537 Acknowledgements

538 The authors acknowledge the experimental unit Pôle d'Expérimentation Avicole de Tours (UE PEAT,
539 INRAE, 2018. Experimental Poultry Facility, DOI: 10.15454/1.5572326250887292E12) where hens were
540 maintained and where the experiments took place.

541

542 Financial support statement

543 This research received the funding support of the PHASE research Department (INRAE) and a PhD
544 fellowship INRAE-Région Centre Val de Loire (France).

545

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649
1 Supplementary Materials ARTICLE 1 - Apply the optimal search strategy: Adult
2 domestic hens learned to infer by exclusion in the sloped-tubes task

3

4

5

6 Degrande R. ª, Cornilleau F. ª, Jardat P. ª, Ferreira V.H.B. ª, Lansade L. ª, Calandreau L. ª

7

8 aCNRS, IFCE, INRAE, Université de Tours, PRC (Physiologie de la Reproduction et des Comportements), F-37380, Nouzilly, Indre-et-Loire, France

10

11 Corresponding author: rachel.degrande@gmail.com ; rachel.degrande@inrae.fr

12

13

14
**SI 1:** Mean performance +/- SD (%) for each trial type at each stage. For each line, the pvalue from the comparison with the chance level is detailed (Wilcoxon test). • = statistical tendencies (P < 0.1) considered when tested individuals are four.

<table><thead><tr><th>Stage</th><th>Trial type</th><th>Mean (%)</th><th>Sd (%)</th><th>P (vs 50%)</th></tr></thead><tbody><tr><td rowspan="4">Free-choice test, M-sessions</td><td>vPT</td><td>89.58</td><td>10.73</td><td>0.0023</td></tr><tr><td>nvPT</td><td>43.05</td><td>12.22</td><td>0.073</td></tr><tr><td>CT</td><td>92.36</td><td>7.5</td><td>0.0021</td></tr><tr><td>ST</td><td>48.26</td><td>9.14</td><td>0.60</td></tr><tr><td rowspan="2">Free-choice test, P-sessions</td><td>vPT</td><td>94.45</td><td>11.42</td><td>0.0015</td></tr><tr><td>nvPT</td><td>31.95</td><td>12.22</td><td>0.022</td></tr><tr><td rowspan="2">Group 1: Different object</td><td>vPT</td><td>95.83</td><td>6.97</td><td>0.031</td></tr><tr><td>nvPT</td><td>29.17</td><td>18.07</td><td>0.057</td></tr><tr><td rowspan="2">Group 2: Forced-choice test (2 first sessions)</td><td>vPT</td><td>97.22</td><td>6.81</td><td>0.026</td></tr><tr><td>nvPT</td><td>42.59</td><td>25.50</td><td>0.58</td></tr><tr><td rowspan="2">Group 2: Forced-choice test, final sessions</td><td>vPT</td><td>91.67</td><td>9.62</td><td>0.095 •</td></tr><tr><td>nvPT</td><td>83.33</td><td>7.85</td><td>0.098 •</td></tr><tr><td rowspan="2">Forced-choice test controls</td><td>CT</td><td>100</td><td>0</td><td>0.072 •</td></tr><tr><td>ST</td><td>52.08</td><td>4.17</td><td>1 •</td></tr></tbody></table>
24 **SI 2: Result for the post-hoc comparisons between each tested condition with a Tukey HSD post-hoc test,**
25 with the absolute value of Cohen's d effect size for each comparison. Performance in inference trials
26 (nvPT) was calculated over 2 sessions for each tested condition to get a relevant number of trials for the
27 analysis (i.e. at least 12 trials). Cohen's d is commonly described as weak at and under 0.2, mean around
28 0.5 and strong at and over 0.8.

29

<table><thead><tr><th>Test conditions compared</th><th></th><th>Difference</th><th>Lower</th><th>Upper</th><th>Adjusted<br>P-value</th><th>Effect size<br>(abs. Cohen's d)</th></tr></thead><tbody><tr><td rowspan="4">Forced-choice, 2<br>last sessions</td><td>Forced-choice, 2 first<br>sessions (group2)</td><td>-40.74</td><td>-74.25</td><td>-7.23</td><td>0.01</td><td>1.78</td></tr><tr><td>Free-choice, <i>M-sessions</i></td><td>-40.28</td><td>-70.25</td><td>-10.31</td><td>&lt; .0001</td><td>3.33</td></tr><tr><td>Free-choice, <i>P-sessions</i></td><td>-51.39</td><td>-81.36</td><td>-21.41</td><td>&lt; .0001</td><td>2.60</td></tr><tr><td>Free-choice with another<br>object (group1)</td><td>-54.17</td><td>-87.68</td><td>-20.66</td><td>&lt; .0001</td><td>3.25</td></tr><tr><td rowspan="3">Forced-choice, 2<br>first sessions</td><td>Free-choice, <i>M-sessions</i></td><td>0.46</td><td>-25.50</td><td>26.42</td><td>1.00</td><td>0.025</td></tr><tr><td>Free-choice, <i>P-sessions</i></td><td>-10.65</td><td>-36.61</td><td>15.31</td><td>0.76</td><td>0.455</td></tr><tr><td>Free-choice with another<br>object (group1)</td><td>-13.43</td><td>-43.40</td><td>16.55</td><td>0.70</td><td>0.561</td></tr><tr><td rowspan="2">Free-choice,<br>Msessions</td><td>Free-choice, <i>P-sessions</i></td><td>-11.11</td><td>-32.30</td><td>10.08</td><td>0.56</td><td>0.632</td></tr><tr><td>Free-choice with another<br>object (group1)</td><td>-13.89</td><td>-39.84</td><td>12.07</td><td>0.55</td><td>0.924</td></tr><tr><td>Free-choice,<br>Psessions</td><td>Free-choice with another<br>object (group1)</td><td>-2.78</td><td>-28.74</td><td>23.18</td><td>1.00</td><td>0.133</td></tr></tbody></table>

30

31

32
SI 3: Analysis of the effect of the individual identity and the trial type on the side chosen first, at each trial, during the *M-sessions* in free-choice conditions. Both the individual identity and the trial type have a significant impact on the side chosen first. The generalized mixed model table is not detailed (median of deviance residuals = -0.51). The results of post-hoc KruskalWallis are detailed for the variable Individual and the variable Trial type. The Dunn post-hoc test (Holm correction) is detailed to compare the side chosen first according to the trial type.

<table><thead><tr><th>Kruskal-Wallis rank sum test</th><th>Chi2</th><th>df</th><th>p-value</th></tr></thead><tbody><tr><td>Side chosen ~ Individual</td><td>162</td><td>11</td><td>&lt;0.001</td></tr><tr><td>Side chosen ~ Trial type</td><td>17.68</td><td>3</td><td>&lt;0.001</td></tr></tbody></table>

Dunn test with Holm correction

<table><thead><tr><th>Group 1</th><th>Group 2</th><th>statistic</th><th>Adjusted p-value</th></tr></thead><tbody><tr><td rowspan="3">CT</td><td>nvPT</td><td>-2.86</td><td>0.020</td></tr><tr><td>vPT</td><td>-0.490</td><td>1</td></tr><tr><td>ST</td><td>-3.44</td><td>0.0035</td></tr><tr><td rowspan="2">nvPT</td><td>vPT</td><td>2.37</td><td>0.054</td></tr><tr><td>ST</td><td>-0.141</td><td>1</td></tr><tr><td>vPT</td><td>ST</td><td>-2.87</td><td>0.020</td></tr></tbody></table>
**SI 4:** Model selection for the effect of the trial type (ST, nvPT, vPT and ST), the visibility of the reward (visible or non-visible), of the presence of a tube with a visible content in the trial (CT, vPT or nvPT) or not (ST). The more accurate model has the smallest corrected Akaike Information Criterion value.

<table><thead><tr><th>AICc model selection</th><th>K</th><th>AICc</th></tr></thead><tbody><tr><td>Side chosen ~ trial type + individual</td><td>15</td><td>761.35</td></tr><tr><td>Side chosen ~ reward visible + individual</td><td>13</td><td>757.67</td></tr><tr><td>Side chosen ~ content visible + individual</td><td>13</td><td>770.13</td></tr></tbody></table>

**SI 5:** The generalized linear model table for the effect of the side of the reward and the visibility of the reward on the performance at each trial, whatever the trial type (individuals as random effects, binomial family).

<table><thead><tr><th>Random effects</th><th>Variance</th><th>Std. Dev.</th></tr></thead><tbody><tr><td>Individuals</td><td>0.23</td><td>0.48</td></tr></tbody></table>

<table><thead><tr><th>Fixed Effects</th><th>Estimate</th><th>Std. Error</th><th>Z value</th><th>Pr(&lt;|z|)</th></tr></thead><tbody><tr><td>Intercept</td><td>0.77</td><td>0.17</td><td>4.46</td><td>&lt;0.001</td></tr><tr><td>Side of the reward</td><td>-2.16</td><td>0.15</td><td>-14.05</td><td>&lt;0.001</td></tr><tr><td>Visibility of the reward</td><td>3.46</td><td>0.20</td><td>17.10</td><td>&lt;0.001</td></tr></tbody></table>
**SI 6:** Detail of the number of time individuals choose to search the reward in one side or the other (percentage of success) depending on the trial type, the test condition, at the group level. There was a significant effect of the trial type on the side chosen first (*P*<0.05 between CT-nvPT, CT-ST and vPT-ST), but no effect of the test condition. Thus, there was an effect of the side of the reward location on the performance, with a significant side bias for right over left in nvPT and ST but no significant difference in CT and vPT. For each boxplot, dots are the mean individual performances, vertical lines are standard deviation, and the horizontal line shows the median value.

<figure><img src="image_44.png" /></figure>
# Deuxième partie : Validation de l'expression spontanée d'une performance par exclusion chez la poule domestique, au travers de la tâche des 2 cups

## INTRODUCTION

Les résultats du test d'inférence par exclusion via la tâche des tubes inclinés mettent en évidence la capacité de la poule domestique à adapter son comportement de recherche en fonction des contraintes, et en fonction de l'information disponible sur la localisation de la récompense (Call & Carpenter, 2001 ; Schloegl et al., 2009). Les résultats de cette expérience ne sont cependant pas suffisants pour confirmer la présence de la capacité d'inférence par exclusion chez la poule. Néanmoins, le fait que les animaux aient été capables d'apprendre à choisir par exclusion est une premier indice permettant de questionner plus avant la présence de capacités d'inférence par exclusion spontanée, hors apprentissage, chez la poule domestique (Duffrene et al., 2022 ; Schloegl & Fischer, 2017).

Dans l'expérience décrite dans l'ARTICLE 1, l'absence d'inférence par exclusion exprimée spontanément, sans apprentissage peut être expliquée par le fait que les animaux ont d'abord été testés en condition de libre choix avant d'être testés dans la condition choix forcé, ce qui demande un réajustement comportemental de l'animal. En passant de la première à la seconde condition de test, l'animal doit faire preuve de flexibilité comportementale et modifier la réponse habituelle qui avait alors été exprimée et renforcée dans le test de choix libre. Ceci peut être limitant sachant que, par exemple, les poules montrent une faible inhibition comportementale par rapport aux corneilles, dans une tâche de flexibilité impliquant la récupération de l'aliment par l'animal lui-même lors du test (Wascher et al., 2021). De plus, la forte composante spatiale dans le test des tubes inclinés pourrait avoir favorisé un biais de latéralité, qui est d'ailleurs confirmé dans la condition de choix libre.

L'objectif de la seconde expérience a été d'adapter à nos poule une tâche d'exclusion plus communément utilisée chez l'animal, la tâche des 2 cups (Völter & Call, 2017). Cette tâche de choix forcé utilise le même principe que celle des tubes inclinés. Dans cette tâche, une cup opaque et une cup transparente sont présentées à l'animal sur une table, l'une des deux contenant une récompense alors que l'autre non. Si l'animal est capable d'inférence par exclusion, comme dans la tâche précédente, il doit comprendre que si la récompense n'est pas sous la cup transparente, elle est nécessairement sous la cup opaque. Cette tâche peut également être complexifiée en recouvrant, après les avoir présentés à l'animal,
les 2 cups par deux grandes cups opaques. Ainsi pour émettre des réponses correctes, quel que soit l'essai, l'animal doit se souvenir de ce qui lui a été présenté. La performance des animaux ne peut donc plus être seulement basée sur ce que l'animal voit, elle implique qu'il ait mémorisé ce qui lui a été présenté avant de faire son choix, argument en faveur de l'utilisation d'une représentation mentale en tant qu'objet de l'inférence (par exemple : Danel et al., 2021 ; Danel, Rebout, & Kemp, 2022 ; Shaw et al., 2013).

Dans cette expérience, les essais ont été réalisés en condition forcée et avec un nombre limité d'essais test, afin de s'assurer de la spontanéité de la réponse d'inférence. Les expériences ont été réalisées sur un nouveau lot d'animaux n'ayant aucune expérience dans les tests d'inférence. Nous avons basé le protocole sur des études réalisées sur d'autres espèces d'oiseaux (Danel et al., 2021 ; Danel, Rebout, & Kemp, 2022 ; Mikolasch et al., 2012 ; Schloegl et al., 2009), ce malgré les réticences exprimées dans l'introduction de l'ARTICLE 1, relatives à la capacité de la poule domestique à suivre la gestuelle humaine (pas d'études à ma connaissance) et les possibles interférences lors de l'intervention de l'expérimentateur (voir par exemple Mikolasch et al., 2012 chez l'oiseau ; discuté chez plusieurs espèces). L'expérience a été réalisée avec le dispositif de la cage d'exposition (voir introduction : dispositifs de test) ; ce dispositif, moins spatial, pourrait permettre de limiter le biais de latéralité. Nous avons analysé la performance d'exclusion des individus au travers du nombre d'essais d'inférence réussis. Des essais-contrôle positifs et négatifs ont été ajoutés afin de nous assurer de l'absence de réponse en faux-positif.

# MATÉRIEL ET MÉTHODE

## 1) Dispositions générales et analyses statistiques

Les tests sont réalisés sur 10 individus femelles de 1 an d'âge appartenant au nouveau lot d'animaux. Les conditions d'élevage et de test des animaux sont les mêmes que précédemment, hormis que l'aliment est cette fois délivré systématiquement entre 16h et 18h tous les jours. Le protocole d'expérimentation a fait l'objet d'une validation par le Comité d'Éthique Centre Val de Loire (numéro APAFIS #40298-202301041435956 v3). Les expérimentations impliquent un choix forcé, et les sessions n'incluent pas d'essais de correction en cas d'échec. Chaque individu a réalisé une session par jour, que ce soit une session d'habituation, d'entrainement ou de test.

Ce test a été réalisé grâce au dispositif de la cage d'exposition. Au début de la session, les animaux sont placés dans la cage d'exposition. La porte grillagée permet à l'expérimentateur d'exposer l'animal à un ou plusieurs stimuli visuels pour la durée souhaitée, puis l'ouverture de la porte grillagée permet à
l'animal d'interagir avec les stimuli (ici : les cups). Une séparation avec écran sans tain entre la cage d'exposition, où est placé l'animal, et l'expérimentateur, permet d'éviter que l'expérimentateur ne donne des indices involontaires sur la réponse correcte à chaque essai. Dans cette expérience, la récompense à retrouver (vers de farine) sera cachée sous des cups. Les cups internes opaques (de couleur noire) et transparentes utilisées sont de 9 cm de hauteur et 6 cm de diamètre. Les cups externes, opaques et plus larges, font 19 cm de hauteur et 8 cm de diamètre et sont de couleur bleu foncé (Figure 20a). Les cups sont placées à droite et à gauche sur une planche en bois amovible, que l'expérimentateur peut rapprocher ou éloigner de la cage d'exposition au cours du déroulement de l'essai (Figure 20b). La distance entre les cups sur la planche est fixe et de 50 cm. En tant que récompense, les vers de farine sont placés dans un bouchon bleu de 2 cm de hauteur et 5.5 cm de diamètre, afin que l'indication de leur localisation soit saillante.

Les analyses statistiques et les graphiques ont été réalisés via le logiciel Rstudio (version 4.2.1 ; R Core Team, 2022). Au niveau du groupe, la recherche de biais de côté en fonction de la configuration des essais ou en fonction des individus a été analysée par un modèle linéaire généralisé. Au niveau des individus, la significativité d'éventuels biais de côté sur l'ensemble des sessions a été évaluée en Posthoc via un test de Wilcoxon bivarié, et les performances ont été comparées au hasard via un test binomial bivarié. Le niveau de hasard est considéré à 50% du total du nombre d'essais, et les résultats sont considérés significatifs lorsque la pvalue est inférieure à 0.05.

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

**Figure 20** : (a) Illustration des cups internes, transparentes ou opaques, et des cups externes opaques. Les cups internes sont présentées à l'animal, puis les cups externes sont placées par-dessus les cups internes avant que l'animal ne puisse donner sa réponse sur l'emplacement de la récompense, ce qui permet d'intégrer une composante mnésique. (b) Illustration de l'emplacement de l'individu testé, de l'expérimentateur, et de l'emplacement des cups. La planche sur laquelle sont posées les cups peut être rapprochée et éloignée de la cage d'exposition. La séparation avec écran sans tain entre l'individu testé et l'expérimentateur n'est pas représentée ici. Le dispositif est placé sur une table.
## 2) Entrainement

Dans un premier temps, les individus ont été entrainés à devoir piquer sur une cup (cup externe) pour récupérer des vers de farine en dessous, contenus dans le bouchon ; les individus ont mis entre 1 minute et 10 minutes pour comprendre la tâche et la réaliser en autonomie sur 4 essais consécutifs (répété le lendemain).

Ensuite, trois sessions d'entrainement de 10 essais chacune ont été réalisées (cups externes). Au début de chaque essai, la poule est dans la cage d'exposition et a un accès visuel à la zone de test. A chaque essai, l'expérimentateur montre le bouchon contenant les vers de farine à la poule pendant 3 secondes, en plaçant sa main à équidistance de sa droite et de sa gauche, et proche de la porte grillagée. Une fois que la poule a porté attention aux vers, ou après un maximum de 10 secondes, l'expérimentateur place le bouchon d'un côté (droite ou gauche) sur la planche en bois, puis place les deux cups aux deux emplacements simultanément. L'expérimentateur avance ensuite la planche vers la cage d'exposition, soulève les deux cups pendant 3 secondes, les replace et recule la planche. Ce geste constitue une seconde exposition de l'animal à l'emplacement de la récompense. Puis, la porte grillagée est ouverte et l'animal a accès aux cups. La cup sur laquelle la poule pique en premier est considérée comme un choix, l'expérimentateur la soulève et la poule a accès au contenu. Si le choix est incorrect, la cup correspondant au choix est soulevée pendant un temps de 5 secondes, et la poule est replacée dans la cage pour la suite de la session. Le critère d'apprentissage demandé pour passer à l'étape suivante est de réussir 8 essais sur 10.

L'étape suivante impliquait le même déroulement, mais cette fois en ajoutant les cups internes transparentes ou opaques aux essais : l'expérimentateur plaçait les vers de la même manière, puis plaçait les cups internes, puis les cups externes. Ceci pour un maximum de 3 sessions de 10 essais. Le critère d'apprentissage demandé pour passer au test était de réussir 8 essais sur 10. Pour l'ensemble des sessions d'entrainement, l'emplacement droite / gauche des cups opaques ou transparentes, ainsi que la localisation de la récompense, étaient pseudo-aléatoires : une configuration de test n'était pas présentée plus de deux fois à la suite, et la récompense n'était pas présentée au même endroit (droite / gauche) plus de deux fois à la suite.
### 3) Test

Après que les animaux aient passé le critère d'apprentissage, 6 sessions de 8 essais ont été programmées, pour totaliser 12 essais d'inférence. Chaque session test comprenait les 4 types d'essais répartis équitablement et de façon pseudo-randomisée au sein des sessions :

- Les essais-contrôle ST, avec 2 cups internes opaques, et donc aucune information sur la localisation de la récompense

- Les essais-contrôle CT, avec 2 cups internes transparentes, et donc la totalité de l'information sur la localisation de la récompense

- Les essais PTv, avec une cup interne opaque et une transparente, la récompense étant placée dans la cup transparente

- Les essais d'inférence PTnv, avec une cup interne opaque et une transparente, la récompense étant placée dans la cup opaque.

Pour cette expérience, les essais sont récompensés de façon différenciée (récompense en cas de succès, absence de récompense en cas d'échec). Le déroulement des essais est équivalent au déroulement des sessions d'entrainement.

## RÉSULTATS

Un modèle linéaire généralisé (gaussian family) montre qu'il existe un effet d'interaction entre les variables *individu* et *côté de la récompense* sur le succès aux essais d'entrainement (2 cups externes opaques ; P<0.05), avec un biais pour se diriger préférentiellement vers la droite au niveau du groupe. Le modèle ne montre pas d'effet du type d'essai (ST, CT, PTv ou PTnv ; p>0.05), et pas de biais de côté qui serait fonction du type d'essai (pas d'interaction ; p>0.05). Au niveau individuel, le biais de côté impacte significativement la réponse aux essais pour Chips (biais pour la droite ; p=0.0013) et Pamela (biais pour la gauche ; p=0.035). La **Figure 21** illustre ce résultat.

Lors des sessions d'entrainement avec les cups opaques externes, aucun individu n'a acquis le critère d'apprentissage dans le temps imparti ; au contraire, la performance des individus s'est globalement dégradée au cours des sessions (nombre maximal d'essais consécutifs réussis par session : performance moyenne session 1 = 2.95+/-1.19 ; session 3 = 1.75+/-1.45 ; W=298, p=0.007). Afin de vérifier que ce comportement n'était pas dû à la nature des cups externes, nous avons réitéré l'entrainement (3 sessions de 10 essais) avec les cups opaques internes. Au cours de ces sessions, la performance a stagné autour de 2 à 3 essais réussis sur 10 pour tous les individus, voire s'est dégradée pour certains, sauf pour Pamela,
qui passe de 2 essais sur 10 réussis lors de la session 1, à 7 essais réussis lors de la session 3, ce qui ne répond toujours pas au critère d'apprentissage (nombre maximal d'essais consécutifs réussis par session : performance moyenne session 1 = 2.25+/-1.48 ; session 3 = 2.1+/-1.89 ; W=217.5, p=0.639).

<figure><img src="image_46.png" /><figcaption>**Figure 21** : Nombre d'essais d'entrainement (cups opaques externes) réussis au total pour chaque individu, selon le côté de l'emplacement de la récompense (gauche en rouge ; droite en bleu).</figcaption></figure>

Les animaux n'ont donc pas été capables de répondre à un test de mémoire simple par rapport à la localisation droite/gauche de la récompense dans le temps imparti. Plusieurs raisons peuvent être évoquées et seront détaillées dans la discussion. Finalement, à la suite de ces 60 essais d'entrainement avec des cups opaques, nous avons décidé de tout de même faire le test d'inférence, mais sans les cups externes, c'est-à-dire sans impliquer la capacité de mémorisation.

Les résultats des sessions test (avec cups internes seulement) montrent que chacun des individus a une performance au hasard pour les essais ST (2 cups opaques ; test binomial bivarié, p>0.05) et une performance significativement supérieure au hasard pour les essais CT (2 cups transparentes ; performance ≥10/12, p<0.05). Dans les essais PTv (une cup opaque vide et une cup transparente contenant les vers), quatre individus ont montré une performance inférieure au hasard, ce qui indique un biais pour les cups opaques, malgré la visibilité des vers dans la cup transparente (Figure 23 ; Tweety, Whitehead,
Chips, Pêche). Pour ces individus, la performance sur les essais d'inférence (PTnv) ne peut pas être prise en compte à cause de ce biais. Finalement, quatre individus montrent une performance significativement supérieure au hasard pour les essais PTv, donc se dirigent bien vers la récompense lorsqu'elle est visible ; et montrent en même temps, une performance significativement supérieure au hasard pour les essais d'inférence (PTnv ; Douce, Sky, Tori, Fluffy ; **Figure 22**). Ces individus ont donc exprimé une performance d'exclusion spontanée en 12 essais test, via le test des 2 cups.

<figure><img src="image_47.png" /><figcaption>**Figure 22** : Nombre d'essais réussis par individu lors des sessions test dans la tâche des 2 cups, pour les essais PTv (en bleu ; la récompense est dans la cup transparente, donc visible) et les essais d'inférence PTnv (en rouge ; la récompense est dans la cup opaque, donc non visible). Quatre individus montrent une performance par exclusion : Douce, Sky, Tori et Fluffy. La ligne noire correspond à la performance qu'il faut atteindre au minimum pour avoir une performance significativement supérieure au hasard (10/12 ; test binomial bivarié).</figcaption></figure>

DISCUSSION

L'objectif de cette expérience était de questionner la capacité spontanée d'inférence par exclusion chez la poule domestique, en complément des résultats qui ont pu être obtenus dans l'ARTICLE 1. Ces derniers avaient montré que la poule est capable de réaliser une performance d'exclusion au bout de plusieurs essais forcés. Cependant, l'absence d'une réponse spontanée est un argument en faveur d'un apprentissage, et un frein à l'argument de la capacité à exprimer une inférence par exclusion via ce dispositif. Nous avons donc testé la capacité d'inférence par exclusion chez la poule de manière spontanée, avec le test des 2 cups.
En suivant le protocole communément utilisé dans la littérature (Völter & Call, 2017), nous avons d'abord entraîné les individus à retrouver des vers de farine parmi deux cups opaques, les vers ayant été placés sous l'une ou l'autre cup à la vue de l'animal. Tous les individus ont montré une réponse au hasard et une absence d'amélioration au cours des sessions d'apprentissage. Il est possible que ce résultat soit en partie dû au fonctionnement du dispositif. En effet, lorsque l'expérimentateur montre l'emplacement des vers de farine à l'animal au début de l'essai, l'animal est retenu par la grille et montre des comportements d'impatience (observation : mouvements de tête rapides de part et d'autre de la grille). Il est donc possible que cet accès visuel, pourtant nécessaire, implique que l'animal cherche à avoir immédiatement accès au dispositif et perde ainsi son attention envers la localisation de la récompense. D'autres travaux ont déjà montré qu'une barrière entre l'animal et l'expérimentateur pouvait significativement diminuer les performances de l'animal, notamment dans des tâches impliquant que l'expérimentateur indique la localisation de la récompense à l'animal (par exemple : Udell et al., 2008).

Une autre possibilité concerne la capacité de permanence de l'objet de la poule domestique. La compréhension de la permanence de l'objet a été testée chez le poussin, qui est capable de montrer une compréhension du stade 3 voire 4 de permanence de l'objet, ce qui correspond à la capacité à suivre un objet vers l'emplacement où il a disparu en dernier. A ma connaissance, toutes les études qui s'intéressent ou qui exploitent la compréhension de la permanence de l'objet chez le poussin impliquent des déplacements d'objets, qui *se déplacent* ou *sont déplacés* derrière une paroi opaque, où ils restent. Une hypothèse est que la mémoire de la localisation d'un objet par l'animal, après avoir observé le déplacement de cet objet (objet animé), serait soit plus forte, soit plus facilement encodée, que la mémoire de la stricte localisation d'un objet (droite/gauche par exemple). Les tests de permanence de l'objet chez la poule domestique ont tous été réalisés chez le poussin, et donc à un jeune âge, ce qui pourrait expliquer une différence en terme de performances cognitives sur ce type de test : la période à laquelle sont testés les poussins (entre 3 et 5 jours) correspond un âge auquel le poussin apprend à identifier des objets alimentaires (Hess, 1964) et sa tendance à suivre un modèle mobile dans une situation d'imprégnation filiale augmente jusqu'à l'âge de 3 ou 4 jours, pour décliner ensuite (Bateson, 1964). Ces points pourraient expliquer une attention non suffisante, et donc l'échec de nos animaux à retrouver une récompense de manière flexible entre les deux cups.

Etant donné les performances lors de l'entrainement, le test d'inférence par exclusion a été réalisé sans les cups opaques externes, donc sans impliquer la capacité de mémorisation. Dans le test des 2 cups, quatre individus ont montré une performance d'exclusion significativement supérieure au hasard en 12 essais. Les autres individus ayant utilisé une stratégie de réponse différente, nous supposons que la
stratégie de résolution d'une tâche relationnelle dépend beaucoup de facteurs individuels. Notamment, plusieurs individus qui n'ont pas réussi la tâche d'exclusion ont montré un biais de préférence pour la cup opaque alors que les vers étaient dans la cup transparente, donc visibles. Ce résultat aura pu être causé par l'implémentation des sessions d'entrainement, au préalable. En effet, les sessions d'entrainement ont comporté uniquement des cups opaques ; malgré une performance au hasard lors de cet entrainement, il est possible que les animaux aient attribué une valeur positive associée à la récompense pour les cups opaques, ce qui n'est pas le cas pour les cups transparentes au début du test. Cette différence aura pu être à l'origine d'un biais de préférence pour les cups opaques pour ces individus.

En opposition, quatre individus ont montré une compréhension de la tâche d'exclusion en choisissant la réponse correcte dans les différentes configurations de test. Ce résultat montre que la poule domestique est capable d'appliquer une règle d'exclusion de manière spontanée. Le fait d'avoir retiré la composante mémoire de l'emplacement, implique qu'il est possible que nos animaux aient plutôt répondu à ce test au travers d'une stratégie de type « *si je ne vois pas la récompense dans la cup transparente alors je choisis l'autre cup* », stratégie qui n'implique pas de représentation mentale de la localisation de la récompense (Duffrene et al., 2022 ; Schloegl & Fischer, 2017).

En résumé, nos résultats supportent l'hypothèse que la poule domestique est capable d'appliquer une règle d'exclusion de manière spontanée. Néanmoins, de futures recherches seront nécessaires pour investiguer précisément les processus cognitifs impliqués dans la résolution de cette tâche chez la poule domestique.
# CHAPITRE 2 : Généralisation de concept dans une tâche de correspondance à l'échantillon différée chez la poule domestique
## Généralisation de concept dans une tâche de correspondance à l'échantillon différée chez la poule domestique

Dans le premier chapitre, nous avons vu que la poule domestique est capable de résoudre une tâche d'inférence par exclusion de manière spontanée. Toutefois, nos résultats ne permettent pas de conclure quant à l'utilisation ou non d'une représentation mentale pour résoudre la tâche. Les inférences inductives et déductives, comme explicitées en introduction de cette thèse, sont des capacités d'inférence basiques qui, elles, mettent obligatoirement en jeu une représentation mentale au travers de la mémoire d'une règle ou d'une catégorie (Völter et Call, 2017). Nous avons donc cherché dans un second temps à tester ce type d'inférence chez la poule domestique

Pour cela nous avons développé une tâche qui implique la capacité de généralisation, qui met en jeu ces deux inférences basiques. En effet, une généralisation implique d'avoir été capable de former une catégorie à partir de plusieurs observations (induction, par exemple : peu importe leur couleur, tous les chats ont des caractéristiques précises qui correspondent au concept de chat). Puis, il est ensuite possible d'utiliser le concept formé pour l'appliquer à de nouveaux stimuli inconnus (déduction, par exemple : un animal violet qui correspond au concept de chat, est par déduction un chat).

La capacité à généraliser a déjà été montrée chez le poussin dans une tâche de discrimination entre deux images (Santolin et al. 2016 ; Versace et al. 2017). Cependant, les auteurs proposent une explication alternative basée sur la mémorisation de couches perceptives (filtres), qui impliquerait que les poussins n'aient pas généralisé un concept appris, mais plutôt reconnu par identité un schéma perceptif particulier dans les nouveaux stimuli.

L'objectif de ce second chapitre a donc été de valider la capacité de généralisation chez la poule domestique, en la testant dans une tâche plus complexe : la tâche de correspondance à l'échantillon (ARTICLE 2). Dans cette tâche, une généralisation implique des capacités d'inférence inductive et inférence déductive, une mémoire du concept appris, et une flexibilité à chaque essai.

Pour cette expérience, nous avons introduit un dispositif écran composé de cellules sensibles qui permettent à la poule d'interagir directement avec les items apparaissant à l'écran. Nous avons dans un premier temps testé la capacité des animaux à utiliser un tel dispositif avec écran, dans une tâche simple de discrimination visuelle (entre A et B, choisir A pour avoir une récompense) et d'inversion de consigne (2ème phase : choisir B ; 3ème phase : choisir A ; 4ème étape : choisir B). Les résultats de cette expérience sont présentés dans l'ARTICLE 2. En résumé, nos animaux ont bien appris la tâche de discrimination et
ont très rapidement montré une flexibilité entre les étapes d'inversion, ce qui suggère une compréhension rapide d'une règle sous-jacente de type *win stay-loose shift* : dès que la récompense n'est plus donnée pour le stimuli A, alors sélectionner B, et inversement (Loconsole et al, 2021 ; van Horik et Emery, 2018).

Nous avons ensuite testé nos animaux dans une tâche de correspondance à l'échantillon différée (exemple : Cook et Wasserman, 2007). Dans un premier temps, un stimulus (l'échantillon, ici : un rond de couleur) apparait au centre de l'écran. Une fois que la poule a piqué dessus, l'échantillon disparait, et après un délai de 10ms, deux stimuli apparaissent, l'un à droite et l'autre à gauche de l'écran (ici : deux ronds de couleurs différentes) dont un qui correspond à l'échantillon (même couleur). La poule est récompensée si elle pique sur le stimulus qui correspond à l'échantillon présenté juste avant.

Le concept testé ici est un concept perceptif d'identité de couleur. L'entrainement est réalisé en utilisant différents stimuli qui correspondent à différentes couleurs (vert, jaune, bleu, rouge), et correspond à l'étape de formation du concept (induction). Le test est réalisé en utilisant des stimuli qui n'ont jamais été utilisés lors de l'entrainement (violet, marron), et correspond à l'étape de généralisation (déduction). .

Nos résultats montrent que pour les essais-test, deux individus (parmi les 6 testés) réalisent une performance qui suggère une généralisation du concept d'identité de couleur aux nouveaux stimuli. Ce résultat vient solidifier la conclusion de l'existence de capacité de généralisation chez la poule domestique, et ce au travers d'une tâche déjà complexe à apprendre pour l'animal qui est la tâche de correspondance à l'échantillon. En dehors de ces deux individus, les autres individus ont montré une performance croissante et instable dans les sessions test, ce qui suggère l'apprentissage d'une règle de choix dans le cas de ces nouveaux stimuli : ces individus n'ont pas réussi à généraliser le concept appris aux nouveaux stimuli.

En conclusion, cette expérience a permis de montrer que la poule domestique possède la capacité de généralisation d'un concept perceptif. Nos résultats suggèrent que la poule est capable d'inférence inductive et déductive, en formant un concept à partir d'un entrainement sur plusieurs cas particuliers, et en appliquant le concept appris à un cas nouveau. Par rapport au CHAPITRE 1, ces résultats confirment que la poule domestique possède des capacités d'inférence qui impliquent la représentation mentale d'une règle. Les inférences que nous avons étudiées jusque-là sont des inférences simples qui s'appuient sur des représentations mentales simples. Dans le cadre de l'étude des capacités de base du raisonnement chez la poule domestique, il sera intéressant d'explorer des capacités d'inférence plus complexes : les inférences relationnelles, qui s'appuient sur la mémoire de plusieurs relations entre différentes informations (représentations mentales relationnelles).
ARTICLE 2 – Domestic hens succeed at serial reversal learning and perceptual concept generalization using a new automated touch-screen device (published)

Rachel Degrande a*, Fabien Cornilleau a, Léa Lansade a, Plotine Jardat a, Violaine Colson b,
Ludovic Calandreau a

<footnote>a CNRS, IFCE, INRAE, Université de Tours, PRC (Physiologie de la Reproduction et des Comportements), F-37380 Nouzilly, Indre-et-Loire, France</footnote>

<footnote>b INRAE, LPGP (Laboratoire de Physiologie et Génomique des Poissons), Campus de Beaulieu, F-35042 Rennes cedex, Ille-et-Vilaine, France</footnote>

<footnote>* Corresponding author: rachel.degrande@gmail.com; rachel.degrande@inrae.fr</footnote>

<footnote>Article published in *Animal, The international journal of animal biosciences*.</footnote>
# Domestic hens succeed at serial reversal learning and perceptual concept generalisation using a new automated touchscreen device

<figure><img src="image_48.png" /></figure>

Rachel Degrande<sup>a,*</sup>, Fabien Cornilleau<sup>a</sup>, Léa Lansade<sup>a</sup>, Plotine Jardat<sup>a</sup>, Violaine Colson<sup>b</sup>, Ludovic Calandreau<sup>a</sup>

## ARTICLE INFO

**Article history:**

Received 21 February 2022

Revised 5 July 2022

Accepted 11 July 2022

**Keywords:**

Behavioural flexibility

Cognition testing

Gallus gallus domesticus

Matching-to-sample

Screen apparatus

## ABSTRACT

Improving the welfare of farm animals depends on our knowledge on how they perceive and interpret their environment; the latter depends on their cognitive abilities. Hence, limited knowledge of the range of cognitive abilities of farm animals is a major concern. An effective approach to explore the cognitive range of a species is to apply automated testing devices, which are still underdeveloped in farm animals. In screen-like studies, the uses of automated devices are few in domestic hens. We developed an original fully automated touchscreen device using digital computer-drawn colour pictures and independent sensible cells adapted for cognitive testing in domestic hens, enabling a wide range of test types from low to high complexity. This study aimed to test the efficiency of our device using two cognitive tests. We focused on tasks related to adaptive capacities to environmental variability, such as flexibility and generalisation capacities as this is a good start to approach more complex cognitive capacities. We implemented a serial reversal learning task, categorised as a simple cognitive test, and a delayed matching-to-sample (dMTS) task on an identity concept, followed by a generalisation test, categorised as more complex. In the serial reversal learning task, the hens performed equally for the two changing reward contingencies in only three reversal stages. In the dMTS task, the hens increased their performance rapidly throughout the training sessions. Moreover, to the best of our knowledge, we present the first positive result of identity concept generalisation in a dMTS task in domestic hens. Our results provide additional information on the behavioural flexibility and concept understanding of domestic hens. They also support the idea that fully automated devices would improve knowledge of farm animals' cognition.

© 2022 The Authors. Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).

## Implications

Understanding farm animals' cognitive capacities is important for improving their welfare. However, information about the cognitive range of many farm animal species, notably poultry, is limited. This study focuses on developing an automated device relevant to farm animals with the aim of testing a larger range of cognitive capacities. We have developed an automated touchscreen device adapted for large-range cognitive testing in domestic hens. Through this device, we collected results that suggest that laying hens possess concept generalisation abilities, thus stressing the interest in applying complex cognition testing devices to farm animals.

## Introduction

From a welfare perspective, improving farm animals' living conditions depends on our knowledge of how they perceive and interpret their environment, which in turn depends closely on their cognitive abilities (ANSES, 2018). The cognition of farm birds is relatively poorly studied even though they are the most abundant farmed animals in the world (FAO, Global Livestock Distribution Data). Among farm birds, the majority of cognition studies have focused on domestic hens, mainly chicks (Garnham and Løvlie, 2018; Vallortigara, 2006, 2021). A few studies have suggested that domestic hens may possess complex cognitive capacities such as retrospective metamemory (Nakamura et al., 2011) or transitive inference (Daisley et al., 2010). However, there are still many capacities to explore in this species, and the methods to study its cognitive modalities still need to be developed.

<footnote><sup>a</sup>CNRS, IFCE, INRAE, Université de Tours, PRC (Physiologie de la Reproduction et des Comportements), F-37380 Nouzilly, Indre-et-Loire, France</footnote>

<footnote><sup>b</sup>INRAE, LPGP (Laboratoire de Physiologie et Génomique des Poissons), Campus de Beaulieu, F-35042 Rennes cedex, Ille-et-Vilaine, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: rachel.degrande@gmail.com, rachel.degrande@inrae.fr
(R. Degrande).</footnote>
A strong relationship exists between the range of cognitive abilities of a given species and its ability to cope with environmental variability. On one hand, behavioural flexibility is the ability to adapt one's behaviour in response to environmental contingencies (Brown and Tait, 2010), which underlies a wide range of adaptive behaviours. For example, individual recognition of social specifics and a flexible memory of their hierarchical position are suited to group living and allow for saving energy in terms of cognitive demand and stress response (Elgar and Riehl, 2021). On the other hand, concept understanding requires the discrimination and generalisation of a rule to many stimuli for which the same behaviour can be applied (Smith et al., 2012; see Versace et al., 2017 in chicks). It enables animals to cope with novelty in their physical environment, thus reducing unpredictability and subsequent related stress. Behavioural flexibility and concept generalisation are the two main components of adaptive capacities in animals. Investigating these capacities facilitates an approach to more complex cognitive capacities. Meanwhile, it offers solid ground to suit husbandry practices to animals' behavioural needs and welfare by understanding how animals cope with environmental variability.

Automated testing devices are an effective approach to explore the cognitive range of a species (Livesey et al., 1972; Gabor and Gerken, 2012). Several automated devices have been developed to investigate or operate simple cognitive tasks in farm animals, such as discriminative learning, notably from the cognitive enrichment perspective (for example in goats: Langbein et al., 2008; in pigs: Zebunke et al., 2013). More specifically, a few screen-like devices have been developed with the aim of operating tasks that require more complex cognitive capacities in farm animals, such as relational learning or concept understanding tasks. Some of these devices have proven effective in fish (Kleiber et al., 2021), horses (Gabor and Gerken, 2010), goats (Meyer et al., 2012), and laying hens (Werner et al., 2005). Automated testing devices remain particularly underdeveloped for farm birds (for a review on farm animal cognition tests, see Bushby et al., 2018). There may be a vested interest in copying screen devices used for primate cognition, for example, because they are automated and flexible enough to easily set up many cognitive tests of increasing complexity (Rowe and Healy, 2014; Calapai et al., 2022). This would allow the exploration of farm animals' cognitive capacities, such as self-control (Beran, 2018), concept understanding (Zentall et al., 2008), and more broadly relational learning and retrieval.

In domestic hens, most cognition tests have been conducted with three-dimensional (3D) apparatus, from the arena and mazes to operant boxes, including detour task structures and location- or object-choice tests (for a review, see Garnham and Lovlie, 2018). In screen-like studies, uses of automated devices in domestic hens, mainly as part of delayed matching-to-sample procedures or stimuli discrimination procedures, are few (but see specific research based on imprinting process in chicks: Zanon et al., 2021). Ryan and Lea (1994) and Deakin et al. (2016) developed non-automated screen-pecking tasks using computer-coloured slides. Forkman and Vallortigara (1999) used an automated system with infrared beams to detect chicks' pecks at symbols displayed on a computer screen. More recently, Dudde et al. (2018) used a touch-kit automated device coupled with a simple screen, in which pecks on the monitor were detected by a mesh of infrared lights from a frame laid over the monitor screen.

We developed a standardised and fully automated touchscreen device for domestic hens, enabling a wide range of test types from low to high complexity under standard conditions. The aim of this study was to introduce and perform experiments with our device using two cognitive tests from existing procedures. The first test was a discrimination learning task followed by a serial reversal learning task (SRL), which is classified as a simple cognitive task as it demands procedural learning. The second test is a delayed

matching-to-sample (dMTS) task followed by a generalisation test for the concept of identity, which is classified as more complex as it demands flexible working memory, a declarative report of the response, and concept understanding. Both tasks typically require the individual to use a given stimulus to conduct its behaviour on a one-trial scale and, at the same time, to use a more general understanding rule asked throughout the task. For the SRL task, we expected a decline in performance from the discrimination to the first reversal learning stage (i.e. more sessions needed to reach the criterion in the first reversal stage) and an increase in performance through the next reversal stages (i.e. fewer sessions needed to reach the criterion in the following stages) (see for example, Brushfield et al., 2008). For the dMTS task, we expected an increase in performance during training (see for example, Cook and Wasserman, 2007). In the generalisation test, we did not have any expectations because concept generalisation through a dMTS task has not yet been described in domestic hens.

## Material and methods

### Animals and housing

The two experiments involved nine and six Isa Brown laying hens, respectively (1–3 years old) maintained at the Pôle d'Expérimentation Avicole de Tours where the experiment was performed (UE PEAT, INRAE, 2018. Experimental Poultry Facility, https://doi.org/10.15454/1.5572326250887292E12). The hens were maintained in a social group of 21 hens on a 6 am to 8 pm daylight cycle, with water ad libitum. Every day when the experiments were completed, food was provided at will. Hens had access to a barn (25 m²) equipped with nesting boxes, perches, and wood chip litter and to an outside enclosure (approximately 30 m²) enriched with perches. All experiments were conducted in the morning between 7 am and 10 am.

### Apparatus

Subjects were tested individually in a playbox (44.5 × 65 × 52 c m) made of condensed wood without direct contact with or view of the experimenter to avoid any influence of the experimenter on its behavioural response. A grid ceiling allowed the experimenter to control the hen's behaviour when needed. A screen (25 × 25 cm) linked to a computer was located at one end of the box. This screen was surmounted by nine sensible cells (8 × 8 cm) that could be independently activated. At the bottom of the screen, a feeder (5 cm diameter × 3 cm depth) was used to automatically deliver mealworms as rewards. A lamp was placed 50 cm above the screen to illuminate the box (260 lx). The playbox was located on a table in a room that was independent and adjacent to the hens' aviary. The items on the screen were computer-coloured filled circles of 7.5 cm diameter. The apparatus, playbox, and the screen are illustrated in Fig. 1. The procedures described below was automated. At the beginning of the procedure, hens had been experienced with the apparatus, that is, they had been familiarised with the apparatus and to peck at different items on the screen for food.

### Testing procedures

#### Discrimination and serial reversal learning

Nine individuals were tested in a four-stage procedure including a discrimination task (stage 1), followed by three reversal stages (stages 2, 3, and 4). Stage 1 consisted of discriminating between a green- and a red-filled circle presented on the screen (Fig. 2). Four hens were rewarded when they pecked at the green circle and five hens when they pecked at the red circle. The hens
<figure><img src="image_49.png" /><figcaption>**Fig. 1.** Schematic view of the testing device used for the two procedures. The photograph illustrates the hen's (Gallus gallus domesticus) positioning when facing the screen at every trial. The apparatus system and the corresponding software were built by © Imetronic. Photograph courtesy of Fabien Cornilleau.</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>**Fig. 2.** Schematic view of the successive screens during the serial reversal learning task (SRL). The two filled circles are presented in the middle left and right sensible cells of the screen. The hen stood in front of the screen and pecked at a filled circle on the screen. A peck at the correct colour induced a reward delivery accompanied by a 2 kHz sound and a white screen. A peck at the wrong colour induced no reward delivery and a black screen accompanied by the 9 kHz sound.</figcaption></figure>

then underwent three successive reversal learning stages: pecking at the non-rewarded colour during the previous stage was now rewarded (e.g. pecking at the red circle if they had to peck at the green circle at the previous stage). Individuals had to meet the criterion of 80% of success in two consecutive sessions to move on to the following stage, with a maximum of 49 sessions for the overall procedure.

Each hen was individually subjected to one session of 30 trials per day, with an inter-trial interval of 3 s. A correct response was always accompanied by a reward of two mealworms, a 2-second 2 kHz sound (62 dB), and a white screen. An incorrect answer was always accompanied by a 2-second 9 kHz sound (51 dB), dark screen, and no reward. A cut-off was set when the hen did not peck at the correct item within 20 s, and the trial was recorded as failed. Item presentations were pseudo-randomised and presented equally on the left and right sides for each session, with the rewarded stimulus presented no more than two consecutive times on the same side. Both the percentage of success at the first session for each stage (PCT) and the number of sessions per stage required to reach the criterion (STC) were scored.

### Delayed matching-to-sample and generalisation task

Six hens were tested in a delayed matching-to-sample task (dMTS). This included two hens from the SRL task, namely Samba and Yuna (among the hens that participated in the SRL task, four

hens deceased before the dMTS task was implemented, and three hens did not respond to the matching rule from the start). Before the testing procedure, individuals were gradually trained to give a five-key pecking at a black circle and then at a coloured circle on the screen to obtain the reward, including the colours used in the dMTS procedure. Overall, six colours were used in the different stages of training and testing: green, red, yellow, blue, brown, and purple.

*General procedure.* For the dMTS task, a trial typically consisted of presenting a coloured filled circle at the centre of the screen (i.e. the sample). A five-key pecking within a maximum of 45 s was required for the hen to move to the choice phase. For the choice, a second screen presented two coloured filled circles as comparison stimuli: one of the filled circles was of the same colour as the sample and the second one was of another colour. The hens were rewarded only if they pecked at the comparison stimulus corresponding to the previous sample (Fig. 3). A correct response was always accompanied by a reward of two mealworms, a 2-second 2 kHz sound (62 dB), and a white screen, and an incorrect answer was always accompanied by a 2-second 9 kHz sound (51 dB) and a dark screen. A cut-off was set when the hen did not achieve the five-key pecks at the sample within 45 s or when the hen did not choose a comparison stimulus within 10 s, and the trial was recorded as incorrect. Three consecutive cut-offs were considered
<figure><img src="image_51.png" /><figcaption>**Fig. 3.** Schematic view of the successive screens during the delayed matching-to-sample task. The sample is presented in the middle cell. The two choices are then presented in the middle left and right cells of the screen. The hen stands in front of the screen and pecks at the colour corresponding to the sample to obtain a reward.</figcaption></figure>

as a lack of motivation and lead to the end of the session. The hen was then taken back again later in the morning. After an incorrect response, correction trials were proposed to rectify the eventual side or colour biases. The correction trials consisted of presenting the exact same trial once again after the presentation of the dark screen and 9 kHz sound, with a maximum of five corrections per trial. One session included 120 trials, not counting the correction trials. The inter-trial interval was fixed at 2 s after a correct response and 5 s after a wrong answer to increase the negative outcome. Three pairs of colours were used progressively: green and red (GR), yellow and blue (YB), and purple and brown (PBr). Item presentations were pseudo-randomised for the left and right sides for each session. A given trial configuration (sample identity and side of the correct comparison stimulus) was presented no more than two consecutive times.

*Training and generalisation test.* First, hens were trained to match the sample to the corresponding choice between the two colours presented: GR. All achieved at least 90% of success for this pair. Following this, hens were trained in a dMTS task with a second pair (YB) until they achieved at least a mean of 70% of success during five consecutive sessions containing at least three sessions with a minimum performance of 75% of success. These two training stages were part of a common pretesting procedure which was not recorded from the start. The hens were then trained with mixed sessions containing equal GR and YB trials until they reached 80% of success for three consecutive sessions before moving to the test. Test sessions included a third unknown pair, PBr. The test lasted for 11 sessions of 120 trials each: 40 test trials with the new pair (PBr) and 80 trials with the training pairs (40 YB trials and 40 GR trials). The test trials were differentially reinforced as the baseline trials. The variable of interest was the mean percentage of success in each session of the test pair.

*Statistical analysis*

Less than 10 individuals were tested; therefore, we chose a non-parametric approach for model fitting and detailed analyses. The results are presented as the mean performance and standard deviation. To analyse the effect of fixed variables on performance, results were analysed using generalised linear models with individuals as repeated measures (Supplementary Material S1). The fixed variables tested were (1) for the SRL task: the stage of learning (1-4) and the group (i.e. if hens started to learn to peck independently at the red or green circle); (2) for the dMTS task: the colour pairs and the stage (training or testing sessions). Independently, we controlled for side or colour bias for the dMTS task. With regard to the fixed variables described above, we compared the likelihood of interaction versus additive models with the

Akaike information criterion (all parameters were otherwise equal), and only the results with the best fits are reported, which are additive models for each analysis. Further analysis was performed using Wilcoxon tests to assess the statistical significance of the performance above chance at the group level and at the individual level, as well as to compare group performance between conditions. At the individual level, binomial tests were conducted to assess the statistical significance of performance above chance within one session. All tests and figures were performed using RStudio (version 1.4.1106). P-values below 0.05 were considered statistically significant for all statistical analyses. Chance level was at 50%.

*Results*

*Serial reversal learning task*

The results of the model analysis for the two response variables showed the same tendency and are presented respectively, that is, the number of sessions to reach the criterion (STC) for each stage (1-4) and the percentage of success at the very first session (PCT) for each stage. Five individuals did not meet the criterion for the fourth reversal stage in the time allowed and were considered not to have passed it (NA). We found no effect of the group (i.e. depending on the first colour learned) on performance throughout stages (for STC: $F = 0.08$, df = 1, $P = 0.79$ and for PCT: $F = 1.89$, df = 1, $P = 0.18$). Moreover, no effect of the group was observed on performance within each stage, indicating that the first colour learned, red or green, had no impact on the performance within and between the stages ($P > 0.05$).

We found an effect of stage on performance (STC: $F = 4.63$, df = 3, $P = 0.01$; PCT: $F = 3.98$, df = 3, $P = 0.02$). As expected, the first reversal stage (stage 2) significantly affected the performance of hens: individuals took 14.44 ± 3.57 sessions to reach the criterion, that is, on average, four sessions more than the discrimination stage (paired Wilcoxon test, $P = 0.03$; Fig. 4). PCT at stage 1 was significantly higher than that at stages 2 and 3 (paired Wilcoxon tests, $P = 0.04$ and $P = 0.04$, respectively; Fig. 5). Performance increased across the three reversal learning stages: PCT at stage 4 was almost significantly higher than that at stage 2 (Wilcoxon test, $P = 0.051$; Fig. 5). Accordingly, the mean STC decreased from the reversal stage 2 to the reversal stage 4.

*Delayed matching-to-sample task*

*Performance for the training pairs (green and red and yellow and blue)*

The six individuals took 17 to 22 sessions to reach the learning criterion of 80% of success in mixed sessions containing equal GR and YB trials (last training phase). The mean percentage of correct
<figure><img src="image_52.png" /><figcaption>**Fig. 4.** Number of sessions required to reach the criterion for the discrimination learning task (stage 1) and for the reversal learning task (stages 2–4). Individual's performances are represented with one colour per individual. \*P-value < 0.05; x P-value < 0.1.</figcaption></figure>

<figure><img src="image_53.png" /><figcaption>**Fig. 5.** Percentage of success of all hens for the very first session of each learning stage. Individual's performances are represented with one colour per individual. \*P-value < 0.05; x P-value < 0.1.</figcaption></figure>

responses for the two stimulus pairs was not significantly different in the three last sessions before testing (paired Wilcoxon test, GR: 87.5 ± 7.00% and YB: 88.3 ± 6.9, P = 0.91). Consistently, the percentage of correct responses was not different between the two training pairs during testing (paired Wilcoxon test, GR: 86.3 ± 9.1% and YB: 83.7 ± 11.1%, P = 0.13); these pairs were therefore analysed as one. At the group level, we found a colour bias among the known pairs for blue over yellow before the testing sessions and for green over red during the testing sessions (paired Wilcoxon tests, P < 0.01). A side bias was found for the right over left, either before or during the testing sessions (paired Wilcoxon test, P < 0.01).

### Performance for the generalisation test pair (purple and brown)

At the group level, the mean percentage of correct responses for PBr trials (11 sessions) was 62.4 ± 10.5% of success, which is lower than that for the two training pairs (paired Wilcoxon test, P < 0.001) but still significantly above the chance level (Wilcoxon test, P < 0.001; Fig. 6). Confirming this, at the individual level, the

<figure><img src="image_54.png" /><figcaption>**Fig. 6.** Mean percentage of success for training pairs during test sessions (GR and YB) and for the test pair (PBr). Each point represents the mean percentage of success for a hen individual over the 11 sessions for the training pairs (GR-YB) and for the test pair (PBr). \*\*P-value < 0.001.</figcaption></figure>

mean percentage of correct responses for the PBr pair was significantly greater than chance for each hen, except for Lys (binomial test, P = 0.11).

Fig. 7 shows individual performances for PBr trials through the sessions. In the first session, group performance achieved 52.92% of success with three hens (*Yuna*, *Celebi* and *Ariel*) performing better than 50%, even if not significant (binomial tests, P > 0.05). *Samba* significantly performed above chance from the third session, that is, within 120 test trials. Similarly, *Casquette* and *Yuna* performed significantly above chance from the fourth session, that is, within 160 test trials. Moreover, *Samba* and *Yuna* consistently and significantly performed above chance until the end of the test (binomial tests for each session for each individual, P < 0.05). In addition, even if it was not consistent throughout the next sessions, *Plume* significantly performed above chance at the fourth session, *Ariel* at the seventh session, and *Lys* and *Célébi* at the eighth session (Fig. 7). Overall, group performance significantly increased from session 1 to session 11 (paired Wilcoxon test for group performance between sessions 1 and 11, P = 0.02). Upon PBr trials, a side bias for right over left (paired Wilcoxon test, P = 0.004) was confirmed for four out of six individuals (binomial tests, P < 0.05), and a colour bias for purple over brown (paired Wilcoxon test, P < 0.001) was confirmed for three out of six individuals (binomial tests, P < 0.05). Only *Ariel* had no colour or side bias.

## Discussion

Our automated screen device allowed us to effectively test one simple and one more complex cognitive task in domestic hens. In the SRL task, hens reached a similar performance for the two changing reward contingencies in only three reversal stages, which refines the existing data on their behavioural flexibility in this task. In the dMTS task, our results suggest that at least two hens (*Samba* and *Yuna*) may have generalised the identity concept as their performance for the novel colours fits in what is mentioned in the literature. Both tasks are discussed below.
<figure><img src="image_55.png" /><figcaption>**Fig. 7.** Percentage of success in PBr trials for each hen at each session. Horizontal dashed lines at 65% and 35% of success represent the percentage for which the individual performance is greater than chance and worse than chance, respectively. Abbreviation: PBr = test pair purple-brown.</figcaption></figure>

## Serial reversal learning task

As expected, learning performance decreased at the first reversal learning stage (PCT stage 2) in comparison with the discrimination stage (PCT stage 1). This tendency has already been observed with spatial or 3D object discrimination procedures in many species (Tello-Ramos et al., 2019), including laying hens (among others: Bona et al., 2018). In our study, the performances of the hens at the discrimination stage (STC stage 1) were similar to those observed by Werner et al. (2005), in which hens also had to interact with a screen-like apparatus. Research investigating reversal learning abilities in hens with a 3D procedure seems to yield better performance results than when hens are assessed for the same cognitive task on a board. This observation is at least true for the discrimination and the first reversal learning stage (Sorato et al., 2018; among others: Loconsole et al., 2021), based on the results of Werner et al. (2005) and the present study, which were designed with screen-like boards (referring to performance as STC). Such a difference between a 3D procedural and a screen reversal task has been observed in keas (O'Hara et al., 2015). This contradicts with the argument of Loconsole et al. (2021) who supports that spatial SRL tasks might not be optimal to allow the study of behavioural flexibility in view of their strong brain hemispheric specialisation. We hypothesised that a larger physical involvement could have an intrinsically deeper reinforcement effect by being more biologically relevant to hens. In line with this view, learning efficiency can be improved by the spatial location of the reward in accordance with comparison stimuli (Williams et al., 1990), the many possible distinctive attributes, and the longer inter-trial intervals at stake in 3D tasks (Pearce, 1997).

Hens performed two more reversal learning stages corresponding to a serial reversal learning task (for a review, see Tello-Ramos et al., 2019). Despite the normal decrease in performance in comparison to the first stage due to the changing reward contingency, the performance increased through successive reversal learning stages (stages 2-4). At the group level, some difficulties must be emphasised because five out of nine hens did not manage to reach the criterion for the third reversal task (stage 4) in the time allowed. These individuals' performance at stages 2 and 3 was the most affected by the change in reward contingency. Variations in individual performance could be explained by several parameters, including personality, hierarchical position, physiological state, and emotional state of the individual (for a review, see Bushby et al., 2018).

When considering the four successful hens, they performed statistically equal to the discrimination stage (stage 1) at the third reversal stage (stage 4), that is, they performed equally for two different rewarded contingencies only after three reversals which is rather fast, thus demonstrating that our device is appropriate for implementing such flexibility tasks. This result is in line with those of other studies which set up to 20 reversals. Following the assumptions of Loconsole et al. (2021) on chicks' performance, hens seem to pass through the novelty of inconsistent orders and to generalise the underlying reversal rule as a *win stay-loose shift* rule according to the last outcome (van Horik and Emery, 2018) within only three reversal learning stages.

## Delayed matching-to-sample task

To our knowledge, this is the first report of identity concept generalisation in a dMTS task in domestic hens. For generalisation trials, the mean performance (11 sessions, i.e. 440 trials) was significantly above chance for every hen but one, and above the 50% threshold for every hen. More precisely, three individuals performed above 50% at the very first session, and two individuals performed significantly and consistently above chance from the third and fourth sessions, respectively.

Transfer studies in animals are not congruent when performance can be referred to as concept generalisation performance. The claim that generalisation must occur at the very first test trial contradicts the fact that chance can be accountable on a one-trial scale, so that more trials are needed. In most studies, concept generalisation is assumed when the performance is above chance at the very first session, either within less than 10 trials (for example: Giurfa et al., 2001 in bees) or within more trials (for example: Wright and Katz, 2006; Truppa et al., 2010 in capuchin monkeys; Obozova et al., 2015 in parrots) with generally approximately 90-120 test trials. Two out of six hens consistently performed above chance from 120 and 160 test trials, respectively, which corresponds to a performance level recognised for validating concept generalisation in other species. Since the test trials were differently reinforced, it is not possible to exclude fast learning occurring for the novel stimuli. However, the fast increase in performance (from sessions 1-3) only concerned the two hens whose performance was still above chance in all subsequent sessions. This fast acquisition with novel stimuli indicates that the hens were able to transfer what they had previously learned in a new situation. This finding supports the application of the concept through the dMTS task to new stimuli presented. Regarding this purpose, together with the neophobic tendencies of hens (Perez et al., 2016) and the intrinsic difficulty of the dMTS task, we may consider that domestic hens are able to transfer a learned concept by generalising it to novel and unknown stimuli. This result is in line with the generalisation abilities of perceptual information shown in chicks (see Versace et al. 2017; Rosa-Salva et al. 2018 through imprinting process; see Santolin et al. 2016 through a two-choice task).

At the individual level, apart from Samba and Yuna, individuals who performed above chance at one session did not show any consistency throughout the sessions, even if their mean performance was still above chance. This intra- and inter-individual inconsistency could be explained by a lack of attention depending on individual personalities. This lack of attention may have caused a sharp decrease in performance, despite better performance in previous sessions. This observation highlights the need to consider more than one session to further evaluate concept generalisation in animals.

Another explanation is that not all hens may have solved the dMTS task in the same approach, and some may have used alternative learning strategies to respond to it. In line with this idea,
Lormant et al. (2018) showed that, to solve the same spatial task, quails with a higher emotionality trait rely on spatial memory which is an explicit form of memory, whereas less emotional quails adopt preferentially cue-based memory, which is an implicit form of memory. Hens who did not effectively acquire the identity concept may have resolved the generalisation test with a cognitive process that does not imply explicit concept learning of the identity rule but procedural learning. This strategy, based on a simple stimulus response, could have allowed hens to rapidly increase their performance over time (Gabor and Gerken, 2010).

When presented with the testing stimuli, that is, PBr, individuals showed a significant bias towards purple over brown. Interestingly, blue was preferred over yellow in the training stage (mixed sessions containing equal GR and YB trials). Jones et al. (2001) showed that chicks who learned to choose two colours (e.g. red and yellow) among many colours, spontaneously generalised their behaviour to an intermediate colour (e.g. orange). Whether the learning parameters for a particular colour affect the behaviour of hens facing colours in a wavelength range remains to be addressed. We also found a side bias for the right over the left side at the group level. Recent studies in chicks have shown that using the right eye is crucial to attend to object-specific cues (Regolin et al., 2005), which could explain the right-side bias observed in our experiment.

## Conclusion

We introduced an original fully automated touchscreen device using digital computer-drawn colour pictures and independent sensible cells adapted for cognitive testing in domestic hens. Our device allowed us to confront individuals both to a simple and a more complex cognitive task, to which they responded positively. We demonstrated that this approach is appropriate for questioning a large range of cognitive capacities in domestic hens, and more generally, for questioning farm animals' cognition.

Knowing the range of cognitive capacities of farm animals is crucial for understanding their perceptions and psychological needs. Husbandry practices can be improved by fundamental cognition research, which can answer common welfare issues of rear-ing systems by understanding the animal point of view (for a review on chickens, see Ferreira et al., 2021). This issue is even more current considering that the impact of the current development of new farm management technologies on animal welfare depends on the cognitive capacities of animals.

## Supplementary material

Supplementary material to this article can be found online at https://doi.org/10.1016/j.animal.2022.100607.

## Ethics approval

The experimental protocol was approved by the Val de Loire Ethics Committee (Approval No.: CE19-2021-0712-1, CEEA VdL, France). Animal care and experimental treatments complied with the French and European guidelines for the housing and care of animals used for scientific purposes (European Union Directive 2010/63/EU).

## Data and model availability statement

The data that support the study findings are deposited in an official repository and are available from the authors upon request at https://doi.org/10.15454/QHEW7B.

## Author ORCIDs

Rachel Degrande: https://orcid.org/0000-0003-4976-1073

Léa Lansade: https://orcid.org/0000-0002-4185-9714

Plotine Jardat: https://orcid.org/0000-0003-0374-5588

Violaine Colson: https://orcid.org/0000-0003-0934-7935

Ludovic Calandreau: https://orcid.org/0000-0002-7535-5733

## Author contributions

Fabien Cornilleau, Ludovic Calandreau, Léa Lansade and Violaine Colson conceived the study. Fabien Cornilleau carried out the experiments. Rachel Degrande analysed the data and drafted the manuscript. Ludovic Calandreau supervised the research. All authors read, reviewed and approved the final manuscript.

## Declaration of interest

None.

## Acknowledgements

The authors acknowledge the experimental unit Pôle d'Expérimentation Avicole de Tours (UE PEAT, INRAE, 2018. Experimental Poultry Facility, DOI: 10.15454/1.5572326250887292E12) where the hens were maintained and where the experiments took place.

## Financial support statement

This research received the support of the PHASE research Department (INRAE) and a PhD fellowship INRAE-Région Centre Val de Loire (France).

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1 Supplementary Materials ARTICLE 2 – Domestic hens succeed at serial reversal

2 learning and perceptual concept generalization using a new automated touchscreen

3 device

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7 Rachel Degrandeª, Fabien Cornilleauª, Léa Lansadeª, Plotine Jardatª, Violaine Colsonᵇ, Ludovic

8 Calandreauª

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10 a CNRS, IFCE, INRAE, Université de Tours, PRC, F-37380, Nouzilly, Indre-et-Loire, France b INRAE,

11 LPGP (Laboratoire de Physiologie et Génomique des Poissons), Campus de Beaulieu, F-35042 Rennes

12 cedex, Ille-et-Vilaine, France

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14 Corresponding author: rachel.degrande@gmail.com; rachel.degrande@inrae.fr

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<caption>Supplementary Table S1: Serial reversal learning, number of sessions to reach the criterion per stage.</caption>

Outputs of the analysis of variance (ANOVA) for the number of sessions to reach the criterion (STC) per stage in the serial reversal learning task. The additive model got a better Akaike Information Criterion (AIC) score than the interaction model. A linear model lmer was directly called since calling a glmer gaussian model is deprecated.

Model implemented: lmer(data=d_rev, nb_sessions~stage+group+(1|individual))

<table><thead><tr><th></th><th>Sum Sq</th><th>MS</th><th>NumDF</th><th>DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td><strong>Fixed variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stage</td><td>115.85</td><td>38.62</td><td>3</td><td>19.49</td><td>4.63</td><td>0.01</td></tr><tr><td>Group</td><td>0.66</td><td>0.66</td><td>1</td><td>6.57</td><td>0.08</td><td>0.79</td></tr><tr><td><strong>Random Effects</strong></td><td>Variance</td><td>SD</td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>3.79</td><td>1.95</td><td></td><td></td><td></td><td></td></tr><tr><td>Residuals</td><td>8.344</td><td>2.89</td><td></td><td></td><td></td><td></td></tr></tbody></table>
Supplementary Table S2: Serial reversal learning, mean percentage of success for the first session per stage. Outputs of the analysis of variance (ANOVA) for the mean percentage of success per stage considering only the very first session, for each stage, in the short serial reversal learning task. The additive model got a better AIC score than the interaction model. A linear model lmer was directly called since calling a glmer gaussian model is deprecated.

Model implemented: lmer(data=first_dsc, percentage~stage+group+(1|individual))

<table><thead><tr><th></th><th>Sum Sq</th><th>MS</th><th>NumDF</th><th>DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td><strong>Fixed variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stage</td><td>282.68</td><td>94.23</td><td>3</td><td>26</td><td>3.98</td><td>0.02</td></tr><tr><td>Group</td><td>44.64</td><td>44.64</td><td>1</td><td>26</td><td>1.89</td><td>0.18</td></tr><tr><td><strong>Random Effects</strong></td><td>Variance</td><td>SD</td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Residuals</td><td>23.66</td><td>4.86</td><td></td><td></td><td></td><td></td></tr></tbody></table>
**Supplementary Table S3: Delayed matching-to-sample, effect of a colour or a side bias during the training stage.** Outputs of the analysis of variance (ANOVA) for the effects of colour or side bias on the performance during the training stage, in the delayed matching-to-sample task. The interaction model got a better AIC score than the additive model. A linear model lmer was directly called since calling a glmer gaussian model is deprecated.

Model implemented: lmer(data=tr, percentage~side:colour+(1|individual))

<table><thead><tr><th></th><th>Sum Sq</th><th>MS</th><th>NumDF</th><th>DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td><strong>Fixed variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Side:Colour</td><td>15569</td><td>2224.2</td><td>7</td><td>875</td><td>11.76</td><td>2.33e-14</td></tr><tr><td><strong>Random Effects</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>32.46</td><td>5.70</td><td></td><td></td><td></td><td></td></tr><tr><td>Residuals</td><td>189.16</td><td>13.75</td><td></td><td></td><td></td><td></td></tr></tbody></table>
**Supplementary Table S4: Delayed matching-to-sample, effect of a colour or a side bias during the generalisation test.** Outputs of the analysis of variance (ANOVA) for the effects of colour or side bias on the performance during the generalisation test, in the delayed matching-to-sample task. The additive model got a better AIC score than the interaction model. A linear model lmer was directly called since calling a glmer gaussian model is deprecated.

Model implemented: lmer(data=te, percentage~side+colour+(1|individual))

<table><thead><tr><th></th><th>Sum Sq</th><th>MS</th><th>NumDF</th><th>DenDF</th><th>F value</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td><strong>Fixed variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Side</td><td>5768</td><td>5768.48</td><td>1</td><td>780</td><td>20.60</td><td>6.57e-06</td></tr><tr><td>Colour</td><td>104862</td><td>20972.4</td><td>5</td><td>780</td><td>74.88</td><td>&lt;2.2e-16</td></tr><tr><td><strong>Random Effects</strong></td><td>Variance</td><td>SD</td><td></td><td></td><td></td><td></td></tr><tr><td>Individual</td><td>26.15</td><td>5.11</td><td></td><td></td><td></td><td></td></tr><tr><td>Residuals</td><td>280.09</td><td>16.74</td><td></td><td></td><td></td><td></td></tr></tbody></table>
# CHAPITRE 3 : Inférence transitive chez la poule domestique adulte dans une série arbitraire de six items
Inférence transitive chez la poule domestique adulte dans une série arbitraire de 6 items

Dans les chapitres précédents, nous avons montré que la poule domestique est capable d'inférences simples qui impliquent potentiellement des représentations mentales. D'autres types d'inférences, dites relationnelles, impliquent une représentation mentale plus sophistiquée comprenant la mémoire de différentes relations entre différentes informations. Ces inférences relationnelles regroupent les deux composantes à la base des capacités de raisonnement : la capacité d'inférence, et la capacité de mémoire relationnelle.

L'inférence transitive est une capacité d'inférence relationnelle qui est très utile dans le contexte social. Cette capacité permet par exemple d'inférer la relation entre deux individus de manière indirecte, en se basant sur les relations directes connues, et de se positionner dans la hiérarchie sociale relativement aux autres individus. Plusieurs paramètres ont permis d'émettre l'hypothèse d'une telle capacité chez la poule domestique dans le contexte social (Craig et al, 1978 ; Hogue et al, 1996). Finalement, une étude en conditions contrôlées chez le poussin suggère que l'espèce est effectivement capable d'inférence transitive (Daisley et al, 2010, 2021).

La tâche communément utilisée pour tester la capacité d'inférence transitive chez l'animal, qui a été utilisée dans l'étude de Daisley et al. chez le poussin, est la tâche des 5 items en série ordonnée (série d'items ABCDE). Cette tâche implique une situation non sociale et met en jeu des relations arbitraires entre les items. Dans cette tâche, l'animal apprend dans un premier temps les relations directes entre les paires d'items successifs, et est récompensé s'il choisit l'item prioritaire dans la paire (A>B, B>C, C>D, D>E ; par exemple : entre A et B, choisir A pour être récompensé). Le test d'inférence consiste à présenter à l'animal une paire d'items non successifs, n'ayant jamais été présentés ensemble auparavant, ici : la paire B-D. Si l'animal est capable d'inférence transitive, alors il utilisera les relations directes B>C et C>D qu'il aura mémorisé, et sera capable d'inférer que B>D, choisissant B dans la paire B-D. Cependant, cette tâche ne permet pas complètement d'exclure une résolution qui serait plutôt basée sur des valeurs de renforcement associatif.

L'objectif de ce troisième chapitre a donc été dans un premier temps de valider, puis de questionner de manière plus approfondie la capacité d'inférence transitive, cette fois chez la poule adulte. Pour consolider et pour approfondir les résultats obtenus chez le poussin, nous avons utilisé une variante plus complète de la tâche d'inférence qui est communément utilisée (ARTICLE 3).
Nous avons entraîné six poules sur une série de 6 items ordonnés (série d’items ABCDEF). Nous avons réalisé un entraînement hybride, combinant des sessions d’apprentissage des paires une à une, et des sessions mixées comprenant l’ensemble des paires apprises (A>B, B>C, C>D, D>E, E>F). Une fois l’apprentissage finalisé, trois types d’essais d’inférence ont pu être proposés : B-D, B-E et C-E (au lieu d’un seul dans la tâche des 5 items), ce qui permet d’établir une conclusion plus solide sur l’existence de la capacité d’inférence. Cette variante plus complète nous a aussi permis de comparer les performances pour un nombre plus important de paires (5 paires d’items successifs et 3 paires d’inférence). En effet, en complément des performances d’inférence en elles-mêmes, comparer les performances entre les paires permet d’établir des hypothèses plus solides sur les processus cognitifs sous-jacents lors de la résolution de la tâche relationnelle (processus associatif ou inférence). La performance des animaux a été mesurée avec le pourcentage de choix corrects par session pour les paires d’items successifs, le pourcentage de choix corrects global sur les essais d’inférence (sur les 12 premiers essais puis sur le total de 36 essais), et le pourcentage de choix corrects global pour chaque type d’essai d’inférence (12 essais au total, par type d’essai d’inférence).

Les deux tiers des individus ont été capables de mémoriser les relations de priorité entre les 5 paires d’items successifs. L’ensemble des individus qui ont réalisé le test d’inférence transitive, a montré une performance significativement supérieure au hasard pour les essais d’inférence. Finalement, l’analyse approfondie de nos résultats suggère que la performance de certains individus peut être expliquée par l’utilisation effective de représentations mentales relationnelles. Pour d’autres cependant, des explications cognitives alternatives basées sur des mécanismes associatifs permettent d’expliquer leur comportement de choix.

En conclusion, cette expérience a permis de montrer que la poule domestique est capable d’inférence transitive. En utilisant une variante plus complète de la tâche, nous avons pu apporter des éléments supplémentaires pour appuyer une résolution de cette tâche par inférence. Ce résultat suggère que la poule est capable d’utiliser des capacités d’inférence qui mettent en jeu des représentations mentales relationnelles, et donc une représentation mentale des relations entre différentes informations mémorisées. Toutefois nos résultats attirent l’attention sur la variabilité inter-individuelle de l’expression de cette capacité d’inférence relationnelle chez l’animal.
ARTICLE 3 – Transitive reasoning in the adult domestic hen in a six-term series task
(in prep.)

Degrande R. ª, Amichaud O. º, Piégu B. ª, Jardat P. ª, Ferreira V.H.B. ª, Colson V. b, Lansade L. ª,
Calandreau L. ª

Corresponding author: rachel.degrande@gmail.com; rachel.degrande@inrae.fr

Article in preparation.

<footnote>a CNRS, IFCE, INRAE, Université de Tours, PRC (Physiologie de la Reproduction et des Comportements), F-37380, Nouzilly, Indre-et-Loire, France</footnote>

<footnote>b Laboratoire de Physiologie et Génomique des Poissons, INRAE, Rennes, France</footnote>

<footnote>c Université de Rennes, France</footnote>
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# Transitive reasoning in the adult domestic hen in a six-term
series task

Degrande R.ª, Amichaud O.ᶜ, Piégu B.ª, Jardat P.ª, Ferreira V.H.B.ª, Colson V.ᵇ, Lansade L.ª,
Calandreau L.ª

ªCNRS, IFCE, INRAE, Université de Tours, PRC (Physiologie de la Reproduction et des Comportements), F-37380, Nouzilly, Indre-et-Loire, France

ᵇLaboratoire de Physiologie et Génomique des Poissons, INRAE, Rennes, France

ᶜUniversité de Rennes, France

Corresponding author: rachel.degrande@gmail.com; rachel.degrande@inrae.fr

## ABSTRACT

Transitive inference (TI) is a disjunctive syllogism that allows to infer a relationship between two
components, indirectly, by knowing their respective direct relationship to a third component (if
A>B and B>C then A>C). Few bird species have been tested for TI to date, which limits our
knowledge of the phylogenetic spread of such reasoning ability. To our knowledge, there have been
three studies investigating TI in a 5+ terms series task in birds before ours (in corvids: Bond et al.
2003; in pigeons: von Fersen et al. 1991; Daniels et al. 2014), with no studies in *Galloanserae* yet.
Here, we tested TI in adult laying hens with the 6-terms series task, that allows to draw more solid
conclusions about TI capacity through learning and testing performance. Six hens were trained to
direct relationships in a sequence of six arbitrary items, varying in color and shape
(A>B>C>D>E>F) in a hybrid training procedure. The birds were trained to always peck at the
priority item among two adjacent ones (e.g., in the pair CD, peck at C). Then, 12 testing sessions
were implemented, comprising 3 non-rewarded inference trials each: BD, BE, and CE. All subjects
showed TI within 12 inference trials and were capable of TI whatever the relative distance between
the items in the series. We found that TI performance was not impacted by the reinforcement ratios
of the items; thus, making it harder to support a purely associative-based resolution of the task. Our
results suggest that TI is based on the same cognitive processes in *Galloanserae* than in *Neoaves*,
and that the cognitive strategy to solve the task might be driven mainly by individual parameters
within species. These results contribute to a better understanding of transitive reasoning in birds.
KEYWORDS: transitive inference; relational memory; cognition; chicken; *Gallus gallus*
*domesticus*

INTRODUCTION

Animals need some specific cognitive capacities to adapt to their living environment, depending on species-specific physical or social constraints. In large groups, the abilities to recognize the individuals and to position themselves in the hierarchy enable individuals to avoid potential injuries from predictable conflicts (Zuberbühler and Byrne 2006; Croney and Newberry 2007). In free-living conditions, hens form social groups from 20 to 30 individuals, that are organized in a strict linear hierarchy maintained through the pecking order (Craig 1978). Some of their socio-cognitive capacities have already been demonstrated, that serve them to navigate such complex social structure. For example, hens are able to recognize the individuals based on several individual features (Abeyesinghe et al. 2009), and to behave properly towards an unknown individual, depending on whether this individual won or lost the interaction with a known, more dominant individual (Hogue et al. 1996).

Among these capacities, transitive inference is the ability to infer the relationship between two individuals indirectly, by knowing their respective relationship with a third individual. This can be illustrated with the three individuals A, B and C: by knowing that A is the dominant over B, and B is the dominant over C, C must infer that A will be dominant over him. Transitive inference (TI) is thus particularly useful in the above-mentioned context of large hierarchical-group living. This complex ability requires a long-term memory about the relationships (relational memory) and the ability to use this knowledge to infer the indirect relationships.

The common task to test for TI in animals is the five-terms series task (Piaget 1928; Bryant and Trabasso 1971; McGonigle and Chalmers 1977). In this task, the individuals have to learn dyadic relationships between successive items in a five-term ordered sequence A > B > C > D > E (where the letters stand for the different items). That is, for example, when faced with the items A and B, individuals are reinforced when choosing A; when faced with B and C, they are reinforced when choosing B, etc. Then the individuals are tested on the unanticipated, nonadjacent pair BD.
Choosing B over D is interpreted as a demonstration of TI, as B and D are indirectly linked through their relationship with the third item C (learned relationships: B > C and C > D).

TI has been studied in chicks (Daisley et al. 2010, 2021) through the five-term series task. In these studies, the chicks showed a transitive response significantly better than chance when faced with the unanticipated, non-adjacent pair BD. However, the use of the five-terms series task has been questioned in the literature (for example: Allen 2006). In a five-terms series, B is directly linked to A, the most positive item in terms of probability of reinforcement, and D is directly linked to E, the most negative item. This configuration involves that B is associated with a more positive residual value, and D with a more negative residual value. Thus, it impedes discussions about the cognitive process at stake both during the relational learning and during TI, that is, either a relational representation of the ordinal series, or a more associative-based representation.

The main purpose of the present study was to train adult hens (*Gallus gallus domesticus*) in a six-term series task. A larger sequence allows to test TI with three nonadjacent and non-endpoint pairs (BD, BE and CE). This approach has already been used in pigeons to draw more solid conclusions about TI, by performing complementary analyses (von Fersen et al. 1991; Bond et al. 2003; Daniels et al. 2014).

We trained hens following a procedure adapted from the hybrid training procedure proposed by Daniels et al. (2014) in pigeons. We implemented a six-terms series of arbitrary items of different shape and colour. Pairs were trained successively (AB, BC, CD, DE and EF), and mixed sessions, containing each previously trained pair, were included at each step. Such hybrid training procedure gradually increasing complexity allows to maximize and equalize the performance for every pair trained and to avoid different delays between training each pair and test sessions (Vasconcelos 2008; Daniels et al. 2014). The 6-item series allowed us to test for three different inference trials: BD, BE and CE, which provides a more robust result for TI by making it harder to respond to, based solely on associative-based processes (Vasconcelos 2008; Bond et al. 2010).

We analyzed the learning performance, the TI performance and the choice latency for each adjacent pair (premise pairs) and for each nonadjacent pair (control and inference trials, respectively), at the group level, and at the individual level to measure eventual inter-individual variations. We put an emphasis on the learning accuracy between the premise pairs as it might give
some indication about the cognitive resolution of the TI task (Bond et al. 2010). Finally, we tested
for alternative cues that could have driven the choice behaviour of individuals through the
configuration of the sessions.

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This experimental procedure was approved by the Val de Loire Ethics Committee (approval
n° CE19 – 2021-0211-1, CEEA VdL, France). Animal care and experimental treatments complied
with the French and European guidelines for housing and care of animals used for scientific
purposes (European Union Directives 2010/63/AU).

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Six adult laying hens (Isa Brown strain), aged 2 to 3 years old, were included in the
procedure. The hens were maintained at the Pôle d'Expérimentation Avicole de Tours, where the
experiment took place (UE PEAT, INRAE, 2018. Experimental Poultry Facility, doi:
10.15454/1.5572326250887292E12). They had access to a wood-chip littered barn (25m²)
equipped with nesting boxes and perches, and had access to an outside enclosure (approx. 30m²)
enriched with perches. Water was provided ad libitum, and food was delivered at will once the
experiments of each testing day were completed. Birds were kept in a stable social group of 21
individuals on a 6am to 6pm daylight cycle. All experiments took place between 9am and 3pm and
the running order of the individuals was counterbalanced each day.

113
114
STIMULI

115
116
117
118
Each individual was attributed a unique sequence of 6 items, composed of the same items
in a different order. The items (4x4cm) were printed in the center of white cards (10x8cm). Each
item had a different color (yellow, red, black, green, blue and pink) and a different shape (square,
circle, cross, diamond, triangle, star) (Figure 1). The colors and the shapes were chosen in order
119 to maximize the difference between the items. With the same purpose, the blank rate and the
120 structural information rate were not controlled. Each individual had a unique sequence order of
121 these items, thus, for clarity, we will name the items depending on their sequence position: A, B,
122 C, D, E and F.

<figure><img src="image_56.png" /><figcaption>124 **Figure 1.** Example of the priority sequence of the items for one individual. The colors and shapes
125 correspond to those that have been printed on the cards for the procedure. The priority order is from the
126 item A to the item F.</figcaption></figure>

128
129 APPARATUS AND GENERAL OPERATIVE

130 1. Apparatus

131 The apparatus consisted in a starting box and a separation between the box and the
132 experimenter, both made in condensed wood. The starting box (69cm long x 55cm large x 80cm
133 high) was closed with a wire-meshed door (Figure 2a). A separation with a one-way mirror,
134 mounted on a wall, stood 20cm away from the starting box. This separation provided a 10cm high
135 x 40cm large opening allowing the experimenter to present the cards to the hen without her being
136 able to see the experimenter (Figure 2b). The pairs of cards were presented on a solid cardboard
137 display (Figure 2c).
<figure><img src="image_57.png" /><figcaption>**Figure 2.** Apparatus, with the starting box and the separation (a). At each trial, the experimenter showed a pair of cards thanks to the display through the opening (b), and the hen had to choose one of the two items presented. Photograph (c): the cards were presented on the display. The films taken by the camera placed on the separation were used to measure the choice latencies when needed.</figcaption></figure>

## 2. General operative

Before training, each individual was habituated individually to the new testing environment and accustomed to peck on a neutral card (black figure printed on a white card with the same dimensions as the test cards) to get mealworms. This habituation was implemented using a clicker-training procedure (see for example Feng et al. 2016).

After this procedure, the stimuli habituation and the training sessions started for each individual. During habituation, the experimenter randomly presented each item of the series, as a prior habituation, and each hen pecked directly on each item regardless of the shapes or the colors. Then, through the training sessions, the hens would learn to peck preferentially (i.e., to choose) on one item in the pair presented, i.e., the prior item among the two, to get the reward.

For each trial, the pair was prepared out of the sight of the individual. Then, the wire-meshed door was opened and the experimenter presented the pair on the display. A choice was considered when the hen pecked on one card. If she gave the incorrect answer, the display was immediately taken back and the hen was brought back in the box for a 10-second wait. Then, a
maximum of 3 correction trials was given with the exact same trial. If she gave the correct answer, the experimenter clicked and waited for a second peck on the same card to give her the reward (either for a successful standard trial or a correction trial: 3 mealworms). After each trial, the hen was brought back in the box and the inter-trial interval was of 15 to 20 seconds. If the hen did not respond within 2 minutes, the experimenter closed the door and waited 10 seconds before trying again. If the hen did not respond for a total of 3 minutes, she was considered unmotivated and the session was postponed.

Sessions could last from 20 to 45 minutes depending on the individual performance (number of correction trials needed) and depending on the number of trials in the session. Each individual had one session per day. For each training or testing session, the prior item for each pair was equally presented right and left among the trials (e.g., in the pair CD, the prior item is C), and a prior item was presented no more than two times in a row on the same side. A learning criterion was demanded at each training step: both in single-pair training and in mixed sessions (detailed below), the hen had to reach a minimum of 75% of success in 2 consecutive sessions with more than 50% of success for each side of presentation of the prior item for each pair. However, if an individual reached 90% successful trials or more for both sides within one session, then the learning criterion was considered to be reached. The learning criteria stood to avoid any potential side bias, i.e., systematically pecking on the left or on the right card, independently of the card. In case of a side bias, a maximum of 8 correction sessions were implemented, that were adapted to the bias and the strength of the bias on a case-by-case basis.

## PROCEDURE

### 1. Training sessions

#### 1.a. Single-pair training of the premise pairs

Each pair of adjacent items (i.e., premise pairs) was learned in the sequence order (e.g., AB, then BC, etc.). These training sessions had 20 trials, with an equal amount of left and right presentations of the prior item. We measured the number of sessions individuals needed to reach the learning criterion for each pair.
1.b. Mixed sessions

To ensure that hens were able to remember the correct choice for the items that were involved in two different situations (e.g., C is the prior item the hen had to peck on in CD but not in BC), mixed sessions, where all pairs previously learned were retrained, were added to our training schedule. These sessions were included after the training of BC, CD, DE and EF, respectively (Figure 3). This hybrid training procedure (successive pair training intermixed in mixed sessions) has been shown to lead to a faster significant performance for the premise pairs (Daniels et al. 2014). The number of presentations of each pair was equal in each session. For a consistency of the number of trials per pair per session, and considering the conditions of prior items presentations, we allocated a total number of trials per session accordingly. Mixed sessions after BC training sessions had a total of 20 trials; those after CD had 18 trials; those after DE had 32 trials; those after EF had 40 trials. We controlled afterwards that the number of presentations of each pair did not influence the performance in inference trials.

We measured the number of sessions the individuals needed to reach the learning criterion for each mixed step. For the last three mixed sessions (which included the 5 premise pairs), a baseline performance (percentage of success) and the response latency was measured for each premise pair.

<figure><img src="image_58.png" /><figcaption>**Figure 3.** Following of the pair training of adjacent items and of the mixed sessions. Individuals had to pass a learning criterion at each step. The outlined items are the prior items in the pair.</figcaption></figure>
212 **2. Test sessions**

213 Each individual had 12 test sessions with 46 trials each: 40 trials of adjacent pairs (8 trials
214 per pair), 3 control trials and 3 inference trials. Control and inference trials were non-rewarded and
215 with no correction trials.

216 Extremity items are not used in inference trials as they are either always reinforced (A) or
217 never reinforced (E). Thus, nonadjacent pairs involving an extremity items (e.g., AC or CE) don't
218 test for TI but allows to control for the basic understanding of associative values according to the
219 reinforcement of each item during training (end-anchor effet, von Fersen et al. 1991). Control trials
220 consisted in nonadjacent pairs with either the very first item A or the very last item F (AC, AD,
221 AE, BF, CF or DF). These A and F control trials were pseudo-randomly distributed and equally
222 presented among the 12 test sessions.

223 Inference trials were trials of nonadjacent items (but no extremity item) for which transitive
224 inference was needed to give the correct response. The pairs involved items that were both equally
225 reinforced (for example, in BD: B+C- and D+E-) and non-reinforced (for example, in BD: A+B-
226 and C+D-) during the training. The six-term series enabled to test TI through three inference trials
227 (BD, BE and CE). Each one of the possible inference trials was presented once per test session.
228 The sides of presentation of the prior item for each possible inference trial, were pseudo-
229 randomized and equalized within two sessions.

230 During the test sessions, the performance for the premise pairs was tracked. In case of a
231 side- or an item-bias, we integrated optional correction sessions with, in main proportion, the
232 difficult pair, and the two adjacent pairs, to ensure an equalized level of performance for every
233 premise pair throughout test sessions. A maximum of two correction sessions was added each two
234 test sessions.

235 We measured the number of successful trials for the premise pair trials, for control trials,
236 and for inference trials. We measured the response latency for control trials and for inference trials.

237
238
STATISTICAL ANALYSIS

239 All statistical analyses were performed using R version 4.2.1 (R Core Team 2022). We
240 considered p-values below 0.05 to be significant, and p-values below 0.1 as a statistical tendency,
241 for all statistical analyses. Chance level was considered at 50% of success. A non-parametric
242 approach was preferred due to the small sample size (6 to 4 individuals).
The response variables were either the number of sessions to criteria, or the percentage of success, or the response latency. The fixed variables were the side for the presentation of the prior item, the trial type, the pair and the number of the session. Anova permutational models were performed when several explanatory variables were to consider, with individuals as random effect (aovperm function, package *permuco*, (Frossard and Renaud 2021); np=10000, type=permutation). Symmetry tests or one-way permutational tests were run to analyze the effect of a fixed variable with more than 2 modalities on the response, when the data were paired or not, respectively (package *coin*, Hothorn et al. 2006; two-tailed comparisons, np=10000). Pairwise permutation t-tests were run for Posthoc analyses to detail the comparison between more than two modalities (pairwise.perm.t.test function, package *RVAideMemoire*, Hervé 2022; np=10000, Holm correction). Two-tailed Wilcoxon rank-sum tests were used to assess the statistical significance of a comparison against the chance level, with Holm correction when appropriate (manual correction). Two-tailed exact binomial tests were used to test the statistical significance of individual performances (binom.test function). Homogeneity of variances was assessed with Levene tests before model fitting and before running multiple comparison analyses. Graphics were performed with the package ggplot2 (Wickham et al. 2019).

For statistical analyses including less than 10 responses, the results are presented as the median and the first and third quartiles (MED [Q1:Q3]). For other statistical analyses, the results are presented as the mean and the standard deviation (MEAN +/- SD).

Supplementary analyses were implemented to control for some eventual response biases because of the sessions' configuration. These analyses are detailed in *Supplementary Materials* (*Supplementary Material S2*).

## 265 RESULTS

267 TRAINING SESSIONS

The details of the number of sessions to reach the learning criterion at each step for each individual are presented in *Supplementary Materials*, *Supplementary Table S1*.

270
271 *Single-pair training of the premise pairs (i.e., pairs of successive items)*. At the group level, the mean number of sessions needed to reach the learning criteria did not differ between the different pairs (variance homogeneity, one-way permutation test, chi2=4.5469, df=4, p=0.337; global median number of sessions=3 [3:4] sessions to reach the criterion).

275

276 *Mixed sessions*. At the group level, the mean number of sessions needed to reach the learning criterion was significantly different depending on the number of pairs included in the session (no variance homogeneity, one-way permutation test, chi2=12.203, df=3, p=0.007). More precisely, the learning speed was higher in the mixed sessions including pairs [AB to BC] compared to other stages of mixed sessions (significant with pairs [AB to CD]: p=0.046; statistical tendency with [AB to DE]: p=0.082 and with [AB to EF]: p=0.074; pairwise permutation t-test).

282

283 *Baseline performance for the premise pairs*. Four out of six individuals successfully finished the training by reaching the learning criteria (*Supplementary Materials*, **Figure S1**). The two others remained stuck at a previous learning stage and were not included in the following analyses. In the last 3 mixed sessions, the mean performance for the premise pairs was of 80.42 +/- 17.88% of success, which is higher than chance level (two-tailed Wilcoxon rank-sum test, V= 1526.5, p<0.001; *Supplementary Materials*, **Figure S2**). We used a permutational anova model analysis to estimate the effect of the pair presented and of the side of the prior item, on the percentage of success. We found an effect of the side (F=8.69, p=0.011), with a global side bias for left over right (mean performance when prior item on the left = 85.69 +/- 19.29%; on the right = 74.81 +/- 25.59%) that was not different between the pairs (F=1.464, p=0.260). Moreover, we found an effect of the pair presented on the performance (F=3.343, p=0.038; **Figure 4**). A multiple pairwise analysis revealed significant performance differences between pairs AB and DE (p=0.035), CD and EF (p=0.007) and DE and EF (p=0.002) and a statistical tendency for the difference between pairs BC and EF (p=0.053; pairwise permutation t-test). The performance for the premise pairs at the end of training for each individual is detailed in **Figure 5**.
<figure><img src="image_59.png" /><figcaption>**Figure 4.** Percentage of success at the group level for each premise pair at the end of the training (3 last mixed sessions – 8 trials per pair per session). \*: p<0.05; \*: p<0.01; \+: p<0.1.</figcaption></figure>

298

299

300
<figure><img src="image_60.png" /><figcaption>**Figure 5.** Percentage of success at the individual level for each premise pair at the end of the training (3 last mixed sessions), for the four individuals that passed the learning criteria. At the individual level, there were no significant difference in the performance of the premise pairs.</figcaption></figure>

*Response latency for the premise pairs in the 3 last mixed sessions.* The mean response latency for the premise pairs was of 3.097 +/-3.335 seconds. We found no difference of response latency between the premise pairs (one-way permutation test, variance homogeneity; chi2=4.9606, df=4, p=0.291). At the individual level, there was a significant difference in the response latency between the pairs for two individuals with a slower response latency for DE and EF (for Dion: between AB and DE: p=0.020; between BC and DE: p=0.014; between AB and EF: p=0.048; between BC and EF: p=0.048; for Daenerys: between AB and EF, p=0.018; pairwise permutation t-test). The response latency for each premise pairs at the end of the training for each individual is detailed in **Figure 6**.
<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /><figcaption>**Figure 6.** Response latency in seconds for each premise pair for each individual, at the end of the training (3 last mixed sessions). \*: p<0.05 (pairwise permutation t-test).</figcaption></figure>

## TEST SESSIONS

Four individuals successfully finished the training and were tested for transitive inference. The test sessions had 40 premise trials, 3 inference trials (BD, BE and CE) and 3 control trials per session. Each individual had 12 test sessions. The performance in control trials validated the basic understanding of the associative rule of the (non-)reinforced extremity items, as every hen performed better than chance level (total of 36 control trials; group median performance = 98.61 [95.14:100] %; binomial tests, p<0.05; performance for A-trials = 97.22 [94.44:100] %; for F-trials = 100 [95.83:100] %).
328 *TI performance.* Each hen performed better than the chance level (total in 36 inference trials; 329 80.56% for Octo, 83.33% for Starr, 94.44% for Dion and 72.22% for Daenerys; binomial tests, 330 p<0.05). The median performance at the group level is of 81.94%. Starr, Dion and Daenerys were 331 successful in their very first inference trial. Within the 12 first inference trials, Dion and Daenerys 332 performed significantly better than the chance level (12/12 and 10/12, respectively; binomial tests, 333 p<0.05; 7/12 for Octo and Starr). We found no side bias for test trials (anova permutation model 334 analysis; F=1.19, p=0.3144) either for control or inference trials (no interaction; F=0.629, 335 p=0.4325). 336

337 *Comparison of the different inference trials.* At the group level, we found a statistical tendency for 338 the difference in the performance between the 3 trial types at the group level with a higher 339 performance for BE than the two others (variance homogeneity, symmetry test, maxT=2.250, 340 p=0.06312; median performance for BE= 100 [97.9:100] %; for BD= 83.30 [70.83:91.60] %; for 341 CE= 66 [64.08:70.47] %). At the individual level, each hen performed better than 50% of success 342 for each trial type (BD, BE and CE). Every individual performed significantly better than chance 343 level when tested with BE, two individuals performed significantly better than chance level when 344 tested with BD, and one individual when tested with CE (statistical significance for more than 10 345 successful trials among 12 for each inference trial possibility; two-tailed binomial test, p<0.05; 346 Figure 7). 347

348 *Response latency.* We found a significant difference in the response latency between premise pairs, 349 control trials and inference trials (One-way permutation test, chi2=7.553, df=2, p=0.023) with a 350 significant difference between premise pairs and control trials (pairwise permutation t-test, 351 p<0.001; mean response latency for premises pairs trials=3.0965 +/-3.33 seconds, for control 352 trials=2.043 +/-1.106 seconds, for inference trials=2.519 +/- 2.189 seconds). We found no 353 significant difference in the response latency between the different pairs presented in control and 354 in inference trials (homogeneity of variances, One-way permutation test, chi2=7.89, df=8, 355 p=0.444). In inference trials, the mean response latency for BD trials was of 2.271 +/- 1.201 356 seconds; for BE trials = 2.308 +/- 1.207 seconds; and for CE trials = 2.933 +/- 3.438 seconds. 357
<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

358

359

**Figure 7.** Detailed performance for each type of inference trial for each individual. Left y-axis: percentage of success in 12 inference trials. Right y-axis: differential ratio of reinforcement in percentage (see *Supplementary Materials*). For example, the higher $D_{BD}$ is, the more B has been reinforced in comparison of D. The performance to reach to be significantly higher than the chance level is 83,33% (10 trials out of 12) which is visualized with the black horizontal line.

365

366

## CONTROL FOR CONFOUNDING FACTORS ON CHOICE BEHAVIOUR AND TI PERFORMANCE

We analyzed whether the hens could have relied on other cues to be successful in test trials. These analyses are detailed in the *Supplementary Materials*. The results show that the reinforcement ratios of the items and the configuration of the test sessions could not have allowed

368

369

370
hens to perform higher than the chance level in test trials, as (1) neither the reinforcement history
371
nor the reinforcement ratio of the items resulting from the training stage influenced the performance
372
in test trials (2) the configuration of the previous pair trial influenced the choice behaviour in the
373
following test trial, (3) hens did not use the rule of a maximum number of trials with the prior item
374
on the same side, to perform better than the chance level in test trials, and (4) the individuals did
375
not have a systematic left-right response scheme that would have enabled them to perform better
376
than chance in inference trials, excepted for Daenerys, for whom 22.22% of her performance in
377
inference trials can be explained by the repetition of a particular scheme (Left-Right-Left-Left).
378

Interestingly, the fact that test trials were not rewarded might have affected the performance
379
of the hens in premise pairs during test sessions. A permutation anova analysis showed that the
380
choice to peck at an item in a premise pair significantly increased when the individual had pecked
381
on this item in the previous non-rewarded test trial (control or inference trial) but only if it was
382
presented on the other side (anova permutation model analysis; interaction: F=7.108, p=0.0078).
383
In parallel, whether hens gave the correct (C<sub>T</sub>) or the incorrect (I<sub>T</sub>) response at test trials did not
384
significantly impact their performance (correct C<sub>H</sub> or incorrect I<sub>H</sub>) at the next premise pair trial
385
which presented one of the same items (two-tailed Wilcoxon tests with Holm correction; mean
386
occurrence of I<sub>T</sub>I<sub>H</sub>=0, I<sub>T</sub>C<sub>H</sub>=10 +/- 4.97, C<sub>T</sub>I<sub>H</sub>=0, C<sub>T</sub>C<sub>H</sub>=42.5 +/- 9.85).
387
388

DISCUSSION
389

Our results show that the hens were capable of transitive inference through the 6-term series
390
task. We found an inter-individual variability in the resolution of the task, and notably, the
391
performance of one hen suggest a resolution of the task through a mental representation of the
392
series. The TI performance of the hens was impacted neither by the reinforcement ratios of the
393
items, nor the configuration of the sessions; thus, making it harder to support a purely associative-
394
based resolution of the task (except for Daenerys). Overall, the present study brings more
395
information about the way chickens can learn and solve a relational task. Unlike other studies that
396
exist in *Galloanserae* (in chicks: Daisley et al. 2010, 2021; in geese: Weiß et al. 2010), the
397
implementation of a six-terms series allowed us to further analyze the performance for the premise
398
pairs and the performance for the different inference trials, that we detail below.
399
400
401
404 Overall, the hens needed a mean of 51.25 +/- 13.43 sessions to complete the training stage.
405 They ended the training of the premise pairs of the six-term series faster than did the graylag geese
406 for a 5-terms series (mean of 83.4 +/-17.1 sessions; Weiss et al. 2010) with a similar hybrid training
407 procedure. Two main reasons might explain this difference. Firstly, the learning criteria may have
408 been more demanding in Weiss et al.'s study. Secondly, our hens were tested in a controlled
409 environment without further diversions, which was not the case for the tested graylag geese.

410 As in graylag geese (Weiß et al. 2010) and in pigeons (Clement and Zentall 2003), our hens
411 learned the first two premise pairs at a similar speed (*Supplementary material*, Figure S1).
412 However, unlike the latter authors, we will not hypothesize a tendency to naturally choose a
413 familiar stimulus over a novel one. At the start of the training sessions for each premise pair (5 to
414 10 first trials), we observed that the hens tended to apply the rule previously learned, that is, they
415 tended to first avoid the item they had not to peck at for the previous pair (e.g., don't peck at B in
416 BC because that was the item to not peck at in AB). A difficulty to step to a new pair has already
417 been found in other species (Treichler and Van Tilburg 1996 in macaques; Bond et al. 2003 in
418 corvids). As a consequence, we would rather hypothesize that, first, hens tended to apply a previous
419 rule when facing new situation, which could be referenced as rule generalization, and then, that the
420 learning speed of the new pair could rather be attributed to a high behavioural flexibility to learn
421 the new rule (see Degrande et al. 2022).

423 The comparison of the performance of the premise pairs has been shown to bring
424 information about the cognitive process that could be at stake in the n-term series task (Bond et al.
425 2010). When comparing the performance between the premise pairs at the end of the training, each
426 hen showed a better performance for AB and EF compared to the other pairs. This result is called
427 the end-anchor effect and is referenced as a serial position effect that implies a better performance
428 when the pair includes an extremity item (here, A or F; Allen 2006). This effect can be explained
429 through the strong conditioning (positive and negative, respectively) that comes with the very first
430 item of the series, that is always reinforced, and the very last, that is never reinforced.
Concerning the response latency, two out of four individuals showed a choice speed that was dependent on the serial position of the pairs, with a slower response for the pairs at a longer distance from A. Some authors argue that this effect suggest a mental representation of the ordinal series (Terrace 2005): the item A being the reference point of the spatial representation of the series, the response latency for the premise pairs increases with the distance from A. This result was significant for Daenerys (AB < EF) and for Dion (AB < DE and EF, and BC < DE and EF). However, the first-item effect is an associative effect that can account for this response latency scheme on the premise pairs (von Fersen et al. 1991). Another hypothesis is that a higher number of presentations of the pairs during training might generate a faster response latency. Thus, the response latency could better reflect the details of the premise pairs differences, rather than the performance itself; this hypothesis needs to be further investigated as the result is not significant at the group level in our study.

At the individual level, two out of our six individuals did not manage to end the training stage, by not being able to mix the first two premise pairs (Elizabeth) or not being able to mix the five premise pairs together (Savana). The fact that some individuals never pass the learning criteria has been found in other species (e.g., in pigeons: von Fersen et al. 1991). The n-term task, that is commonly used for TI testing, involves some individual parameters that are prerequisites to the task and that can impact strongly the performance, independent of the ability to perform TI. Independently of inferential and relational memory abilities, this task requires a long-term memory, a retrieval capacity, a good behavioural flexibility, and the ability to apply a different rule for the same item depending on the context of presentation (i.e., depending on the pair). Personality and social structure issues have been shown to impact these parameters in birds (Fortin et al. 2018; see for example in chicks: Daisley et al. 2021; in geese: Weiss et al. 2010). This observation calls for precautions when testing for TI in animals with the common n-term series task.

Finally, we found a significant side bias for left over right in the last mixed sessions of the relational training. In the same way, Daisley et al. (2010) found that chicks that could use their left eye only had a better TI performance in comparison to chicks that could use their right eye only. As the bird's visual pathway have been shown to be contralateral (Deng and Rogers 1998), the authors conclude that the right hemisphere may be more implicated in TI, which is consistent with
a development of TI capacities through relational representations in mammals (see for example Dusek and Eichenbaum 1997). However, we did not find a significant side bias for the TI performance, which could mean that the cognitive process implied in relational learning is different than the one implied in relational retrieval in birds.

467

468 TRANSITIVE INFERENCE PERFORMANCE

469

The hens showed transitive inference in the 6-term series task. Each hen performed significantly better than chance in 36 inference trials (non-rewarded test trials), and two hens did it in 12 trials. Notably, one individual (Dion) performed significantly better than chance level for the three different trials (BD, BE and CE). Further analyses showed that the hens could not have used some alternative strategy than transitive inference to perform better than chance in inference trials, as we controlled for a strategy based on the reinforcement ratio of the items and for a performance based on the configuration of the sessions (see *Supplementary Materials*). TI performance was not dependent on the reinforcement ratio of the items, which is an argument against an associative-based response. Our hens performed better than the chicks in Daisley et al. (2010; series ABCDE with 70 +/-5.88 % in 20 trials, compared to 81.94 [78.47:86.11] % in 36 trials or 75 [58.33:100] % in 12 trials in the present study). The reason might be that the chicks had a different learning procedure for the premise pairs (sequential learning without mixed sessions) which has been discussed as less optimal (Daniels et al. 2014).

483

The 6-term series enabled to study further the cognitive resolution of the task through the three inference trials. The hens performed better in inference trials with more distant items, i.e., in the nonadjacent pair BE (performance significantly better than chance for the four hens), compared to pairs BD and CE (sign. for two hens and for one hen, respectively). This performance related to the distance between two items is referred to as the symbolic distance effect (Moyers et al. 2018). It has been found in other species as pigeons (von Fersen et al. 1991; Daniels et al. 2014), corvids (Bond et al. 2003) or even in humans (for example: Bryant and Trabasso 1971) and is consistent with linear models of TI as a prediction of a serial position effect (von Fersen et al. 1991; Daniels et al. 2014). However, the premise pairs were trained in a temporal order that was consistent with their ordinal rank, thus, we cannot state from our results whether the hens mentally organized the
items of the series along a linear representation (Couvillon and Bitterman 1992; MacLean et al. 2008). This symbolic distance effect is also explainable by the Value Transfer Theory (VTT) from von Fersen et al. (1991) or derived associative-based models (see for example Zentall et al. 1996; discussed in: Allen 2006; Vasconcelos 2008; Guez and Audley 2013). In these models, each item is being transferred a partial associative value from its adjacent items in the series, depending on the reward contingency that occurs in the pairs presented. This way, A transfers its positive value along the adjacent items, and F transfers its non-rewarding, negative value along its adjacent items. This transfer mechanism could thus make it easier to respond to BE through associative values than BD or CE.

According to the symbolic distance between the items, the performance for the nonadjacent pairs BD and CE should have been similar. However, we found that the performance for BD was better than the one for CE for two out of four individuals (not significant). The decreasing TI performance from BE to BD to CE has also been found in pigeons in Daniels et al. (2014) at the group level. The associative model proposed by Bond et al. (2003) predicts a better accuracy for BD than CE; at the individual level, the performance of Octo matches this model well. It is possible that the worse performance in CE is related to a recency effect for the learning of the pair EF (with E rewarded), which hypothesis must be validated through further studies.

Focusing on the performance of each individual separately, we show that the hens might have used different cognitive strategies to solve the TI task. At one extreme, Dion probably solved the task through a relational representation of the series: she showed a high performance for the premise pairs and an almost perfect performance for the three different inference trials. Her performance highlights the relevance of studying the response latency to observe potential response differences between the pairs at a high level of performance. At the other extreme, Daenerys used a more associative strategy, as her performance could be predicted by the values of the differential ratio of reinforcement at the end of her training. Her learning performances and her inference performances match an associative model including a transfer of associative values through the items. More broadly, such relational learning might be multiply encoded and simultaneous cognitive- (through a relational representation) or associative- strategies (through associative-based models) could be used in the TI task, as designed in most experiments (Jacobs 2006; Lazareva and Wasserman 2006; Bond et al. 2010).
Test trials were not rewarded in our study and the hens remained constant in their TI performance through the sessions, which is conflicting with the hypothesis of Daisley et al. (2010) that assumed that the lack of feedback in TI trials could produce an additional learning. However, we found that the lack of feedback in test trials might have an impact on the choice behaviour of hens in following premise pairs trials, when it included one of the items contained in the previous test trials. It might be interesting to question further the effect of non-rewarded test trials in birds on the baseline performance of the premise pairs, as the majority of the studies in TI imply non-rewarded test trials (for example: von Fersen et al. 1991; Greene et al. 2001; Lazareva and Wasserman 2006; Aust et al. 2008; Avarguès-Weber et al. 2010; Weiß et al. 2010; Daisley et al. 2010, 2021; Mikolasch et al. 2013; Werchan and Gómez 2013; Galizio et al. 2017, but see other methodologies for example in Lazareva et al. 2020; Zentall et al. 2019; Camarena et al. 2018).

## CONCLUSION AND PERSPECTIVES

To our knowledge, this is the first attempt to further study transitive inference and the underlying process of TI in the domestic chicken, and more generally, in *Galloanserae*. Our results confirm that adult chickens are capable of transitive inference, and support that their transitive responding cannot be explained just in terms of an associative-based model. Moreover, we found that some hens might be able to solve the transitive task through a mental representation of the series. We suggest that different resolution processes might be at stake depending on the individual. Since our results resemble those found in *Neoaves* (pigeons and corvids), our hypothesis is that TI is based on the same cognitive processes in both phylogenetic groups, and that the cognitive strategy to solve the transitive task might be driven mainly by individual parameters within species. Our study brings additional information about the cognitive capacities in chicken, which might be more complex than what is often assumed in the collective mind.

## ETHICS APPROVAL

This experimental procedure was approved by the Val de Loire Ethics Committee (approval n° CE19 – 2021-0211-1, CEEA VdL, France). Animal care and experimental treatments complied with the French and European guidelines for housing and care of animals used for scientific purposes (European Union Directives 2010/63/AU).
555 SUPPLEMENTARY MATERIALS

556 Supplementary analyses are detailed in the *Supplementary Materials*.

557

558 DATA AND MODEL AVAILABILITY STATEMENT

559 The data that support the study findings are available from the authors upon request. Private URL
560 for the submission process: https://entrepot.recherche.data.gouv.fr/privateurl.xhtml?token=2c5e2dc6-e372-
561 4866-b6ec-8100e70ae6d4

562

563 AUTHORS ORCIDs

564 Rachel Degrande: https://orcid.org/0000-0003-4976-1073

565 Ludovic Calandreau: https://orcid.org/0000-0002-7535-5733

566 Léa Lansade: https://orcid.org/0000-0002-4185-9714

567 Plotine Jardat: https://orcid.org/0000-0003-0374-5588

568 Vitor Hugo Bessa Ferreira: https://orcid.org/0000-0001-7752-2382

569 Océane Amichaud: https://orcid.org/0009-0006-8788-5770

570 Violaine Colson: https://orcid.org/0000-0003-0934-7935

571 Benoît Piégu: https://orcid.org/0000-0003-4384-1726

572

573 AUTHORS CONTRIBUTION

574 Rachel Degrande, Fabien Cornilleau, Ludovic Calandreau, Léa Lansade and Océane Amichaud
575 contributed to the study design. The habituation and the first training stages of the hens with clicker
576 training were performed by Océane Amichaud. Material preparation, data collection and analysis
577 were performed by Rachel Degrande. The first draft of the manuscript was written by Rachel
578 Degrande and all authors commented on previous versions of the manuscript. Benoit Piegu worked
579 on the program and model corresponding to the last control analysis presented in *Supplementary
580 Materials*, i.e., whether the individuals could use a scheme of left/right following to perform better
581 than chance in inference trials. Ludovic Calandreau supervised the research. All authors read and
582 approved the final manuscript.

583

584
585 DECLARATION OF INTEREST

586 The authors have no conflict of interest to disclose.

587

588 ACKNOWLEDGEMENTS

589 The authors acknowledge the experimental unit Pôle d'Expérimentation Avicole de Tours (UE
590 PEAT, INRAE, 2018. Experimental Poultry Facility, DOI: 10.15454/1.5572326250887292E12)
591 where hens were maintained and where the experiments took place.

592

593 FINANCIAL SUPPORT STATEMENT

594 This research received the funding support of the PHASE research Department (INRAE) and a
595 PhD fellowship INRAE-Région Centre Val de Loire (France).

596

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Supplementary Material S1: Details about the training and testing performances

Supplementary Table S1. Number of sessions each individual (columns) needed to reach the learning criterion for each training step (lines). NC: the individual did not reach the learning criterion in the constraints imposed and is thus removed from the experiment.

<table><thead><tr><th></th><th>Savana</th><th>Elizabeth</th><th>Octo</th><th>Starr</th><th>Dion</th><th>Daenerys</th><th>Mean</th></tr></thead><tbody><tr><td>AB</td><td>4</td><td>4</td><td>3</td><td>3</td><td>3</td><td>2</td><td>3.17</td></tr><tr><td>BC</td><td>4</td><td>3</td><td>3</td><td>2</td><td>4</td><td>4</td><td>3.33</td></tr><tr><td>CD</td><td>5</td><td></td><td>3</td><td>3</td><td>3</td><td>3</td><td>3.40</td></tr><tr><td>DE</td><td>3</td><td></td><td>4</td><td>3</td><td>2</td><td>3</td><td>3.00</td></tr><tr><td>EF</td><td>5</td><td></td><td>9</td><td>3</td><td>3</td><td>3</td><td>4.60</td></tr><tr><td>Mixed sessions with AB to BC</td><td>2</td><td>NC</td><td>2</td><td>2</td><td>3</td><td>3</td><td>2.40</td></tr><tr><td>Mixed sessions with AB to CD</td><td>9</td><td></td><td>10</td><td>9</td><td>6</td><td>7</td><td>8.20</td></tr><tr><td>Mixed sessions with AB to DE</td><td>11</td><td></td><td>3</td><td>4</td><td>9</td><td>13</td><td>8.00</td></tr><tr><td>Mixed sessions with AB to EF</td><td>NC</td><td></td><td>11</td><td>10</td><td>10</td><td>16</td><td>11.75</td></tr><tr><td>Total sessions to end the training</td><td></td><td></td><td>69</td><td>39</td><td>43</td><td>54</td><td>51,25</td></tr></tbody></table>
36 **Supplementary Table S2.** Detail of the learning and test (lines) performances at the individual level (columns) for a more visual comparison. The two individuals who did not reach the learning criteria for all training steps are not included.

39

<table><thead><tr><th></th><th>Starr</th><th>Dion</th><th>Daenerys</th><th>Octo</th><th>Group mean</th></tr></thead><tbody><tr><td>total sessions to criteria</td><td>39</td><td>43</td><td>54</td><td>69</td><td>51.25</td></tr><tr><td>% success at the end of the training</td><td>78.3</td><td>90</td><td>72.5</td><td>80.8</td><td>80.42</td></tr><tr><td>% AB</td><td>87.5</td><td>87.5</td><td>83.3</td><td>95.8</td><td>88.53</td></tr><tr><td>% BC</td><td>79.2</td><td>95.8</td><td>62.5</td><td>58.3</td><td>73.95</td></tr><tr><td>% CD</td><td>70.8</td><td>83.3</td><td>62.5</td><td>83.3</td><td>74.97</td></tr><tr><td>% DE</td><td>58.3</td><td>83.3</td><td>66.7</td><td>75</td><td>70.83</td></tr><tr><td>% EF</td><td>95.8</td><td>100</td><td>87.5</td><td>91.7</td><td>93.75</td></tr><tr><td>% success for control trials (36 trials)</td><td>100</td><td>100</td><td>88.89</td><td>97.22</td><td>96.53</td></tr><tr><td>% success for inference trials (12 trials per pair)</td><td>80.56</td><td>94.45</td><td>72.23</td><td>77.78</td><td>81.26</td></tr><tr><td>% BD</td><td>91.67</td><td>91.67</td><td>58.33</td><td>75</td><td>79.17</td></tr><tr><td>% BE</td><td>91.67</td><td>100</td><td>91.67</td><td>91.67</td><td>93.75</td></tr><tr><td>% CE</td><td>58.33</td><td>91.67</td><td>66.67</td><td>66.67</td><td>70.84</td></tr></tbody></table>

40

41
Supplementary Material S2: Controls of confounding factors on choice behavior and performance during test sessions

1) The reinforcement ratio of the items resulting from the training stage did not influence the performance in test trials

We measured the reinforcement ratio of the items involved in the inference trials (B, C, D and E) to observe if there would be any effect on the transitive response (Lazareva et al. 2004, Daniels et al. 2014). The reinforcement ratio of each item X was calculated as the total number of trials presented during the training (including correction sessions) where X was reinforced, divided by the total number of trials presented with X (reinforced and non-reinforced). For example, the reinforcement ratio of B was calculated as follows: $R_B=N_{BC}/(N_{AB}+N_{BC})$. R<sub>B</sub> was used to calculate the differential of reinforcement ratios of the two items involved in each inference trial type. For example, the differential reinforcement ratio in BD was calculated as follows: $D_{BD}=R_B-R_D$. The higher D<sub>BD</sub> is, the more the individual would have been reinforced for the item B in comparison to the item D. We measured the correlation between D and the TI performance (number of successful trials) for each trial type (BD, BE or CE) for each individual.

<table><thead><tr><th>(a)</th><th>Item B</th><th>Item C</th><th>Item D</th><th>Item E</th></tr></thead><tbody><tr><td>Starr</td><td>0.538</td><td>0.514</td><td>0.464</td><td>0.388</td></tr><tr><td>Octo</td><td>0.585</td><td>0.474</td><td>0.469</td><td>0.513</td></tr><tr><td>Dion</td><td>0.556</td><td>0.495</td><td>0.462</td><td>0.364</td></tr><tr><td>Daenerys</td><td>0.617</td><td>0.528</td><td>0.448</td><td>0.284</td></tr><tr><td>Median Ratio</td><td>0.571</td><td>0.505</td><td>0.463</td><td>0.376</td></tr></tbody></table>

<table><thead><tr><th>(b)</th><th>Pair</th><th>Mean % of success</th><th>Differential reinforcement ratio</th></tr></thead><tbody><tr><td rowspan="3">Starr</td><td>BD</td><td>91.7</td><td>0.074</td></tr><tr><td>BE</td><td>100</td><td>0.150</td></tr><tr><td>CE</td><td>58.3</td><td>0.126</td></tr><tr><td rowspan="3">Octo</td><td>BD</td><td>75</td><td>0.116</td></tr><tr><td>BE</td><td>100</td><td>0.072</td></tr><tr><td>CE</td><td>66.7</td><td>-0.039</td></tr><tr><td rowspan="3">Dion</td><td>BD</td><td>91.7</td><td>0.094</td></tr><tr><td>BE</td><td>100</td><td>0.192</td></tr><tr><td>CE</td><td>91.7</td><td>0.131</td></tr><tr><td rowspan="3">Daenerys</td><td>BD</td><td>58.3</td><td>0.169</td></tr><tr><td>BE</td><td>91.7</td><td>0.333</td></tr><tr><td>CE</td><td>66.7</td><td>0.244</td></tr></tbody></table>
**Supplementary Table S3.** (a) Detail of the reinforcement ratio for each term of the series (except the extremity items) at the end of the training stage, at the individual level. (b) Detail of the TI performance and the differential reinforcement ratio for each nonadjacent pair (inference trials) at the individual level. The performance is indicated as the percentage of success for the 12 trials for each pair. The differential reinforcement ratio is calculated as, with the example for the pair BD: $D_{BD}=R_B-R_D$ with $R_B=N_{B+C.}/(N_{A+B.}+N_{B+C.})$ and $R_D=N_{D+E.}/(N_{C+D.}+N_{D+E.})$.

At the group level, the differential of the reinforcement ratio of the items cannot explain the performance (Spearman correlation test, S=267.78, p=0.8441, rho=0.0637). At the individual level, this result is confirmed except for Daenerys whose TI performance increases with the differential ratio reinforcement.

In addition, we measured whether the number of presentations of an item during the whole training procedure and its reinforcement history could have an effect on the performance of individuals when this item was presented in an inference trial. First, we measured the number of presentations of each item (B, C, D, E) during the whole training procedure, either (1) in a pair in which this item is the reinforced item (RI; e.g., B in BC), or (2) in a pair in which this item is not the reinforced item (NRI; e.g. B in AB). Second, we detailed the individual performance for each inference trial, either (1) when the trial included the item as the prior item (PI; e.g. BD for the item B), or (2) when the trial included the item as the non-prior item (NPI; e/g/ BD for the item D). We found neither an effect of the total number of presentations of an item during training on the TI performance (F=0.08847, p=0.7676), nor an interaction with the priority of this item in the inference trial (F=0.0003911, p=0.9856; anova permutation model analysis).

2) The configuration of the previous pair trial did not influence the choice behaviour in the following test trial

We tested whether the relationship between the configuration of the previous training pair trial $H_{n-1}$ and the configuration of the test trial $T_n$ would have an influence on the success at this test trial $T_n$. This relationship was described in terms of whether $H_{n-1}$ and $T_n$ had the same item or not (we analyzed it for the prior and the non-prior item); if this item was presented on the same side or not; and whether the prior item was presented on the same side in both ($H_{n-1}$ and $T_n$) or not.
None of these parameters did significantly influence the performance in test trials, either control or inference trials (anova permutational model; same item: F=0.1566, p=0.9316; same side if same item: F=0.2944, p=0.8114; same side prior item: F=0.2318, p=0.7647).

3) *The configuration of the previous pair trial did not influence the choice behaviour in the following test trial*

The building of the test sessions implied that no more than two consecutive trials could present a prior item on the same side. We tested whether the hens could have used this rule to succeed more than chance in test trials (e.g., Hn-2 pair: BC, Hn-1 pair: DE, Tn pair: DB). An anova permutational model showed that the percentage of success was not significantly different when the test trial was at the end of such sequence or not (F=0.04904, p=0.8331). This conclusion was true both for inference trials and for control trials (no interaction effect: F=3.0888, p=0.1542).

4) *The individuals did not have a systematic left-right response scheme that would have enabled them to perform better than chance in inference trials, except one individual.*

We questioned whether the individuals repeated a particular left-right scheme in consecutive trials for their response choice during test sessions, and if so, whether this was positively related to the inference performance of the individual. Here, a scheme means a sequence of left and right choice responses in consecutive trials, for example: R-L-L-R-R (R for choosing the right card, and L for choosing the left card). If the repetition of a particular scheme during test sessions can be positively correlated to the correctness of the individual's choices in test trials, then its performance would not have been an inference performance. First, we searched for left-right schemes that appeared more than chance in the choice response of each individual in test sessions. Then, we compared this result with the inference performance for each individual.

## 1. Count of the repetitions of left-right schemes in test sessions by a python program

The principle of this program was to count, for each individual, the number of repetitions of a scheme of a particular length, to the chance level. We included schemes of a length of 4 to 8 items (R or L choices). The analysis was made for each individual, independently. For each
individual, the program considered all the possible schemes of each length, by taking into account
the way the test sessions were conducted (12 sessions of 46 trials).

First, for each length, the program returned the most frequent scheme and the number of
times that it appeared in the sequence of the choice responses of the individual. We compared this
number to the number of times it appeared in the scheduled sessions (i.e., the number of times such
scheme appeared due to the way the sessions were built).

Second, a program (kmer_permut, written in rust) allowed us to do a permutation test.
Random datasets were created considering the way the test sessions were conducted. The
distribution of the maximum number of repetitions (repmax) that would appear at random was
obtained (within 12 sessions of 46 trials per individual; np=100 000). From this distribution, we
obtained a corresponding p-value for each maximum number of repetitions (repmax) of the scheme
(ecdf function, in R). This allowed to compare the number of repetitions of the scheme in the
individual's responses to the chance level. The length of 5 items showed the responses that derived
the most from the chance level, and thus is the length for which we present the results below.

The schemes (that could be different depending on the individual) which were kept for the
next analysis were the schemes which were repeated more that chance (data from the permutations)
and that were repeated more than they were in the scheduled session (data from the comparison).

## 2. Correlation with the inference performance

The result shows that the individual Daenerys is the only who shows a scheme with a
repetition higher than what was presented in the scheduled sessions (scheme L-R-L-L; 51
repetitions versus 32 in the scheduled sessions), and with a number of repetitions that was
significantly higher than chance (ecdf function, p=0.041).

This individual is the one who performed the worse, both in control trials (88.89%) and in
inference trials (72.22%). We analyzed more precisely the co-occurrences of the scheme and her
success in inference trials. This co-occurrence was of 30.55% (11 co-occurrences in 36 inference
trials), and she succeeded in 8 out of these 11 inference trials (72.72%). Finally, 22.22% of her
performance in test trials could have been explained by the repetition of this scheme (8 successful
trials out of 36; 1/12 for BD, 3/12 for CE and 3/12 for BE), which brings her overall inference
performance down to 50% of success.
# DISCUSSION GÉNÉRALE
Cette thèse, centrée autour de l'étude des capacités d'inférence, avait pour objectif d'améliorer nos connaissances sur les capacités cognitives à la base des capacités de raisonnement chez la poule domestique adulte. Nous avons cherché à évaluer la présence de différents types d'inférence chez la poule, impliquant des capacités de mémoire relationnelle plus ou moins complexes. Nos résultats suggèrent que la poule domestique est capable d'inférence et de capacités de mémoire relationnelle complexes, et viennent préciser et appuyer les conclusions tirées de la littérature sur le poussin. Après avoir rappelé les principaux résultats de ces travaux, nous discuterons de la pertinence des concepts de représentation mentale et de raisonnement chez la poule domestique, à la lumière des expériences menées. Nous aborderons ensuite l'importance des paramètres individuels dans l'expression des capacités de raisonnement chez l'animal. Enfin, nous terminerons en abordant les principales limitations et implications potentielles de nos résultats concernant les connaissances des capacités de raisonnement chez les volailles, et plus largement chez l'oiseau.

# I) La poule domestique possède des capacités d'inférence et de mémoire relationnelle

## Rappel des résultats principaux

Dans le premier chapitre, nous avons testé la capacité d'inférence par exclusion chez la poule domestique. C'est un type d'inférence simple qui a été étudié chez plusieurs espèces mais pour laquelle il n'existe pas d'étude chez la poule. Notre première expérience (chapitre 1, ARTICLE 1) montre que la poule domestique n'utilise pas spontanément l'inférence par exclusion, lorsque les conditions externes permettent d'appliquer une autre stratégie qui permet de récupérer la récompense. Notre seconde expérience (chapitre 1, deuxième partie) montre que la poule est capable d'appliquer une règle d'exclusion de manière spontanée en condition de choix forcé. Cependant, ces résultats à eux seuls n'ont pour l'instant pas permis de confirmer l'utilisation effective d'une représentation mentale de la localisation de la récompense, et donc de confirmer une résolution par inférence.

Dans le deuxième chapitre, nous avons testé la capacité de généralisation de la poule domestique sur un concept perceptif : l'identité de couleur (ARTICLE 2). Cette tâche requiert des capacités d'inférence inductive et déductive. Nos résultats suggèrent que certains individus ont réussi le test : elles ont adapté leur réponse face à des stimuli inconnus, grâce à la représentation
mentale du concept appris. Par rapport au premier chapitre, ceci appuie l'hypothèse de capacités d'inférence chez la poule domestique, au travers de la représentation mentale d'un concept.

Jusqu'alors, moins d'une dizaine d'études s'étaient intéressées aux capacités d'inférence chez la poule domestique, la plupart ayant été menées sur le poussin autour de 3 à 4 jours d'âge. L'hypothèse de capacités d'inférence simple chez le poussin (inférence diagnostique ou inférence déductive : Chiandetti & Vallortigara, 2011 ; Mascalzoni et al., 2010 ; Santolin, Rosa-Salva, Vallortigara, et al., 2016 ; Versace et al., 2017) pouvait être remise en cause, en raison d'explications cognitives alternatives basées sur la perception et la mémorisation fine d'indices perceptifs par le poussin, qui possède alors une attention prononcée à ce type de stimuli (Vallortigara et al., 2005). Nos résultats sur la généralisation de concept chez la poule adulte viennent appuyer l'hypothèse de capacités d'inférence simple, et donc de l'utilisation de représentations mentales simples chez cette espèce.

Dans le troisième chapitre, nous avons testé une capacité d'inférence relationnelle : l'inférence transitive, qui met en jeu des capacités de mémoire relationnelle plus complexes au travers de représentations mentales relationnelles. Nos résultats montrent que la poule est capable d'inférence transitive ; de plus, les résultats de certains individus suggèrent fortement l'utilisation effective de représentations mentales relationnelles chez cette espèce.

De précédentes études avaient déjà montré des capacités de mémorisation d'informations relationnelles chez la poule et chez le poussin, dans le cadre de tests d'orientation spatiale (Chiesa et al., 2006 ; Ferreira, Barbarat, et al., 2020 ; Ferreira et al., 2019 ; Tommasi & Vallortigara, 2000), et chez le poussin dans un test de mémoire contextuelle (« où + dans quel contexte » ; Loconsole, Mascalzoni, et al., 2021). Daisley et al. (2010, 2021) ont ensuite étudié la capacité à réaliser des inférences sur des informations relationnelles dans un test d'inférence transitive, chez le poussin. En ayant mis en place une série plus longue (basée sur 6 items) dans la tâche d'inférence transitive, nos résultats nous ont permis de discuter d'avantage des processus cognitifs sous-jacents, et de conforter l'hypothèse de capacités de représentations mentales relationnelles dans cette tâche chez la poule domestique.

En plus de conforter l'hypothèse de capacités d'inférence et de mémoire relationnelle complexe chez la poule, nos résultats suggèrent que cette espèce est capable de différents types d'inférences à partir d'informations relationnelles mémorisées, et donc qu'elle serait capable de
représentations mentales relationnelles. Globalement, ces résultats contribuent à l'amélioration des connaissances fondamentales sur les capacités de raisonnement de la poule domestique, et sur l'étendue phylogénétique des capacités de raisonnement chez l'oiseau.

## Le rôle central des capacités de mémoire relationnelle en tant que support des capacités de raisonnement

L'inférence est le processus cognitif qui permet de mettre en relation différentes informations mémorisées, et d'en tirer une conclusion. Autrement dit, inférer, c'est être capable de relier une expérience perceptive<fnref n="15" /> avec des informations mémorisées au travers d'une expérience passée, sur la base d'une relation entre les deux. C'est le mécanisme fondamental des capacités de raisonnement, qui sert aussi de base aux processus de prise de décision, de résolution de problème, d'apprentissage de causalités par exemple.

Ce processus d'inférence repose sur la capacité à établir des représentations mentales flexibles. Ces représentations mentales flexibles sont supportées par la mémoire relationnelle, qui a donc un rôle central en tant que support des capacités de raisonnement. La complexité de la mémoire relationnelle est déterminée par la variété des opérations combinatoires possibles qui permettent à l'animal d'établir des conclusions par inférence (Gallistel, 1990). Eichenbaum et Cohen (2014) ont proposé une visualisation mécanistique du concept de représentation mentale flexible, qui a pu être ensuite soutenue par des résultats à l'échelle neurobiologique (« the Tolman-Eichenbaum Machine » ; Figure 23 ; Roy et al., 2011 ; Tolman, 1948 ; Whittington et al., 2020). Ainsi, la mémoire relationnelle serait structurée sous forme de grille, permettant d'encoder les éléments d'une scène en tant qu'informations dissociées mais reliées entre elles. Cet encodage permettrait d'avoir accès : soit aux informations en tant que telles (un élément d'une scène), soit aux différents liens entre toutes ou certaines relations entre les éléments de la scène (généralisation, établissement de liens indirects), ou même en tant qu'unités structurelles par exemple pour l'établissement de comportements innovants (généralisation d'une solution trouvée dans la scène A, à la scène B, car les structures relationnelles entre les éléments des deux scènes sont similaires).

<footnote n="15">¹⁵ Les capacités d'inférence concernant des informations uniquement en mémoire sont particulières : c'est, par exemple, être capable de répondre à une question sur le lien entre deux éléments non perceptibles sur le moment (voir représentations détachées, partie III de la discussion). L'observation de cette capacité n'est possible qu'au travers d'un intermédiaire de communication qui permettrait à l'individu questionné d'exprimer sa réflexion et/ou sa conclusion à l'expérimentateur.</footnote>
<figure><img src="image_69.png" /></figure>

**Figure 23** : La figure correspond à une partie de la Figure 1 de Whittington et al. (2020). Les illustrations schématisent l'idée de structure en réseau pour la mémoire des relations entre différentes informations. La Figure (E) présente l'exemple du réseau des liens familiaux. La Figure (F) présente l'idée selon laquelle, grâce à cette visualisation conceptuelle, il n'y a pas besoin de connaitre l'ensemble des relations entre tous les éléments du réseau. L'intégration d'un nouvel élément dans le réseau à travers sa relation avec un des éléments du réseau peut permettre d'inférer les liens de ce nouvel élément avec les autres éléments du réseau, par inférence.

Cette visualisation permet d'appréhender l'importance de ce support qu'est la mémoire relationnelle, nécessaire aux processus d'inférence médiate. A partir des relations encodées dans la mémoire relationnelle, le processus d'inférence consiste à mobiliser la flexibilité de la mémoire relationnelle pour mettre en relation ou combiner les informations, pour en tirer une conclusion / solution adaptative. Nos expériences ont montré que la poule possède une mémoire relationnelle flexible, au travers des tests d'inférence simple (par exemple : déduction) ou d'inférence relationnelle (inférence transitive). Ces résultats originaux suggèrent l'existence de représentations mentales relationnelles chez la poule domestique.

## II) Peut-on parler de représentations mentales chez la poule ?

### Réflexion sur le concept de représentation mentale

Une représentation mentale est une relation entre deux ou plusieurs éléments, encodée en mémoire, qui permet potentiellement d'inférer des conclusions à partir d'informations qui ne sont pas directement perceptibles (règle, nature des relations). Certains auteurs remettent en cause la capacité de raisonnement basée sur l'utilisation flexible de représentations mentales chez l'animal. Bien qu'elle reste pertinente pour expliquer ce qui sous-tend les processus de raisonnement (Roy et al., 2011), la notion de représentation mentale est un concept d'analyse psychologique, et il
n'existe pas de consensus sur le format de stockage des unités d'information en mémoire (Pitt, s. d.).

Ainsi, certains auteurs réfutent le concept de représentation mentale et se positionnent en antithèse d'un système de résolution par inférence chez l'animal (par exemple: McLaren et al., 2014; Penn & Povinelli, 2007; Shanks, 2010). La grande majorité des processus cognitifs alternatifs proposés se basent sur le principe de modélisations mathématiques, qui permettent de reproduire certaines performances d'inférence sur la base de suites d'opérations booléennes et/ou de combinaisons de réponses conditionnées (Allen, 2006; Dickinson, 2012; Kruschke, 2008). Ces explications alternatives impliquent cependant tout aussi bien la réalisation de combinaisons sur plusieurs informations en mémoire, quel que soit le format de représentation de ces informations.

À ce sujet, Smith et al. (2008) écrivent que « la possibilité de recréer un phénomène avec des continua et des critères n'implique ni n'exclut la possibilité que le phénomène soit associatif ou cognitif (...). Par exemple, les performances humaines pourraient être modélisées de cette manière, même si ces performances étaient explicites, conscientes, méta-représentatives et métacognitives. ». Cette réflexion nous incite à rester prudent lors de la formulation d'hypothèses plus simples, concernant la réponse comportementale de nos animaux.

Finalement, certains travaux de neurobiologie comme ceux de Whittington et al. (2020) rendent le concept de représentation mentale relationnelle très pertinent pour exprimer le format de stockage des informations dans la mémoire relationnelle. Ceci nous permet, au vu des conclusions de nos expériences, de soutenir la notion de représentations mentales relationnelles chez la poule domestique.

## Inférence ou mécanismes associatifs ?

Malgré cela, parmi les auteurs qui remettent en question la notion de représentation mentale, beaucoup estiment que s'il est possible de modéliser<fnref n="16" /> mathématiquement (même à posteriori) les réponses cognitives de l'animal, alors cette explication prend le pas sur l'hypothèse de représentations mentales (voir les nombreux articles qui débattent des résultats de Blaisdell et al., 2006). Nous pouvons mettre cette idée en lien avec la réponse de certains individus lors de notre expérience sur l'inférence transitive (ARTICLE 3) : l'hypothèse la plus probable concernant la

<footnote n="16">¹⁶ Une modélisation est la mise en place d'un système capable de reproduire au moins en partie le fonctionnement dynamique de ce qu'il modélise.</footnote>
performance de ces individus aux essais d'inférence, est une résolution rendue possible par un ensemble de mécanismes associatifs.

La manière la plus évidente de trancher en faveur d'une résolution par inférence est de travailler sur des tests de généralisation. En effet, ce qui différencie une inférence d'un mécanisme associatif est le caractère flexible, et la directionnalité des représentations mentales. La notion de *flexibilité* implique que la capacité d'inférence puisse être généralisée à de nouveaux stimuli, voire à des situations complètement nouvelles. Bien sûr, la transposition de certaines tâches d'inférence, dans le but de tester la capacité de généralisation de l'animal sur cette inférence, peut sembler difficile à mettre en place sans impliquer une combinaison trop importante de capacités cognitives (inférentielles ou non) qu'il serait nécessaire d'avoir, en plus de la capacité questionnée. Beckers et al. (2016) soulignent notamment qu'il reste des progrès à faire pour pouvoir discriminer entre une résolution de tâches par inférence ou par combinaisons associatives chez l'animal. Par exemple, les inférences relationnelles, qui impliquent l'utilisation d'informations de type *relation*, sont des capacités qui peuvent difficilement être reproduite par des conditionnements associatifs, comme par exemple le raisonnement par analogie (Penn & Povinelli, 2007 ; Smirnova et al., 2021). Ce type de capacité mériterait d'être testé chez la poule domestique afin de valider l'hypothèse de capacités d'inférence relationnelle chez cette espèce ; cela pourrait être fait au travers de la tâche de correspondance à l'échantillon (voir par exemple T. Obozova et al., 2015).

Finalement, les processus de résolution inférentiels et associatifs ne sont pas forcément mutuellement exclusifs voire parfois difficilement discriminables. Certains auteurs proposent que ces processus agissent de manière couplée dans le cadre des capacités d'inférence (Bond et al., 2010 ; Jacobs, 2006 ; Lazareva & Wasserman, 2006 ; Völter & Call, 2017). Ceci donne naissance à la notion de *multi-action* des systèmes de résolution impliqués dans les capacités d'inférence (Moors, 2014), qui permet notamment de parler d'échelle graduelle continue au sein de la dichotomie associatif – inférence (Petty, 2006). Par exemple, les capacités d'inférence peuvent apparaître utiles en soutien à l'apprentissage de causalités ; ou encore, un apprentissage associatif peut venir en tant qu'information supplémentaire dans le cadre d'inférences.

Cette notion de *multi-action* des systèmes de résolution convient au concept de représentations mentales tel qu'intégré dans la catégorisation conceptuelle des systèmes de mémoire par Cohen et Eichenbaum (1993) : les informations stockées en mémoire relationnelle
peuvent être accessibles en tant qu’unités indépendantes, ou en relation les unes avec les autres.
Ceci permet d’introduire, au vu de nos résultats, l’hypothèse que le système de restitution des
informations relationnelles pourra dépendre de facteurs individuels. Par exemple, le trait d’anxiété
de l’individu peut avoir un impact sur l’utilisation de sa mémoire relationnelle dans une tâche
d’orientation spatiale (Ferreira, 2020 ; Lormant et al., 2018).

### III) Le concept de raisonnement en cognition animale

*Discussion sur la définition de raisonnement*

Au-delà du concept de représentation mentale, la définition de raisonnement elle-même ne
fait pas consensus au sein de la communauté scientifique ; la définition utilisée pourra impacter les
conclusions sur l’existence de capacités de raisonnement chez l’animal.

Comme mentionné en introduction, la définition de raisonnement chez l’humain implique
la notion de conscience de ses propres processus mentaux, ce qui est une forme de métacognition.
La notion de métacognition est aussi présente dans d’autres approches qui insistent sur la nature
des informations qui sont utilisées dans les représentations (Gärdenfors, 1995). Dans cette
approche, un processus cognitif est considéré être un raisonnement seulement lors de l’utilisation
de représentations dites détachées : le raisonnement a lieu en dehors de toute activation par des
indices perceptifs.

Outre le fait que nous n’ayons pas accès aux capacités de conscience et de métacognition
de l’animal, ces capacités ne sont pas un prérequis à la capacité d’inférence (Custers & Aarts, 2011
; Gärdenfors, 1995). Chez l’animal, le raisonnement est défini par sa logique inférentielle, c’est-à-
dire par la capacité de l’individu à utiliser sa mémoire relationnelle et relier plusieurs informations
entre elles, pour émettre une conclusion (Völter & Call, 2017). D’après cette définition, les résultats
de cette thèse soutiennent l’hypothèse que la poule domestique est capable de raisonnement.

En parallèle, certains auteurs considèrent que l'acquisition du langage est un prérequis aux capacités de raisonnement, et plus spécifiquement à la formation de concepts. Cette approche trouve son origine dans la tradition philosophique, et a été contre-argumentée par plusieurs auteurs. Ces derniers proposent que la capacité à utiliser et combiner des représentations mentales de manière flexible est un critère nécessaire et suffisant pour permettre la formation de concepts (Maury 1964 ; Camp, 2009).
Enfin, une approche majeure est celle de l'opposition entre la logique inférentielle et la logique propositionnelle (Aguilera, 2018 ; Bermúdez, 2006). La logique propositionnelle a cela de plus qu'elle produit un raisonnement valide : les formes propositionnelles sont des représentations complètes dont la cohérence est susceptible d'être évaluée de façon binaire (oui/non ; évaluation métacognitive). Nous ne nous positionnons pas dans ce cadre théorique car nous considérons qu'un raisonnement peut aussi être fallacieux (par exemple : formation d'une catégorie par inférence inductive, basée sur des éléments qui ne sont pas représentatifs d'une situation réelle – exemple des préjugés), que ce soit chez l'humain ou l'animal (Völter & Call, 2017). De plus, nous ne considérons pas qu'il y ait lieu d'opposition entre ces deux logiques : dans le cas de l'existence d'une logique propositionnelle, cette logique se baserait sur le principe de la logique inférentielle, en y ajoutant des éléments qui permettent d'évaluer la véracité des conclusions ou du moins des probabilités de réalisation.

## Discussion autour de la spontanéité des capacités de raisonnement

Un critère couramment utilisé pour faire la différence entre une résolution par association ou par inférence chez l'animal est le caractère spontané de la réponse inférentielle (Maier & Schneirla, 1964 ; Premack & Premack, 1994). Cette exigence d'une réponse spontanée implique que l'animal résolve la tâche proposée en un nombre limité d'essais test. Cependant, dans la littérature, et ce même pour une même capacité cognitive et une même espèce, il n'y a pas de nombre d'essais-test clairement établi pour lequel nous pourrions (in-)valider une capacité de raisonnement chez l'animal (voir aussi discussion ARTICLE 2). Une idée pourra être de se limiter au nombre minimum qui permet de tester statistiquement la significativité<fnref n="17" /> de la réponse, c'est-à-dire 10 succès sur 12 tentatives. Mais ceci implique d'accepter que l'animal qui aura réussi 9 fois sera catégorisé comme *n'ayant pas exprimé* la capacité en question dans ce test.

Nous pouvons aussi discuter de l'aspect de spontanéité sous un autre angle, pour considérer la facilité avec laquelle l'animal résout une tâche d'inférence : (cas 1) directement, sans avoir eu accès au dispositif ni à une expérience similaire par le passé ; (cas 2) directement, mais en ayant eu besoin d'apprendre auparavant les caractéristiques du dispositif ou de la situation de test au travers de sessions d'entrainement ; (cas 3) directement, mais par transfert, c'est-à-dire que

<footnote n="17">¹⁷ Mais voir les discussions sur la (non-)pertinence de l'utilisation de la p-value telle qu'elle est faite dans la majorité des cas en sciences (Einav & O'Connor, 2019; Wasserstein et al., 2019).</footnote>
l'animal aura dû apprendre à résoudre la tâche par inférence, et que cette capacité d'inférence sera validée au travers d'un test de généralisation dans une autre situation de test. Dans une perspective d'étude des capacités d'inférence, devons-nous faire une différence entre le cas 1, dans lequel l'animal est capable de mobiliser de manière flexible d'autres capacités ou représentations qui lui permettent de comprendre le dispositif sans l'avoir expérimenté ; et le cas 3, dans lequel l'animal doit apprendre la tâche d'inférence ? Il semble incorrect de faire une évaluation des capacités d'inférence entre les espèces en fonction de leur compréhension spontanée ou non des caractéristiques des dispositifs, car ces capacités dépendent de l'écologie comportementale des espèces (Henke-von der Malsburg et al., 2020). Cependant nous pourrons proposer que l'animal qui réalise une inférence sans avoir eu besoin d'apprendre la règle d'inférence au préalable, montre une meilleure capacité à utiliser de manière flexible des représentations mentales, au contraire de celui qui aura eu besoin d'en apprendre le principe au préalable.

L'autre point de discussion à ce propos concerne la limitation du nombre d'essais d'entrainement, qui peut avoir un impact important sur les performances d'inférences de l'animal dans le test. En effet, un nombre trop important d'essais d'entrainement est un facteur qui augmente la probabilité de mise en place de comportements habituels (Balleine & Dezfouli, 2019 ; Dickinson, 1985). L'acquisition d'une telle stratégie habituelle remplace alors la réponse dite *orientée vers un but*, qui, elle, implique le maintien du lien de causalité entre la situation de test et la réponse comportementale, et permet ainsi que l'individu adapte sa réponse de manière flexible (Dickinson et al., 1995 ; voir aussi discussion ARTICLE 1). Or il n'existe pas de données comparatives nous permettant de savoir avec quelle rapidité peut se faire l'acquisition d'une habitude chez la poule, et cet aspect a été problématique lors de nos expériences. En effet, la majorité des animaux testés a montré un biais systématique dans les réponses de choix très rapidement, sous 10 à 30 essais d'entrainement en fonction de la tâche. Un nombre trop important d'essais d'entrainement peut donc dans certains cas *effacer* la spontanéité de la performance d'inférence, alors qu'un plus petit nombre d'essais d'entrainement aurait peut-être permis de le mettre en évidence.