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Serotonergic-Dependent Awareness Is Required for Trace Conditioning in Honey Bees

Catherine Macri, Marco Paoli, Andrea Alamia, Michel Gho, Guillaume Isabel, Martin Giurfa

PII: S2589-0042(25)02484-8

DOI: https://doi.org/10.1016/j.isci.2025.114223

Reference: ISCI 114223

To appear in: *iScience*

Received Date: 31 July 2025

Revised Date: 19 September 2025

Accepted Date: 21 November 2025

Please cite this article as: Macri, C., Paoli, M., Alamia, A., Gho, M., Isabel, G., Giurfa, M., Serotonergic-Dependent Awareness Is Required for Trace Conditioning in Honey Bees, *iScience* (2025), doi: https://doi.org/10.1016/j.isci.2025.114223.

This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability. This version will undergo additional copyediting, typesetting and review before it is published in its final form. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Please note that Elsevier's sharing policy for the Published Journal Article applies to this version, see: https://www.elsevier.com/about/policies-and-standards/sharing#4-published-journal-article. Please also note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Delay
conditioning

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Easy to learn

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Performance undisturbed by a distractor
'automatic' association without awareness

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Serotoninergic signalling unnecessary

Trace
conditioning

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Awareness
of CS-US
contingency

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Challenging to learn

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Performance disturbed by a distractor
awareness involved

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Serotoninergic signalling required

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1 Serotonergic-Dependent Awareness Is Required for Trace

2 Conditioning in Honey Bees

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4 Author list: Catherine Macri¹,²,³, Marco Paoli¹,²,*, Andrea Alamia⁴, Michel Gho¹,⁵, Guillaume Isabel³,£, Martin

5 Giurfa¹,²,£

6 Affiliations:

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(1) Sorbonne Université, CNRS, Inserm, Institut de Biologie Paris-Seine, IBPS, F-75005 Paris, France

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(2) Sorbonne Université, CNRS, Inserm, Centre de Neuroscience Neuro-SU, F-75005 Paris, France

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(3) Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), University of Toulouse, CNRS, UPS, F-31062 Toulouse cedex 9, France

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(4) CERCO, CNRS Université de Toulouse, Toulouse 31059, France

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(5) Sorbonne Université, CNRS, Inserm, Développement Adaptation et Vieillissement, Dev2A, F-75005 Paris, France

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* : Present address: Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne, F-21000 Dijon

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£ : * : Guillaume isabel and Martin Giurfa jointly supervised this work.

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Lead contact: Martin Giurfa (martin.giurfa@sorbonne-universite.fr)

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**Summary**

In humans, trace conditioning—where a temporal gap separates conditioned (CS) and unconditioned (US) stimuli—requires awareness and is sensitive to distraction, unlike delay conditioning. Here, we show that honey bees exhibit comparable features, suggesting cognitively demanding processes akin to awareness. Using olfactory conditioning of the proboscis extension response, we found that bees learned in both paradigms, but trace conditioning resulted in weaker acquisition and memory. High-resolution video analysis revealed delayed response latencies and altered licking dynamics during trace learning. Strikingly, trace—but not delay—conditioning was impaired by visual and mechanosensory distractors. Neuropharmacological blockade of serotonin signaling selectively disrupted trace conditioning, implicating serotonergic circuits in bridging CS–US intervals. A computational model of the bee olfactory network supported this interpretation. Our findings reveal that bees engage higher-order cognitive mechanisms under temporal uncertainty, suggesting that core features of awareness may be evolutionarily conserved across distant taxa.

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**Keywords:** Awareness, Insect cognition, *Apis mellifera*, Trace conditioning, Attention

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42 **Introduction**

43 Awareness is considered a facet of consciousness, often described as the specific experience of being
44 cognizant of a particular event at a specific moment¹. It can be either focused (i.e., paying attention
45 to a particular stimulus) or diffuse (such as being generally aware of one's context without focusing
46 on any specific detail)². Awareness, therefore, represents active engagement with particular aspects
47 of one's mental state at any given moment.

48 In humans, awareness of stimulus contingencies can be studied under controlled
49 experimental conditions through participants' reports of their subjective experiences³. A classic
50 example is found in studies that use distractors during trace and delay conditioning⁴⁻⁶. These two
51 forms of Pavlovian conditioning differ in the timing between the conditioned stimulus (CS) and the
52 unconditioned stimulus (US). In delay conditioning, the CS is presented and remains active until the
53 US is introduced, with both stimuli partially overlapping. In trace conditioning, a gap separates the CS
54 and US presentations so that for conditioning to occur, the subject must maintain a memory "trace"
55 of the CS during the gap. While this difference seems minor, it has substantial implications for
56 learning: typically, trace conditioning proves more difficult to learn than delay conditioning⁷,⁸; yet, it
57 can provide a significant advantage in scenarios where danger or reward signals are delayed. In
58 addition, the two conditioning types depend on different neural substrates as delay conditioning
59 does not require an intact hippocampus and neocortex⁹,¹⁰, while trace conditioning does⁴,¹¹⁻¹⁴.

60 Comparative studies of delay and trace conditioning in humans and other mammals
61 frequently use the conditioning of the eyeblink response¹⁵,¹⁶. In this paradigm, a CS (e.g., a tone or a
62 light) predicts an aversive air puff to the eye, eliciting an eyeblink to prevent the air puff. Repeated
63 pairings result in the acquisition of a conditioned eyeblink response timed to the CS that predicts the
64 air puff¹⁷. Auditory versions of delay and trace eyeblink conditioning have been used to explore the
65 role of awareness. In⁶, during eyeblink conditioning, participants watched a silent movie and were
66 later questioned about its content and their learning from the conditioning experience (contingency
67 between tone/light and air puff). In trace conditioning, only participants who became aware of the
68 CS-US relationship successfully learned the task, whereas awareness was not required for delay
69 conditioning⁶. To investigate this issue further, participants underwent trace conditioning while
70 performing a distracting task that involved detecting a series of three odd digits in a sequence and
71 pressing a button to report them. These participants failed to develop awareness and performed
72 significantly worse than a control group that did not experience the distractive task, learning the
73 association between tone and air puff. These findings suggest that awareness is essential for
74 successful trace conditioning⁶,¹⁸.
The question of whether awareness is necessary for trace conditioning but not delay conditioning has not been investigated so far in the honey bee, a model organism for cognitive research among invertebrates¹⁹⁻²¹. Honey bees can be conditioned in the laboratory using a Pavlovian protocol known as olfactory conditioning of the proboscis extension response (PER)²²,²³. In this protocol, harnessed bees learn to associate an odor (CS) with a sucrose solution (US), and the conditioned response is the proboscis extension to the odorant that predicts sucrose delivery. While delay conditioning is commonly used in these experiments²²⁻²⁴, trace conditioning variants have been employed to study neural activity during the trace interval²⁵. However, it remains unclear whether distraction impairs trace conditioning in honey bees, which would indicate the need for an awareness-like state.

In this study, we combined trace and delay conditioning with various types of distractors to assess their effects on learning success and memory retention in honey bees. Additionally, we developed a neural model to explain the success of trace conditioning and tested it using a neuropharmacological approach. Our findings show that distractors impair trace, but not delay, conditioning and indicate that serotonergic signalling may play a crucial role in successful trace learning. These results imply that, similar to humans, honey bees may rely on an awareness of the CS-US contingency during trace conditioning, and that serotonergic signalling plays a crucial role in this process, suggesting that this cognitive state may extend to invertebrates.

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## Results

### Delay and Trace Conditioning Induce Different Learning and Retention Performances.

In this experiment, we aimed to compare the acquisition and retention performance of honey bees conditioned using delay and trace conditioning. During each trial, a single odorant (CS) was paired with a 50% (w/w) sucrose solution (US). Two odorants, 1-nonanol and 2-hexanol, were used as CS, with different groups of bees assigned to each.

In delay conditioning, the odorant was presented for 6 s, followed by the sucrose solution for 3 s, with a 5 s interstimulus interval (ISI), resulting in a 1 s overlap between the stimuli (Fig. 1A). In the trace conditioning protocol, the odorant was presented for 0.5 s, followed by the sucrose solution for 3 s, with the same ISI of 5 s as in delay conditioning, resulting in a CS-US gap of 4.5 s (Fig. 1A). This trace conditioning protocol was adopted from a previous study that optimized stimulus timing for olfactory trace conditioning in honey bees²⁵. In both protocols, successful learning was demonstrated by a proboscis extension response (PER) to the conditioned odorant (Fig. 1B). To
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prevent residual odorants from interfering with the trace conditioning paradigm, an air extractor was
placed behind the bees. Its effectiveness was confirmed using a photoionization detector (Fig. S1).

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After conditioning, retention performance was assessed by presenting the trained odorant
and a novel odorant (to test memory specificity) in a random sequence without reward, 1 hour (mid-
term memory, MTM) and 24 hours (long-term memory, LTM) after conditioning. MTM is known to be
independent of protein synthesis, whereas LTM depends on this process<sup>26,27</sup>. Separate groups of
bees, conditioned in parallel, were used for each retention test.

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Learning (i.e. the percentage of bees responding to the CS) did not differ significantly among
the eight trained groups (four for delay conditioning and four for trace conditioning), which varied by
the odorant used as the CS (1-nonanol or 2-hexanol) and the retention test time (1 h or 24 h)
(GLMER, Odor effect, *z* = -1.08, *p* = 0.28; *Retention time effect*, *z* = -0.63, *p* = 0.53). Therefore, the
learning performances were pooled and represented as two learning curves, showing the percentage
of PER to the CS across trials: one for delay conditioning and the other for trace conditioning (Fig.
1C). Under both conditioning regimes, bees increased their responses to the CS across trials
(Cochran's Q test, delay conditioning: *Q* = 196, *df*: 4 *p* < 0.001; trace conditioning: *Q* = 117, *df*: 4, *p* <
0.001). However, acquisition levels were significantly higher in delay conditioning compared to trace
conditioning (GLMER, Group x Trial Interaction, *z* = -4.26, *p* < 0.001), indicating that trace
conditioning was more cognitively challenging than delay conditioning. Similar results were obtained
when varying the duration of the CS in comparable trace conditioning experiments (Fig. S2),
demonstrating that this parameter did not influence our results and conclusions.

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Retention performance, evaluated both as the percentage of bees showing PER to the CS
and the novel odorant (NOd) (Fig. S3A) and as the percentage of bees exhibiting specific memory
(i.e., responding to the CS but not the NOd; Fig. S3B), confirmed the greater difficulty of trace
conditioning compared to delay conditioning. Twenty-four hours after conditioning, trace-
conditioned bees exhibited lower long-term retention levels (Fig. S3A) and specific memory (Fig. S3B)
than delay-conditioned bees.

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### Delay and Trace Conditioning Induce Different Response Latencies and Licking Frequency.

Having established significant differences in learning and retention between delay and trace
conditioning, we next sought to determine whether these two conditioning methods result in
qualitative differences in PER, reflecting differences in the timing of the CS-US interval<sup>28</sup>. To address
this question, we trained two groups of bees: one with delay (Delay) and the other with trace
conditioning (Trace, Fig. 2A). Since these two groups differed not only in the temporal relationship
between CS and US but also in learning performance and CS duration, we conducted two additional experiments to determine if lower learning performance and shorter odorant duration could influence PER dynamics. In one experiment, we conditioned a group of bees using delay-conditioning with a lower-quality sucrose reward (20% w/w), which should reduce learning performance (Delay20, Fig. 2A). In the other experiment, we conditioned bees using trace conditioning featuring a longer CS duration (Trace5s, Fig. 2A). The performances of bees from the four groups trained with 2-hexanol or 1-nonanol as a CS were pooled because learning did not differ significantly between odorants (Fig. 2B, GLMER, Odor effect, *z* = 0.71, *p* = 0.48).

In terms of learning performances (Fig. 2B), the Delay group consistently exhibited higher conditioning levels than the Trace group, as observed in the previous experiment, and also outperformed the two additional groups (Delay20 and Trace5s) (*post hoc* analysis of the Group effect, Delay20 vs. Delay, *z* = 3.44, *p* = 0.003, Trace vs. Delay, *z* = 3.95, *p* < 0.001, Trace5s vs. Delay, *z* = 2.94, *p* = 0.018). The three groups with lower performance (Trace, Trace5s, and Delay20) did not differ significantly from one another (*post hoc* analysis of the Group effect, Delay20 vs. Trace, *z* = 0.39, *p* = 0.98, Delay20 vs. Trace5s, *z* = -0.80, *p* = 0.86, Trace vs. Trace5s, *z* = -1.25, *p* = 0.59). These results confirm our previous findings that trace conditioning is more cognitively demanding than delay conditioning (Fig. 1C) and that CS duration does not affect trace learning efficiency (Fig. S2B). Furthermore, they demonstrate that a lower-quality US in the Delay20 group resulted in lower learning performances comparable to those observed in the trace conditioning variants.

One hour after conditioning, we recorded PER responses to odorant delivery in all four groups using a video camera positioned above each bee's head. These video recordings enabled us to quantify both the latency to the maximal proboscis extension and the number of licking events (glossa extensions) (Fig. 2C). The latency to maximal PER differed between the Delay and the three other groups (Fig. 2D). Bees from the Delay group extended their proboscis earlier than those from the Trace group (Wilcoxon test, *W* = 236.5, *p* = 0.04), and the within-group variability in latencies was significantly lower in the Delay group compared to the Trace group (one-tailed F test, *F*26,25 = 0.05, *p* < 0.001). While the latency to maximal PER in the Trace5s group was similar to that of the Trace group (Fig. 2D, *W* = 438, *p* = 0.74), it did not differ significantly from the Delay group either (*W* = 342, *p* = 0.18). Overall, delay conditioning elicited an almost automatic response with reduced variability, whereas trace conditioning resulted in more variable latencies, suggesting hesitation about when to extend the proboscis. This higher variability in PER to the expected US in the trace-conditioned group could indicate that some bees attempted to time the CS-US interval; however, it may also reflect lower learning performance. The latency to full PER in the Delay20 group (with a lower-quality 20% sucrose reward) did not differ from that of the Trace group (*W* = 348.5, *p* = 0.80), but was
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significantly different from that of the Delay group trained with the 50% sucrose solution (Fig. 2D, *W* = 261.5, *p* = 0.05). Overall, these results suggest that the high variability observed in the responses of the trace-conditioned bees does not necessarily reflect timing of the CS-US interval, but may instead result from reduced learning efficiency associated with trace conditioning.

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Considering now the number of licking events (Fig. 2E), bees from the Trace group showed significantly fewer licking attempts than delay-conditioned bees (Trace vs. Delay, *W* = 540, *p* < 0.001, Trace vs. Delay20, *W* = 633.5, *p* < 0.001). However, the Trace5s group, conditioned with a 5 s CS, exhibited a significantly higher number of licking events than the Trace group conditioned with a 0.5 s CS (Fig. 2E, *W* = 233, *p* = 0.003). These findings suggest that the absence of licking events initially observed in the trace-conditioned group with a 0.5 s CS is likely attributable to the short duration of the odorant predicting the reward, rather than being an inherent behavioral feature of trace-conditioned PER.

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**Distractors Significantly Impair Trace Conditioning, but Not Delay Conditioning.**

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We next investigated whether, similar to humans, trace conditioning requires an awareness-like state of stimulus contingencies, which may be dispensable in delay conditioning. To explore this, we examined whether a distractor stimulus differentially affects trace and delay conditioning. Specifically, we compared the acquisition and retention performances of bees conditioned under the two regimes in the presence of a brief visual distractor (a 100 ms, 50 Hz white flash) delivered at a random time within the CS-US interval during each trial (Fig. 3A). Bees were placed within an isolation chamber to minimize contextual cues and enhance distractor salience (Fig. 3B).

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As in the previous experiment, no differences in learning were observed based on the odorant (1-nonanol or 2-hexanol) used as the CS (GLMER on delay-conditioned bees, Odorant as CS effect, *z* = 0.01, *p* = 0.99, GLMER on trace-conditioned bees, Odorant as CS effect, *z* = -0.84, *p* = 0.40). Therefore, performances were presented as a single pooled curve for each conditioning type. One hour after conditioning, responses to the CS, the NOd (to assess memory specificity), and the distractor were measured in the absence of reinforcement to assess whether competition between the CS and the distractor for association with the US occurred during learning. Figure 3D shows the learning performance of delay-conditioned bees, comparing those not subjected to the visual distractor (controls) with the distracted bees. In both groups, bees increased their conditioned responses across trials (Cochran's Q test, delay conditioning control: *Q* = 116, *df* = 3, *p* < 0.001; delay conditioning distraction: *Q* = 122, *df* = 3, *p* < 0.001). No significant difference was found between the performance of the two groups (GLMER on Delay conditioned bees, Distraction effect, *z* = 0.013, *p* = 0.99), indicating that the visual distractor had no impact on acquisition under the delay conditioning
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regime. Retention performance recorded 1 hour after conditioning also showed no difference between control and distracted bees (Fig. S4A; $\chi^2 = 0$, df = 1, p = 1).

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In contrast, the learning performance of control and distracted bees differed significantly under the trace conditioning regime (Fig. 3E). While both groups of bees increased their response to the CS across trials (Cochran's Q test, trace conditioning control: $Q = 44.1$, df = 3, p < 0.001; trace conditioning distractor: $Q = 21.3$, df = 3, p < 0.001), the visual distractor presented during the gap between the CS and US significantly impacted learning performance (Fig. 3E). Distracted bees exhibited lower acquisition than control bees (GLMER on Trace conditioned bees, Distraction effect, z = -3.45, p < 0.001). A similar result was observed in a retention test performed 1 h after conditioning (Fig. S4A; $\chi^2 = 5.98$, df = 1, p = 0.014). Overall, these results indicate that the visual distractor selectively interfered with trace conditioning but not with delay conditioning. A combined analysis further supported this conclusion (GLMER analysis on all bees, Distraction x Group effect, z = -3.266, p = 0.001).

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To evaluate the general validity of this conclusion, we repeated the previous experiments, this time using a mechanosensory distractor. We designed a device in which a feather delivered a 100 ms mechanosensory contact to the bee's thorax during conditioning (Fig. 3C). Then, we compared its effects on delay- and trace-conditioned bees. The odorant used as the conditioned stimulus (CS) did not significantly affect learning performance in either delay conditioning (GLMER on Delay conditioned bees, Odorant used as a CS effect, z = -0.49, p = 0.63) or trace conditioning (GLMER on Trace conditioned bees, Odorant used as a CS effect, z = -1.12, p = 0.26). Therefore, we combined the acquisition performance into a single learning curve for each conditioning type. Delay-conditioned control and mechanically-distracted bees (Fig. 3F) significantly increased their responses to the CS over the course of conditioning trials (Cochran's Q test, delay conditioning control: $Q = 78$, df = 3, p < 0.001; delay conditioning distraction: $Q = 81.1$, df = 3, p < 0.001) regardless of the mechanosensory stimulation (GLMER on Delay conditioned bees, Distraction effect, z = 0.142, p = 0.89). Similar results were observed in a retention test conducted 1 hour after conditioning (Fig. S4B, control vs. distracted bees with specific memory: $\chi^2 = 0.06$, df = 1, p = 0.80). Thus, as with the visual distractor, the mechanosensory distractor had no detectable effect on either acquisition or retention performance in delay conditioning.

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A different result was observed in the case of trace conditioning. Although bees significantly increased their responses to the CS across conditioning trials (Cochran's Q test, trace conditioning control: $Q = 40.5$, df = 3, p < 0.001; trace conditioning distractor: $Q = 21.3$, df = 3, p = 0.001), acquisition was impaired in the distracted group (Fig. 3G) compared to the control, non-
241 distracted group (GLMER on Trace conditioned bees, Distraction effect, *z* = -3.83, *p* < 0.001).

242 Consistently, the retention performance of distracted bees was lower than that of control bees (Fig.

243 S4B; percentage of specific learners: $\chi^2 = 7.72, df = 1, p = 0.005$).

244 In conclusion, both experiments show the selective interference of distractors, regardless
245 of their modality, with trace but not delay conditioning. This conclusion is further supported by a
246 combined analysis (GLMER on all bees, Distraction × Group effect: *z* = -3.33, *p* < 0.001). Since delay
247 conditioning remained unaffected across conditions, we propose that distinct cognitive processes
248 underlie trace and delay conditioning in honey bees, consistent with the idea that awareness of the
249 stimulus contingencies is important for successful trace conditioning but is unnecessary for delay
250 conditioning.

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252 Trace Conditioning Impairment by a Distractor is Not Caused by Stimulus Competition or Negative
253 Valence.

254 The impairment in learning performance caused by distractors during trace conditioning could be
255 due not to a distortion of perceptual awareness but to a competition between the conditioned
256 odorant and the distractor for associative strength based on their concurrent temporal relationship
257 with the US. To test this hypothesis, we presented delay- and trace-conditioned bees with the
258 distractor used during conditioning—either the 100 ms light flash or the 100 ms feather contact—
259 one hour after the end of training, and quantified their PER scores. As shown in Table 1, there was a
260 significant absence of appetitive responses to distractors after conditioning, confirming their neutral
261 nature in our experiments. Thus, the reduction in CS responses during trace conditioning induced by
262 distractors was attributable to their interference with awareness of stimulus contingencies rather
263 than a shared associative strength with the CS.

264 An alternative hypothesis for the performance impairment observed during trace
265 conditioning is that the distractor might have had an innate aversive valence, which interfered with
266 the bee's ability to learn the appetitive odor-sucrose association. To test this, we subjected two
267 groups of bees to one trace or one delay conditioning trial in which bees experienced the odorant
268 paired with sucrose, followed by a second trial (Fig. 4A). In the second trial, control bees were
269 exposed to the odorant alone (extinction trial), while distracted bees encountered the odorant
270 followed by the distractor—the 100 ms light flash or the 100 ms feather contact. One hour after the
271 second conditioning trial, bees were tested by presenting the CS alone and a novel odorant. If the
272 distractor had an innate aversive valence, it should reduce CS responses in the distracted bees
273 beyond the extinction effect observed in control bees exposed only to the odorant in the second
274 trial.
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In the test, the percentage of bees exhibiting specific memory—defined as those responding to the CS but not to the NOd—did not differ between the control group and either of the two distracted groups (feather group or flash-light group) (Fig. 4B). Response rates were similar between control and distracted delay-conditioned bees ($\chi^2 = 1.65$, *df* = 2, *p* = 0.44) as well as between control and distracted trace-conditioned bees ($\chi^2 = 0.01$, *df* = 2, *p* = 0.99). These results rule out the possibility that the distractors had an innate aversive valence, which could have selectively impaired performance during trace conditioning. Instead, they support the interpretation that distractors interfered with awareness of stimulus contingencies during trace, but not delay, conditioning.

## A Neural Network Model Reveals the Importance of a Timing Element in Keeping the Trace.

After identifying the reported differences between delay and trace conditioning, we adopted a modelling approach to investigate whether different neural mechanisms underlie these two processes, as previously suggested by experimental studies in *Drosophila*<sup>29</sup>. We hypothesized that the mushroom bodies (MBs), a multimodal brain structure involved in learning and memory<sup>30</sup>, play a crucial role in olfactory trace conditioning. To test this hypothesis, we implemented a spiking neural network model of the MBs to identify the computational mechanisms required for trace conditioning tasks, and compared its performance to predictions under a delay conditioning regime (Fig. 5A).

As shown in Fig. 5B, the model is implemented based on the essential components of the honey bee brain required for associative olfactory learning. In blue, the olfactory input (CS) is relayed from the antennal lobe – the primary olfactory center – to the MB Kenyon cells (KCs) *via* projection neurons (PNs). In red, the distractor input is also conveyed to the MB KCs via other neural pathways, which vary depending on the distractor’s sensory modality. Importantly, associative learning occurs through spike-time-dependent-plasticity (STDP)<sup>31</sup>, enabling KCs to associate the olfactory CS with the appropriate motor response -specifically, the proboscis extension reflex to obtain the sucrose reward. This response is mediated by mushroom bodies output neurons (MBONs, green in Fig 5B; see supplementary Fig. S5A for a rasterplot obtained for a representative trial in all experimental conditions). The model incorporates two neuromodulatory mechanisms that influence learning: an octopaminergic system (OA), which functions as an instructive reward signal<sup>32</sup> and is essential for the STPD learning dynamic (see Methods for details), and a simplified serotonergic system (5-HT), mediated by the dorsal paired medial neurons (DPMs). The latter is particularly relevant for trace conditioning, as it extends the coincidence detection window, thereby bridging the temporal gap between the CS and US, consistent with recent findings in *Drosophila*<sup>33</sup>. In our model, the serotonergic system acts as an attentional mechanism, filtering out irrelevant stimuli by accelerating the decay of their eligibility traces during the temporal gap between the CS and US. The eligibility
trace serves as a temporary memory of an event, gradually decaying over time (i.e., through a given time constant τ; see Methods for details). Specifically, the serotonergic system³³ maintains the time constant τ of the CS during the CS-US interval, while reducing the time constant τ of the Distractor, thereby weakening the impact of irrelevant stimuli on learning (see Methods for details).

As mentioned above, honey bees learn the CS-US association in the delay-conditioning protocol, regardless of the distractor's presence. However, in trace conditioning, learning was less efficient, and this effect was even more pronounced in the presence of a distractor (Figs. 3D, 3F). Our simulations replicate these empirical observations (Fig. 5C). Specifically, we simulated 12 trials of 1000-time steps (equivalent to 1000 ms) for each condition (delay/trace, with/without distractors) and repeated the training 20 times with different initializations (see Fig. S5B to visualize the synaptic changes over 12 trials in one of the initializations). In each trial, the CS was presented after 100 ms and lasted for 500 ms. In delay conditioning, the US was delivered during the final 200 ms of the CS, whereas in trace conditioning, the US was presented 200 ms after the CS ended. If the distractor was included, it appeared randomly before the US, and in trace conditioning, it always occurred after the end of the CS.

We then tested MBON activation by presenting only the CS or the distractor after each trial. The percentage of proboscis extension responses (%PER) shown in Figures 5C-E is proportional to the number of spikes generated by the MBON (Mushroom Body Output Neuron) in each trial following training. As shown in Fig. 5C, our results show that the model successfully learns the CS-US association in delay conditioning, but to a lesser extent in trace conditioning, mirroring our experimental data. In addition, as in the real data, the effect of the distractor was significant for trace, but not for delay, conditioning. Importantly, the model did not respond to the distractor stimuli (Fig. 5D), demonstrating that the attentional mechanisms mediated by the DPM neurons effectively prevented the association of irrelevant stimuli.

To further examine the role of this 'awareness system', we simulated a 'lesion study' by suppressing the DPM module from the model, thereby eliminating its modulation of the time constant of the eligibility trace. Under these conditions, our simulations (Fig. 5E) revealed a reduction of the response rate exclusively in trace conditioning. We thus aimed to test this prediction experimentally by pharmacologically blocking serotonin signalling.

## Testing the Model: Serotonergic Blockade Impairs Trace but Not Delay Conditioning.

Our model, as well as empirical studies (30), suggests that serotonergic DPM neurons play a central role in successful trace conditioning. Genetic methods used to block 5-HT neurons are not feasible in
a non-transgenic organism like the honey bee. Therefore, we specifically aimed at inhibiting serotonergic signaling at the level of the mushroom bodies by injecting 5-HT antagonists *via* the ocellar nerve —a thick fiber bundle approximately 40 µm in diameter that extends medially and caudally from the dorsal margin of the head capsule to a depth of 300 µm into the protocerebrum³⁴.

After carefully removing the lens of the median ocellus, we injected either phosphate-buffered saline (PBS) as a control, or a mixture of the two serotonin-receptor antagonists methiothepin mesylate and ketanserin tartrate dissolved in PBS³⁵,³⁶, at concentrations of 1 mM or 10 mM. Following a 30-minute rest period, bees were conditioned under either a trace or delay conditioning regime, and their memory retention was tested after one hour. According to our model, serotonergic blockade should impair trace but not delay conditioning.

Consistent with our previous experiments, learning performances did not differ based on the conditioned odorant (1-nonanol or 2-hexanol), as shown by the GLMER analysis (delay-conditioned bees: Odorant as CS effect, *z* = -0.94, *p* = 0.35; trace-conditioned bees: Odorant as CS effect, *z* = -1.31, *p* = 0.19). Consequently, performance data were pooled into a single curve for each conditioning type. The learning performance of three groups of bees (i.e., control bees, and bees injected with 1 mM or 10 mM of the serotonin antagonist cocktail) trained under a delay-conditioning regime is shown in Fig. 6A. *Post hoc* comparisons revealed that learning performance did not differ significantly between PBS-injected bees and those injected with the serotonin antagonist cocktail, regardless of the concentration used in this case (GLMER on delay conditioned bees, *post hoc* comparisons for Treatment effect, PBS vs. Cocktail 1mM, *z* = -0.07, *p* = 0.99, PBS vs. Cocktail 10 mM, *z* = -0.06, *p* = 0.99, Tukey-adjusted). Memory retention 1 h after conditioning was significant in all three groups, as each group showed a stronger response to the conditioned stimulus than to the novel odorant (Fig. S6A; McNemar tests for each group; PBS: χ² = 20.02, *p* < 0.001, Cocktail 1mM: χ² = 14.02, *p* < 0.001, Cocktail 10 mM: χ² = 13.59, *p* < 0.001). Retention levels were similar across the three groups, as the percentage of bees exhibiting specific memory did not differ significantly (Fig. S6C; χ² = 0.53, *df* = 2, *p* = 0.77). Overall, these results demonstrate that serotonergic blockade did neither affect olfactory delay conditioning nor the memory resulting from this form of conditioning.

The learning performance of the three groups of bees trained under a trace-conditioning regime is shown in Fig. 6B. *Post hoc* comparisons revealed that bees subjected to serotonergic blockade at the higher concentration exhibited significantly lower learning compared to PBS-injected bees (GLMER on trace conditioned bees, *post hoc* comparisons for Treatment effect, PBS vs. Cocktail 10 mM, *z* = 3.02, *p* = 0.007). Bees injected with the lower concentration of the serotonin antagonist
cocktail showed an intermediate performance, trending lower than PBS-injected bees but not
significantly different from those receiving the higher concentration (GLMER on trace conditioned
bees, post hoc comparisons for Treatment effect, PBS vs. Cocktail 1 mM, z = -2.22, p = 0.068, Cocktail
1 mM vs. Cocktail 10 mM, z = -0.89, p = 0.65, Tukey-adjusted). Memory retention, assessed 1 hour
after conditioning, followed a similar pattern. Only the PBS-injected bees displayed a higher response
to the conditioned stimulus compared to the novel odorant, consistent with successful memory
retention (Fig. S6B; McNemar test: $\chi^2 = 8.26, p = 0.004$). In contrast, bees injected with either 1 mM
or 10 mM of the antagonist cocktail did not show a higher response to the CS than to the NOd
(McNemar test for Cocktail 1 mM: $\chi^2 = 2.12, p = 0.15$, McNemar test for Cocktail 10 mM: $\chi^2 = 0.84, p
= 0.36$), indicating a failure to form a specific associative memory. However, specific memory was not
different between control and treated bees (Fig. S6C; $\chi^2 = 3.99, p = 0.136$), probably because memory
levels were already low in the PBS-injected control bees. Thus, in line with the predictions of the
model described above, the blockade of serotonergic signaling specifically impaired trace
conditioning while leaving delay conditioning unaffected.

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## Discussion

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Our study shows that honey bees can learn and memorize odor-sucrose associations both under a
delay and a trace conditioning regime. Moreover, we have shown that the presence of distracting
stimuli selectively impairs trace conditioning while leaving delay conditioning unaffected. Finally,
both modelling and neuropharmacology highlight a central role of serotonergic signalling for trace
conditioning. These findings suggest that, similar to humans, honey bees may rely on an awareness
of the CS-US relationship during trace conditioning, but not during delay conditioning, thus extending
the presence of this cognitive state to an invertebrate species.

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### Distractors Impair Trace, but not Delay Conditioning.

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Our results showed that despite the fact that bees learned the odor-sucrose association under both
conditioning regimes, learning performance and memory retention were lower in trace conditioning
than in delay conditioning. These results agree with previous findings on olfactory trace conditioning
in bees²⁵ and confirm that these two conditioning protocols differ in cognitive complexity.

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To successfully associate the CS and US in trace conditioning, the animal must retain a
representation of the CS across the temporal gap separating it from the US. This process likely
engages working memory, which allows for the active maintenance and manipulation of information
over relatively long periods³⁷. The involvement of working memory in trace conditioning suggests a
role for attentional processes, which can be categorized as either bottom-up (stimulus-driven, based on salience) or top-down (volitional, dependent on past experiences and goal-directed processing; ³⁸,³⁹). Given that trace conditioning requires bridging a temporal gap, it is more likely mediated by top-down attentional control, whereby the animal actively maintains the CS representation while suppressing irrelevant or competing stimuli. This interpretation aligns with theories suggesting that cognitive abilities such as trace conditioning, rapid reversal learning, and cross-modal learning may be underpinned by forms of consciousness⁴⁰,⁴¹. Indeed, it has been proposed that failure to achieve trace conditioning in the presence of distractors – despite successful performance under undistracted conditions – could serve as one among several potential tests for animal consciousness⁴². Our results demonstrate that introducing a distractor during the CS-US interval significantly impairs learning in trace-conditioned bees, but not in delay-conditioned bees. This result is consistent with findings reported for humans subjected to eyeblink trace and delay conditioning⁴⁻⁶, which led to the conclusion that awareness of the stimulus contingencies is required for successful trace conditioning but not for delay conditioning. In that sense, our study did not seek to re-establish the known differences between trace and delay conditioning in honey bees, nor to simply reproduce findings on serotonergic modulation. Instead, our central objective was to examine how external distractors differentially affect trace and delay conditioning—a question that has never been addressed in honey bees. The ultimate goal was to determine if these two forms of learning differ in their requirement of awareness. This distinction is not peripheral, but rather central to our contribution.

The distractors used in our experiments were neutral stimuli that did not compete with the CS for associative strength. Similar to experiments in rodents⁴² and in *Drosophila*⁴³, we used a neutral stimulus that selectively disrupted trace - but not delay - conditioning, remained non-associative, and exerted interference across modalities. Additionally, our distractors differed in their sensory modality from the olfactory CS to prevent enhancement of associative learning. In fact, prior experiments²⁵ showed that the presentation of an additional odorant during the gap increased trace-conditioning performances.

Successful trace conditioning relies on the ability to maintain an internal representation of the CS until the US is presented. The fact that a distractor disrupts this process suggests that the bees must actively maintain a CS representation in time, a cognitive function potentially requiring attentional control or awareness⁴⁴. This idea is supported by two findings: first, learning is constrained by time as bees fail to associate the CS and US when the gap exceeds six seconds²⁵, suggesting a limit to how long they can sustain the CS representation. Second, prior studies indicate that bees can gradually learn to associate a CS and a US over increasingly long inter-stimulus
intervals²⁵, a cognitive feature that resembles attentional learning mechanisms and is inconsistent
with rigid, automatic mechanisms. This interpretation aligns with findings in *Drosophila*, where
 distractors have similarly been shown to impair trace visual learning⁴³, reinforcing the idea that
 attentional processes, and more specifically awareness of stimulus contingencies, play a key role in
 trace conditioning across species⁴⁵.

447

**448 Evidence for Awareness Without Precise Timing.**

449 Our results suggest that bees exhibit awareness of stimulus contingencies during trace conditioning
450 but do not encode precise timing between the CS and US. In contrast, studies in humans4-6 and
451 mammals46 have shown that during trace conditioning, subjects exhibit temporal tuning of their
452 responses, with peak activity gradually shifting toward the expected US arrival time. In our study, the
453 absence of such timing behavior may be attributed to the limited number of training trials, which
454 could have constrained temporal encoding, or to a potential trade-off between learning speed and
455 temporal specificity. Notably, a lack of precisely timed conditioned responses has been consistently
456 reported in all insect trace conditioning studies to date47,48. However, bees are capable of learning
457 temporal relationships between events, as shown by experiments in bumble bees trained under a
458 fixed-interval procedure49. In this paradigm, bees received a sucrose reward for the first response
459 (extending the proboscis through a hole in a container) that occurred after a fixed time interval had
460 elapsed. Bumble bees trained with an initial reward and a variable gap (6 s, 12 s, or 36 s) adjusted
461 their proboscis extension responses to approximately one-third of the interval, indicating an ability to
462 encode temporal information under certain training conditions.

463

**464 The Neural Underpinnings of Awareness of Stimulus Contingencies under Trace Conditioning in**
**465 Honey Bees.**

Since distractors were from a different sensory modality than the odorants used for conditioning,
their effects on learning performance likely emerge at higher-order processing centers—where
different sensory modalities converge—such as the mushroom bodies (MBs) or the central complex.
In honey bees, the MBs are well known for their multimodal sensory integration capacities and are
strong candidates for maintaining conditioned stimulus representations over time. Persistent odor
representations—or "aftersmells"—have been observed at the level of projection neurons conveying
olfactory information from the antennal lobe to the MBs<sup>25,50,51</sup>. Modeling these responses at the level
of Kenyon-cell (KC) connectivity supported the idea that the MBs may sustain neural activity across
the CS-US gap (46). Theoretical models<sup>44</sup> propose that such persistent activity could enable trace
learning. This hypothesis is further supported by evidence from *Drosophila*, where MB-dependent trace conditioning has been demonstrated using olfactory stimuli⁵⁰,⁵¹. Conversely, Grover *et al*.⁴³ recently showed that MBs are not required for visual trace conditioning in *Drosophila*, where the central complex appears to assume this role. These differences may reflect modality-specific circuit engagement: in flies, MBs receive sparse visual input⁵², suggesting a reduced involvement in visual trace learning despite participation in simpler forms of visual associative learning⁵³,⁵⁴. In contrast, the MBs of honey bees are deeply involved in both olfactory and visual learning and memory¹⁹,²⁶,³⁰, making them strong candidates for supporting trace learning.

Importantly, the *Drosophila* study did not included analyses of response timing⁴³ and emphasized a role for dopaminergic signaling in trace conditioning. Our study highlighted the importance of serotonergic signalling for successful trace conditioning. In *Drosophila*, the dorsal paired medial (DPM) neuron—a serotonergic neuron—extends the time window for CS-US association³³, acting as a timing modulator. This finding was incorporated into our model and experimentally validated through pharmacological blockade of serotonergic signalling, which selectively impaired trace, but not delay, conditioning. Although a direct homologue of the *Drosophila* DPM neuron has yet to be identified in bees, our results suggest a conserved role for serotonergic signalling in attentional processes or in the maintenance of CS representations across the trace interval.

While the previous study on *Drosophila* DPM serotonergic neurons showed that they can extend the time window for CS-US associations during trace conditioning³³, it did not investigate the impact of distractors on that process. In contrast, our work explores how serotonergic modulation relates specifically to the differential vulnerability of trace versus delay conditioning to distraction. This nuance is central to both our experimental results and the conceptual framework of our model, where serotonergic modulation is used not to explain trace conditioning itself and the length of the temporal gap, but rather the selective disruption of trace conditioning by distractors.

Overall, our results indicate that trace learning may depend on species- and modality-specific neural circuitry, while also relying on shared neuromodulatory mechanisms such as serotonin-mediated temporal integration. These findings further support the role of the MBs as a central site for integrating and sustaining CS representations during the temporal gap in trace conditioning⁴⁴.

## Modelling Olfactory Learning Performance with a Minimal yet Informative Framework

Other models have been developed to capture various aspects of insect—primarily *Drosophila*—olfactory learning. For instance, Bennett et al.⁵⁵ proposed that dopaminergic neurons signal prediction
errors rather than absolute reward by integrating feedback from mushroom body output neurons (MBONs), thereby modulating plasticity at Kenyon cell → MBON synapses. This approach contrasts with our model, which relies on a more classical associative mechanism without an explicit prediction-error component. However, the two frameworks are not mutually exclusive, and future work could explore hybrid models where even simple associative tasks are solved through error-driven learning.

Springer and Nawrot⁵⁶ introduced a model featuring parallel memory traces (appetitive and aversive), an idea conceptually related to our assumption of distinct traces for cues and distractors, reflected in the use of different time constants (τ). Their model also includes feedback from MBONs to dopaminergic neurons to generate reward predictions and adjust synaptic strength, adding a layer of dynamical plasticity not explicitly represented in our framework.

Jürgensen et al.⁵⁷ focused on second-order conditioning in *Drosophila* larvae, investigating how different circuit motifs—varying in feedback and feedforward connectivity—support higher-order associative learning. While their model incorporates dopaminergic signaling similarly to our use of octopaminergic neurons to signal reinforcement, their emphasis on second-order conditioning and task-specific performance optimization limits the generalizability of their findings to our paradigm of absolute conditioning.

In summary, while these models incorporate greater biological detail—especially through feedback loops and reward prediction dynamics—our model maintains a simpler structure, which facilitates analysis and interpretation. It provides a minimal yet sufficient implementation for the associative learning task at hand, without the need to explicitly model feedback connections.

## Limitations of the Study

A key assumption of our study and modeling framework is the existence of serotonergic neurons in honey bees functionally analogous to the Dorsal Paired Medial (DPM) neurons described in *Drosophila*, which are known to extend the temporal window for CS-US associations during trace conditioning³³. While such neurons have not yet been identified in bees, our model posits that serotonergic signaling modulates stimulus processing by enhancing task-relevant representations and suppressing irrelevant or contextual ones. This functional asymmetry is implemented through biologically grounded mechanisms, including differential eligibility trace dynamics and spike-timing-dependent plasticity (STDP). Although direct experimental evidence for these neurons and their modulatory role in bees is currently lacking, our model remains neurobiologically plausible and offers specific, testable
539 predictions. Future work can use these predictions to guide investigations into the neuroanatomy and
540 neuromodulatory processes underlying trace learning in bees.

541

542 Resource Availability

543 Lead contact

544 Further information and requests for resources and reagents should be directed to and will be

545 fulfilled by the lead contact, Prof. Dr. Martin Giurfa (martin.giurfa@sorbonne-universite.fr)

546 Materials availability

547 The materials used were purchased from standard chemical suppliers listed in the Key Resources

548 Table. No custom materials were created or used.

549 Data and code availability

550 The datasets of this study are available in the Figshare

551 repository: https://figshare.com/s/ee65ad8bc64e7920156f

552 The code of the model can be found at: https://github.com/artipago/honeybee-spiking-network-

553 model

554 Other items

555 Any additional information required to reanalyse the data reported in this paper are available from

556 the lead contact upon request.

557

558

559

560 Author Contributions

561 C. M., M. P., G.I. and M.G. conceived and designed the study. C.M. conducted the experiments. M.Gh.

562 helped with the experiments. A.A. designed and implemented the model. C.M. analyzed the data. All

563 experiments were supervised by M.G. and G.I. Funding was obtained by M.G. C.M. and M.G. wrote the

564 manuscript. All authors reviewed and approved the final manuscript.

565
566 Acknowledgements

567 We thank two anonymous reviewers for valuable and encouraging comments. We also thank Benjamin
568 Paffhausen for designing, 3D-printing and assisting in the implementation of the isolation chamber and
569 the feather presentation using a fine motor. We also thank Nathan Gaubil for his implication in drafting
570 the model's code and Olivier Fernandez and Alexandre Catonné for managing the hives in the
571 experimental apiaries of Toulouse and Paris, respectively. This work was supported by the European
572 Research Council (ERC Advanced Grant COGNIBRAINS to M. Giurfa), the Toulouse Mind and Brain
573 Institute (TMBI grant to A. Alamia and M. Giurfa), and by funding from Sorbonne university, the French
574 Research Council (CNRS) and the French National Institute of Health and Medical Research (INSERM).

575

576 Declaration of interests

577 The authors declare no competing interests.

578

579 Declaration of Generative AI And AI-Assisted Technologies

580 During the preparation of this work, the authors did not use AI assisted technologies.

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584
585 **Figures**

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**Figure 1. Honey bees learn odor-sucrose associations under both delay and trace conditioning. (A)**

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In delay conditioning, the conditioned stimulus (CS) and unconditioned stimulus (US) overlap in time (blue area). In trace conditioning, there is a stimulus-free gap between the offset of the CS and the onset of the US, known as the “trace” period (orange area). ISI: interstimulus interval, defined as the interval between the CS and US onsets. (B) Example of a harnessed bee displaying the proboscis extension reflex (PER). Image from Matsumoto et al.<sup>24</sup> (C) Percentage of bees that exhibited a conditioned response during conditioning trials. Shaded areas represent the 95% confidence intervals for performance in delay (blue) and trace (orange) conditioning. Sample sizes (N) are indicated. ***, *p* < 0.001, GLMER, Group x Trial interaction.

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**Figure 2. Learning performances and features of the conditioned response (PER) to the odor during the memory test. (A)** Experimental designs of the four groups of bees included in the video analysis: delay conditioning (Delay), delay conditioning with 20% sucrose (Delay20), and trace conditioning (with CS durations of 0.5 s or 5 s, labelled Trace and Trace5s, respectively). (B) Percentage of bees that exhibited a conditioned response during conditioning trials for the groups Delay, Delay20, Trace, and Trace5s. The shaded area around the curves represents the 95% confidence interval. **, *p* < 0.01, *Group Effect, pairwise comparisons*. (C) Schematic of the frame-by-frame PER analysis. Odorant delivery onset was detected via illumination of a LED on the Arduino board. Proboscis extension latency was calculated by counting the number of frames until maximal response and converting it to seconds. The number of licking events—instances where the bee extended its glossa—was also recorded. (D) Latency of maximum proboscis extension for each group. The full line indicates the median and the dotted lines the interquartile range *, *p* < 0.05, Wilcoxon test. (E) Number of licking events for each group. The box plots show the median and the interquartile range**, *p* < 0.01; ***, *p* < 0.001, Wilcoxon test.

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**Figure 3. Distractors impair learning performance in trace, but not delay, conditioning. (A)**

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Experimental designs of distractor delivery for both delay and trace conditioning. The distractor lasted 100 ms and was randomly delivered within a 3-s window centered around the midpoint of the 5-s interstimulus interval (ISI). (B) 3D-printed isolation chamber used to perform PER conditioning while minimizing contextual cues and enhancing distractor salience. (C) Example of a harnessed bee being stroked on the back with a feather, serving as a mechanosensory distractor. The feather was motor-controlled and applied for exactly 100 ms. (D, E) Learning curves (% of PER to the CS) in delay-conditioned (D) and trace-conditioned (E) bees from control groups (blue for delay, orange for trace) and visually distracted groups (black). (F, G) Learning curves (% of PER to the CS) in delay-conditioned (F) and trace-conditioned (G) bees from control groups (blue for delay, orange for trace) and mechanosensory distracted groups (black). Shaded areas around the curves represent 95% confidence intervals. ***, *p* < 0.001, GLMER, Distraction effect.
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**Figure 4.** Distracting stimuli do not affect negatively olfactory memory retention. (A) Design of the extinction experiment used to assess whether the distractor carried neutral valence. The first trial was a standard conditioning trial (either trace or delay). The second trial was an extinction trial in which either the CS was presented alone ('Nothing') or concurrently with a visual or mechanosensory distractor ('LED/Feather'). A memory test was conducted 1 hour later. (B) Percentage of CS-specific responders for each of the six groups. Three groups were conditioned using delay conditioning (blue) and three using trace conditioning (orange). For each conditioning variant, one group underwent extinction without a distractor (control), and two groups were exposed to either a visual or mechanosensory distractor following the CS presentation. Memory was tested 1 hour post-conditioning. N=23-29 for each group. Error bars indicate 95% confidence intervals.

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**Figure 5.** A spiking neural model accounts for learning performances in delay and trace conditioning. (A) Schematic of the delay and trace conditioning protocols, showing the conditioned stimulus (CS; odorant, blue), unconditioned stimulus (US; sucrose, green), and distractor (DIS; red). (B) Graphical representation of the network architecture. PN: projection neurons (odorant input); LPN: lobula projection neurons (visual input); GAN: giant ascending neuron (mechanosensory input); KC: Kenyon cells; DPM: dorsal paired medial neurons; 5-HT: serotonin; MBON: mushroom body output neuron; OA: octopamine; US: unconditioned stimulus; PER: proboscis extension response. (C) Averaged acquisition curves from simulated data (% of PER to the CS) over 20 initializations for delay-conditioned (blue) and trace-conditioned (orange) neural networks. Solid lines represent control conditions; dotted lines indicate distracted conditions. Shaded areas around the curves represent the SD. (D) Average response levels of the model to the distractor stimulus. (E) Averaged acquisition curves of simulated data (% of PER to the CS) over 20 initializations for delay-conditioned (blue) and trace-conditioned (orange) neural networks with the DPM module excluded from the model. Shaded areas show the SD.

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**Figure 6.** Serotonergic blockade specifically impairs learning performance in trace, but not in delay, conditioning. (A) Acquisition curves showing the percentage of bees exhibiting a proboscis extension response (% of PER) to the conditioned stimulus across training trials in delay conditioning. (B) Same as in (A) for trace-conditioned bees. Control groups are shown in blue (Delay PBS) and orange (Trace PBS); pharmacologically treated groups received a serotonergic antagonist cocktail of methiothepin mesylate and ketanserin tartrate at 1 mM (pink) or 10 mM (purple). Shaded areas around the curves represent 95% confidence intervals. **, p < 0.01, GLMER, Treatment effect, pairwise comparisons.

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<table><thead><tr><th>Distractor</th><th>Conditioning variant</th><th>Group</th><th>Bees showing PER</th></tr></thead><tbody><tr><td rowspan="4">Visual experiment<br>White LED flashing at 50 Hz for 0.1 s</td><td rowspan="2">Delay</td><td>Control</td><td>0/46</td></tr><tr><td>Distraction</td><td>0/46</td></tr><tr><td rowspan="2">Trace</td><td>Control</td><td>0/56</td></tr><tr><td>Distraction</td><td>0/61</td></tr><tr><td rowspan="4">Mechanosensory experiment<br>Feather stroking the back of the bees for 0.1 s</td><td rowspan="2">Delay</td><td>Control</td><td>0/42</td></tr><tr><td>Distraction</td><td>2/44</td></tr><tr><td rowspan="2">Trace</td><td>Control</td><td>0/46</td></tr><tr><td>Distraction</td><td>0/45</td></tr></tbody></table>

**Table 1.** Distractors used are not learned in association with sucrose reward. Number of bees exhibiting a proboscis extension response (PER) to the distracting stimuli. The dataset includes eight groups: four from the visual distractor experiment and four from the mechanosensory distractor experiment. Within each experiment, bees were trained using either delay or trace conditioning. For each conditioning variant, one group served as a control (no distractor), while the other was exposed to a distractor during the interstimulus interval. Responses to the distractor alone were tested after memory tests for the conditioned stimulus and the novel odorant.
<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Chemicals, peptides, and recombinant proteins</strong></td></tr><tr><td>2-hexanol</td><td>Sigma Aldrich</td><td>Cas No. 111-27-3</td></tr><tr><td>1-nonanol</td><td>Sigma Aldrich</td><td>Cas No. 143-08-8</td></tr><tr><td>Methiothepin mesylate</td><td>Sigma Aldrich</td><td>CAS No. 74611-28-2</td></tr><tr><td>Ketanserin tartrate</td><td>Sigma Aldrich</td><td>CAS No. 83846-83-7</td></tr><tr><td colspan="3"><strong>Deposited data</strong></td></tr><tr><td>The datasets generated and/or analysed during the current study are available in Figshare</td><td>The authors of this article</td><td>https://figshare.com/s/ee65ad8bc64e7920156f</td></tr><tr><td>The code used in the modelling section is available in Github</td><td>The authors of this article</td><td>https://github.com/artipago/honeybee-spiking-network-model</td></tr><tr><td colspan="3"><strong>Experimental models: Organisms/strains</strong></td></tr><tr><td>Honey bee workers Apis mellifera</td><td>Apiary of the University Paul Sabatier & apiary of Sorbonne university.</td><td>N.A.</td></tr><tr><td colspan="3"><strong>Software and algorithms</strong></td></tr><tr><td>R 4.3.2 Statistical software</td><td>R Project for Statistical Computing</td><td>https://cran.r-project.org/bin/windows/base/</td></tr><tr><td>Graphpad Prism 8.0.2</td><td>GraphPad Software</td><td>www.graphpad.com</td></tr><tr><td>ImageJ 1.54f</td><td>ImageJ Software</td><td>https://imagej.net/ij/index.html</td></tr><tr><td>Arduino 2.3.6</td><td>Arduino IDE</td><td>https://www.Arduino.cc/en/software/</td></tr><tr><td>MATLAB 2024b</td><td>The MatWorks, Inc.</td><td>https://www.mathworks.com/products/matlab.html</td></tr><tr><td colspan="3"><strong>Other</strong></td></tr><tr><td>Saccharose</td><td>Sigma-Aldrich</td><td>Cas No. 57-50-1</td></tr><tr><td>Phosphate buffered saline</td><td>Sigma Aldrich</td><td>MDL MFCD00131855</td></tr></tbody></table>
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# EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

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## Insects

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Female worker honey bees (*Apis mellifera*) were collected from hives located in our university campus apiary. No institutional permission or ethical guidelines are required for research work on insects. Bees were captured in small glass vials and transported to the laboratory, located a few hundred meters away. Upon arrival, bees were briefly cooled on ice for 5 minutes and individually harnessed in plastic tubes (inner diameter ~ 0.9 cm; wall thickness: 1-2 mm). The bees' heads protruded from the tubes, allowing free movement of the antennae and mouthparts. Approximately 30 min after harnessing, the bees were fed 5 µL of 30% (w/w) sucrose solution to prevent excessive starvation and to standardize satiety level across individuals, and kept for 3 h in a dark, humid container maintained at 28°C. One hour before conditioning, proboscis extension reflex (PER) was assessed by touching the antennae with a toothpick dipped in 30% sucrose solution. Only bees showing a full PER were included in the experiments.

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# METHOD DETAILS

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## Olfactory Stimulation.

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Olfactory stimulation was controlled using a computer-controlled olfactometer that precisely timed the delivery of odorant pulses⁵⁸. To eliminate residual odorants and maintain the temporal integrity of the trace conditioning protocol, an air extractor was placed behind the bees to remove lingering volatiles. The absence of residual odorants during the interstimulus gap was verified (Fig. S1) using a photoionization detector (miniPID, model 200a, Aurora Scientific Inc., Canada). Additionally, the airflow strength was carefully controlled to avoid unintended mechanical stimulation.

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## Absolute Conditioning.

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All experiments employed absolute conditioning, in which a single conditioned stimulus (CS; odorant) predicted the delivery of an unconditioned stimulus (US; sucrose reward). Bees were trained with one of two odorants (CS: either 1-nonalol or 2-hexanol; Sigma-Aldrich, Saint-Quentin-Fallavier, France), while the other odorant was used as a novel odorant (NOd) during the memory retention tests to assess memory specificity. We followed a standardized conditioning protocol²⁴ using 4 or 5 CS-US pairings with an inter-trial interval (ITI) of approximately 10 min.

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Each trial began with 15-s habituation period in the experimental setup to allow bees to acclimate to the airflow and context. Both delay and trace conditioning used the same inter-stimulus
interval (ISI; interval between the CS and US onsets) of 5 s and the same US duration of 3 s, but
differed in CS duration (Fig. 1A). In delay conditioning, the CS lasted 6 s and overlapped with the US.
In trace conditioning, the CS lasted 0.5 s and terminated 4.5 s before US onset, maintaining a
constant ISI of 5 s across paradigms. In addition, we performed control experiments using longer CS
durations in trace conditioning, which did not affect our conclusions (Fig. S2). After each CS-US
pairing, the bee remained in the experimental setup for 15 additional seconds, to minimize
contextual learning. Each trial lasted 44 s.

The presence (+) or absence (-) of the proboscis extension response (PER) was recorded
during the 5-s interval between odorant onset and the onset of sucrose delivery (Fig. 1A, B). Bees
that responded spontaneously to the odorant in the first trial were excluded from the analysis²⁴ since
the CS must be neutral at baseline⁷.

Memory retention was tested either 1 h or 24 h after the last conditioning trial. Retention
was assessed by sequentially presenting the CS and NOd in randomized order, with a 10-min inter-
test interval. PER was recorded for each odorant presentation. Finally, the unconditioned response
(PER to sucrose) was tested by applying sucrose directly to the antennae; bees that failed to exhibit
PER were excluded from the analysis. To minimize extinction or fatigue effects, different groups of
bees were conditioned and tested in parallel for each retention interval.

## Video Analysis for PER Latency and Licking Events.

Video-recordings were performed during memory tests in bees that underwent delay and trace
conditioning procedures. In this way, we examined whether these conditioning regimes elicited
qualitative differences in PER. Recordings were made using a video camera (SONY Handycam HDR-
CX625, 50fps) and analyzed manually on a frame-by-frame basis with ImageJ. To determine the
precise latency of PER onset, an Arduino-controlled LED was programmed to turn on at odor
presentation and used as time reference. PER latency was defined as the time (in s) from LED onset
to the first timepoint at which the proboscis reached its maximum extension (Fig. 2C). Latency was
calculated based on frame number and thus expressed as decimal values (e.g., 1.58 s). Additionally,
we quantified the number of licking events, defined as discrete extensions of the glossa beyond the
proboscis sheath. While the proboscis refers to the entire feeding organ used for lapping and sucking
nectar, the glossa is its central, tongue-like component. Thus, a bee may extend its proboscis without
necessarily engaging in licking behavior—i.e., without rhythmic glossa movements beyond the
proboscis sheath.

The video analysis was conducted by a single observer. To assess the reliability of this scoring
and ensure that no observer bias affected the results, we asked a co-author to independently re-
analyze a random subset of 40 videos (≈35% of the 113 total videos). The second observer was blind to the individual-level data extracted by the first observer, though both observers could not be blind to the conditioning group (delay vs. trace), since LED indicators on the Arduino marked CS and US onsets/offsets in the videos. These LED cues, necessary to define time zero (odor onset), also conveyed the temporal dynamics of the conditioning protocol, making full blinding to group assignment impossible. To quantify inter-observer reliability, we compared the measurements for latency and number of licking events. We used the following approaches: 1) **Intraclass Correlation Coefficient (ICC)**: We used a two-way agreement model. The ICC was 98.3% (95% CI: 96.8–99.1) for licking events and 95.3% (95% CI: 90.9–97.5) for latency, indicating excellent agreement between observers. 2) **Bland–Altman analysis**: The mean difference between observers was close to zero (–0.09s for latency, –0.25 events for licking), and most points fell within ±1.96 SD, suggesting no systematic bias or drift between observers. These complementary analyses confirm a high degree of inter-observer reliability and support the robustness of the data collected.

756

### Distractor Delivery.

To investigate the effect of distractive stimuli on learning under delay and trace conditioning protocols, we placed each bee inside a custom 3D-printed isolation chamber to minimize contextual cues and enhance distractor salience (Fig. 3A). Two types of distractors were used: visual stimulation (light flashes from a white LED at 50 Hz) and mechanosensory stimulation (a feather that gently stroked the back of the bees) (Fig. 3B). Distractors were delivered during the interstimulus interval (ISI) in both conditioning paradigms and lasted 100 ms. To prevent bees from predicting the distractor onset, its presentation was randomly timed within a 3-s window centered around the midpoint of the ISI. Distractors were applied in every conditioning trial. To determine whether the distractor stimuli were encoded as competing cues, we tested each bee's behavioral response to the distractors following the memory retention tests. This assessment allowed us to evaluate whether distractors had acquired associative strength or remained neutral.

769

770

### Model.

The model architecture, shown in Figs. 5A and 5B, was inspired by previous studies on insect olfactory learning⁵⁹. Importantly, it is not intended to replicate all anatomical connections in detail, but rather to provide a minimal yet informative framework capable of performing the task, as in⁵⁹.
The network consists of two layers: the first layer comprises 78 projection neurons (PNs for odorant inputs, LPNs and GANs for visual and mechanosensory distractor input, respectively), which relay signals to the next layer, composed of 560 Kenyon cells (KCs). These KCs constitute the mushroom bodies and project onto a single motor output neuron (mushroom body output neuron, MBON), which governs the proboscis extension reflex, the behavioral response to the sucrose reward. Each neuron is modelled using the biologically plausible spiking dynamics proposed by Izhikevich et al.⁶⁰. Specifically, the membrane potential $v$ evolves according to the following equation:

$$782 \qquad C\dot{v} = k(v - v_r)(v - v_t) - u + I$$

Where $\underline{u}$ represents the recovery current:

$$784 \qquad \dot{u} = \alpha(\beta(v - v_r) - u)$$

If the membrane potential reaches the threshold $v_t$, both the current and the membrane potential are set to $v = \gamma$ and $u = u + \delta$.

Importantly, $\alpha$, $\beta$, $\gamma$, $\delta$ and $k$ are model parameters determining the neurons' dynamics and firing rate. We set the neurons' parameters to model the firing rate of honey bee KCs⁶¹. Specifically, we set $\alpha = 0.01$, $\beta = -0.3$, $\gamma = -65$, $\delta = 8$, $k = 0.015$, $C = 4$, $v_t = -25$ and $v_r = -85$. Learning occurs via STDP mechanisms, as described by Izhikevich³¹, mimicking the role of octopaminergic neuromodulation. In honey bees, octopamine serves as the functional equivalent of dopamine in flies, playing a crucial role in reward-based learning³². In short, the synaptic strength $s$ between pre- and post-synaptic neurons is modulated by the octopaminergic concentration $d$ and an eligibility trace, which captures the temporal dynamics of synaptic modifications:

$$795 \qquad \dot{s} = cd$$

where the eligibility trace $c$ is defined as:

$$797 \qquad \dot{c} = -\frac{c}{\tau_c} + STDP(\tau)\delta(t - t_{pre/post})$$

Here, $\tau = t_{post} - t_{pre}$, where $\tau$ is the interspike interval. This variable, which reflects the relative timing between pre- and postsynaptic spikes, decays exponentially to zero with a time constant $\tau_c = 100$ ms.

As in previous work³¹, the STDP function was defined as:

$$801 \qquad STDP(tpost - tpre) = \begin{cases} A e^{\frac{(tpost - tpre)}{\tau_{STDP}}} & \text{if } tpost - tpre < 0 \\ A e^{-\frac{(tpost - tpre)}{\tau_{STDP}}} & \text{if } tpost - tpre \ge 0 \end{cases}$$

Where $\tau_{STDP}$ was set to 20ms
The octopaminergic concentration is defined as:

$$ \dot{d} = - \frac{d}{\tau_d} + OA(t) $$

where the octopaminergic time constant $\tau_d$ was set to 200ms for all neurons, and OA(t) represents the pool of octopamine at any given moment. In particular, OA(t) is distributed to the KC neurons proportionally to the temporal delay between each spike and the delivery of the US (i.e., it was set to 0 in absence of the US)

Intuitively, the eligibility trace functions as a memory signal that gradually decays over time, with a time-constant $\tau_c$ set to 100ms for the neurons processing the cue signal. Additionally, we included the effect of serotonergic DPM neurons, which modulate the eligibility trace decay (i.e., $\tau_c$) of KCs, acting as an attentional mechanism. Specifically, DPM modulation reduces the trace time-constant $\tau_c$ from 100ms to 10ms in neurons processing distractor stimuli, effectively limiting their influence on learning.

For each condition, the model was trained twenty times over 12 trials, each trial consisting of 1000 time steps of 1 ms. In each trial, the conditioned stimulus (CS) was presented at $t = 100$ ms and lasted for 500 ms. In delay conditioning, the US was delivered during the final 200 ms of the CS presentation. In contrast, during trace conditioning, the US was delivered 200 ms after the CS had ended. Random stimuli were generated independently for both the CS and the distractors on each trial. When present, a distractor was introduced at a random time point preceding the US. In the trace conditioning condition, the distractor always occurred after the CS offset. For each condition, we trained the twenty models with different initializations for 12 trials.

## Pharmacological Blockade of Serotonergic Signaling in the Bee Brain.

To inhibit serotonergic signaling at the level of the mushroom bodies, we injected 5-HT antagonists via the ocellar tract. A 10 µL microsyringe (World Precision Instruments, Friedberg, Germany) was used to inject 250 nL of drug solution into the brain of immobilized bees. The syringe tip was inserted approximately 50 µm through a small hole pierced in the cornea of the median ocellus, following the procedure described by Tedjakumala et al.³⁴. This technique has been validated in previous studies, demonstrating that drugs injected through the ocellar tract diffuse into the brain, achieving homogeneous distribution within less than 5 min⁶². Serotonergic neurons and circuits in the honey bee brain are primarily localized in the mushroom bodies and optic lobes, with additional staining observed in the central complex⁶³,⁶⁴. Notably, both the optic lobes and the central complex are
involved in visual processing, whereas our conditioning experiments were purely olfactory in nature.
Therefore, any off-target effects of our serotonergic blockade beyond the mushroom bodies would
likely have impacted primarily visual circuits, which are not relevant to our experimental design.

To ensure sufficient effective pharmacological effect, injections were performed 30 minutes
before conditioning, in accordance with previous works demonstrating that biogenic amine receptor
antagonists reach peak efficacy during this time window ³⁴,³⁶,⁶⁵. The treatment consisted of a
serotonin antagonist-receptor cocktail containing methiothepin mesylate and ketanserin tartrate
(both from Sigma Aldrich, Saint-Quentin-Fallavier, France). Two concentrations were tested: 1 mM
and 10 mM, in phosphate-buffered saline (PBS). Control bees were injected with PBS alone.

## QUANTIFICATION AND STATISTICAL ANALYSES

The percentage of bees exhibiting a conditioned PER (%PER) in response to the odorant stimulation was calculated for each trial and test. Ninety-five percent confidence intervals were computed using a bootstrap method. Learning performance was analyzed using a Generalized Linear Mixed Effects Model (GLMER) for binomial family. Individual identity was included as a random factor, while fixed effects included conditioning type (delay vs. trace), memory test time (1 h vs. 24 h), conditioned stimulus (1-nonanol vs. 2-hexanol) and trial number. For the distraction experiment, distractor presence (yes/no) was added as an additional fixed factor. In the pharmacological experiment, treatment group (control, cocktail 1 mM, cocktail 10 mM) was included as a fixed factor. For both delay and trace conditioning, model selection was performed using an information theoretic approach based on Akaike Information Criterion (AIC). Z-values and *p*-values are reported throughout the manuscript.

Within-group comparisons of CS vs. NOd responses were conducted using McNemar tests,
appropriate for paired binomial data. Between-group comparisons (CS and NOd response levels
across groups) and comparisons of the percentage of specific learners (bees responding to the CS but
not the NOd) were analyzed using χ² tests with reported degrees of freedom. All statistical analyses
were conducted using the R 4.0.3 software (R project). GLMERs were performed using the lme4
package. Post-hoc analyses were done using the emmeans and lsmeans packages.

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1022

1023
A

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

B

<figure><img src="image_15.png" /></figure>

C

Learning curves comparing
delay and trace performance

<figure><img src="image_16.png" /></figure>
A

Delay

<figure><img src="image_17.png" /></figure>

Delay20

<figure><img src="image_18.png" /></figure>

Trace

<figure><img src="image_19.png" /></figure>

Trace5s

<figure><img src="image_20.png" /></figure>

B

Learning curves of groups included in the video analysis

<figure><img src="image_21.png" /></figure>

C

<figure><img src="image_22.png" /></figure>

D

Latency to maximum PER

<figure><img src="image_23.png" /></figure>

E

Number of licking events

<figure><img src="image_24.png" /></figure>
A

Delay
conditioning

<figure><img src="image_25.png" /></figure>

B

<figure><img src="image_26.png" /></figure>

D

Delay learning curves
with a visual distractor

<figure><img src="image_27.png" /></figure>

F

Delay learning curves
with a mechanosensory distractor

<figure><img src="image_28.png" /></figure>

Trace
conditioning

<figure><img src="image_29.png" /></figure>

C

<figure><img src="image_30.png" /></figure>

E

Trace learning curves
with a visual distractor

<figure><img src="image_31.png" /></figure>

G

Trace learning curves
with a mechanosensory distractor

<figure><img src="image_32.png" /></figure>
A

Trial 1
Association

Delay

<figure><img src="image_33.png" /></figure>

Trace

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

Trial 2
Extinction

<figure><img src="image_35.png" /></figure>

Memory
test

<figure><img src="image_36.png" /></figure>

B

Specific learners

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>
A

<figure><img src="image_39.png" /></figure>

B

<figure><img src="image_40.png" /></figure>

C

Model results:
response to CS with DPM included

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

D

Model results:
response to Dis with DPM included

<figure><img src="image_42.png" /></figure>

E

Model results:
response to CS with DPM excluded

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

Delay
Control

Delay
Distraction

Trace
Control

Trace
Distraction
A

Delay conditioning learning curves
with a 5-HT blockade

<figure><img src="image_44.png" /></figure>

B

Trace conditioning learning curves
with a 5-HT blockade

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>
* Bees learn odor-sugar associations in both delay and trace conditioning

* Trace conditioning yields lower performance than delay conditioning

* Distractors impair trace but not delay conditioning, showing awareness demand

* 5-HT mediates trace but not delay conditioning, showing a role in stimulus bridging