# Evidence for the effect of brief exposure to food, but not learning interference, on maze solving in desert ants

Tomer Gilad, Arik Dorfman, Aziz Subach, Romain Libbrecht, Susanne Foitzik, Inon Scharf

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## To cite this version:

Tomer Gilad, Arik Dorfman, Aziz Subach, Romain Libbrecht, Susanne Foitzik, et al., Evidence for the effect of brief exposure to food, but not learning interference, on maze solving in desert ants. Integrative Zoology, 2022, 17 (5), pp.704 - 714. 10.1111/1749-4877.12622. hal-04730500

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Submitted on 10 Oct 2024

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# Evidence for the effect of brief exposure to food, but not learning interference, on maze solving in desert ants

Tomer GILAD,¹ Arik DORFMAN,¹ Aziz SUBACH,¹ Romain LIBBRECHT,² Susanne FOITZIK² and Inon SCHARF¹

¹School of Zoology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel and ²Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany

## Abstract

Theories of forgetting highlight 2 active mechanisms through which animals forget prior knowledge by reciprocal disruption of memories. According to “proactive interference,” information learned previously interferes with the acquisition of new information, whereas “retroactive interference” suggests that newly gathered information interferes with already existing information. Our goal was to examine the possible effect of both mechanisms in the desert ant *Cataglyphis niger*, which does not use pheromone recruitment, when learning spatial information while searching for food in a maze. Our experiment indicated that neither proactive nor retroactive interference took place in this system although this awaits confirmation with individual-level learning assays. Rather, the ants’ persistence or readiness to search for food grew with successive runs in the maze. Elevated persistence led to more ant workers arriving at the food when retested a day later, even if the maze was shifted between runs. We support this finding in a second experiment, where ant workers reached the food reward at the maze end in higher numbers after encountering food in the maze entry compared to a treatment, in which food was present only at the maze end. This result suggests that spatial learning and search persistence are 2 parallel behavioral mechanisms, both assisting foraging ants. We suggest that their relative contribution should depend on habitat complexity.

**Key words:** binary-tree maze, desert ants, forgetting, memory, motivation

## INTRODUCTION

Spatial learning can be defined as the process through which animals form a neuronal representation of their surroundings (Capaldi *et al.* 1999; Brodbeck 2012). Spatial learning is especially beneficial for central-place foragers when they can return to collect food from the same food sources or to previously stored food (Healy &

Hurly 2004; VanderSal 2008). It helps to minimize travel costs by arriving directly at the desired destination and therefore saves time and energy (Saleh & Chittka 2007; Bracis *et al.* 2015). Spatial learning also reduces preparation risk, which is positively correlated with foraging persistence (e.g. Lima *et al.* 1985; Gotthard 2000).

Until recently, forgetting has been mostly considered as a constraint on learning, instead of an adaptive trait in itself subjected to natural selection (Kraemer & Golding 1997; Gonzalo *et al.* 2009; Mery 2013). Information might lose value in changing environments and when and if animals form memories depending on the context

*Correspondence*: Inon Scharf, School of Zoology, Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel.
Email: scharfi@tauex.tau.ac.il
or environment. As a result, it makes more sense to maintain memories, such as food or predator location, only as long as they are beneficial (McNamara & Houston 1985; Krebs & Inman 1992; Cuthill et al. 1994). For example renewable food sources are expected to be remembered longer than non-renewable ones (Dunlap & Stephens 2012; Freas et al. 2017). Two non-mutually exclusive theories of forgetting are: (i) the “decay theory,” which refers to the passive process of memories decaying with time; and (ii) the “interference theory,” a more active process, which involves either “proactive interference” or “retroactive interference.” In the former one, prior information interferes with the acquisition of new information, whereas in the latter, newly gathered information interferes with already existing memories (Wixted 2004a,b; Roediger et al. 2007). In insects, most evidence for memory interference comes from food handling, for example when butterflies or bees learn how to efficiently handle specific flower types (Lewis 1986; Woodward & Laverty 1992; Goulson et al. 1997; Gegear & Laverty 1998), and examining interference with spatial learning in insects is less common (but see studies on memory interference in honeybees searching for a food reward; Cheng & Wignall 2006; Menzel 2009).

Spatial memory is one behavioral mechanism that can shorten the time required to reach a food source because a searching animal can use the shortest path available when the destination is known. A parallel mechanism, which shortens food-discovery time is the intensity of searching and its persistence. Search persistence can change in response to a variety of factors, unrelated to spatial learning. For example hungry animals are more persistent in their search for food than satiated animals, learn better foraging-related tasks than satiated animals, and prefer the food provided when hungry in choice tests conducted later (Ramond 1954; Croy & Hughes 1991; Marsh et al. 2004; Scharf 2016). Elevated predation risk reduces the foraging motivation of animals (Verdolin 2006) but hungry animals more often ignore such predation risks (Whitham & Mathis 2000; Berger-Tal et al. 2010). Search persistence depends also on the certainty of food discovery and the reward attractiveness, in addition to spatial knowledge (Roces 1993; Warburton 2003; Anselme & Güntürkün 2019). For instance, the search persistence of ants is higher in spring than autumn and ants learn faster to search for a more attractive reward type (Oberhauser et al. 2018; Saar et al. 2020). Higher search persistence can be achieved by different means, such as faster movement, moving across longer distances, giving up more slowly after commencing foraging, and a faster switch to area-restricted search upon an encounter

with food (Wallin & Ekbom 1994; Berger-Tal et al. 2010; Czaczkes et al. 2011). In social insects, higher search persistence can be achieved also by allocating more workers to foraging when deemed necessary. In short, more successful foraging can result from both spatial learning and higher search persistence (Bega et al. 2020).

Our aim was to examine the possible effect of proactive and retroactive interference as well as search persistence on spatial learning and maze solving in the ant *Cataglyphis niger*. Foraging in this *Cataglyphis* species is characterized by single foragers, which may elicit a few other workers to leave the colony and search for food but do not use pheromone-based recruitment (Razin et al. 2013). *Cataglyphis* foragers are excellent navigators using diverse tools, such as landmarks, path integration, and panoramic view of the environment (Wehner 2003; Collett 2010; Fleischmann et al. 2018). *Cataglyphis* foragers are exposed to many risks, such as desiccation and predation outside of the nest, and often survive this phase of their life for not more than 2 weeks (Fleischmann et al. 2016). We examined whether *C. niger* ant workers, trained to solve a maze of one layout, take longer to solve the same maze, after an encounter with a maze of a different layout. Previous studies already demonstrated that the focal species solves such mazes faster with successive runs within and between days, even 5 times faster on the third run than the first one when offered honey as a reward (Saar et al. 2017, 2020). The relevant memory holds for ~16 days after which maze-solving times return to their original levels before training (Saar et al. 2017). To differentiate between retroactive and proactive interference, we set the experiments up so that the ants encounter the maze of a different layout (but of the same complexity) either before or after the main training. Mazes, from simple ones, such as a T-maze, to more complex ones, have been frequently used in ants to examine spatial learning (e.g. Chameron et al. 1998; Beugnon & Macquart 2016; Pasquier & Grüter 2016; Czaczkes 2018).

In parallel, we examined whether an increase in search persistence can explain the arrival in higher numbers at the food reward. If so, search persistence should be a parallel behavioral mechanism to spatial learning, which assists in solving navigation problems. We expected a change in the maze layout during the learning process (i.e. learning interference) to slow down the arrival of workers at the food reward. We also expected that prior exposure to the food reward would increase the search persistence on successive tests because it increases the foraging benefits or the certainty that food exists nearby.
<figure><img src="image_3.png" /><figcaption>**Figure 1** (a) Scheme of Experiment 1. In the retroactive interference treatment (i), colonies were tested on the maze (layout 1) they were trained in prior to exposure to a different maze (layout 2). In the proactive interference treatment (ii), colonies were tested in the maze (layout 1) they were trained in, but after exposure to a different maze (layout 2). The control (iii) encountered only a single maze type (layout 1). The vertical grey rectangles stand for the 2 comparisons among treatments we conducted. (b) Mazes of the 2 layouts, which were a mirror image of one another. “S” and “H” stand for the start point and the food reward, “*” stand for the deepest cells in the maze, “@” stand for the location of the empty or food dish on the first run of Experiment 2, and “|” stands for layout. The white circles represent paths leading to the food reward while the grey circles stand for paths leading to a dead-end.</figcaption></figure>

# MATERIALS AND METHODS

## Ant collection and maintenance

We collected 11 and 9 colonies of *C. niger* (>200 workers each) for the first and second experiments, respectively, in June 2020 and March of 2021. Colonies were collected from Tel-Baruch sand dunes (32.132°N, 34.788°E). This area (~1.5 × 0.5 km), located in north-west Tel Aviv, is an enclave of disturbed natural habitat, surrounded by the city, and is characterized by stabilized and stabilized sand dunes. See Saar *et al.* (2018) for the plant species and other ant species common in the area. Colonies were kept under laboratory conditions (~28°C; 12:12 L:D) and within 7 days after collection, we split each colony into 3 units. Neither queens nor brood were included in the newly created 30-worker units. Colonies without a queen or brood keep long in the lab

and forage readily for food. Splitting colonies is a powerful method in the research of eusocial insects and enables a stronger comparison among treatments (e.g. Schmid-Hempel & Schmid-Hempel 1998). Each colony unit was kept separately in a plastic container of 20 × 20 × 6 cm. We let the colony units acclimatize for 7 days before the experiment commenced, during which the colony unit had access to water but received no food to keep the foraging motivation high.

## Experiment 1: spatial learning, proactive, and retroactive interference

The first experiment comprised 3 treatments: (a) retroactive interference, (b) proactive interference, and (c) the control (11 colonies, each split into 3 colony units of 30 workers and allocated to 1 treatment; Fig. 1a). All
workers started in the nest, which was connected to a
maze using a corridor (Fig. 1b). Each experimental run
started by opening the path enabling the workers to move
from the nest to the corridor and then to the maze. We
used 2 layouts of the same maze (layout 1 and layout
2; Fig. 1b). The searching workers in these mazes could
choose among 14 entries (excluding the entry to the maze
itself), of which only 3 led to the reward. On day 1, the
proactive interference treatment was exposed to a maze
of layout 2, with honey reward (0.5 g) placed in an Ep-
pendorf lid at its one end. The colony units were exposed
to the maze 3 successive times, with an interval of 30 min-
utes in between. Each run was stopped 10 minutes follow-
ing the arrival of the first worker at the reward or after 50
minutes if no workers reached the reward, after which all
searching workers were brought back to the nest. On day
2, during the training phase, all colonies were exposed to
a maze of layout 1, 3 successive times. This is the first
exposure to the maze of the retroactive interference and
control groups. The colony units were kept between day
3 and day 6 in their nests without access to the maze. On
day 7, the retroactive interference treatment was exposed
to a maze of layout 2 for 3 runs. Finally, all treatments
were exposed on day 8 to a maze of layout 1 for a single,
test run (Fig. 1a). The experiment lasted 8 days, a period
chosen as the midpoint of 16 days following training af-
ter which maze-solving time returns to its original level
(Saar *et al.* 2017). The proactive treatment experienced a
maze of layout 2 a day before training on a maze of lay-
out 1, and the retroactive treatment experienced a maze
of layout 2 a day before being tested on a maze of lay-
out 1. All runs were filmed. The colony units tested in the
control group (Fig. 1a, iii) are expected to learn the best
because they encounter only one maze layout without any
interference. The treatment encountering the maze of lay-
out 2 just before the test (Fig. 1a, i) should experience
retroactive interference, as the recent information gath-
ered (maze layout 2) is expected to interfere with already
existing information (maze layout 1). The treatment en-
countering the maze of layout 2 before encountering maze
of layout 1 in the training phase (Fig. 1a, ii) should expe-
rience proactive interference, as the information learned
previously (maze layout 2) interferes with the acquisition
of new information (maze layout 1).

Experiment 2: spatial learning and search
persistence

We split 9 colonies into 3 colony units of 21 workers
and allowed them 7 days to acclimate. We applied 3 treat-

ments on the first run: (a) honey (0.5 g) placed at the entry
to the maze (food at the entry; Fig. 1b); (b) honey placed
at the end of the maze (food at the end) similar to Exper-
iment 1; and (c) an empty dish (an Eppendorf lid) placed
at the entry to the maze (empty dish). The first run was
stopped 10 min. after the arrival of the first worker to the
honey reward (or empty dish). The second run, or the test
run, took place in the same maze after a 30-minute in-
terval with the honey reward at its end cell (as in treat-
ment b). All runs were filmed. We expected colony units
exposed to the food reward at the entry to their maze to
have later the highest search persistence in the maze be-
cause they have already encountered food and may have
a higher reward expectation than those that have not yet
encountered food.

Response variables, statistical analyses, and tests

We observed the films of each run and wrote down all
entry events to any maze cell as well as arrivals at the
honey reward. We refer to 5 response variables: (a) the
time required for the first worker to reach the honey re-
ward at the end of the maze (food-discovery time); (b) the
number of workers reaching the honey reward (food ar-
rivals); (c) the number of workers entering any of the cells
leading to the honey reward divided by entries to all cells
considering only movement forward in the maze (proportion
of correct moves); (d) the number of workers entering
the maze (maze entries); and (e) the number of workers
reaching the deepest cells in the maze (deep-maze entries;
Fig. 1b). Variables a, b, d, and e were square-root trans-
formed, due to their deviation from a normal distribution
according to Kolmogorov–Smirnov tests. Variable c was
arcsin-transformed, as common for proportions (Gotelli
& Ellison 2004, ch. 8).

We analyzed the data using linear mixed-model regres-
sions with the colony as a random explanatory variable,
treatment as a fixed explanatory variable, and 4 of the
5-mentioned response variables as the dependent vari-
ables (food arrivals, maze entries, and deep-maze entries).
Food-discovery time is analyzed in Table S2, Supporting
Information. We compared the retroactive treatment to the
2 other treatments in Experiment 1, day 8 (the test run);
the proactive treatment to the other treatments in Exper-
iment 1, day 2 (the training phase); and the food-at-the-
entry treatment to the 2 other treatments in the test run of
Experiment 2. We wished to verify there is indeed spa-
tial learning. We therefore compared food-discovery time
and the proportion of correct moves between runs 1 and
3, when the colonies were first exposed to the maze. We
did so by using a similar linear mixed-model, taking the
<figure><img src="image_4.png" /><figcaption>**Figure 2** (a,c) Food arrivals, or the number of workers arriving at the food, and (b,d) deep-maze entries, or the number of workers entering the deepest cells in the maze, on (a,b) the test phase on day 8 of the experiment, and on (c,d) day 2. Violin plots were plotted using a script in MATLAB (Bechtold 2016). Medians, quartiles, range, and all data points are shown. Note that the range of the response variables differs among panels.</figcaption></figure>

colony as a random variable, and run number and treatment as fixed variables. The result is presented in Table S3, Fig. S1, Supporting Information. Next, we examined whether the response variables are inter-correlated and used a principal component analysis (PCA) on the z-score transformed 5 response variables. We chose the last run of Experiment 1 (test run on day 8) for this analysis because this was the last run after applying the different treatments. In 3 cases, in which workers did not reach the honey reward, food-discovery time was given the maximal value of 3000 s. In Experiment 2, we compared the above-mentioned 5 response variables in the test phase similar to Experiment 1. We conducted 24 separate tests. We accounted for multiple testing using the Benjamini-Hochberg False Discovery Rate method with a false discovery rate of 15% (Benjamini & Hochberg 1995). The analysis revealed that all significant results held. Analyses were performed using SYSTAT v. 13 (Systat Software, San Jose, CA, USA).

# RESULTS

## Experiment 1: spatial learning, proactive, and retroactive interference

Test phase, interference with learning (day 8): The first goal was to examine whether interference with learning delays maze solving compared to a control. Food arrivals (number of workers arriving at the food) and deep-maze entries (entries to the deepest level at the maze) were higher in the retroactive interference treatment than the 2 other treatments (Fig. 2a,b; Food arrivals: retroactive vs. proactive: *t* = -2.163, df = 18, *P* = 0.044, *d* [Cohen's *d* effect size] = 0.936; retroactive vs. control: *t* = -2.862, df = 18, *P* = 0.010, *d* = 1.033. Deep-maze entries: retroactive vs. proactive: *t* = -2.630, df = 18, *P* = 0.017, *d* = 1.250; retroactive vs. control: *t* = -2.777, df = 18, *P* = 0.012, *d* = 1.119; Fig. 2a,b). No difference was found in maze entries among the 2 interference
treatments and the control (retroactive vs. proactive: $t = -1.086$, df = 20, $P = 0.291$; retroactive vs. control: $t = -1.731$, df = 20, $P = 0.099$) as well as in the proportion of correct moves (retroactive vs. proactive: $t = -0.537$, df = 17, $P = 0.598$; retroactive vs. control: $t = -0.634$, df = 17, $P = 0.534$). See Table S1, Supporting Information for means and standard deviations of all examined response variables according to treatments.

**Training phase (day 2):** The goal here was to examine whether prior exposure to the food reward triggers higher search persistence, as a mechanism to reach the food reward, which is parallel to spatial learning. The results were qualitatively similar to the test phase, with an important distinction: the proactive treatment was the exception this time. The reason for this difference, however, is plausibly not related to learning interference, but rather to the prior exposure of the workers to the food reward a day before (Fig. 1a). Food arrivals and deep-maze entries were higher or tended to be higher in the proactive interference treatment than the 2 other treatments (Fig. 2c,d; Food arrivals: proactive vs. retroactive: $t = -3.082$, df = 18, $P = 0.006$, $d = 1.005$; proactive vs. control: $t = -3.939$, df = 18, $P = 0.001$, $d = 1.301$. Deep-maze entries: proactive vs. retroactive: $t = -1.946$, df = 18, $P = 0.067$; proactive vs. control: $t = -2.756$, df = 18, $P = 0.013$, $d = 0.996$). No differences were found in maze entries among treatments (proactive vs. retroactive: $t = -0.392$, df = 20, $P = 0.699$; proactive vs. control: $t = -1.081$, df = 20, $P = 0.292$) as well as in the proportion of correct moves (proactive vs. retroactive: $t = -0.678$, df = 17, $P = 0.507$; proactive vs. control: $t = -0.412$, df = 17, $P = 0.686$).

**Correlations among the response variables:** The PCA on the 5 response variables resulted in 2 PCs with eigenvalues larger than 1 (Table 1). PC1 demonstrated a trade-off between food arrivals, deep-maze entries, and maze entries in positive values versus food-discovery time, with the proportion of correct moves loading low on PC1. PC2 demonstrated another trade-off, between food-discovery time and deep-maze entries versus the proportion of correct moves, while both maze entries and food arrivals loaded low on this PC.

## Experiment 2: spatial learning and search persistence

Food arrivals and maze entries were higher in the food-at-the-entry treatment than the food-at-the-end treatment (Fig. 3; Food arrivals: entry vs. end: $t = -2.762$, df = 15, $P = 0.015$, $d = 1.236$; entry vs. empty: $t = -0.986$, df = 15, $P = 0.340$. Maze entries: entry vs. end: $t = -3.548$,

<table><caption>**Table 1** Results of the principal component analysis on the 5 response variables. PC1 represents a negative trade-off between food-discovery time and all other response variables. PC2 stands for a trade-off between food-discovery time and deep-maze entries vs. the proportion of correct moves</caption><thead><tr><th></th><th>PC1</th><th>PC2</th></tr></thead><tbody><tr><td>Eigenvalue</td><td>2.3915</td><td>1.6085</td></tr><tr><td>% variance explained</td><td>47.83%</td><td>32.17%</td></tr><tr><td>Food arrivals</td><td>+0.6120</td><td>-0.0159</td></tr><tr><td>Maze entries</td><td>+0.4726</td><td>-0.1822</td></tr><tr><td>Deep-maze entries</td><td>+0.4347</td><td>-0.5283</td></tr><tr><td>Proportion of correct moves</td><td>+0.1922</td><td>+0.6902</td></tr><tr><td>Food-discovery time</td><td>-0.4196</td><td>-0.4595</td></tr></tbody></table>

$df = 16, P = 0.003, d = 1.311$; entry vs. empty: $t = -1.599$, df = 16, $P = 0.130$). The empty-dish treatment was in-between the 2 other treatments. No differences were found among treatments in deep-maze entries (entry vs. end: $t = -1.763$, df = 15, $P = 0.098$; entry vs. empty: $t = -1.480$, df = 15, $P = 0.160$) as well as in the proportion of correct moves (entry vs. end: $t = 0.206$, df = 14, $P = 0.840$; entry vs. empty: $t = 0.505$, df = 14, $P = 0.622$).

## DISCUSSION

We examined whether the spatial memory of the ant *C. niger* declines owing to either retroactive or proactive interference. Neither had a negative effect on any of the response variables related to searching for a food reward in a maze. Our results suggest a different process taking place—the change in the workers’ search persistence, owing to non-spatial but perhaps operant learning that there is food somewhere in the maze and probably non-recruitment-based upregulation of foraging. This leads to a more persistent search and arrival at deeper cells in the maze, which consequently ends in more arrivals at the food. This process does not involve spatial learning, and evidently, there is no change in the proportion of correct moves in the maze. The strong contribution of search persistence to food discovery is demonstrated in the test phase of the second experiment. More worker arrivals to the food were detected in the group initially exposed to food at the maze entry, rather than at the end of the maze. In short, our study presents 2 parallel, non-mutually exclusive mechanisms, contributing to foraging in complex habitats: spatial learning and elevated search persistence.
<figure><img src="image_5.png" /><figcaption>**Figure 3** (a) Maze entries, or the number of workers entering the maze, and (b) food arrivals, or the number of workers arriving at the food cell on the second run in the 3 treatments: The food reward was placed on the first run at the maze entry (Entry) or the maze end (End), or an empty dish was placed at the maze entry (Empty). Violin plots are presented, similarly to Fig. 2.</figcaption></figure>

We did not discover any evidence for interference with spatial learning. In other words, the workers as a group were not slowed down by searching in a different maze layout, either before or after exposure to the test layout of the maze. It could be that spatial memory is less susceptible to interference than other types of learning, such as associative learning (Shettleworth 2010, ch. 7). Evidence for interference with spatial learning or memory is relatively rare although there is much evidence for interference with associative learning in insects (Gegear & Laverty 1998; Reaume *et al.* 2011; Tapia *et al.* 2015).

In eusocial insects, it is easy to compensate for low learning levels with high search persistence, simply by having more workers searching. The same compensation is harder in solitary animals, which cannot search simultaneously at different locations. The relative contribution of spatial learning and search persistence to successful foraging remains to be tested and compared between eusocial and solitary insects. One possible explanation for the absence of learning interference is that we brought the workers back to the nest after each run and did not allow them to return independently. Experiencing both outward and inward journeys may improve learning (Sakiyama & Gunji 2013) and hence also strengthen the effect of interference with learning.

Spatial learning is likely to exist in our experiment. The analysis in Table S3, Fig. S1, Supporting Information demonstrates that when encountering the same maze, workers reached the food faster and the proportion of correct moves was higher (i.e. they made fewer mistakes on the way to the food reward). While the former result may stem from a higher search persistence, the latter one is already a clear indication of spatial learning. The proportion of wrong or correct moves is a common measure of spatial learning in mazes (Punzo 1980; Hyde *et al.* 2000; Grüter *et al.* 2015), which is indeed more convincing evidence for spatial learning than changes in maze-solving time. Although learning takes place at the individual level whereas we examined whole colonies, we see no other likely explanation for the diminishing number of errors in the maze with successive runs, except for spatial learning. Moreover, it makes also sense to examine the performance of the whole colony, as this is the unit of selection in social insects, forming a “superorganism” (Seeley 1989; Korb & Heinze 2004). That said, it is unclear which cues the workers used for their orientation in the maze. They might have used minor irregularities in the maze as landmarks or simply remember the required turns and their order.

The tested response variables were inter-correlated in a way that emphasizes the 2 distinct mechanisms improving foraging: spatial learning and search persistence. Food-discovery time was negatively correlated with the proportion of correct moves, pointing to the contribution of spatial knowledge to maze solving (PC2). Food-discovery time was also negatively correlated with the number of workers entering the maze and the number of workers reaching the deepest level of the maze (PC1), where the food reward was placed. The fact that a higher number of workers in the maze discover food faster than a lower number of workers (even if simply by chance)
indicates the contribution of search persistence to maze solving at the colony level.

Foraging is clearly influenced by search persistence. For instance, hungrier ant colonies recruit more nestmate workers towards detected food or have more workers leaving the colony and searching for food (Traniello 1977; Cosens & Toussaint 1986; Liefke et al. 2001). This may easily lead to faster discovery of food or its more persistent exploitation. Here, knowledge on the existence of food nearby increased the worker's motivation to leave the nest and search when the path to the maze was opened (potentially an external stimulus). Other studies on the same ant species demonstrated supporting results. First, pre-exposure to food, even out of the maze context, resulted in faster solving times (in comparison to a non-exposed control; Bega et al. 2020). Second, switching mazes at mid-experiment slowed food-discovery time, but never down to the level of a naïve colony at its first run (Saar et al. 2020). Third, ants demonstrated faster spatial learning of a preferred food over food of second priority (Saar et al. 2020). These results may be also related to a difference in search persistence, when workers are more motivated to search again for higher-ranked food or when they already know that food is available.

Although we did not demonstrate any type of learning interference, we bring 3 pieces of evidence for the contribution of search persistence to foraging. We show twice in Experiment 1 that exposing workers a day before to a maze of a different layout elevated search persistence and led to more workers reaching the food reward. In Experiment 2, we built on this result and demonstrated that exposure to food at the entry to the maze elevated search persistence more than exposure to food at the maze end. We suggest that after discovering food and sharing it with nestmates, such nestmates were more motivated to search in the maze than before. It is not surprising that the availability of food leads to upregulation of foraging (e.g. Jandt & Jeanne 2005; Greene et al. 2013). However, the fact that such elevated search persistence leads to faster maze solving, as a parallel mechanism of spatial learning, is more interesting. Furthermore, workers previously finding food could have been more persistent in their search on the next run by giving up more slowly if they did not find food. Animals should forage as long as the foraging benefits are higher than its costs (Mitchell et al. 1990; Olsson & Brown 2006). The experiment results can be explained using a foraging framework as well: prior knowledge on the existence of food increases the certainty foragers have regarding the foraging benefits, making them more likely to initiate foraging under such conditions (Real 1981; Kagel et al. 1986).

Both the number of workers entering the maze and those arriving at the food were higher when on the previous run food was presented in the maze entry than at the maze end, with an intermediate result demonstrated when an empty dish was placed in the maze entry. The remaining question is why an empty dish somewhat elevates search persistence. First, an empty dish may be a landmark triggering attention. Desert ants, as individual foragers, rely also on landmarks while foraging (Boulay et al. 2017; Fleischmann et al. 2018), and searching in landmark-rich habitats might be more persistent than that in simpler habitats. For example when encountering a known landmark, *Cataglyphis* workers can change their state from homing to foraging (Merkle & Wehner 2008). An alternative explanation refers to the change in the maze entry area between the first and second runs, which took place in the food-at-the-entry and empty-dish treatments but not in the food at the maze treatment (the dish placed at the entry was moved away). A spatial change in the habitat may trigger more persistent searching or exploration than a non-changing habitat (Thinus-Blanc et al. 1987; Poucet 1989).

In conclusion, we suggest that workers searched more persistently and reached the food reward at the maze end in higher numbers after exposure to food than before it owing to an increase in their search persistence. Neither proactive nor retroactive interference to spatial learning was evident, suggesting that colonies can “flood” the maze with searching workers when they deem this beneficial. It will be of interest to examine in which habitats the benefit of such an action should be higher. While high search persistence may be more helpful than spatial learning in simple habitats, it may not hold true in more complex habitats.

## ACKNOWLEDGMENT

We thank the German Research Foundation for funding this research project (DFG; grant no. FO 298/31-1).

## CONFLICT OF INTEREST

We declare no conflict of interest. No permissions were required to conduct this study.

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## SUPPLEMENTARY MATERIALS

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1** Means ± SDs for all 5 response variables described in the manuscript.

**Table S2** Analysis of food-discovery time.

**Table S3** Analysis of the training phase (a comparison between run 1 and run 3 in the maze).

**Figure S1** The difference between run 1 and run 3 in: (a) food-discovery time, and (b) the proportion of correct moves.

**Cite this article as:**

Gilad JT, Dorfman A, Subach A, Libbrecht R, Foitzik S, Scharf I (2022). Evidence for the effect of brief exposure to food, but not learning interference, on maze solving in desert ants. *Integrative Zoology* **17**, 704–14.