a blueprint for prediction of SlitOR
ligands based on the interaction of phylogeny and chemical
structure (de Fouchier et al., 2017). Given evidences of conserved
function between CpomOR19 and SlitOR19, prediction of
SlitOR ligands may benefit future studies on deorphanization of
CpomORs.

PHYSIOLOGICAL PROPERTIES OF
CpomORs

Expression of CpomOR genes in HEK cells was undertaken
by co-transfecting the CpomOrco co-receptor subunit of the
codling moth (Figure 1B). Functional studies of CpomOrco demonstrated heteromeric complexes of the co-receptor with OR subunits being more sensitive than homomeric co-receptor complexes, as previously demonstrated for Orco-based channels of other insects (Jones et al., 2011; Pask et al., 2011; Kumar et al., 2013; Turner et al., 2014). By the use of the main ligand VUAA (Jones et al., 2011), calcium response was characterized by faster activation/deactivation kinetics for CpomORco+OR than CpomORco alone. Testing inhibitors like amiloride derivatives (ADs; Pask et al., 2013; Röllecke et al., 2013) demonstrated similar effects for both homomeric and heteromeric complexes. When HEK cells were tested by whole-cell and outside-out patch-clamp recordings, activation of CpomOrco+OR complexes resembled modalities of ligand-gated cation channels: responses to multiple stimulations were characterized by constant amplitudes and stable kinetic parameters, which is indicative of the ionotropic nature of insect OR receptors (Sato et al., 2008).

Despite that the molecular mechanisms at the base of signal transduction of insect olfactory systems still remain unknown (Krieger and Breer, 1999; Jacquin-Joly and Merlin, 2004; Sakurai et al., 2014), results on the functional characterization of *C. pomonella* ORs are consistent with the idea that all insect or, perhaps, even all arthropod chemosensory receptor channels (among ORs and IRs) can be characterized by somewhat common pharmacology (Bobkov and Ache, 2007; Abuin et al., 2011; Bobkov et al., 2014). Although, this might be called into question given some evidence pointing towards metabotropic signaling modalities for insect ORs (Sargsyan et al., 2011; Getahun et al., 2013; Ignatious Raja et al., 2014).

## TRANSIENT RECEPTOR POTENTIAL CHANNELS OF *Cydia pomonella*

A second analysis of sequencing data from Bengtsson et al. (2012), unveiled further transcripts related with ligand-gated cation channels belonging to the class of TRP. In several organisms, TRP-channels enable sensing of multiple stimuli from the environment (Liedtke and Heller, 2007). Among chemical stimuli, several compounds commonly found in food plants and spices are reported to activate TRPs (Caterina et al., 1997; Jordt et al., 2004; Xu et al., 2006; Bautista et al., 2007). Interestingly, TRP-active compounds are reported for their ability to repel insects (Leung and Foster, 1996; Barnard, 1999) and, in particular, to activate the olfactory system of tortricid and noctuid moths (Cattaneo et al., 2014; Wei et al., 2015).

In *C. pomonella*, five TRPs have been found in the antennae belonging to the TRPC (TRP, TRPC) and the TRPA subfamily (Pyrexia, water witch, TRPA5; Cattaneo et al., 2016). Up to now, *CpomTRPA5* is the only TRP of the codling moth that has been extended to the full length. Interestingly, five variants of the spliced-coding sequence have been found, demonstrating different expression patterns among body parts of the codling moth. Analysis of the *CpomTRPA5* mRNA sequence demonstrated the transcript undergoing to mRNA editing by insertion of 15 additional nucleotides within the third exon of the full-length sequence, which is a mechanism

occurring for $K^+$ channels of multiple organisms, including insects (Holmgren and Rosenthal, 2015). Evolutionary studies suggested the relatedness of *TRPA5* gene to the thermal sensor *Pyrexia* (Peng et al., 2015), which has also been described as a thermal-gated $K^+$-channel of insect (Lee et al., 2005).

Identification of TRPs in *C. pomonella* represented the first documented finding within this species for this particular class of chemoreceptors. Identification of *CpomTRPA5* and its spliceforms is among the first documented existences of this particular subunit for arthropod TRPAs (Peng et al., 2015). Relatedness of *CpomTRPA5* with *Pyrexia* suggested a possible role of the *CpomTRPA5* receptor as a thermal sensor, which is consistent with behavioral evidences for the codling moth of odor-guided responses in relation with temperature (Witzgall et al., 1999).

By means of methods adopted to test activation of mammalian TRPAs expressed in HEK cells (Bassoli et al., 2009, 2013; Cattaneo et al., 2017a), functional characterization studies of CpomTRPA5 may be conducted to better elucidate possible roles of this receptor in chemical and physical sensing modalities of the codling moth.

## FUTURE PERSPECTIVES

The technologies adapted to the setup of transcriptomic and heterologous expression studies for the functional characterization of chemosensory receptors of *C. pomonella*, may offer new opportunities to address longstanding questions in the field of insect ecology, with a practical outcome for the implementation of its control strategies.

Two out of the three codling moth ORs that have been deorphanized, belong to the clade of putative Pheromone Receptors. Although attempted, the receptor for the main pheromone codlemone has not been functionally characterized. To validate a possible role of CpomOR1 as a main candidate sensor (Bengtsson et al., 2012; Walker et al., 2016; Cattaneo et al., 2017b), future experiments will verify if co-expression of CpomOR1 with CpomOR6a in *Drosophila* aT1 neurons is sensitive to codlemone. This approach is supported by evidences of response to codlemone acetates from OSNs of *C. pomonella* responding to codlemone (Bäckman et al., 2000), which may suggest a possible role of the CpomOR6a subunit to sense this pheromone. In addition, studies on several insects demonstrated co-expression of different OR subunits in the same OSN (Couto et al., 2005; Fishilevich and Vosshall, 2005; Goldman et al., 2005; Ray et al., 2007; Koutroumpa et al., 2014; Karner et al., 2015; Lebreton et al., 2017), and stoichiometry of OR heteromers is still debated (Larsson et al., 2004; Benton et al., 2006; Wicher, 2018).

In support of the control of the codling moth with mating disruption, novel trends are leading the direction of studies to integrate targeting of sensing modalities of codling moth females. Indeed, methods based on mating disruption demonstrated inefficacy to the control of the codling moth at high population in the orchards, as well as on the top of tree branches, where the pheromone cloud is limited (Witzgall et al., 1999).
Identification of CpomORs with a female-biased expression (Bengtsson et al., 2012; Walker et al., 2016) motivates the use of heterologous methods to address their functional characterization (Swedish Research Council Formas, Project Reg. No. 2016-01281 "Control of Apple Pest Insects with Fruit and Yeast Odorants"). This approach may identify novel ligands active on female olfactory systems. Among these ligands, odors emitted by fruits and their associated microbes may be tested, given the importance of yeasts for attractiveness of egg-lying females (Witzgall et al., 2012).

Recent studies based on CRISPR/Cas9 editing of the codling moth, demonstrated efficacy of this method to address knockdown of functional OR proteins, which resulted in affection of fecundity and fertility, with edited females producing nonviable eggs (Garczynski et al., 2017). Future targets may combine heterologous expression methods, with the use of CRISPR/Cas9 to generate OR-edited insects, as a complementary approach to address the functional characterization of codling moth receptors.

Future trends integrating research on the olfactory system of *C. pomonella* may target larval chemical sensing as complementary to the current approaches addressing the functional characterization of adult ORs (Formas Mobility Starting Grant Reg. No. 2018-00891 "Control of Fruit Pests by Targeting Larval Chemical Sensing," submitted). Indeed, chemosensory mechanisms at the base of larval behavior are long renowned for the codling moth (Knight and Light, 2001; Jumean et al., 2005) and expression of CpomORs with a larval-bias has been reported (Walker et al., 2016).

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Broader discoveries on the molecular bases of the olfactory mechanisms of *C. pomonella* will enhance current control strategies interfering with the insect's chemosensory communication. Development of novel methods targeting olfaction may help limit the use of insecticides, with beneficial effects on the quality of life for apple growers, consumers, as well as public living around the orchard areas, reducing further the conflict between agricultural and urban worlds.

## AUTHOR CONTRIBUTIONS

AMC wrote the manuscript.

## FUNDING

The financial support for this study has been provided by the Craaford Foundation (Ref. No: 20170728), integrated with the Formas Project Reg. No.: 2016-01281 "Control of Apple Pest Insects with Fruit and Yeast odorants"—Swedish Research Council. Costs for submission will be covered by the *Martha och Dagny Larssons fond*—Swedish University of Agricultural Sciences (Protokoll 172-174, Sammanträdesdatum 2018-04-24).

## ACKNOWLEDGMENTS

The author acknowledges Dr. William B. Walker III and Prof. Peter Witzgall for general discussion, personal communications and availability in phase of writing this contribution. Language editing has been courtesy performed by Dr. William Walker.

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**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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# Using Temporal Expectation to Assess Auditory Streaming in Mice

Gaëlle A. Chapuis¹* and Paul T. Chadderton¹,²*

¹Department of Bioengineering, Imperial College London, London, United Kingdom, ²School of Physiology, Pharmacology and Neuroscience, University Walk, University of Bristol, Bristol, United Kingdom

Auditory streaming is the process by which environmental sound is segregated into discrete perceptual objects. The auditory system has a remarkable capability in this regard as revealed in psychophysical experiments in humans and other primates. However, little is known about the underlying neuronal mechanisms, in part because of the lack of suitable behavioural paradigms in non-primate species. The mouse is an increasingly popular model for studying the neural mechanisms of perception and action because of the range of molecular tools enabling precise manipulation of neural circuitry. Here we present a novel behavioural task that can be used to assess perceptual aspects of auditory streaming in head-fixed mice. Animals were trained to detect a target sound in a one of two simultaneously presented, isochronous pure tone sequences. Temporal expectation was manipulated by presenting the target sound in a particular stream either early (~2 s) or late (~4 s) with respect to trial onset in blocks of 25-30 trials. Animals reached high performance on this task (d' > 1 overall), and notably their false alarms were very instructive of their behavioural state. Indeed, false alarm timing was markedly delayed for late blocks compared to early ones, indicating that the animals associated a different context to an otherwise identical stimulus. More finely, we observed that the false alarms were timed to the onset of the sounds present in the target stream. This suggests that the animals could selectively follow the target stream despite the presence of a distractor stream. Extracellular electrophysiological recordings during the task revealed that sound processing is flexibly modulated in a manner consistent with the optimisation of behavioural outcome. Together, these results indicate that the perceptual streaming can be inferred via the timing of false alarms in mice, and provide a new paradigm with which to investigate neuronal mechanisms of selective attention.

**Keywords:** auditory cortex (AC), scene analysis, psychoacoustic, selective attention, top-down pathways, false alarm (FA)

## 1. INTRODUCTION

In an acoustic scene comprising multiple auditory sources, humans and animals can readily identify and track a relevant sound source amongst the background noise, and switch from tracking that source to follow another (Fritz et al., 2007; Shamma et al., 2011). Such ease however belies the complexity of the underlying processes: the auditory system must parse sounds from various sources into discrete perceptual objects (a process known as “auditory stream segregation”; Bregman, 1994), and may further enhance the representation of relevant inputs to best support behavioural needs (Crick, 1984; Fritz et al., 2007; Shamma et al., 2011). Switching attention from

**OPEN ACCESS**

**Edited by:**
Gérard Manière,
Université de Bourgogne, France

**Reviewed by:**
Etsuro Hori,
University of Toyama, Toyama, Japan
Anna Magnusson,
Karolinska Institutet (KI), Sweden

**\*Correspondence:**
Gaëlle A. Chapuis
gaëlle.chapuis11@imperial.ac.uk
Paul T. Chadderton
p.chadderton@bristol.ac.uk

**Received:** 03 July 2018
**Accepted:** 17 August 2018
**Published:** 11 September 2018

**Citation:**
Chapuis GA and Chadderton PT (2018) Using Temporal Expectation to Assess Auditory Streaming in Mice. *Front. Behav. Neurosci.* 12:205. doi: 10.3389/fnbeh.2018.00205
one source to another has been shown to modulate sensory
signal representation at various levels of the auditory pathway,
notably in the auditory cortex (AC) (Fritz et al., 2003, 2007;
Mesgarani and Chang, 2012; Lakatos et al., 2013; Rodgers and
DeWeese, 2014). However, little is known about the underlying
mechanisms. This may be due to the complex and numerous
neural pathways involved in modulating the activity in AC
during attentional processes (Froemke et al., 2012; Pinto et al.,
2013; Nelson and Mooney, 2016; Winkowski et al., 2017). To
understand the contribution of each pathway, it is necessary
to develop experimental paradigms enabling their specific
manipulation.

The mouse is an attractive experimental model because of the
wealth of genetic and molecular approaches that are available
to manipulate anatomically defined neural circuits (Havekes and
Abel, 2009; Garner and Mayford, 2012; Harris et al., 2014; Park
and Carmel, 2016). Moreover, mice rely on auditory signals
for a wide range of behaviourally relevant tasks (Pereira et al.,
2012; Konopka and Roberts, 2016; Itatani and Klump, 2017),
and are capable of flexible behaviour (Bissonette and Powell,
2012; Jaramillo and Zador, 2014; Hamilton and Brigman, 2015).
However, no behavioural paradigm exists yet in mice to study
the effect of selective attention on auditory streaming. Current
paradigms of auditory selective attention in mice (Ahrens et al.,
2014; Rodgers and DeWeese, 2014; Wimmer et al., 2015) mostly
rely on briefly presented stimuli, which do not allow for auditory
stream formation (Bregman, 1994; Moore and Gockel, 2012). In
contrast, behavioural tasks are far more extensively developed
in primates (Lakatos et al., 2013; Calderone et al., 2014), owing
to their greater capacity to learn complex rules (Bissonette and
Powell, 2012). Translating behavioural tasks from primates to
mice would enable deeper insight of the neural mechanisms
underlying auditory processing and perception.

Here, we present a novel behavioural task inspired from
primate models (Lakatos et al., 2013), that can be used to assess
the effect of selective attention on auditory streaming in head-
fixed mice. By introducing biases in the acoustic stimuli and
studying the timing of mice behavioural decisions, we were
able to assess whether the animals switched from listening
to one sound source to another within an auditory mixture
in single behavioural sessions. Our paradigm is advantageous
as it enables the quantification of attentional state upon
electrophysiological signals acquired acutely. By recording neural
activity in the AC of mice during ongoing behaviour, we
reveal that sound processing is flexibly modulated in a manner
consistent with the optimisation of behavioural outcome. Future
use of this behavioural paradigm combined with molecular tools
to manipulate neural circuits would offer great insight on the
underlying basis of auditory selective attention and streaming.

## 2. RESULTS

### 2.1. Design and Validation of a New
Auditory Task Involving Selective Attention
and Auditory Streaming

We developed a Go/No-Go auditory selective attention paradigm
in mice, modelled after a related study in primates (Lakatos et al.,

2013). Two auditory streams were simultaneously presented
to the subject (Figure 1A); one stream was composed of high
frequency tones each separated by short time intervals (High
stream), and the other stream was composed of low frequency
tones separated by longer time intervals (Low stream). Mice were
rewarded for correctly detecting a target frequency-modulated
sound embedded within one of the two streams (Figure 1B). We
guided the subject toward attending to one specific stream by
fixing the target features for blocks of consecutive trials. During
the first trials of a block, only the target of a given stream was
presented (target only condition; S0), then the single stream
associated with that target was also presented (single stream
condition; S1), and finally both streams were simultaneously
presented (dual stream condition; S2) (Figure 1C). The target
alone (S0) and single stream (S1) conditions were intended as
cues indicating upcoming target features in the dual stream (S2)
condition. We ensured that the animals could switch attentional
state during single behavioural sessions by using blocks of ~40
trials, and by presenting the different blocks in strictly alternating
fashion.

As we used a rather small number of trial in each block
compared to previous studies in rodents (Jaramillo and Zador,
2010, 2014), it was critical to ensure that our subjects could
identify the change in target feature probability. Because
rodents are known to be particularly sensitive to temporal
biases (Buhusi et al., 2009; Jaramillo and Zador, 2010; Tosun
et al., 2016), we differentiated the target timing for each
of the two streams. Specifically, high frequency targets were
presented early (~2 s from trial onset; Early trial block), and
low frequency targets were presented late (~6 s from trial
onset; Late trial block). Any difference in behavioural response
timing observed across blocks would indicate that mice were
sensitive to the block design despite the small number of trial
used per block. It is important to highlight that in the dual
stream (S2) condition, the stimuli presented before the targets
were identical in both block types, as displayed in Figure 1A.
Mice achieved high performance in this target detection task
(Figure 1D and Supplementary Figures 1,2; $d' > 1$ for both
blocks, S2 condition). Furthermore, they were sensitive to the
trial block design, as revealed by the change in their false
alarm (FA) response pattern between Early and Late blocks
(Figure 2). Notably, FA in the Late block were significantly
delayed compared to those in the Early block (Figure 2B; FA
response time: Early block = 1,358 ± 57 ms, Late block = 3,720
± 633 ms, median ± median absolute deviation; $p < 0.001$, WSR
test, $N = 25$ sessions in 4 mice, only FA in S1 and S2 were used).

Having shown that mice timed their behavioural responses
according to known target temporal biases (i.e., Early or Late),
we explored whether mice specifically attended to the cued
auditory stream. To answer this question, we analysed FA
response patterns more finely. Notably, FA responses appeared
aligned to tone onsets in the single stream (S1) but not in the
target-only (S0) condition (Figure 3A). We formally quantified
the locking of FA to tone onset by measuring the vector
strength (VS) of the FA reaction time (RT) distribution in
both conditions (Figure 3B). For all mice, the VS was lower
in the target alone (S0) condition compared with the single
stream (S1) condition (Figures 3C,D; $VS = 0.46 \pm 0.06$ in
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**FIGURE 1** | Complex discrimination task involving auditory selective attention. (A) The stimulus consisted of two trains of 100 ms pure tone; one with High frequency tones separated by 300 ms, and one with Low frequency tones separated by 517 ms. A frequency-modulated target could occur at three positions with equal probability in a trial (shown by the colored squares). High frequency targets were presented Early, and Low frequency targets were presented Late.
(B) Water-restricted mice were head-fixed onto an apparatus enabling the detection of licking events. The first lick detected in a trial was counted as the response (represented as the darker filled dot on top of the stimulus trace). The mouse was required to not lick before the occurrence of a target. If the mouse responded in the target window, a drop of water was administered (Hit). (C) Target timing and frequency content was biased using a trial block design. A block was composed of three trial types: target only (S0), single stream (S1), and dual stream (S2). Once the S2 condition was reached, mice had to perform 20-25 correct trials for the stimulus to switch to the other block type. (D) Averaged *d'* over the last 3 training sessions prior to electrophysiological recording for each mouse in the S2 Early and Late condition. *d'*-values above 1 indicate good performance.

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**FIGURE 2** | Mice were sensitive to temporal bias as revealed by their false alarm timing. (A) Example session of a mouse (ID 3). Each dot represents a response (Hit or FA). FA are color-coded based on the block type (Early or Late). (B) Significant increase in the median of FA time (measured from trial onset) in the Late vs. Early condition. Error bars represent median absolute deviation. ***$P < 0.001$.

S1 condition, VS = 0.07 ± 0.01 in S0 condition; *p* < 0.01, WSR test). Moreover, significant VS was only found in the single stream (S1) condition (Figure 3C, starred distributions), indicating that mice did not use the trial onset to time their response with such specificity. This result confirmed that FA were not randomly executed, but instead timed to stream tone onsets.

Based on this finding, we hypothesised that mice sensitive to the target spectral bias (i.e., High or Low) may selectively attempt to respond to tones of the cued stream rather than in the non-cued stream, a strategy that increases the chance of responding correctly. To test this hypothesis, we reported the FA in the dual stream (S2) condition to the nearest High and Low tone onset, in cases where either the High stream

(Early block) or Low stream (Late block) was cued (Figure 4). The difference between RT distributions indicated whether the subject adapted its behaviour according to the spectral probability of the target. We observed considerable heterogeneity in behavioural response strategies of the individual mice: one animal executed FAs mainly in response to tones in the High stream, two animals executed FAs mainly to the Low stream, and one animal (# 2; Figure 4) timed its FAs to the cued stream. These results indicate that mice can be trained on this selective attention paradigm, and that different animals employ different behavioural strategies under the same task conditions. Assessment of FA timing patterns to high and low streams can be used to establish which behavioural strategy is being followed.
<figure><img src="image_40.png" /></figure>

**FIGURE 3** | Mice timed their false alarm responses to tone onsets. (A) Example FA during the Late block in a single session. Each dot represents a FA response, in the S0 (grey) or S1 (blue) condition. Vertical grey lines indicate sound onsets in S1 condition. (B) FA reaction time (RT) distributions for S0 and S1 conditions (same colors as in A). The Vector Strength (VS) indicates the peak strength of the distribution, and is reported in the inset (\*p < 0.05). (C). Normalised RT distribution for FA in S1 conditions for each mice (one mouse per column, the mouse ID is presented in the square box) computed using all sessions. RT are measured either from High (top) or Low (bottom) tone onsets. The distributions are color-coded based on the block type (Early or Late). The RT distribution of a mouse generating FA timed to sound onsets presents a peak, as assessed by the VS (\*p < 0.05). RT distributions are smoothed (1 bin std Gaussian filter) for display. (D) The VS was significantly higher in the S1 than S0 condition. Each line indicates the VS change between S1 and S0 condition for a mouse generating FA in a specific block type (Early or Late).
\*\*P < 0.005.

<figure><img src="image_41.png" /></figure>

**FIGURE 4** | Mice were sensitive to spectral bias as revealed by their false alarm timing. Normalised RT distribution for FA in S2 condition for each mice (one mouse per column, the mouse ID is presented in the square box) computed using all sessions. RT are measured either from High (top) or Low (bottom) tone onsets. The distributions are color-coded based on the block type (Early or Late). RT distributions are smoothed (1 bin std Gaussian filter) for display. The *p*-value from a KS test comparing the RT distributions in Early and Late conditions is displayed as an indication of response bias (\*\*p < 0.001, \*p < 0.01, \*p < 0.05; Bonferroni correction factor 8). The first mouse times its FA mainly to the High tones. The second mouse times its FA to the cued tones. The third and fourth mice time their FA mainly to the Low tones.

## 2.2. Selective Attention Modulated Neural Responses in Auditory Cortex Irrespectively of Frequency Tuning

Having shown that mice can be trained to selectively attend to the cued stream, we sought to determine whether neural activity in the AC varied according to the identity of the cued stream. We recorded single cells in the AC of behaving mice using tetrodes.

Each cell was assigned to a frequency-preference group (High responder or Low responder) based on its response to tones in the single stream (S1) condition (Figures 5A,B). All cells classified as High responder or Low responder were significantly activated by pure tones of a single frequency, either to the High frequency (*n* = 11 cells) or to the Low frequency (*n* = 6 cells) tones only.

We first focused the analysis on the activity recorded during the Early time zone (EZ; 0–1.6s from trial onset) occurring before
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

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**FIGURE 5** | Increase in spiking activity prior to target occurrence irrespective of frequency tuning. (A) Increase in spiking activity in Early vs. Late trials for an example cell tuned to the High frequency sound. Top and second panels: Raster plots and corresponding mean PSTHs of spiking activity during Hit trials in S2 condition, color-coded based on block type (Early or Late). The time axis is truncated so as to encompass only the Early zone (EZ; 0–1.6 s from trial onset). The orange arrow marks the onset time of the first target in the Early block. The vertical lines indicate tone onset (black: High frequency, grey: Low frequency). Third panel: Firing rate modulation (measured as the difference) between the Early and Late PSTHs. Values above zero indicate an increase in firing rate in the Early vs. Late condition. (B) Same as in (A), but for a cell tuned to the Low frequency. (C) Same as in (A), but for a cell classed as Task Modulated (T.M.). (D) Modulation of the firing rate between Early and Late conditions, displayed for each cell type. A value above 0 indicates an increase in the Early vs. Late trials. Each dot represents a cell, and the dot is filled with grey color if the firing rate of the cell significantly differed in Early vs. Late condition.

the first Early target onset in the dual stream (S2) condition. Since the stimuli in the dual stream (S2) Early and Late blocks were identical during the EZ, any difference in stimulus processing between Early and Late blocks could be attributed to top-down influences reflecting expectations. **Figure 5D** presents the modulation of the summed activity in the Early compared to Late

block for the different cell types, with values above 0 indicating an increase in firing rate in the Early block. Both High- and Low-responder cell populations displayed an increase in firing rate, as assessed by the average modulation index above 0. This increase was significant at the single cell ($n = 5$ cells, $p < 0.05$, WSR test) and population ($p < 0.01$, WSR test) levels.
To further confirm whether this increase in activity was not dependent on the cell's tuning, we measured the firing rate modulation of cells which were not significantly tuned to either Low or High tones, but which exhibited a high discrimination performance between Early and Late trials (AUROC > 0.6, using the sum of the PSTH to assess firing rate change in the EZ). These cells were labelled as Task Modulated (Figure 5C), and while they were not significantly responsive to High and Low tones, they might still be responsive to other sounds or to other aspects of the pure tones such as the offset (Sollini et al., 2018). As displayed in Figure 5D, the average modulation index for the Task Modulated cell population was also above 0, indicative of an increase in firing rate in Early compared to Late trials. Most of these Task Modulated cells had a significant modulation ($p < 0.05$, WRS test), which can be accounted for by the selection criterion based on high AUROC values. Therefore neural activity in the EZ of Early block trials was elevated relative to Late block trials, irrespective of the frequency tuning of individual cells, suggesting that the temporal proximity of an action (e.g., a lick) and/or reward is associated with a global increase in excitability in the AC.

To confirm whether the increase in firing rate was localised to the time prior to likely target occurrence, we measured the firing rate modulation of cells during Late trials at two different time intervals, one near trial onset and one near Late targets. These intervals were defined as early (0–1.6 s from trial onset) and late (4–5.6 s from trial onset), as is illustrated in **Figure 6A**. Importantly, both time zones encompassed a similar amount of Low and High tones (although organised in a different pattern relative to each other), and had similar duration. This allowed for a direct comparison of the mean firing rate during these two periods. Since High and Low cells were mostly responsive to single tones, the difference in tone presentation pattern was considered negligible when comparing the firing rates between the intervals. The **Figure 6A** presents an example cell (classed as Task Modulated) displaying an increase in firing rate over the course of Late trials. This effect was not an artefact, as analysis of the autocorrelogram and spike waveforms presented in **Figure 6B** confirmed. The minimal contamination in the refractory period argues against electrical noise generating the increase in activity visible in **Figure 6A**. This observation was highly consistent across cells, as most cells displayed an increase in firing rate during the period close to the Late target (Figure 6C). This effect was significant both at the single cell ($p < 0.05$, WSR test) and population ($p < 0.01$, WSR) levels. However, the Task Modulated cells were selected based on their ability to discriminate late and early zones (AUROC > 0.6), and might thus constitute a different population that the one presented in **Figure 5D**.

## 2.3. Neural Activity in Auditory Cortex Was Modulated by Upcoming Behavioural Decision

The previous finding indicated that the increase in firing rate observed preceding a likely target was not involved in the specific enhancement of sensory processing. Instead, this modulation

might reflect response preparation or reward anticipation, and might thus occur prior to any response, be it a Hit or FA. To test this hypothesis, we assessed whether cells with a high discrimination performance between Early and Late Hit trials could accurately classify the FA response types (Figure 7). Throughout this section, the activity during the Early and Late Hit trials is labelled as training data, whilst the activity during the Early and Late FA trials is labelled as test data. Early FA were defined as the FA responses made in the 1.2–2.8 s time window post trial onset in the Early S2 block, and Late FA were defined as the FA responses made in the 4–7 s time window post trial onset in the Late S2 block. For all trials, the activity considered was the average firing rate in the 0–1.2 s time window post trial onset. Firstly, a cell was classified as informative if its discrimination performance (i.e., the AUROC) was above 0.6 when comparing the activity in Early and Late S2 Hit trials (training data; Figure 7A). Then, a logistic regression classifier was trained on the training data (removal of 5% of trials for cross-validation). The accuracy of this classification typically matched the AUROC value. Then, the activity during each FA trial (test data) was presented to the classifier (see Figure 7B) and classification accuracy calculated. Since the number of Early and Late FA was generally unequal, it was important to build a baseline distribution for the classification accuracy. For example, a cell classifying any activity as originating from the class Early could obtain 90% classification accuracy if 9 out of 10 trials were truly Early. To generate a baseline classification accuracy distribution, the order of the true labels was shuffled, and these newly generated labels were compared against the predicted labels (process repeated 500 times). Out of the cells with high classification accuracy for Hit trials ($n = 30$), 9 cells were found to classify FA trial types above chance ($p < 0.05$; $n = 1/3$ for High cells; $n = 1/3$ for Low cells; $n = 7/24$ for Other cells). On average, classification accuracy was $66.5 \pm 2.2\%$ for those cells, whilst the mean of the baseline distribution was $51.7 \pm 1.4\%$. These results confirmed that the spiking activity of a subpopulation of cortical neurons mirrored the behavioural response specificity.

# 3. DISCUSSION

In a world where the senses are continuously stimulated, optimisation of information processing is believed crucial for perception and resulting behavioural actions(Crick, 1984; Fritz et al., 2007; Harris and Mrsic-Flogel, 2013). To understand how such optimisation occurs is a major quest in sensory neuroscience. The mouse is an advantageous model in which to study the neural processes underlying sensory refinement, as it is possible to precisely record and manipulate defined neural circuit components.

## 3.1. Auditory Attention and Streaming Could Be Inferred From the Timing of False Alarms

Here, we developed a novel behavioural task in mice to assess perceptual aspects of auditory streaming. Animals were trained to detect a target sound in one of two simultaneously presented,
<figure><img src="image_46.png" /></figure>

**FIGURE 6** | Dynamic increase in spiking activity prior to target occurrence. (A) Example raster and mean PSTH of spiking activity for a cell (classified as Task Modulated) during Late trials (S2 Hit only). The raster displays trials from a single block judged most representative, whilst the PSTH represents the mean activity over all blocks. The early and late zones define the portions of time used to compute the modulation of firing rate presented in (C). Both time zones are 1.6 s long. The early zone starts at trial onset, whilst the late zone ends prior to the first Late target. (B) Left panel: Auto-correlogram of the cell presented in (A). The 3 ms refractory period is marked in yellow. This cell presents minimal contamination in the refractory period, and it thus considered well isolated. Right panel: Waveforms of the cell presented in (A). The grey traces are individual waveform traces, and the black trace is the mean. Note that four sets of waveforms are presented since the cell was recorded using a tetrode comprising four electrodes. The horizontal bar indicates 0.4 ms, and the vertical bar indicates 10μV. (C) Modulation in the firing rate between the early and late time zones during Late trials, displayed for each cell type. A value above 0 indicates an increase in the late vs. early zone. Each dot represents a cell, and the dot is filled with grey color if the firing rate of the cell significantly differed in the early vs. late zone.

isochronous pure tone sequences. Whilst mice reached high performance in this task, incorrect responses (false alarms; FA) were highly informative on the individual's strategy. Using FA responses together with classical measures of performance such as *d'* enabled us to critically assess each animal's behavioural state. Notably, we show that a subset of animals was able to adapt their behaviour according to task specificity.

Mice are known to adjust the timing of their behavioural decisions according to learned probabilities so as to maximise reward outcome (Buhusi et al., 2009; Tosun et al., 2016). We therefore propose that the observation of incorrect response timing during any behavioural task may offer great insight on the subject's behavioural strategy and attentional state.

## 3.2. Activity in Auditory Cortex Depends on Behavioural Context

By recording in auditory cortex during ongoing behaviour, we related modulation of sensory encoding to changes in behavioural states. Specifically, activity in the AC was modulated by target feature expectation, in a manner that reflected the timing of the upcoming target, but not the spectral content of that target. Most cells displayed an increase in spiking activity prior to the first target during early trials, independently of their tuning profile. Moreover, a classifier trained based on the activity of a subset of AC cells during Hit trials was sufficient to decode response timing during FA trials. This implies that mice use similar behavioural strategies during Hit and FA, and that these strategies impact on neural processing at the level of the AC.

Given these findings, it appears that the prime difference in activity between Early and Late trials was not related to sensory processing *per se*, but rather reflects other aspects of the behavioural context. This was surprising, as the activity in auditory cortex is traditionally thought of as dedicated to the processing of auditory signals. These modulations might instead reflect the encoding of other task aspects, such as movement preparation, reward expectation, or task rule (Hu, 2003; Shuler and Bear, 2006; Rodgers and DeWeese, 2014; Bagur et al., 2017). Neural activity at the level of AC has recently been proposed to encode more than simply stimulus features (Rodgers and DeWeese, 2014; Bagur et al., 2017; Francis et al., 2018), however the underlying mechanisms are yet unknown. Future experiments will enable to delineate the contribution of specific anatomical pathways, including both sub-cortical (Thorn et al., 2010; Froemke et al., 2012; Pinto et al., 2013; Wimmer et al., 2015; Nelson and Mooney, 2016) and cortical structures (Gremel and Costa, 2013; Rodgers and DeWeese, 2014; Winkowski et al., 2017), in the modulation of AC activity during varying attentional states.

# 4. MATERIALS AND METHODS

## 4.1. Animals

Mice were housed under a 12/12 h light/dark cycle with food and water available *ad libitum*, except during behavioural training days. Electrophysiological recordings and behavioural training were performed during the dark phase of the cycle. All experiments were conducted under the UK Animals (Scientific
<figure><img src="image_47.png" /></figure>

**FIGURE 7** | Predicting FA specificity using neural responses during Hit trials. (A) The activity in the 0–1.2 ms in Early and Late Hit trials (S2 condition only) was used to train the classifier in distinguishing between Early and Late responses. The orange and blue traces are the mean PSTHs in Early and Late Hit trials respectively, for an example cell tuned to the Low frequency. The green box at the end of the PSTH displays the time window in which the mouse generated Hit responses. (B) The activity in the 0-1.2 ms in Early and Late FA trials (S2 condition only) was used to test the classifier. The orange and blue traces are the mean PSTHs in Early and Late FA trials respectively, for the example cell in (A). The red box at the end of the PSTH displays the time window in which the mouse generated FA responses.

Procedures) Act 1986. Four adult female mice (C57BL/6, Charles River UK Ltd & Harlan UK Ltd) were used in this study. Mice were aged 16–18 weeks and weighed 25–35 g on the day of electrophysiological recording.

## 4.2. Head-Implantation

Mice were anaesthetised with isoflurane (1–2 % v/v) and placed in a stereotaxic frame (Angle 2, Leica Microsystems, Germany). A custom-made plastic head-implant was fixed to the exposed cranium using tissue glue (Histoacryl, Braun Corporation, USA) and dental cement (Associate Dental Products Ltd). A grounding pin was inserted above the cerebellum, and secured using dental cement. The location of the future craniotomy was measured using a pipette referenced to Bregma (–2.7 mm AP, ±2.8 mm ML), and marked by a cross made on the skull using a surgical blade. The exposed skull was then covered with Kwikcast (World Precision Instruments), and the animal recovered. Analgesia was provided by injecting carprofen (5 mg/kg) sub-cutaneously (SC) 20 min prior to recovery.

## 4.3. *In vivo* Electrophysiology

Upon the day of electrophysiological recording, the animal was anaesthetised using isoflurane and surgically prepared. The Kwikcast was removed, exposing the skull over both hemispheres. Right- and left-hemisphere craniotomies (1 × 1 mm) were made over the cross-marked locations. The dura was

removed, and the brain was lubricated with PBS. Agar (1 % in PBS) was applied over the PBS as a moisturising sealant. Once hardened, the agar was covered with a layer of Kwikcast. The mouse was administered with analgesics subcutaneously, and left to recover in a heating chamber until locomotor and grooming activity were fully recovered.

Once the animal was recovered from the craniotomy, it was fixed in the apparatus using zinc screws attached to the head-implant. The back of the animal was gently restrained using a half-tube composed of soft fabric, clamped down by a grounded metal plate. Once a craniotomy was made, up to two subsequent recordings were made in that hemisphere. Recordings were made in the other hemisphere successively. Mice underwent left or right craniotomies first in balanced proportion across the cohort.

All recordings were made using silicon microelectrode comprising multiple tetrodes (A32, 4x2Tet, NeuroNexus, USA), advanced in the brain using a micromanipulator (IVM, Scientifica, UK) tilted by a 35 degree angle from the vertical line. The electrode penetration depth was typically 2.610 mm. Data were acquired via Digital Lynx 16SX system (Neuralynx, USA) and stored on a PC.

## 4.4. Auditory Stimulus Presentation

Auditory stimuli were pre-generated and calibrated (5–100 kHz flat spectrum ± 1.5 dB SPL) using Matlab (Mathworks, USA) and presented free-field (ES1; Tucker Davis Technologies, USA) via an RZ6 Processor (using RPvdsEX software; Tucker Davis Technologies, USA). The start and end of all stimuli were ramped with a 3 ms cosine ramp. All sounds presented were 100 ms long. A High (14 kHz) and Low (5 kHz) carrier frequency oddball streams were generated, by presenting pure tones isochronously and by introducing a target instead of a pure tone. Per trial, only one target could be presented. A target was a rising frequency modulated sweep (4.4 octave/s) starting at the stream carrier frequency (i.e., at either 5 or 14 kHz). The stimulus onset asynchrony (SOA) of the low and high streams was fixed at 617 ms and 400 ms respectively. At the beginning of a trial, the two streams started concurrently.

## 4.5. Behavioural Setup

During behavioural training, mice were fixed onto a rigid metal platform, also used during electrophysiological recording. An Arduino UNO (www.Arduino.cc) served as a controller, interfacing with a PC via the serial port. Outputs from the Arduino were sent to the PC and saved in text format using custom-written scripts in Python. Data from the text file were further analysed and plotted using custom-written Python scripts so as to display the mouse performance online during training sessions. During electrophysiological recording, arduino digital commands were sent to the digital port of the Digital Lynx so as to align neural traces with digital triggers during post-processing.

A lick port was used to monitor the animal's behaviour. The lick port was composed of an infra-red LED and sensor electrical circuit, a water delivery system and a vacuum system used for water removal. Briefly, a drop of water was delivered upon the opening of a solenoid valve clamping the water delivery tube. The control signal was a digital square pulse sent by an
Arduino. Similarly, a vacuum removal system was triggered upon the activation of a solenoid valve, connected to a negative air pressure. The lick port was mounted onto micromanipulators so as to position it finely compared to the mouse jaw. The speaker was positioned in front of the animal.

## 4.6. Behavioural Paradigm

Licking for a water reward was used to indicate a response. A single lick was considered as a Go-response. Mice were trained on a dual-stream oddball paradigm. An oddball stream was composed of a sequence of pure tones of a given frequency (High or Low), containing a target (frequency modulated) sound. Mice were cued to expect a target in a particular stream using a trial block design. Per block, the target alone was first presented (S0 condition) until the mouse reached 5 Hit trials. Subsequently, the oddball stream associated with that target was presented (S1 condition) until the mouse performed 10 Hit trials. Finally, the two streams were presented concurrently (S2 condition) until the mouse performed 20–25 Hit trials. High frequency target occurred Early (~2 s from trial onset), whilst Low frequency target occurred Late (~6 s from trial onset).

At the end of each trial, the vacuum pump was turned on for 500 ms to remove the excess water present in the lick port. A random delay (100–150 ms) was applied before presenting any stimulus following vacuum offset. The reward window started 100 ms after target onset, and ended when the next sound of the corresponding stream occured, i.e., the length of the reward window was the stream SOA minus 100 ms.

## 4.7. Measure of Behavioural Performance

All sounds presented before the target window were considered as one No-Go cue, and all sounds presented after as another No-Go cue. One trial could thus give 4 outcomes depending on the mouse response timing: (1) if the mouse licked before the target window, a false alarm (FA) was counted; (2) if the mouse licked in the target window, a correct rejection (CR) + Hit were counted; (3) if the mouse licked after the target window, a CR + Miss + FA were counted; (4) if the mouse did not lick during the trial, a CR + Miss + CR were counted.

Performance was measured using the sensitivity index *d*', defined as $z(P_{Hit}) - z(P_{FA})$, where *z* is the z-transform, $P_{Hit}$ and $P_{FA}$ the probability of Hit and FA respectively. $P_{Hit}$ was defined as $N_{Hit}/N_{Go}$, i.e., the number of Hit trials divided by the number of Go cues presented (note that $N_{Go} = N_{Miss} + N_{Hit}$). Similarly, $P_{FA}$ was defined as $N_{FA}/N_{No-Go}$, i.e., the number of FA trials divided by the number of No-Go cues presented (note that $N_{No-Go} = N_{CR} + N_{FA}$). In the case of $P_{Hit} = 0$ or $P_{FA} = 0$, the value was replaced with $1/N_{Go}$ or $1/N_{No-Go}$ respectively. In the case of $P_{Hit} = 1$ or $P_{FA} = 1$, the value was replaced with $(N_{Go} - 1)/N_{Go}$ or $(N_{No-Go} - 1)/N_{No-Go}$ respectively.

## 4.8. Training Protocols

### 4.8.1. Water Regulation

Following a minimum of 5 days post-surgery recovery, the mouse was placed under water restriction. On the first day of water restriction, no water was given. On the second day, 1mL of water

was given. Subsequently, the mouse began its training protocol on the behavioural setup, where it received most of its daily water dose. The minimal water dose given per day was fixed to 1 mL. If the mouse did not perform enough trials in a single session so as to reach the daily dose, the complementary water volume was given at the end of the training session once the mouse had been removed from the head-fixing apparatus. Mice were typically trained 6–7 days per week.

Once under water restriction, the weight of the mouse was measured daily, prior to the delivery of water. In the case of the animal's weight decreasing below 80% of an aged-match, water unrestricted litter-mate, the animal was given water ad libitum for a day. In order to give a precise dose to an animal in isolation, the mouse was placed on a scale, and its weight recorded. The dose of water was then administered either directly to the mouse's mouth using a pipette, or by squirting the water out onto the scale's plastic flooring. The difference in body weight measurement pre- and post- water delivery ensured that the animal had drunk its daily dose.

### 4.8.2. Habituation

The first 3 training sessions consisted of habituating the mouse to the head fixation apparatus and learning the association between lick port and water delivery. During these habituation sessions, the reward delivery valve was replaced by a syringe full of water that could be manipulated by the experimenter so as to deliver water into the lick port on demand.

During each session, the mouse was fixed into the setup, and a pipette full of water was first advanced to the animal's mouth so as to prompt licking behaviour. Once the mouse had started to express exploratory licking behaviour, the lick port was filled with water and advanced so as to be reached by the animal's tongue. The lick port was filled with water until the animal displayed signs of satiation, i.e., stopped licking and attempted to push away the lick port with its forelimbs. The mouse was then removed from the apparatus, and supplementary water was given if necessary.

### 4.8.3. Target-Reward Association

Following the habituation sessions, the mouse underwent typically 7 sessions to associate the water delivery with the target sounds. During the first 4 sessions, an automatic reward (water drop of 5 µL) was given 100 ms after sound onset. During the 3 consecutive sessions, the mouse had to trigger a reward by licking in the reward time window. The two (High and Low) target sounds were presented per session, using a 20–30 trials block design.

### 4.8.4. Refrain Licking Prior to Target Onset

During 5 sessions following the target-reward association sessions, the mouse was required not to lick before the occurrence of a sound (note that unlike in the FM discrimination task, all sounds presented at this stage are targets in the case of oddball tasks). The length of time the mouse was required to withhold licking for was randomly selected from a uniform distribution whose boudaries were incrementally increased over