Raphaëlle Malassis

Arnaud Rey

Joël Fagot
email: joel.fagot@univ-amu.fr

Running head: NON-ADJACENT DEPENDENCIES PROCESSING IN PRIMATES Non-adjacent Dependencies Processing in Human and Non-human Primates

Keywords: language evolution, statistical learning, sequence learning, long-distance dependencies, animal cognition

Human and non-human primates share the ability to extract adjacent dependencies and, under certain conditions, non-adjacent dependencies (i.e., predictive relationships between elements that are separated by one or several intervening elements in a sequence). In this study, we explore the online extraction dynamics of non-adjacent dependencies in humans and baboons using a serial reaction time task. Participants had to produce three-target sequences containing deterministic relationships between the first and last target locations. In Experiment 1, participants from the two species could extract these non-adjacent dependencies, but humans required less exposure than baboons. In Experiment 2, the data show for the first time in a non-human primate species the successful generalization of sequential non-adjacent dependencies over novel intervening items. These findings provide new evidence to further constrain current theories about the nature and the evolutionary origins of the learning mechanisms allowing the extraction of non-adjacent dependencies.

Statistical learning can be defined as the implicit learning of regularities embedded in the environment, which has been proposed to play an important role in language acquisition (for a review, see [START_REF] Romberg | Statistical learning and language acquisition[END_REF] and many other aspects of cognition (e.g., in visual perception, [START_REF] Fiser | Unsupervised statistical learning of higher-order spatial structures from visual scenes[END_REF]. Several experimental paradigms have been used to study these fundamental learning mechanisms such as the Artificial Grammar Learning paradigm (AGL, [START_REF] Reber | Implicit learning of artificial grammars[END_REF], the Artificial Language Learning paradigm (ALL, [START_REF] Saffran | Statistical learning by 8-month-old infants[END_REF] or the Serial Response Time paradigm (SRT, [START_REF] Nissen | Attentional requirements of learning: Evidence from performance measures[END_REF]. In these experiments, participants are typically exposed to sequences of nonsense stimuli (e.g., syllables, tones or visual shapes) that are organized with a specific grammar. Learning of the predictive relationships embedded in the input can then be assessed by presenting novel sequences that are either consistent or inconsistent with this grammar.

Comparative human/non-human studies can inform us about the nature and dynamic of these learning mechanisms, and their occurrence during the evolution, by distinguishing domain-general and evolutionary old processes from those that might have appeared more recently in the human lineage. Comparative experiments have demonstrated so far that animals, like humans, are highly proficient in learning predictive relationships between adjacent elements (i.e., elements that are presented one after the other in a sequence, without any delay or element in between; see for a review: [START_REF] Cate | Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning[END_REF][START_REF] Conway | Sequential learning in non-human primates[END_REF][START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF]. This ability has been reported in several primates (tamarins: [START_REF] Hauser | Segmentation of the speech stream in a non-human primate: statistical learning in cotton-top tamarins[END_REF]macaques: Wilson et al., 2013;Wilson, Smith, & Petkov, 2015;and baboons: Minier, Fagot, & Rey, 2016), and nonprimate species (pigeons: [START_REF] Froehlich | Anticipating by Pigeons Depends on Local Statistical Information in a Serial Response Time Task[END_REF]rats: Toro & Trobalón, 2005;and songbirds: Takahasi, Yamada, & Okanoya, 2010). Along with neuroimaging data (e.g., [START_REF] Wilson | Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans[END_REF], these behavioral data have led to the hypothesis that the extraction of adjacent dependencies relies on evolutionarily conserved mechanisms [START_REF] Friederici | Processing local transitions versus long-distance syntactic hierarchies[END_REF][START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF].

In contrast, the evolutionary origins of the capacity to extract non-adjacent dependencies (i.e., predictive relationships between elements that are separated by one or several intervening elements in a sequence, hereafter "NADs") remain a matter of debate. A standard example of NADs in English is the relationship between auxiliaries and inflectional morphemes that are separated in the speech stream by the verbal root (e.g., is reading).

Crucially, extracting and recognizing these dependencies requires generalization over a variable verb. Studies conducted in humans indicate that NADs extraction is more challenging for humans than the extraction of regularities between adjacent elements [START_REF] Cleeremans | Learning the structure of event sequences[END_REF][START_REF] Gebhart | Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds[END_REF]Newport & Aslin, 2004;[START_REF] Pacton | Is an attention-based associative account of adjacent and nonadjacent dependency learning valid?[END_REF]Perruchet & Rey, 2005;Wilson, Smith, et al., 2015). This capacity develops later in human infancy [START_REF] Gómez | The Developmental Trajectory of Nonadjacent Dependency Learning[END_REF] than the sensitivity to adjacent dependencies [START_REF] Saffran | Statistical learning by 8-month-old infants[END_REF]; but see [START_REF] Marchetto | Words and possible words in early language acquisition[END_REF] for different findings). Moreover, the extraction of NADs looks especially challenging in human adults outside some specific facilitative contexts. For instance, it is facilitated when the non-adjacent elements have a high degree of perceptual (e.g., phonological) similarity [START_REF] Creel | Distant melodies: statistical learning of nonadjacent dependencies in tone sequences[END_REF][START_REF] Gebhart | Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds[END_REF][START_REF] Onnis | Phonology impacts segmentation in online speech processing[END_REF], when the intervening elements are highly variable [START_REF] Gómez | Variability and detection of invariant structure[END_REF][START_REF] Onnis | Variability is the spice of learning, and a crucial ingredient for detecting and generalizing in nonadjacent dependencies[END_REF], or when the non-adjacent elements are located at the edge of the sequences [START_REF] Peña | Signal-driven computations in speech processing[END_REF]. Studies also revealed similar performances and constraints on NADs learning in experiments using linguistic and nonlinguistic stimuli, such as tones [START_REF] Creel | Distant melodies: statistical learning of nonadjacent dependencies in tone sequences[END_REF][START_REF] Endress | Learning melodies from non-adjacent tones[END_REF][START_REF] Gebhart | Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds[END_REF], or actions [START_REF] Endress | From movements to actions: two mechanisms for learning action sequences[END_REF], suggesting that the learning mechanisms involved in this ability are not language-specific. Several experiments aimed to test whether animals can also extract NADs. Wilson et al. (2015) exposed human participants and macaques to a complex grammar involving multiple adjacent and non-adjacent dependencies. All participants from both species detected violations of the adjacent dependencies. By contrast, half of the tested humans, and none of the monkeys were sensitive to the NADs. The authors concluded that when multiple regularities are present, monkeys rely preferentially on local ones, whereas humans exhibit more flexibility. Human learners can encounter some difficulties with the learning of several regularities simultaneously (e.g., [START_REF] Kovács | Flexible Learning of Multiple Speech Structures in Bilingual Infants[END_REF], but Wilson at al. (2015) suggests that this might be even more difficult for non-human learners. Animals' focus on various local cues rather than on NADs has also been reported when multiple embedded NADs must be processed, as in the so-called center-embedded grammars (e.g., [START_REF] Heijningen | Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species[END_REF]. However, these results may point to a limitation in the monkeys' ability to track several NADs simultaneously, rather than an inability to extract NADs per se.

Complementary information on animals' ability to learn NADs has been obtained

from experiments using simpler grammars of the form AX (n) B. In these grammars, A and B are two paired elements, with a non-adjacent transitional probability of 1, and X (n) is one or several (n) variable intervening elements. Two main types of NAD have been investigated thus far using these grammars: dependencies between elements that are perceptually more similar compared to the interspersed element(s) (i.e., that belong to the same perceptual category, hereafter "feature-based NAD"), and learning of NADs without such perceptual cues (often called "arbitrary associations").

Successful learning of feature-based NADs has been recently reported in two primate species, with visual [START_REF] Sonnweber | Non-adjacent visual dependency learning in chimpanzees[END_REF] and auditory stimuli [START_REF] Ravignani | Action at a distance: dependency sensitivity in a New World primate[END_REF]. Successful generalization across isomorphic visual and auditory sequences was also demonstrated recently [START_REF] Ravignani | Chimpanzees process structural isomorphisms across sensory modalities[END_REF]. In addition, positive results were obtained in rats, with NADs between elements that were phonologically similar (i.e., vowels vs. consonants, and vice-versa;[START_REF] De La Mora | Rule learning over consonants and vowels in a nonhuman animal[END_REF], whereas previous experiments conducted on NADs extraction in this species led to negative results in the absence of phonological similarity [START_REF] Toro | Statistical computations over a speech stream in a rodent[END_REF]. Perceptual similarity therefore appears to facilitate the extraction of NADs in some non-human animal species, as it does in humans (e.g., Onnis et al., 2005). These findings support the hypothesis that NADs extraction is sustained by a general-purpose learning mechanism, interacting with general perceptual constraints (i.e., extra-linguistic, such as Gestalt principle of similarity, [START_REF] Creel | Distant melodies: statistical learning of nonadjacent dependencies in tone sequences[END_REF], both being shared by phylogenetically distant species (Newport & Aslin, 2004; see however de la [START_REF] De La Mora | Rule learning over consonants and vowels in a nonhuman animal[END_REF][START_REF] De La Mora | Rule learning over consonants and vowels in a nonhuman animal[END_REF][START_REF] Toro | Finding words and rules in a speech stream: functional differences between vowels and consonants[END_REF][START_REF] Toro | Finding words and rules in a speech stream: functional differences between vowels and consonants[END_REF], for a discussion of potential human-specific constraints on speech processing).

Evidence for successful NADs learning in the absence of such perceptual cues can also be found in non-human animals. To date, three distinct experiments were conducted on that topic. [START_REF] Newport | Learning at a distance II. Statistical learning of non-adjacent dependencies in a non-human primate[END_REF] assessed the ability of tamarins (Saguinus oedipus) to extract dependencies between non-adjacent syllables in a speech stream, using a familiarization procedure. Six 3-syllable nonsense words of the form AXB (three instantiations of A-B pairs and one of two X syllables inserted in the middle) were presented in a continuous stream during several minutes via a loudspeaker. After this familiarization phase, the tamarins' reactions to words (e.g., A1XB1) versus part-words (B1A2X) were tested.

Tamarins exhibited a higher rate of orientation responses towards the speaker after a partword than after a word, suggesting that they learned the NADs. The authors concluded that the tamarins accurately segmented this continuous stream into triplets of syllables based on these NADs. However, one limitation of this study is that the tamarins' ability to generalize these dependencies over novel intervening X syllables was not tested. Therefore, this experiment leaves unexplored the question of how the NADs were processed and stored in their memory. Indeed, they could have extracted the three A-B non-adjacent relationships, or instead memorized the six AXB "words" as wholes. The same limitation also applies to [START_REF] Milne | Evolutionary origins of non-adjacent sequence processing in primate brain potentials[END_REF] who used a passive listening method in rhesus macaques (Macaca mulatta) and the recording of event-related potentials. Despite the results suggesting the learning of NADs, this study failed to provide generalization tests to confirm that the learned NADs could be maintained over novel intervening events.

To our knowledge, [START_REF] Sonnweber | Non-adjacent visual dependency learning in chimpanzees[END_REF] is the only study proposing this kind of test in a non-human species. These data were obtained in one subgroup of chimpanzees (Pan troglodytes) referred to as the "arbitrary associative dependencies" group. These chimpanzees were presented on each trial with two "strings" of visual shapes depicted concurrently on a screen in a two-alternative forced choice task. The task required to choose the strings of the form AX n B (with five instantiations of A-B pairs and 60 instantiations of X items, with n = 1 or 2) over XX n X strings. What the participants precisely learned was evaluated by a series of tests proposed after the training phase. The two main generalization tests (Test 2: extension of the number of X elements, and Test 3: introduction of novel X shapes) were successfully completed by one participant over the three tested chimpanzees. However, the fact that the items were presented concurrently rather than sequentially raises potential issues. The processing of associations between spatially distant elements, especially at rather small distance (up to 12 cm from each other during training, with a maximum of 30 cm in Test 2, according to the reported pixel measures), can arguably rely on strategies different from those available for the extraction of sequential NADs. For instance, once the chimpanzees have learned that only the left-and right-most shapes of each string matter in this task, they can make quick eye-movements from one to the other and process them as if they were adjacent, despite the spatial distance. Extraction and recognition of sequential NADs, in contrast, may require to hold an element in working memory and relate it to another element occurring later in the sequence [START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF]. Therefore, [START_REF] Sonnweber | Non-adjacent visual dependency learning in chimpanzees[END_REF] offers promising findings about non-human primates' ability to generalize NADs, but these researches would need to be extended to the case of sequential NADs. Another limitation of the comparative literature is that it contains no comparative data on the temporal dynamics of NADs learning in human and non-human animal species. Earlier, we reported that baboons and humans learned adjacent dependencies at the same speed [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF]Rey, Minier, Malassis, Bogaerts, & Fagot, submitted). A remaining issue is whether this is also true for NADs.

The aim of the present study was twofold in this context. It was firstly to provide human and baboon comparative data on the fine-grained dynamics of NADs extraction. It was secondly to test whether baboons can generalize sequential NADs over novel intervening elements. In Experiment 1, baboons and humans performed a serial response time task (SRT, [START_REF] Nissen | Attentional requirements of learning: Evidence from performance measures[END_REF], requiring to follow a stimulus appearing sequentially at different locations on a touchscreen. In a subset of the presented sequences, the first target location predicted the last target location, while the second target location varied. Response times provided an online behavioral metric of NADs learning. In Experiment 2, generalization of the NADs over novel intervening locations was tested. During this test, novel consistent sequences were contrasted with novel sequences violating the dependencies. This violation method was inspired from [START_REF] Gómez | Variability and detection of invariant structure[END_REF]. Only baboons took part in this second part of the study, as generalization has already been demonstrated in humans [START_REF] Endress | Rapid learning of syllable classes from a perceptually continuous speech stream[END_REF][START_REF] Frost | Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech[END_REF][START_REF] Marchetto | Finding words and word structure in artificial speech: the development of infants' sensitivity to morphosyntactic regularities[END_REF].

Experiment 1

Method

Baboons

Participants and apparatus

Participants are 16 Guinea baboons (Papio papio, 11 females, age range 4-21 years) from the CNRS primate facility (Rousset-sur-Arc, France). They live within a larger group of 26 individuals, within a 700 m2 outdoor enclosure and had a permanent access to ten Automated Learning Devices for Monkeys (ALDM, for a detailed description, see [START_REF] Fagot | Automated testing of cognitive performance in monkeys: use of a battery of computerized test systems by a troop of semi-free-ranging baboons (Papio papio)[END_REF][START_REF] Fagot | Automatic testing of cognitive performance in baboons maintained in social groups[END_REF] equipped with a 19-inch touch screen and a food dispenser. The main feature of ALDM equipment is that a radio frequency identification reader (RFID) identifies each baboon via a microchip implanted in each arm. The baboons can therefore participate to the research at will, without being captured, as the test programs recognize them automatically. All baboons had previously participated to numerous computerized experiments using the ALDM test systems, including experiments on sequence processing [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF], but they have never been exposed to tasks requiring the learning of NADs. The experiment was controlled by the EPrime software (Version 2.0, Psychology Software Tools, Pittsburgh).

Procedure

Participants had to perform a serial response time (SRT) task which required following and touching a dot moving in a pre-defined order on a touch screen. Each trial began by the display of a fixation cross (120x120 pixels) presented at the bottom of the screen (see Fig. 1a). Touching this stimulus triggered the display of a matrix of 9 locations (3x3) which contained 8 white crosses (60x60 pixels each) and a red circle target (80x80 pixels).

Participants had to touch the target. The target then disappeared and moved to the next location on the screen. There was no delay between the offset of the target and its next display. A trial consisted in a sequence of three targets. An accurate completion of the 3-target sequence delivered grains of dry wheat inside the ALDM test unit. An incorrect response (i.e., selection of an incorrect location) stopped the trial and triggered a 5-sec timeout without food reward. Trials in which the participants failed to select a stimulus within 5-sec after the target's appearance were aborted and presented again in the next trial. The time elapsed between the appearance of the target (i.e., the red circle) and the baboon's touch on this target was recorded as the response time. Response times were recorded for each response in the 3target sequence. Two types of 3-target sequences were presented. In the first type ("NAD" condition), the first location of the target systematically predicted its last location, leading to a nonadjacent Transitional Probability (TP) of 1. Two pairs of A-B locations were used (A1-B1 and A2-B2), and a variable X location was inserted between A and B locations. The sequences were therefore of the form A1XB1 and A2XB2. The full set of 9 possible locations consisted in 4 locations for A1, B1, A2 and B2, and 5 locations for X elements (i.e., X1, X2, X3, X4, X5; see Fig. 1b). These locations were chosen pseudo-randomly among the nine possible spatial locations of the matrix, with the sole constraint that neither A1 and B1, nor A2 and B2, could be located next to each other. As the five X locations were presented at equal frequencies, the second location was uninformative about the third target location. The TP between the first and the second target locations was equal to 0.2, and the TP between the second and the third was equal to 0.5. A total of ten (two A-B pairs * five X locations) NAD sequences were used (see Table 1).

The second type of sequences had an XXB1 and XXB2 structure. These sequences were used in the control condition. To equate for the number of sequences per condition, 10 control sequences were constructed, as illustrated in Table 1. TPs between the first and third locations, as well as between the second and third locations, were equal to 0.5 in these sequences. It was therefore impossible to use adjacent or non-adjacent dependencies to predict the third target location. NAD and control sequences were presented at equal frequencies across the experiment. This equates motor practice in the two conditions. Experiment 1 involved a total of twenty 100-trial blocks. Each block contained five randomly intermixed presentations of the 10 NAD and 10 control sequences described above. All baboons were presented with the same sequences. Custom-written Python code (www.python.org) was used to generate the sequences.

Table 1 The 20 sequences presented in Experiment 1.

NAD Control

A1 X1 B1 X3 X1 B1 A1 X2 B1 X5 X2 B1 A1 X3 B1 X2 X3 B1 A1 X4 B1 X1 X4 B1 A1 X5 B1 X4 X5 B1 A2 X1 B2 X3 X1 B2 A2 X2 B2 X5 X2 B2 A2 X3 B2 X2 X3 B2 A2 X4 B2 X1 X4 B2 A2 X5 B2 X4 X5 B2

Training

The baboons were familiar with the general principle of the task at the beginning of the experiment, as they previously performed a SRT task involving 9-target sequences [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF]. Before the experimental phase described above, the baboons received an initial training phase of twenty100-trial blocks in which the first, second and third target locations were selected randomly in the matrix. These random sequences were constructed with the constraint to systematically avoid the repetition of any given target location within the 3-target sequence. The aim of the training phase was twofold: (1) to familiarize the baboons with the 3-target sequences (instead of 9 as in [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF], and (2) to limit any long-term effect of the regularities previously learned.

Data analyses

Our set of statistical analyses compared response times on the third target (RTs) in the NAD and control conditions. Remember that the NAD and control sequences were presented at equal frequencies across the experiment. This procedure allowed us distinguishing response time accelerations due to a practice effect, as participants performed the control and NAD sequences an equal number of times, and those attributable to the learning of the predictive relationships present in the NAD sequences. Importantly, the final transitions between the second and the third target locations were the same across conditions (i.e., X1-5-B1 and X1-5-B2). Motor constraints were therefore controlled for that last transition. Shorter RTs in the NAD compared to the control condition would therefore indicate successful learning of the NADs.

For statistical analyses, the data of the exposure phase were grouped in five blocks of 400 trials. For each participant, mean response times on the third target (RTs) was computed for each condition and block. These data were then analyzed using repeated-measures ANOVA involving the Condition (NAD and Control) and Block (1-5) as within-participant factors. Post-hoc comparisons were performed using Tukey Honestly tests (p < .05)

On average, the sixteen baboons required five days of testing (range 3-7 days) to complete the experiment. Incorrect trials were removed from the data set (1.6%), as were removed all the trials with response times greater than two standard deviations from the mean (5.0% of the remaining trials, computed for each participant and each block).

Humans

Participants and apparatus

Ten human participants participated in this experiment (five females, age range 19-24). All participants were students of Aix-Marseille University and were paid for their participation. All participants were right-handed, had normal or corrected-to-normal vision, and were naïve as to the goal of the study. Participants performed the task while sited in front of a 19-inch touch screen. The experiment lasted approximately three hours per participant, and was divided in two sessions. Testing sessions were separated by two days at most.

Procedure and data analyses

The task and stimuli were similar in their general principles for humans and for baboons, with only slight differences between species. First, the number of blocks was reduced by half in humans (n = 10), in comparison to baboons (n = 20). This reduction was motivated by the long duration of the experiment (i.e., three hours with the current design).

Second, humans did not receive a food reward at the end of each correct trial, contrary to baboons. The duration of the time-out and inter-trial interval were otherwise similar in the two species. Third, humans were instructed at the beginning of the experiment to follow the red target as quickly and accurately as possible. They were also told that a green screen meant that they did not correctly touch the target. Participants were interviewed at the end of the experiment to assess their declarative knowledge of the regularities embedded in the sequences. They were asked: "Did you notice any regularity, or rule, in some of the sequences?".

Regarding the training phase, human participants were first trained on the SRT task using the same procedure as for baboons, but their number of training trials was reduced to ten 100-trial blocks. Statistical analyses were the same as for baboons, except that blocks of the exposure phase were grouped in five blocks of 200 trials (instead of 400).

Incorrect trials were removed from the data set (7.3%), as well as trials with response times greater than two standard deviations from the mean (3.3% of the remaining trials, computed for each participant and each block).

Results

Baboons

Mean RTs obtained in baboons are reported for each condition and block in Fig. 2a.

The repeated measures ANOVA revealed that the effect of Condition did not reach the standard significance level (i.e., .05), F (1, 15) = 4.21, p = .06, ηp 2 = .22. No main effect of Block was found 1 , F (1, 15) = 1.21, p = .29, but there was a significant interaction between these two factors, F (1, 15) = 13.73, p < .01, ηp 2 = .48. Post-hoc analyses revealed that RTs were significantly shorter in NAD compared to the control trials for the last block (Block 5, Cohen's d = .29), but not for the previous blocks (1-4). Altogether, the findings suggest that baboons successfully learned the NADs in our task, but that this learning took a substantial number of trials.

1 As noted by some reviewers, an increase in RTs for both conditions is observed between Blocks 3 and 4 (see Fig. 2a). We have no definitive explanation to this increase, which can be explained by a series of factors not directly related to the task, such as an interfering social event in the group of baboons, or a change in weather conditions. reliable main effect of Block was found as well, F (1, 9) = 30.99, p < .001, ηp 2 = .77, as was found a significant Block*Condition interaction, F (1, 9) = 11.87, p < .01, ηp 2 = .57. Post hoc analyses on this interaction indicated a RTs advantage in the NAD condition compared to the control condition that was significant from the first block (ds = Block 1: .25, Block 2: .42, Block 3: .39, Block 4: .39, Block 5: .70). These results suggest that humans began to learn the NADs earlier than baboons, during the first block, and that this learning was amplified throughout the course of the experiment.

Post-experiment interviews revealed that none of the participants reported the existence of a predictive relationship between the first and the last locations (i.e., neither A1-B1 nor A2-B2 relationships were reported). However, 4 out of 10 participants correctly reported that two locations only were used at the last step of the sequence. In addition, four participants could explicitly verbalize some of the triplets (from one to four triplets, depending on the participant). Six of these triplets of locations were NAD sequences, and four were control sequences. To assess whether the results we obtained were triggered by the participants' declarative knowledge of this subset of sequences, we replicated the analysis described above after removing from the dataset the corresponding trials, and these exclusions did not change the pattern of statistical results. Overall, these data suggest that human participants could extract the NADs. They further suggest that while human participants exhibited learning of the regularities, as inferred from their response times, this learning was implicit.

Discussion

In Experiment 1, baboons and humans could extract the NADs, as both species responded faster to the third target when its location was predicted by the first target location (NAD condition) than when it was not (control condition). Fig. 1 suggests that this difference progressively increased in both species, with substantial differences between them. A significant difference in RTs between the two conditions was found from the first block of exposure in humans. This effect only emerged after five blocks in baboons, revealing a substantial difference in the amount of exposure necessary for each species to exhibit successful learning of the NADs. Considering that each of the two NADs was presented 100 times within a 400-trial block, we estimate that baboons demonstrated learning after 400 (100 * 4 blocks) presentations of each NAD. In contrast, this advantage in response times reached significance since the first 200-trial block in humans, meaning that learning took place within the first 50 presentations of these NADs. Moreover, effect size of the difference between the two conditions on the last block was small to medium in baboons (d = .29), while it was medium to large in humans (d = .70).

However, a discrepancy in task practice between the two species might partly explain these differences between humans and baboons. Indeed, all our baboons had already participated to a study involving a SRT task [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF], and performed 20 blocks of random sequences before completing the current experiment. In contrast, human participants only performed 10 random blocks and never had been trained to the SRT task before. This discrepancy in task practice between the two species might explain why humans' response times decreased progressively throughout the experiment in both the control and the NAD conditions, whereas baboons only showed this acceleration in the NAD condition.

Importantly, the baboons responded faster on average than humans on the first block (Mean ± SD = 406 ± 43 and 460 ± 62 ms, respectively, t (24) = 2.58, p = .02, d = .99), probably as a consequence of their extensive practice with the task, while the two species show comparable data suggest that a ceiling effect might have limited baboon's acceleration in the NAD condition.

One limitation of this first experiment is that it does not rule out the possibility that the participants progressively learned the NAD sequences by rote, or a subset of them, and responded faster at the third location because of this rote learning, but without extracting the NADs embedded in these sequences. Experiment 2 addresses this hypothesis in baboons by testing their ability to generalize the NADs over novel intervening locations.

Experiment 2

Experiment 2 tested whether baboons merely learned the ten A1XB1 and A2XB2 sequences as wholes, or extracted instead the A1_B1 and A2_B2 non-adjacent relationships.

Baboons were exposed successively to two different "languages" 2 in a within-participant design, and two experimental phases were proposed for each language. The first phase corresponded to the exposure phase of Experiment 1, except that the set of possible locations was increased from 9 to 16 locations. Six of these locations remained unused during that phase. During the second phase, which is referred to as the test phase, the baboons were presented with two types of novel sequences. In half of these sequences, the NADs learnt during the exposure phase were presented over the six locations not used during the exposure phase. The dependencies were inconsistent in the remaining half of the sequences (i.e., A1

was presented with B2 and A2 with B1). Importantly, the consistent sequences in Language 1

were the inconsistent sequences in Language 2, and vice-versa. This two-language design therefore guaranties that any difference in response times between the conditions will reflect the learning and generalization of the NADs, rather than idiosyncratic sequence features related to differences in motor constraints.

Methods

Participants and apparatus

Experiment 2 involved 14 (10 females, age range 4-21 years) out of 16 baboons of Experiment 1, because we were unable to test two of the previous participants for reasons unrelated to our research. The apparatus was the same as in the previous experiment. The general testing procedure was also the same as in Experiment 1, except that the display contained 16 (4x4) possible locations rather than only 9 (3x3).

Procedure

Table 2 provides the full lists of sequences used in Languages 1 and 2. The first exposure phase was identical to Experiment 1 in both languages. In Language 1, sequences took the form A1XB1 and A2XB2 for the NAD trials (50% of the trials), and XXB1 and XXB2

for the control trials (50% of the trials; see Table 2), with six different instantiations of the X locations. In Language 2, the sequences took the form A1XB2 and A2XB1 for the NAD trials, and control sequences were the same as in Language 1. The remaining six locations (noted Y1-6) were never used during the exposure phase. Locations have been chosen pseudo-NON-ADJACENT DEPENDENCIES PROCESSING IN PRIMATES 22 randomly, with the sole constraint that A1 and B1, as well as A2 and B2, could not be located next to each other in the matrix. Fig. 3 shows the sixteen locations on the touchscreen that were used in Experiment 2. Note that the locations were the same in both languages, and that only the NADs differed between them.

Baboons then received three types of sequences during the test phase. The first type (50% of the trials) corresponded to the NAD sequences of the exposure phase. For the two other types of trials, sequences were either consistent with the NADs of the exposure phase ("Consistent" condition, 25% of the test trials), or inconsistent with those dependencies ("Inconsistent" condition, 25%). In these two latter conditions, the second location of the sequence was drawn from the set of Y locations never used during the exposure phase.

Therefore, sequences were novel in these two conditions. In Language 1, consistent sequences took the form A1YB1 and A2YB2, while inconsistent sequences took the form A1YB2 and A2YB1. In Language 2, the consistent sequences were the inconsistent sequences of Language 1 (i.e., A1YB2 and A2YB1), while the inconsistent sequences were the consistent ones of Language 1 (i.e., A1YB1 and A2YB2). Twelve sequences were constructed for each condition.

NAD, consistent and inconsistent trials were randomly intermixed within each block of the test phase.

Each baboon participated successively to the two language sessions. For each language, the baboons received 40 blocks of 96 trials during the exposure phase, and 20 blocks of 96 trials in the test phase. Note that the amount of exposure changed from 20 blocks in Experiment 1 to 40 in Experiment 2, for both languages, because we hypothesized that learning was still in progress during the last blocks of Experiment 1. To limit potential influences of Language 1 acquisition on the learning of Language 2, six months elapsed between the two language sessions, during which the baboons were exposed to different unrelated tasks. Furthermore, a random phase consisting of 35 blocks of 96 trial blocks was given to the baboons at the beginning of the Language 2 session.

Figure 3. Locations on the touchscreen in Experiment 2. Letters are given for illustrative purposes but were not displayed. Note that the locations were the same for Languages 1 and 2.

Table 2. The 48 sequences presented during Experiment 2, for Language 1 (L1) and Language 2 (L2).

NAD (L1) NAD (L2) Control (L1 & L2) Consistent (L1) / Inconsistent (L2) Inconsistent (L1) / Consistent (L2) A1 X1 B1 A2 X1 B1 X2 X1 B1 A1 Y1 B1 A2 Y1 B1 A1 X2 B1 A2 X2 B1 X3 X2 B1 A1 Y2 B1 A2 Y2 B1 A1 X3 B1 A2 X3 B1 X6 X3 B1 A1 Y3 B1 A2 Y3 B1 A1 X4 B1 A2 X4 B1 X1 X4 B1 A1 Y4 B1 A2 Y4 B1 A1 X5 B1 A2 X5 B1 X4 X5 B1 A1 Y5 B1 A2 Y5 B1 A1 X6 B1 A2 X6 B1 X5 X6 B1 A1 Y6 B1 A2 Y6 B1 A2 X1 B2 A1 X1 B2 X2 X1 B2 A2 Y1 B2 A1 Y1 B2 A2 X2 B2 A1 X2 B2 X3 X2 B2 A2 Y2 B2 A1 Y2 B2 A2 X3 B2 A1 X3 B2 X6 X3 B2 A2 Y3 B2 A1 Y3 B2 A2 X4 B2 A1 X4 B2 X1 X4 B2 A2 Y4 B2 A1 Y4 B2 A2 X5 B2 A1 X5 B2 X4 X5 B2 A2 Y5 B2 A1 Y5 B2 A2 X6 B2 A1 X6 B2 X5 X6 B2 A2 Y6 B2 A1 Y6 B2

Data analyses

Two separated sets of analyses were conducted on the exposure and test phases. We also investigated potential effects of spatial factors on the generalization of NADs.

This analysis was conducted with an ANOVA using the Condition (Consistent, Inconsistent)

and Pair (A1-B1, A2-B2, A1-B2, A2-B1) as within-participant factors, and the RTs obtained in the first test block as dependent variable. An additional set of analyses tested whether generalization occurred for every novel Y location. One-tailed paired t-tests (p < .05) were used to assess if RTs were reliably longer in the inconsistent compared to the consistent condition for each Y location.

To complete Language 1 session, the fourteen baboons required 8 days (range 5-12) on average. The same baboons required on average 10 days (range 5-17) of testing to complete Language 2 session. Incorrect trials were removed from the data set (Language 1:

1.7%, Language 2: 2.1%), as well as trials with RTs greater than two standard deviations from the mean (4.9 % and 5.1%, respectively, computed for each participant and each block).

Main Results

Mean RTs are reported in Fig. 4 for each language, phase and condition. For the exposure phase, the main effect of Condition was significant, F (1, 13) = 16.28, p = .001, ηp 2 = .56, corresponding to faster response times in the NAD (Mean ± SD = 422 ± 44 ms) than in the control condition (430 ± 43 ms). Also significant was the main effect of Block, F (1, 13) = 16.88, p = .001, ηp 2 = .56, showing that RTs decreased with practice. Finally, the main effect of Language was also significant, F (1, 13) = 6.60, p < .01, ηp 2 = .34, indicating faster response times on average for the second (414 ± 32 ms) compared to the first language (438 ± 50 ms). The Condition*Block interaction was close to significance level, F (1, 13) = 3.21, p = .10, ηp 2 = .20. There were no other interactions (all ps > .42). The main effect of condition, along with the absence of Condition*Language interaction, indicate that the baboons were able to learn the NADs in both languages. ). An ANOVA conducted on the first test block confirmed that a significant difference between the consistent and inconsistent conditions was already present at the onset of that phase, as revealed by a main effect of Condition, F (1, 13) = 9.53, p < .01, ηp 2 = .42. No other main effect or interaction were found (ps > 12).

Spatial factors

An ANOVA revealed a significant interaction between Condition and Pair, F (3, 39) = 4.56, p < .01, ηp 2 = .26. Post-hoc tests revealed significantly longer RTs in the inconsistent compared to the consistent condition for two pairs: A2-B2 (416 ± 68 and 389 ± 44 ms, respectively, d = .44) and A2-B1 (467 ± 61 and 438 ± 53 ms, d = .50), but not for the A1-B1

(458 ± 38 and 463 ± 58 ms) and A1-B2 (379 ± 45 and 384 ± 43 ms) pairs. These results suggest that baboons have been able to generalize two out of the four NADs.

Table 3 reports the results of the one-tailed paired t-tests evaluating whether the generalization obtained with the two NADs mentioned above occurred on every novel Y location. RTs were longer in the inconsistent compared to the consistent condition for all Y locations, and this difference was significant for four out of the six Y locations (i.e., Y2, Y3, Y4, Y6), and approached significance for a fifth one (i.e., Y5). Therefore, these results suggest that the baboons generalized the NADs over several novel Y locations, with only one exception for Y1.

Discussion

Two results were obtained in Experiment 2. First, baboons successfully learned the NADs during the exposure phase, replicating the findings already obtained in Experiment 1.

Second, for two NADs response times were slower for inconsistent than for consistent test trials. This finding suggests generalization of these NADs over the novel intervening locations. Importantly, these results suggesting generalization cannot be accounted for by learning of the consistent sequences, since each beginning of a sequence (i.e., A1-Y1-6 and A2-Y1-6) was followed by a B1 or B2 location at equal frequency within each language session.

Analyses moreover confirmed that the speed advantage for consistent sequences was already present at the onset of the test phase.

Interestingly, reliable generalization was only observed for the two NADs beginning by A2 which was located in the lower-left corner of the matrix (see Fig. 3). By contrast, the baboons failed to properly generalize the two NADs beginning at the A1 location, which was not located in a corner. This result suggests that the saliency of the predictive target had an impact on baboons' ability to generalize NADs in this task. We also note that generalization of the above two NADs was not observed for the only Y element located in a corner of the matrix (Y1), possibly because of its greater saliency, or eccentricity in the matrix, compared to the other Y locations which were more central. Taken together, these findings suggest that the baboons can generalize NADs over novel locations, but that this ability may be hindered by spatial factors.

General discussion

In Experiment 1, humans and baboons responded faster to the last target in the NAD compared to the control sequences. Importantly, participants did not merely learn that the sequences always finished by one of two locations (with a probability of .5 for each), but processed the conditional probabilities of the third location, given the first location. This result was replicated in baboons in Experiment 2. These data overall provide evidence of baboons' ability to learn NADs. However, while humans presented a significant difference in response times between the two conditions within the first exposure block, this difference appeared after approximately 400 presentations of each NAD in baboons. This difference in learning speed differs from what has been found for adjacent dependencies [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF]Rey et al., in press). In these earlier studies, baboons and humans learned at similar speed (in less than 200 trials) the predictive relationships between two locations presented at the end of three different triplets. This suggests that learning of NADs is more difficult for baboons than humans, while these two species seem equally fast for learning adjacent dependencies. Importantly, the amount of exposure required by the baboons to extract NADs in the current experiment is in the same range as previously found with habituation-dishabituation paradigms in other primate species (e.g., 672 presentations in Newport et al., 2004). This amount may serve as a reference for designing future experiments investigating related topics.

For two NADs we found that when presented with novel intervening locations the baboons were slower to respond when the sequences were inconsistent with the learned NADs than when the dependencies were preserved. To our knowledge, this result is the first demonstration in a non-human species of a successful generalization of sequentially nonadjacent dependencies over novel intervening elements. The current results therefore extend to the case of sequential NADs Sonnweber et al. 's findings (2015), in which generalization of NADs was tested between visual shapes presented concurrently. Along with previous findings in humans (e.g., [START_REF] Frost | Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech[END_REF], our study indicates that this ability is shared across different primate species. However, successful generalization of the NADs in the current SRT study appeared to be modulated by spatial features of the stimuli. Indeed, the baboons only generalized the two NADs whose predictive target was located in a corner of the matrix and generalization was not observed for one of the novel intervening location which was also located in a corner. These data present some similarities with previous findings in the auditory domain. For instance, Endress, Scholl, and Mehler (2005, see also [START_REF] Peña | Signal-driven computations in speech processing[END_REF] reported that human participants could properly extract some specific regularities from auditory sequences only when the relevant stimuli were presented at an edge of the sequences. Moreover, several studies manipulating the phonological parameters of the stimuli suggested that the relative saliency of the non-adjacent and intervening elements impacts the extraction of NADs [START_REF] Creel | Distant melodies: statistical learning of nonadjacent dependencies in tone sequences[END_REF]Onnis et al., 2005;[START_REF] Toro | Statistical computations over a speech stream in a rodent[END_REF]. Taken together, these findings suggest (1) that perceptual factors constrain the extraction of NADs, and (2) that this effect occurs in both the auditory and visuo-spatial domains.

The exact nature of the processes involved in NADs extraction and recognition is still debated in the literature, and at least three theories are proposed. According to a first theory, the mechanisms that support NADs extraction are the same associative mechanisms as those allowing the learning of adjacent dependencies [START_REF] Perruchet | Learning nonadjacent dependencies: no need for algebraic-like computations[END_REF]). This single-process account proposes that the joint attentional processing of two elements is a necessary and sufficient condition to associate them, even when these are distant in space or time [START_REF] Pacton | An attention-based associative account of adjacent and nonadjacent dependency learning[END_REF]. A second and complementary theory proposes that the extraction of adjacent and non-adjacent dependencies involves similar statistical learning mechanisms, but emphasize the role played by working memory for NADs extraction [START_REF] Conway | Multiple Routes to Implicit Statistical Learning? A Dual-System Perspective[END_REF][START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF]. According to this dualsystem theory, the detection of adjacent relationships would be sustained by modality-specific processes involving perceptual-motor neural networks [START_REF] Conway | Multiple Routes to Implicit Statistical Learning? A Dual-System Perspective[END_REF], or ventral regions of the frontal cortex [START_REF] Wilson | Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans[END_REF], while the extraction of nonadjacent relationships would additionally recruit more dorsal areas [START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF].

According to a last hypothesis [START_REF] Endress | Rapid learning of syllable classes from a perceptually continuous speech stream[END_REF][START_REF] Endress | Perceptual and memory constraints on language acquisition[END_REF][START_REF] Peña | Signal-driven computations in speech processing[END_REF], humans and possibly other animals [START_REF] Endress | The apes' edge: positional learning in chimpanzees and humans[END_REF]) may be endowed with some rule-based mechanisms dedicated to fast learning of "classes" of elements occurring at the edges of sequences (e.g., here, learning that A1 and A2 always occur in the first position), as well as statistical learning mechanisms for the computation of transitional probabilities (e.g. A1_B1). The current experiment did not aim at assessing the edge-based positional learning hypothesis but examined the extraction time course of those transitional probabilities.

In humans, learning occurred early in the experiment, and was strengthened throughout the entire exposure phase. In baboons, learning of the NADs occurred later in the experiment, but a similar progressive emergence was observed. In previous SRT experiments, we found that the learning of adjacent dependencies in triplets [START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF], or pairs of shapes [START_REF] Fagot | The processing of positional information in a two-item sequence limits the emergence of symmetry in baboons (Papio papio), but not in humans (Homo sapiens)[END_REF] gave rise to similar progressive (albeit earlier) decrease in response times throughout the exposure to these regularities. These data, along with previous findings [START_REF] Frost | Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech[END_REF][START_REF] Romberg | All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language[END_REF][START_REF] Vuong | Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies[END_REF], suggest that adjacent and non-adjacent dependencies might be extracted by similar statistical learning mechanisms. However, they do not allow disentangling between the singleprocess and the dual-system proposals described above. Indeed, the difference in speed learning that is observed between baboons and humans might reflect an involvement of working memory in NADs extraction, as working memory capacity differ between these species (e.g., [START_REF] Fagot | A comparative study of working memory: Immediate serial spatial recall in baboons (Papio papio) and humans[END_REF]. However, addressing this question would require additional behavioral and brain imaging studies. Regarding behavioral studies, correlational designs in humans and non-human primates would allow examining individual differences in statistical learning ability across different types of dependencies (i.e., adjacent and nonadjacent, [START_REF] Siegelman | Towards a theory of individual differences in statistical learning[END_REF]Siegelman & Frost, 2015), as well as exploring the relationships between working memory capacity and NADs extraction.

Comparative neuroimaging studies have already suggested that adjacent dependencies are processed by evolutionary conserved brain structures, including the frontal operculum and anterior insula [START_REF] Wilson | Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans[END_REF]. However, further researches are also needed in this domain to discover whether NADs processing is sustained by similar or different neural substrates in human and non-human primates [START_REF] Milne | Evolutionary origins of non-adjacent sequence processing in primate brain potentials[END_REF][START_REF] Wilson | Conserved Sequence Processing in Primate Frontal Cortex[END_REF]. They also express greater difficulties in processing NADs in an AGL-SRT task [START_REF] Hsu | Impaired statistical learning of nonadjacent dependencies in adolescents with specific language impairment[END_REF]. In another study, [START_REF] Misyak | Sequential Expectations: The Role of Prediction-Based Learning in Language[END_REF] found a positive correlation between typically-developing individuals' ability to process longdistance dependencies from a natural language and NADs in an AGL-SRT task. On the other hand, statistical learning appears to be subject to modality and stimulus specificity (e.g., [START_REF] Conway | Modality-constrained statistical learning of tactile, visual, and auditory sequences[END_REF][START_REF] Conway | Statistical learning within and between modalities: pitting abstract against stimulus-specific representations[END_REF]. [START_REF] Siegelman | Statistical learning as an individual ability: Theoretical perspectives and empirical evidence[END_REF] reported for instance that the performance of their participants did not correlate between any of four statistical learning tasks they have used (auditory-verbal, auditory-nonverbal, visual, and SRT tasks). These results suggest the existence of modality and stimulus specific constraints on statistical learning mechanisms, which prevent drawing strong conclusions regarding the similarity between NADs extraction in natural languages and in our visuo-spatial task.

Another
From an evolutionary standpoint, the ability to keep track of predictive relationships between non-adjacent events might present various functional advantages, for instance for planning complex behavioral or motor sequences, or for monitoring social interactions and their outcomes [START_REF] Sonnweber | Non-adjacent visual dependency learning in chimpanzees[END_REF]. In baboons, this process may allow for instance to detect that a threat behavior from an individual A is followed by a scream from an individual B, even if other individuals emit unrelated vocalizations in between. One general advantage of the learning of NADs might also be a reduction in memory demands. For instance in song birds, admitting an optional or variable song element between two others (thereby being nonadjacent) could limit the number of pairwise transitions needed to be memorized [START_REF] Petkov | On the pursuit of the brain network for proto-syntactic learning in non-human primates: conceptual issues and neurobiological hypotheses[END_REF]. In our experiments, the extraction of two NADs may be cognitively less demanding than the learning and storage of ten sequences.

Figure 1 :

 1 Figure 1: (a) General procedure of the serial response time task in Experiment 1. After touching the fixation cross, participants had to touch the red circle target that appeared successively at three different locations. (b) Locations on the touchscreen. Letters are given for illustrative purposes but were not displayed.

Figure 2 .

 2 Figure 2. Response times for the third target (RTs) depending on the block, for the control and the non-adjacent dependencies (NAD) conditions in Experiment 1, for baboons (a) and for humans (b). The bars represent standard errors from normalized data.

  Blocks were grouped for these analyses in 10 (Exposure) and 5 (Test) blocks of 384 trials each. Learning of NADs during the exposure phase was evaluated with repeated-measures ANOVA performed on RTs and involving the Condition (NAD, Control), Language (1, 2) and Block (1-10) as within-participant factors. Post-hoc comparisons were performed using Tukey Honestly tests (p < .05).Generalization was evaluated in the test phase with repeated-measures ANOVA on RTs involving the Condition (NAD, Consistent, Inconsistent), Language (1, 2) and Block (1-5) as within-participant factors. RTs in the consistent and inconsistent conditions were further compared for the first test block using repeated-measures ANOVA involving the Condition (Consistent, Inconsistent) and Language (1, 2) as within-participant factors. RTs in the consistent and inconsistent conditions were further compared for the first test block using a repeated-measures ANOVA involving the Condition (Consistent, Inconsistent) and Language (Language 1, Language 2).

Figure 4 .

 4 Figure 4. Mean response times (RTs) on the third target obtained in each condition of the exposure and test phases of Experiment 2 and for each language. Bars represent standard errors from normalized data.

  important question is the domain generality of the mechanisms involved during the learning of NADs. For instance, are the extraction of NADs in the current SRT task and the extraction of NADs in natural languages (such as in is reading) supported by the same learning mechanisms? Evidences accumulated so far reveal a contrasted pattern[START_REF] Frost | Domain generality versus modality specificity: the paradox of statistical learning[END_REF]. On the one hand, individuals with specific language impairment have been found to exhibit poor performance in auditory statistical learning tasks (e.g.,[START_REF] Evans | Statistical Learning in Children With Specific Language Impairment[END_REF] as well as in SRT tasks[START_REF] Lum | Procedural learning deficits in specific language impairment (SLI): A meta-analysis of serial reaction time task performance[END_REF], compared with typically-developing individuals.

Table 3

 3 Response times (Mean ± SD, ms) and results of the one-tailed paired t-tests assessing generalization over each novel Y locations, for the A2-B1 and A2-B2 NADs during the first test block. dfs were all equal to 13.

		Y1	Y2	Y3	Y4	Y5	Y6
	Inconsistent 426 ± 33 445 ± 69 434 ± 37 472 ± 76 435 ± 54 436 ± 49
	Consistent	419 ± 39 391 ± 40 413 ± 35 423 ± 49 419 ± 65 415 ± 38
	t	0.91	3.51	3.36	2.72	1.32	1.80
	Cohen's d	.15	.96	.60	.77	.27	.48
	p	.19	< .01	< .01	< .01	.10	< .05

RTs on the last block (400 ± 54 and 420 ± 56 ms, respectively, t (24) = 0.89, p = .38). These

We borrow here the term "language" to the literature on artificial language learning, the set of sequences being considered here as a set of "words".

Acknowledgments

This work, carried out within the Labex BLRI (ANR-11-LABX-0036) and the Institut Convergence ILCB (ANR-16-CONV-0002), has benefited from support from the French government, managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A*MIDEX). This research was supported by the Premilang2 ANR-project (#ANR-13-BSH2-0002) and the Chunked ANR-project (#ANR-17-CE28-0013-02).


Arnaud Rey
email: arnaud.rey@univ-amu.fr

Laure Minier

Raphaëlle Malassis

Louisa Bogaerts

Joël Fagot

Regularity extraction across species: associative learning mechanisms shared by human and non-human primates

Keywords: regularity extraction, statistical learning, implicit learning, associative learning, verbal recoding

come

Statistical learning -the ability to extract and encode environmental regularitiesappears as a key feature of human cognitive systems and has been a domain of intensive research over the last 20 years (see [START_REF] Frost | Domain generality versus modality specificity: The paradox of statistical learning[END_REF][START_REF] Frost | Domain generality versus modality specificity: The paradox of statistical learning[END_REF][START_REF] Thiessen | The extraction and integration framework: A two-process account of statistical learning[END_REF][START_REF] Thiessen | The extraction and integration framework: A two-process account of statistical learning[END_REF], for reviews, but also e.g., [START_REF] Estes | Toward a statistical theory of learning[END_REF][START_REF] Restle | Theory of serial pattern learning: Structural trees[END_REF] for earlier pioneer work). It is thought to play a key role in the segmentation of continuous input, prediction, discrimination and categorization, shaping our basics representations of the environment [START_REF] Frost | Domain generality versus modality specificity: The paradox of statistical learning[END_REF].

Historically, before the 'statistical learning' wave, similar issues were addressed in the field of 'implicit learning'. The latter term captures the observation that some forms of learning occur after the mere repeated exposure to regularities (or rule systems, as in artificial grammar learning experiments, e.g., [START_REF] Reber | Implicit learning of artificial grammars[END_REF], with no intention of learning and no clear awareness of what has been learned [START_REF] Cleeremans | Implicit learning: News from the front[END_REF][START_REF] Shanks | Implicit learning[END_REF]. Some authors, such as [START_REF] Conway | Statistical learning within and between modalities: Pitting abstract against stimulus-specific representations[END_REF] see also Christiansen, this issue), have proposed to merge the two terms (i.e., 'implicit statistical learning') to cover the general learning ability to extract and encode different forms of environmental regularities.

Others, like [START_REF] Perruchet | Statistical approaches to language acquisition and the self-organizing consciousness: A reversal of perspective[END_REF] see also [START_REF] Perruchet | Word segmentation: Trading the (new, but poor) concept of statistical computation for the (old, but richer) associative approach[END_REF], have argued that 'statistical learning' is nothing more than a new terminology corresponding, in fact, to the old domain of associative learning (e.g., [START_REF] Hebb | Distinctive features of learning in the higher animal[END_REF]Mitchell & Le Pelley, 2010;[START_REF] Shanks | The Psychology of Associative Learning[END_REF]. In this view, the extraction of regularities is understood as the formation of new larger units also called 'chunks'. This process is supported by associative learning mechanisms, which itself is considered a by-product of the concurrent attentional processing of multiple elements (or small units). Learning can also be qualified as 'implicit' as it doesn't require awareness of what is learnt nor the intention to learn (see also [START_REF] Perruchet | Implicit learning and statistical learning: One phenomenon, two approaches[END_REF][START_REF] Perruchet | The self-organizing consciousness[END_REF] 1 . In that perspective, implicit statistical learning and associative learning are considered as two sides of the same coin, the former having a broader scope (including issues related to various aspects of language processing) compared to the latter that was more concerned by the formation of simple associations or by operant conditioning behaviors.

Basic associative (or implicit statistical) learning mechanisms are certainly shared by human and non-human primates (e.g., [START_REF] Wilson | Conserved sequence processing in primate frontal cortex[END_REF], and possibly by all species equipped with a nervous system (e.g., chicks [START_REF] Santolin | Generalization of visual regularities in newly hatched chicks (Gallus gallus)[END_REF]songbirds: Chen, van Rossum, & Ten Cate, 2015;rodents: Toro, Nespor & Gervain, 2016). This raises the question which aspects of the human ability for regularity extraction can be related to those in non-human primates. The present study aims to address this question by a direct behavioral comparison between humans and Guinea baboons (Papio papio) in the same experimental paradigm. This paradigm provides an online measure of regularity extraction at different levels of integration. We argue that the use of an online learning measure on the one hand and the study of non-human primates on the other hand form a valuable approach to overcome two difficulties that characterize the existing literature.

The first difficulty is that the temporal dynamics of regularity extraction is frequently not considered. The seminal study of [START_REF] Saffran | Statistical learning by 8-month-old infants[END_REF] is often considered as the critical starting point in the study of statistical learning, and several theoretical accounts and computational models of this phenomenon have been proposed (e.g., Frank, Goldwater, Griffiths, & Tenenbaum, 2010; French, Addyman, & Mareschal, 2011; Perruchet 1 In [START_REF] Perruchet | The self-organizing consciousness[END_REF] theory, learning involves attention or, more precisely, attentional processing of the learned information. In that sense, attention or attentional processing are equivalent to consciousness, but not awareness. According to this view, one can learn a regularity by paying attention to that regularity while being unaware of that regularity (i.e., not being able to report the exact content of that regularity). [START_REF] Perruchet | PARSER: A model for word segmentation[END_REF][START_REF] Pothos | Theories of artificial grammar learning[END_REF]. The empirical evidence used to elaborate these models is mainly derived from studies using the same artificial language learning paradigm. In this task, participants are exposed to an artificial language composed of few polysyllabic nonsense words (e.g., 4) that are auditorily presented without any pause between words. After this exposure phase, participants are asked to indicate which one of two test sequences looks more familiar. The main dependent variable used to test models is therefore an offline measure that informs us about which kind of regularities can be learned but that does not provide any information regarding the temporal dynamics of learning (see e.g., Siegelman, Bogaerts, & Frost, 2017, for a detailed discussion of the problems of offline learning measures). In contrast to offline measures, online measures are obtained by recording the performance of participants during the exposure to the regularities and can therefore provide information on the temporal dynamics of regularity extraction. The serial response time task [START_REF] Nissen | Attentional requirements of learning: Evidence from performance measures[END_REF] is one good example of such online measure. In this task, participants respond to sequences of stimuli that appear one-by-one at various locations on a computer screen. If the sequences include regularities (allowing for the prediction of upcoming locations), then participants speed their response times (RTs) if they manage to extract these regularities (see also [START_REF] Batterink | Rapid Statistical Learning Supporting Word Extraction From Continuous Speech[END_REF]Christiansen, this issue;[START_REF] Misyak | On-line individual differences in statistical learning predict language processing[END_REF][START_REF] Siegelman | Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities[END_REF] for other examples of online learning measures).

A second difficulty is paradoxically the strong focus on human (adult) participants. Indeed, during the exposure phase, participants may develop strategies or explicit learning procedures (like repeating internally some sequences of syllables) and their resulting performance is certainly influenced and biased by various forms of explicit recoding processes [START_REF] Batterink | Implicit and explicit contributions to statistical learning[END_REF]Siegelman et al., 2017). Testing human infants without any advanced language and reasoning abilities (like the original Saffran et al.

study did, followed by many others, e.g., [START_REF] Bulf | Visual statistical learning in the newborn infant[END_REF][START_REF] Singh | Infant word segmentation and childhood vocabulary development: a longitudinal analysis[END_REF]) could be a solution to overcome that difficulty. However, the study of infants also brings other procedural difficulties because the measures that can be recorded for this population (for example, the head-turn procedure) are indirect and much noisier (i.e., less reliable). Testing non-human primates allows us to study regularity extraction in the absence of any bias due to language experience and the related ability to verbally recode regularities.

Previous studies that adopted a comparative approach to study statistical learning were mainly concerned with discovering differences between human and non-human primates (e.g., [START_REF] Fitch | Computational constraints on syntactic processing in a nonhuman primate[END_REF][START_REF] Wang | Representation of Numerical and Sequential Patterns in Macaque and Human Brains[END_REF]. That logic is derived from a research tradition that searched for a critical distinctive feature that could distinguish animal communication systems from human language. For instance, [START_REF] Hauser | The faculty of language: what is it, who has it, and how did it evolve[END_REF] put forward the ability to process and produce recursive structures as a fundamental distinctive feature that sets humans apart from other species. However, as argued by [START_REF] Pinker | The faculty of language: what's special about it?[END_REF], there are certainly many other critical differences between human and non-human primates that can account for their distinct cognitive trajectories (see also [START_REF] Fagot | Comparing human and nonhuman animal performance on domain-general functions: Towards a multiple bottleneck scenario of language evolution[END_REF].

Here we propose an alternative perspective stating that we can increase our understanding of the precise dynamics of regularity learning processes by looking at the common patterns produced by human and non-human primates in online experimental paradigms. As mentioned above, it is obvious that human adults can make use of both implicit associative learning mechanisms and explicit recoding strategies based on their language abilities to perform regularity extraction tasks. Non-human primates miss the language tools that humans can use to explicitly recode regularities. One can therefore safely assume that non-human primates mainly rely on associative learning mechanisms in these tasks, and that these mechanisms are shared with humans. Therefore, any common pattern of performance between human and non-human primates should reflect common fundamental properties of these learning mechanisms. Alternatively, any difference could be related to the use of language recoding abilities and should therefore be considered with caution for constraining the development of computational models.

We now illustrate this proposition by comparing the performance of human and nonhuman primates in a novel task that has been designed to combine the serial response time task and the artificial language paradigm [START_REF] Franco | Chunking or not chunking? How do we find words in artificial language learning[END_REF]. Human adults and baboons had to touch a red circle appearing successively at nine different locations of a computer screen. The dot to follow moved on the screen along paths containing statistical regularities. The use of this task allowed us (1) to test the two populations with the exact same experimental procedure, (2) to provide detailed information on the temporal dynamics of regularity extraction, and (3) to compare humans and baboons at different levels of integration, at the local level within the sequence (i.e., co-occurrences between individual elements), or at the level of the global sequence structure (see the Method section below for an operational definition of the distinction between local and more global regularities). Note that part of the primate data was reported in [START_REF] Minier | The temporal dynamics of regularity extraction in nonhuman primates[END_REF].

Methods

Participants were ten Guinea baboons (Papio papio, age range 3-15.5 years) from the CNRS primate facility in Rousset (France) and 5 human adults (age range 20-24 years) from Aix-Marseille University.

Baboons live within a larger group of 26 individuals, within a 700 m2 outdoor enclosure and had a permanent access to ten Automated Learning Devices for Monkeys (ALDM, for a detailed description, see [START_REF] Fagot | Automated testing of cognitive performance in monkeys: Use of a battery of computerized test systems by a troop of semi-free-ranging baboons (Papio papio)[END_REF][START_REF] Fagot | Automatic testing of cognitive performance in baboons maintained in social groups[END_REF] equipped with a 19-inch touch screen and a food dispenser. The main feature of ALDM equipment is that a radio frequency identification reader (RFID) identifies each baboon via a microchip implanted in each arm. The baboons can therefore participate to the research at will, without being captured, as the test programs recognize them automatically. All baboons had previously participated to numerous computerized experiments using the ALDM test systems. The experiment was controlled by the E-Prime software (Version 2.0, Psychology Software Tools, Pittsburgh).

A trial began with the presentation of a fixation cross at the bottom of a touch screen.

The cross stayed on the screen till it was pressed. After pressing on it, the fixation-cross disappeared and immediately 9 crosses representing locations were displayed on the screen, one of them being replaced by the target, a red circle. When the target was touched, it disappeared and was replaced by the cross. The next cross/location in the sequence was then replaced by the red circle until the end of the sequence. Reward (a small pellet) was provided at the end of a sequence of nine touches.

If participants touched an inappropriate location (incorrect trial), or failed to touch the screen within 5,000ms after the red circle's appearance (aborted trial), a green screen was displayed for 3000ms (baboons) / 1000ms (humans) as a marker of failure. Aborted trials were not counted as trials and were therefore presented again, while incorrect trials were not.

The time elapsed between the appearance of the red circle and the participant's touch on this circle was recorded as the response time (RT). To learn the task, baboons initially received trials that were rewarded after one touch, after which the number of touches in a trial was progressively increased up to nine. Humans were simply instructed to touch the red circle as quickly as possible. They were also informed that each trial began by the presentation of the yellow cross at the bottom of the screen followed by a sequence of nine touches on the moving red circle.

To equate for the motor difficulty of the sequences to be produced, each participant was first tested on 5 blocks (baboons) / 4 blocks (humans) of 200 random trials (called "Random I phase"), each composed of a random ordering of nine locations, without immediate repetition. A baseline measure for all possible transitions from one location to another was computed by calculating mean RTs for each transition at the group level (i.e., the entire group of baboons on the one hand and of human participants on the other hand).

After this Random I phase, participants were exposed to trials including statistical regularities (hereafter, "Pattern phase"). Three independent regularities, each composed of three fixed locations were constructed (R1 = 'A1B1C1'; R2 = 'A2B2C2'; R3 = 'A3B3C3'). To study the extraction dynamic of these regularities, we carefully selected these three patterns so that the mean RTs for the initial transition ('AiBi') and final transition ('BiCi') would not be statistically different considering the baseline measurements obtained for these transitions during the random phase. By matching these local adjacent dependencies at the onset of the Pattern phase, it was possible to study the fine-grained evolution of these RTs that provide a direct behavioral index of regularity extraction.

Moreover, to study the extraction dynamic at a more complex and global level, the three regular patterns appeared in each sequence of nine touches, increasing the predictability of each pattern in the sequence (1/3, 1/2, 1). For example, if participants are exposed to the regular patterns R3 followed by R1, then the last regular pattern will be R2. If they manage to extract such a complex regularity, we should then observe a decrease in RTs for the first element of the last displayed regular pattern (in our example, for A2).

During the Pattern phase, participants performed 10 (baboons) / 7 (humans) blocks of 200 trials each, one trial being composed of a random combination of the three regular patterns2 . Finally, participants performed a last series of 5 (baboons) / 1 (humans) random blocks ("Random II phase") that had the same structure as the Random I phase (see Figure 1). < Insert Figure 1 here >

Results

Incorrect trials were removed from the dataset (7.6% for baboons and 5.9% for humans), as well as RTs greater than two standard deviations from the mean (computed for each subject and each block, 4.2% for baboons and 3.6% for humans).

The first analysis concerned regularity extraction at the local level. [START_REF] Minier | The temporal dynamics of regularity extraction in nonhuman primates[END_REF] reported an advantage of the second transition (i.e., BC) over the first one (i.e., AB) in baboons, with RTs on C decreasing faster than RTs on B. We therefore ran the same 2 (Transitions) * 7 (Block) repeated measures ANOVA on the mean RTs of humans. Mauchly's test indicated a violation of the sphericity assumption for the block factor (c 2 (44)=71.7, p<0.05), so Greenhouse-Geisser estimates (e=0.11) were used to correct for degrees of freedom. We found a significant effect of Transition (F(1,9)=14.9, p<0.01, η 2 p=0.62), first transition RTs being slower than those of second transitions, and a significant effect of Block (F(2.88, 25.9)= 36.5, p<0.001, η 2 p=0.80) as the response latencies decreased through learning.

Finally, the Transition * Block interaction was also significant (F(9,81)=4.9, p<0.001,η 2 p=0.33). Contrasts between first and second transitions for each block revealed a significant difference in blocks 1 to 8, but not in blocks 9 and 10. As shown in Figure 2, the human results match those of baboons with a faster decrease of RTs on C than on B.

< Insert Figure 2 here >

The second analysis concerned regularity extraction at a more global level, specifically, the predictability of the third pattern in the sequence given the first and second.

As shown in Figure 3 (upper panel), the predictability within regular patterns is always equal to 1. Indeed, in each regular sequence, Ai was always followed by Bi that was always followed by Ci. However, the predictability of A varied across patterns depending on their position in the sequence of 9 touches. The first A of the first ABC regular pattern could occur with a probability of 1/3, the second A with a probability of 1/2, and the last A with a probability of 1. Of course, these probabilities depend on the ability of participants to extract the global statistical structure of the experiment.

We thus considered RTs for the three patterns on the three different positions in the sequence. Because humans and baboons did not receive the same number of blocks, we ran two different ANOVAs, both including Place in the pattern (first = A, second = B or third = C), Blocks (1 to 10 for baboons and 1 to 7 for humans) and Position of the patterns in the sequence (first, second or third) as within-subject factors.

For baboons, Mauchly's test indicated a violation of the assumption of sphericity for the Block factor (χ2 (44)=67.5, p<0.05) as well as for the Position factor (χ2 (2)=6.3, p<0.05), so Greenhouse-Geisser estimates (ε=0,31 and ε= 0.62, respectively) were used to correct for degrees of freedom. We found a significant effect of Position (F(1.23,11.2)=8,0, p<0,013, η 2 p=0,47), and a significant effect of Block (F(2.8, 25.2)= 3.7, p<0.001, η 2 p=0.29). The interaction between Block and Position were found to be significant as well (F(18,162)=1.8, p=0.026, η 2 p=0.17). Helmert contrasts showed that the effect of Position was between the first position and the last two (F(1,9)=8.9, p=0.02, η 2 p = 0.5) but not between the second position and the third (F(1,9)=1.74, p=0.22, η 2 p =0.16). Another ANOVA ran on the mean RTs of the first place of the patterns (i.e., A) with only the last learning block and Position as a within-participant factor with 3 levels (1st, 2nd and 3rd) showed a significant effect (F(2,18)=4.9, p<0.05, η 2 p =0,35). Helmert contrasts confirmed that this effect was significant between the first and two last positions (F(1,9)=6.2, p=0.04, η 2 p =0.40) but not between the second and the third positions (F(1,9)=0.67, p=0.43, η 2 p =0.07).

For humans, we found a significant effect of Position (F(2,8)=36,83, p<0,001, η 2 p=0,90), and a significant effect of Block (F(6, 24)= 7,0, p<0.001, η 2 p=0.64). The interaction between Position and Block was also significant (F(12,48)=13.57, p<0.001, η 2 p=0.77). Helmert comparisons showed that the effect of Position was between the first position and the two last (F(1,4)=51.5, p=0.02, η 2 p= 0.98), and between the second position and the third position (F(1,4)=8.03, p=0.047, η 2 p=0.67). A final ANOVA considered mean RTs of the first place in the patterns (i.e., A) in the last learning block only. Position was entered as within-participant factor with 3 levels (1st, 2nd and 3rd). This analysis showed a significant effect (F(2,8)=36.7, p<0.0001, η 2 p=0,90). Helmert contrasts indicated that this effect was significant between the first and two last positions (F(1,4)=68.0, p<0.001, η 2 p=0.94) and between the second and the third position as well (F(1,4)=14.3, p=0.02, η 2 p=0.78). The evolution of mean RTs for baboons and humans over each block, each position within the sequence, and each place within the regular pattern is provided in Figure 3 (lower panels). A clear difference between baboons and humans appears for the first element of the triplet when it occurred in Position 3 of the sequence (i.e., A3): RTs decreased across learning blocks for humans while no decrease was observed for baboons. < Insert Figure 3 here > Discussion Two main findings were obtained in the present comparative study in which the performance of human adults and baboons were collected in the same regularity learning task.

For regularities at the local level, human and non-human primates displayed a similar statistical learning dynamic: RTs decreased faster on the third location of a regular pattern (i.e., C) than on the second (i.e., B). At a more global level, we found different results for humans and baboons. In humans, RTs for the first element (i.e., A) of the third displayed regular pattern (i.e., the 7 th element in the sequence, which should become predictable over the course of learning) decreased gradually across learning blocks while no such improvement was observed for baboons.

The first result, already reported for baboons by [START_REF] Minier | The temporal dynamics of regularity extraction in nonhuman primates[END_REF], indicates that the two species had a similar dynamic of regularity extraction. In both species, RTs decreased faster on C than on B in ABC regular patterns, suggesting that it is a general property that should be captured by current computational models. As argued previously by [START_REF] Minier | The temporal dynamics of regularity extraction in nonhuman primates[END_REF], this pattern of results is inconsistent with theoretical propositions resting on the notion of transitional probabilities between adjacent elements. Indeed, in our task, the transitional probabilities from A to B and from B to C are all equal to one and are therefore insufficient to account for the present data. Similarly, the TRACX model by [START_REF] French | TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction[END_REF] predicts that RTs should decrease faster on B compared to C given that the model is based on a left-toright acquisition of statistical regularities. Only the SRN model [START_REF] Elman | Finding structure in time[END_REF] predicts the learning advantage of C over B. SRN assumes that learning is based on the development of associations between the successive elements of the input that are frequently and consistently encountered together. SRN encodes also the contextual information that precedes the successive elements, and therefore C benefits from richer contextual information (i.e., the preceding systematic sequence AB) while B benefits only from the systematic occurrence of A.

Our second result indicates that only humans capture the more complex regularity related to the predictability of the third regular pattern in the larger sequence. In that situation, following the logic that we proposed, the absence of a common behavioral pattern of results between human and non-human primates suggests that this kind of regularity cannot be extracted by mere associative learning mechanisms. Computational models are therefore not expected to account for this phenomenon that may require language recoding abilities.

Conclusions and avenues for future research

The comparative approach taken in the current paper suggests that the mechanisms underlying regularity extraction can be further understood by looking at patterns of performance that are common to human and non-human primates. Based on the present results we suggest that shared associative learning mechanisms can account for the extraction of local regularities (i.e., co-occurrences) and that a general theory of regularity learning could benefit from the large existing literature on associative learning (Mitchell & Le Pelley, 2010;[START_REF] Shanks | The Psychology of Associative Learning[END_REF]. Moreover, the temporal dynamics of co-occurrence learning we observed in both species suggests a learning advantage for the final stimulus of a repeated (triplet) pattern, providing an important empirical constraint for models of regularity extraction. Sensitivity to global regularities at the level of the entire sequence was observed for humans only and might call for a theory that accounts for the interaction between basic associative learning mechanisms and verbal recoding strategies.

Several important issues remain to be addressed to reach a general theory of associative learning mechanisms and their role for regularity extraction in different situations.

We propose that the use a comparative approach can be a fruitful avenue for future research to address a series of remaining theoretical questions, which we outline in more detail below.

A first series of questions concerns the role of the timing of co-occurrences in the sensory input. Suppose that A and B are two sources of information that co-occur systematically, A being always followed by B. Assuming no interfering information between A and B, how does the dynamic of regularity extraction change as a function of the delay between the presentation of A and the presentation of B? How many times A-B has to be repeated in order to observe an extraction of the co-occurrence? What is the role of the delay between two repetitions of A-B?

A second series of questions is related to the sources of information surrounding the A-B co-occurrence to be extracted. A general theory of regularity learning should be able to specify the role of factors such as the number and nature of interfering information between two repetitions of A-B, the role of the similarity between the interfering information and each element of the co-occurrence (A and B), the role of forward and backward inconsistencies (i.e., the role of the statistical co-occurrence of A with another source of information, e.g., C, and the interference of A-C on the extraction of A-B or, respectively, the co-occurrence of B with a preceding source of information, e.g., D, and the interference of D-B on the extraction of A-B), and finally, the effect of the presence of other co-occurrences (e.g., like E-F) appearing between two repetitions of A-B.

A third series of questions concerns the number of co-occurring elements. What is the regularity extraction dynamic when A-B is systematically followed by another information C (an issue that has started to be addressed in the present study)? Similarly, what will happen if we increase the chain of co-occurring elements? For ABCD sequences, for example, will we observe an even faster decrease of RTs on D compare to C (because of the richer context) or is the role of contextual information limited to a restricted time-window? And for longer sequences, what is the effect of repeating a previously encountered element (e.g., like in the sequence ABCAD)?

A fourth set of questions is related to the learning of non-adjacent co-occurrences [START_REF] Gómez | Variability and detection of invariant structure[END_REF][START_REF] Pacton | An attention-based associative account of adjacent and nonadjacent dependency learning[END_REF][START_REF] Pacton | Is an attention-based account of adjacent and nonadjacent dependency learning valid ?[END_REF]Wilson, Spierings, Ravignani, Mueller, Mintz, Wijnen, van der Kant, Smith, Rey, submitted). Under which conditions can we extract the co-occurrence of A and B when other sources of information are placed between A and B? Clearly, if co-occurrence learning is supported by associative learning mechanisms, the presence of interfering information between A and B will strongly decrease the probability to extract that regularity. However, baboons have shown some ability to process long-distance dependencies [START_REF] Rey | Centre-embedded structures are a by-product of associative learning and working memory constraints: Evidence from baboons (Papio Papio)[END_REF] indicating that the learning of non-adjacent regularities should certainly be accounted for by current computational models of regularity learning.

To conclude, we have argued that the domains of statistical learning and implicit learning, recently chunked under the term 'implicit statistical learning' (e.g., Christiansen, this issue), are fundamentally rooted in the domain of associative learning. We have also proposed an alternative way of using comparative studies to better comprehend the fundamental mechanisms supporting regularity extraction and the temporal dynamics of associative learning mechanisms. Rather than focusing on the differences between species, we suggest focusing on common patterns of performances to determine the empirical evidence that should constrain the elaboration of computational models. By employing online measures, we can generate fine-grained behavioral results that can inform models about the precise dynamics of regularity extraction mechanisms. We have suggested that this approach can be quite powerful in testing the predictions of computational models. Finally, we have outlined a

  Following a different line of reasoning, one may argue that the present results point to a possible limitation of the learning capacities in non-human primates. The extraction of these global regularities might indeed require a more complex learning mechanism or a larger memory span for preceding elements. One would need however to specify what is the precise nature of this learning mechanism and what would be the role of short-term memory span in facilitating the extraction of such global regularities.

Figure captions Figure 1 :

 captions1 Figure captions

Figure 2 :

 2 Figure 2: Evolution of mean RTs for the first (AB) and second (BC) transitions over blocks

Figure 3 :

 3 Figure 3: Upper central panel: Predictability of each location in the sequence of nine touches.

Figure 1 Figure 2

 12 Figure 1

Figure 3

 3 Figure 3

The number of blocks in humans and baboons are not exactly the same, because if the baboons could do the task when they wanted, humans had to complete the blocks in one long session. That is the reason why the number of blocks for humans has been slightly reduced.

Acknowledgments

This work, carried out within the Labex BLRI (ANR-11-LABX-0036) and the Institut Convergence ILCB (ANR-16-CONV-0002), has benefited from support from the French government, managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A*MIDEX). This research was also supported by the Premilang2 ANR-project (#ANR-13-BSH2-0002) and the Chunked ANRproject (#ANR-17-CE28-0013-02).

list of questions that would need to be addressed to reach a deeper understanding of associative learning mechanisms in the context of regularity extraction.