Conserved Sequence Processing in Primate Frontal Cortex
Authors: Benjamin Wilson, William D. Marslen-Wilson, Christopher I. Petkov
Journal: Trends in Neurosciences (2017)

Cross-species studies of sequence processing are providing insights into combinatorial learning in nonhuman animals that may represent evolutionary precursors to human language-related operations. Recent comparative neuroimaging studies in humans and monkeys provide neurobiological evidence for evolutionarily conserved sequencing processes, supported by functionally homologous subregions of frontal cortex in humans and monkeys. These findings point to further development of animal model systems in which the neuronal mechanisms that support these operations can be studied in ways not feasible in humans. A heuristic 'ventrodorsal gradient' model is proposed of primate frontal cortex engagement that depends on sequencing complexity. An important aspect of animal perception and cognition is learning to recognize relationships between environmental events that predict others in time, a form of relational knowledge that can be assessed using sequence-learning paradigms. Humans are exquisitely sensitive to sequencing relationships, and their combinatorial capacities, most saliently in the domain of language, are unparalleled. Recent comparative research in human and nonhuman primates has obtained behavioral and neuroimaging evidence for evolutionarily conserved substrates involved in sequence processing. The findings carry implications for the origins of domain-general capacities underlying core language functions in humans. Here, we synthesize this research into a 'ventrodorsal gradient' model, where frontal cortex engagement along this axis depends on sequencing complexity, mapping onto the sequencing capacities of different species. An important aspect of animal perception and cognition is learning to recognize relationships between environmental events that predict others in time, a form of relational knowledge that can be assessed using sequence-learning paradigms. Humans are exquisitely sensitive to sequencing relationships, and their combinatorial capacities, most saliently in the domain of language, are unparalleled. Recent comparative research in human and nonhuman primates has obtained behavioral and neuroimaging evidence for evolutionarily conserved substrates involved in sequence processing. The findings carry implications for the origins of domain-general capacities underlying core language functions in humans. Here, we synthesize this research into a 'ventrodorsal gradient' model, where frontal cortex engagement along this axis depends on sequencing complexity, mapping onto the sequencing capacities of different species. Human language is an unrivalled mode of communication. It reflects our vast combinatorial capacity to generate and recognize an infinite number of novel communicative sequences, combining syntactic knowledge (rules of language) with semantic labels (words and their meanings). Viewed as an evolved neurobiological system, language depends on neurocognitive processes and neural substrates that may be specific to the domain of language (domain-specific; see Glossary) or require access to more domain-general neurocomputational capacities. An explanatory dissection of these systems will require a multilevel, cross-species approach. Language-specific and domain-general processes can be compared and contrasted in humans [1Fedorenko E. et al.Language-selective and domain-general regions lie side by side within broca's area.Curr. Biol. 2012; 22: 2059-2062Abstract Full Text Full Text PDF PubMed Scopus (254) Google Scholar, 2Brownsett S.L.E. et al.Cognitive control and its impact on recovery from aphasic stroke.Brain. 2014; 137: 242-254Crossref PubMed Scopus (168) Google Scholar], while cross-species comparisons can identify which domain-general capabilities are evolutionarily conserved [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar, 4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 5Saffran J. et al.Grammatical pattern learning by human infants and cotton-top Tamarin monkeys.Cognition. 2008; 107: 479-500Crossref PubMed Scopus (123) Google Scholar, 6Newport E.L. et al.Learning at a distance ii. Statistical learning of non-adjacent dependencies in a non-human primate.Cogn. Psychol. 2004; 49: 85-117Crossref PubMed Scopus (134) Google Scholar, 7Spierings M.J. ten Cate C. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment.Proc. Natl. Acad. Sci. U.S.A. 2016; 113: E3977-E3984Crossref PubMed Scopus (51) Google Scholar, 8Wilson B. et al.Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.Eur. J. Neurosci. 2015; 41: 568-578Crossref PubMed Scopus (40) Google Scholar] and thereby which functionally conserved neurobiological processes support human behavior [9Wilson B. et al.Auditory sequence processing engages evolutionarily conserved regions of frontal cortex in macaques and humans.Nat. Commun. 2015; 6: 8901Crossref PubMed Scopus (71) Google Scholar, 10Wang L. et al.Representation of numerical and sequential patterns in macaque and human brains.Curr. Biol. 2015; 25: 1966-1974Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar]. Here, we focus on the sequential nature of linguistic communication and the relationship of this to sequence processing as a conserved but variable capacity across the Primate order. Structured sequence-learning tasks, including statistical learning and artificial grammar-learning paradigms, can be used to determine whether an individual is sensitive to different types of ordering relationships between items in a sequence (Figure 1). Performance on these tasks has been shown to correlate with performance on language tasks [11Frost R. et al.Domain generality versus modality specificity: the paradox of statistical learning.Trends. Cogn. Sci. 2015; 19: 117-125Abstract Full Text Full Text PDF PubMed Scopus (294) Google Scholar, 12Gómez R.L. Gerken L. Infant artificial language learning and language acquisition.Trends. Cogn. Sci. 2000; 4: 178-186Abstract Full Text Full Text PDF PubMed Scopus (314) Google Scholar, 13Conway C.M. Pisoni D.B. Neurocognitive basis of implicit learning of sequential structure and its relation to language processing.Ann. N. Y. Acad. Sci. 2008; 1145: 113-131Crossref PubMed Scopus (118) Google Scholar], engages brain areas within the human language network [14Friederici A.D. The brain basis of language processing: from structure to function.Physiol. Rev. 2011; 91: 1357-1392Crossref PubMed Scopus (957) Google Scholar, 15Petersson K.M. et al.Artificial syntactic violations activate broca's region.Cog. Sci. 2004; 28: 383-407Google Scholar], and is impaired in patients with agrammatic aphasia [13Conway C.M. Pisoni D.B. Neurocognitive basis of implicit learning of sequential structure and its relation to language processing.Ann. N. Y. Acad. Sci. 2008; 1145: 113-131Crossref PubMed Scopus (118) Google Scholar, 16Grube M. et al.Core auditory processing deficits in primary progressive aphasia.Brain. 2016; 139: 1817-1829Crossref PubMed Scopus (44) Google Scholar, 17Christiansen M.H. et al.Impaired artificial grammar learning in agrammatism.Cognition. 2010; 116: 382-393Crossref PubMed Scopus (71) Google Scholar]. This evidence indicates that sequencing operations share neural mechanisms with language-related processes in the human brain. Shared sequence-processing capabilities have been identified behaviorally in human and nonhuman animals and sequence processing has been proposed as a potential precursor to language syntax (e.g., [18Fitch W.T. et al.Pattern perception and computational complexity: Introduction to the special issue.Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 2012; 367: 1925-1932Crossref PubMed Scopus (22) Google Scholar, 19Hurford J.R. The Origins of Grammar: Language in the Light of Evolution II. Oxford University Press, 2012Google Scholar]). Moreover, recent comparative neuroimaging experiments have provided novel evidence for evolutionarily conserved, functionally homologous neural substrates for sequence processing in humans and monkeys [9Wilson B. et al.Auditory sequence processing engages evolutionarily conserved regions of frontal cortex in macaques and humans.Nat. Commun. 2015; 6: 8901Crossref PubMed Scopus (71) Google Scholar, 10Wang L. et al.Representation of numerical and sequential patterns in macaque and human brains.Curr. Biol. 2015; 25: 1966-1974Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar, 20Uhrig L. et al.A hierarchy of responses to auditory regularities in the macaque brain.J. Neurosci. 2014; 34: 1127-1132Crossref PubMed Scopus (53) Google Scholar]. Here, we review the comparative behavioral and neuroimaging studies of sequence learning in humans and monkeys and propose a heuristic model describing the involvement of different regions of frontal cortex, within a distributed network of regions, in processing increasingly complex sequencing relationships. Testing this model will require further research (see Outstanding Questions). The recent identification of evolutionarily conserved neural substrates provides critical evidence that structured sequence-learning tasks can provide important insights into how language evolved and identify which specific conserved neural processes related to language can be unraveled mechanistically in animal models. More generally, this research also has implications for understanding how the brain supports complex behaviors, such as the ability to perceive the order of temporal relationships in the world [21Lashley K.S. The problem of serial order in behavior.in: Jeffress L.A. Cerebral Mechanisms in Behavior. Wiley, 1951: 112-131Google Scholar]. Most nonhuman animals do not organize their vocalizations into complex structured sequences in the same way as humans, songbirds, and a few other species [22Fitch W.T. Jarvis E.D. Birdsong and other animal models for human speech, song, and vocal learning.in: Arbib M. Language, Music and the Brain. MIT Press, 2012: 499-540Google Scholar]. Some monkeys are capable of combining their vocalizations in functionally meaningful ways [23Arnold K. Zuberbuhler K. Language evolution: semantic combinations in primate calls.Nature. 2006; 441: 303Crossref PubMed Scopus (254) Google Scholar], but the combinatorial operations involved are minimal. Furthermore, it is clear that the vocal production systems of extant nonhuman primates differ considerably from the complex sensorimotor systems underpinning speech articulation and combinatorial phonology in the modern human (e.g., [24Bouchard K.E. et al.Functional organization of human sensorimotor cortex for speech articulation.Nature. 2013; 495: 327-332Crossref PubMed Scopus (356) Google Scholar, 25Fitch W.T. The evolution of speech: a comparative review.Trends Cogn. Sci. 2000; 4: 258-267Abstract Full Text Full Text PDF PubMed Scopus (471) Google Scholar]). However, differences in these vocal output capacities do not exclude that nonhuman primates (and other mammals) may have substantial perceptual capacities for assessing the temporal relationships between environmental events [26Petkov C.I. Jarvis E.D. Birds, primates, and spoken language origins: behavioral phenotypes and neurobiological substrates.Front. Evol. Neurosci. 2012; 4: 12Crossref PubMed Scopus (265) Google Scholar]. In this regard, investigative paradigms originally developed to study the sequence-learning capabilities of human adults and infants have been successfully adapted to study similar capacities in a range of nonhuman species, including rodents, songbirds, and a variety of primates [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar, 4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 5Saffran J. et al.Grammatical pattern learning by human infants and cotton-top Tamarin monkeys.Cognition. 2008; 107: 479-500Crossref PubMed Scopus (123) Google Scholar, 6Newport E.L. et al.Learning at a distance ii. Statistical learning of non-adjacent dependencies in a non-human primate.Cogn. Psychol. 2004; 49: 85-117Crossref PubMed Scopus (134) Google Scholar, 7Spierings M.J. ten Cate C. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment.Proc. Natl. Acad. Sci. U.S.A. 2016; 113: E3977-E3984Crossref PubMed Scopus (51) Google Scholar, 8Wilson B. et al.Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.Eur. J. Neurosci. 2015; 41: 568-578Crossref PubMed Scopus (40) Google Scholar, 27Sonnweber R. et al.Non-adjacent visual dependency learning in chimpanzees.Animal Cogn. 2015; : 1-13Google Scholar, 28Ravignani A. et al.Action at a distance: dependency sensitivity in a new world primate.Biol. Lett. 2013; 9: 20130852Crossref PubMed Scopus (48) Google Scholar, 29Murphy R.A. et al.Rule learning by rats.Science. 2008; 319: 1849-1851Crossref PubMed Scopus (127) Google Scholar, 30Hauser M.D. Glynn D. Can free-ranging rhesus monkeys (Macaca mulatta) extract artificially created rules comprised of natural vocalizations?.J. Comp. Psych. 2009; 123: 161-167Crossref PubMed Scopus (38) Google Scholar, 31Toro J. Trobalón J. Statistical computations over a speech stream in a rodent.Percept. Psychophys. 2005; 67: 867-875Crossref PubMed Scopus (166) Google Scholar, 32van Heijningen C.A. et al.Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 20538-20543Crossref PubMed Scopus (136) Google Scholar, 33Gentner T.Q. et al.Recursive syntactic pattern learning by songbirds.Nature. 2006; 440: 1204-1207Crossref PubMed Scopus (399) Google Scholar]. In these tasks, typically using artificial grammar-learning paradigms, participants are first exposed to exemplary sequences of stimuli that contain sequencing regularities or dependencies between the elements in the sequence (sounds, pictures, etc.). The participants are then tested with novel sequences that either follow or violate these regularities to assess which ordering dependencies they can learn and what strategies they might use to do so. The complexity of such dependencies can be parametrically varied to study more or less language-like ordering relationships [4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 34Dehaene S. et al.The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees.Neuron. 2015; 88: 2-19Abstract Full Text Full Text PDF PubMed Scopus (218) Google Scholar] (Figure 1). A relatively simple sequencing operation, for example, might involve recognizing relationships between two adjacent items in a short sequence (e.g., [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar] in Figure 1A), which many species are capable of learning [4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 5Saffran J. et al.Grammatical pattern learning by human infants and cotton-top Tamarin monkeys.Cognition. 2008; 107: 479-500Crossref PubMed Scopus (123) Google Scholar, 6Newport E.L. et al.Learning at a distance ii. Statistical learning of non-adjacent dependencies in a non-human primate.Cogn. Psychol. 2004; 49: 85-117Crossref PubMed Scopus (134) Google Scholar, 8Wilson B. et al.Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.Eur. J. Neurosci. 2015; 41: 568-578Crossref PubMed Scopus (40) Google Scholar, 9Wilson B. et al.Auditory sequence processing engages evolutionarily conserved regions of frontal cortex in macaques and humans.Nat. Commun. 2015; 6: 8901Crossref PubMed Scopus (71) Google Scholar, 27Sonnweber R. et al.Non-adjacent visual dependency learning in chimpanzees.Animal Cogn. 2015; : 1-13Google Scholar, 28Ravignani A. et al.Action at a distance: dependency sensitivity in a new world primate.Biol. Lett. 2013; 9: 20130852Crossref PubMed Scopus (48) Google Scholar, 29Murphy R.A. et al.Rule learning by rats.Science. 2008; 319: 1849-1851Crossref PubMed Scopus (127) Google Scholar, 30Hauser M.D. Glynn D. Can free-ranging rhesus monkeys (Macaca mulatta) extract artificially created rules comprised of natural vocalizations?.J. Comp. Psych. 2009; 123: 161-167Crossref PubMed Scopus (38) Google Scholar, 35Petrides M. Functional specialization within the dorsolateral frontal cortex for serial order memory.Proc. R. Soc. Lond. B: Biol. Sci. 1991; 246: 299-306Crossref PubMed Scopus (134) Google Scholar]. However, even with adjacent relationships ​, demands on learning and memory can increase as greater variability is introduced - for example, when the transitional probabilities between items in longer sequences become less predictable (as illustrated along the X-axis in Figure 1 [4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 5Saffran J. et al.Grammatical pattern learning by human infants and cotton-top Tamarin monkeys.Cognition. 2008; 107: 479-500Crossref PubMed Scopus (123) Google Scholar, 8Wilson B. et al.Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.Eur. J. Neurosci. 2015; 41: 568-578Crossref PubMed Scopus (40) Google Scholar]). Nonadjacent relationships generate a further increase in sequencing complexity (Figure 1B). There is accumulating evidence that some animals are sensitive to temporally separated, nonadjacent sequencing relationships ​[6Newport E.L. et al.Learning at a distance ii. Statistical learning of non-adjacent dependencies in a non-human primate.Cogn. Psychol. 2004; 49: 85-117Crossref PubMed Scopus (134) Google Scholar, 28Ravignani A. et al.Action at a distance: dependency sensitivity in a new world primate.Biol. Lett. 2013; 9: 20130852Crossref PubMed Scopus (48) Google Scholar, 29Murphy R.A. et al.Rule learning by rats.Science. 2008; 319: 1849-1851Crossref PubMed Scopus (127) Google Scholar, 35Petrides M. Functional specialization within the dorsolateral frontal cortex for serial order memory.Proc. R. Soc. Lond. B: Biol. Sci. 1991; 246: 299-306Crossref PubMed Scopus (134) Google Scholar, 36Lu K. Vicario D.S. Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain.Proc. Natl. Acad. Sci. U.S.A. 2014; 111: 14553-14558Crossref PubMed Scopus (35) Google Scholar, 37Endress A.D. et al.Evidence of an evolutionary precursor to human language affixation in a non-human primate.Biol. Lett. 2009; 5: 749-751Crossref PubMed Scopus (27) Google Scholar], in which an item must be held in working memory over time for these relationships to be recognized (Figure 1B). Hierarchically organized sequences, containing nested or recursive relationships between items, have been widely argued to reflect more language-like structures [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar, 38Hauser M.D. et al.The faculty of language: what is it, who has it, and how did it evolve?.Science. 2002; 298: 1569-1579Crossref PubMed Scopus (2666) Google Scholar, 39Friederici A.D. et al.The brain differentiates human and non-human grammars: functional localization and structural connectivity.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2458-2463Crossref PubMed Scopus (455) Google Scholar] (Figure 1C [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar]). However, despite several studies testing whether birds or monkeys can process these more complex sequences [3Fitch W.T. Hauser M.D. Computational constraints on syntactic processing in a nonhuman primate.Science. 2004; 303: 377-380Crossref PubMed Scopus (419) Google Scholar, 27Sonnweber R. et al.Non-adjacent visual dependency learning in chimpanzees.Animal Cogn. 2015; : 1-13Google Scholar, 33Gentner T.Q. et al.Recursive syntactic pattern learning by songbirds.Nature. 2006; 440: 1204-1207Crossref PubMed Scopus (399) Google Scholar, 40Abe K. Watanabe D. Songbirds possess the spontaneous ability to discriminate syntactic rules.Nat. Neurosci. 2011; 14: 1067-1074Crossref PubMed Scopus (136) Google Scholar], it remains unclear whether nonhuman animals can learn such relationships [32van Heijningen C.A. et al.Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 20538-20543Crossref PubMed Scopus (136) Google Scholar, 41Beckers G.J.L. et al.Birdsong neurolinguistics: songbird context-free grammar claim is premature.Neuroreport. 2012; 23: 139-145Crossref PubMed Scopus (79) Google Scholar]. While this leaves unresolved the ultimate limits of nonhuman sequence-processing capabilities and where the cognitive capabilities of humans and other animals may diverge [42Corballis M.C. The Recursive Mind: The Origins of Human Language, Thought, and Civilization. Princeton University Press, 2014Crossref Google Scholar, 43Fitch W.T. Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition.Phys. Life Rev. 2014; 11: 329-364Crossref PubMed Scopus (119) Google Scholar] (see Outstanding Questions), the current artificial grammar-learning literature demonstrates that several species have significant capabilities in this domain, involving both adjacent and nonadjacent sequencing operations [4Wilson B. et al.Auditory artificial grammar learning in macaque and marmoset monkeys.J. Neurosci. 2013; 33: 18825-18835Crossref PubMed Scopus (70) Google Scholar, 5Saffran J. et al.Grammatical pattern learning by human infants and cotton-top Tamarin monkeys.Cognition. 2008; 107: 479-500Crossref PubMed Scopus (123) Google Scholar, 6Newport E.L. et al.Learning at a distance ii. Statistical learning of non-adjacent dependencies in a non-human primate.Cogn. Psychol. 2004; 49: 85-117Crossref PubMed Scopus (134) Google Scholar, 8Wilson B. et al.Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.Eur. J. Neurosci. 2015; 41: 568-578Crossref PubMed Scopus (40) Google Scholar, 9Wilson B. et al.Auditory sequence processing engages evolutionarily conserved regions of frontal cortex in macaques and humans.Nat. Commun. 2015; 6: 8901Crossref PubMed Scopus (71) Google Scholar, 27Sonnweber R. et al.Non-adjacent visual dependency learning in chimpanzees.Animal Cogn. 2015; : 1-13Google Scholar, 28Ravignani A. et al.Action at a distance: dependency sensitivity in a new world primate.Biol. Lett. 2013; 9: 20130852Crossref PubMed Scopus (48) Google Scholar, 29Murphy R.A. et al.Rule learning by rats.Science. 2008; 319: 1849-1851Crossref PubMed Scopus (127) Google Scholar, 30Hauser M.D. Glynn D. Can free-ranging rhesus monkeys (Macaca mulatta) extract artificially created rules comprised of natural vocalizations?.J. Comp. Psych. 2009; 123: 161-167Crossref PubMed Scopus (38) Google Scholar, 35Petrides M. Functional specialization within the dorsolateral frontal cortex for serial order memory.Proc. R. Soc. Lond. B: Biol. Sci. 1991; 246: 299-306Crossref PubMed Scopus (134) Google Scholar, 36Lu K. Vicario D.S. Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain.Proc. Natl. Acad. Sci. U.S.A. 2014; 111: 14553-14558Crossref PubMed Scopus (35) Google Scholar, 37Endress A.D. et al.Evidence of an evolutionary precursor to human language affixation in a non-human primate.Biol. Lett. 2009; 5: 749-751Crossref PubMed Scopus (27) Google Scholar]. The neural substrates supporting these capacities can now be studied across species using neurobiological techniques. In humans, sequence-learning and processing tasks can engage similar regions of frontal cortex to those involved in processing natural language [14Friederici A.D. The brain basis of language processing: from structure to function.Physiol. Rev. 2011; 91: 1357-1392Crossref PubMed Scopus (957) Google Scholar, 15Petersson K.M. et al.Artificial syntactic violations activate broca's region.Cog. Sci. 2004; 28: 383-407Google Scholar]. The level of engagement of frontal regions depends critically on the complexity of the sequencing operations [39Friederici A.D. et al.The brain differentiates human and non-human grammars: functional localization and structural connectivity.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2458-2463Crossref PubMed Scopus (455) Google Scholar, 44Bahlmann J. et al.Neural circuits of hierarchical visuo-spatial sequence processing.Brain Res. 2009; 1298: 161-170Crossref PubMed Scopus (77) Google Scholar, 45Petersson K.M. et al.What artificial grammar learning reveals about the neurobiology of syntax.Brain Lang. 2012; 120: 83-95Crossref PubMed Scopus (127) Google Scholar], with adjacent sequencing operations involving more ventral frontal regions, such as the frontal operculum, while more complex nonadjacent or hierarchically structured relationships also involve Brodmann Areas 44/45 (BA 44/45) [14Friederici A.D. The brain basis of language processing: from structure to function.Physiol. Rev. 2011; 91: 1357-1392Crossref PubMed Scopus (957) Google Scholar, 45Petersson K.M. et al.What artificial grammar learning reveals about the neurobiology of syntax.Brain Lang. 2012; 120: 83-95Crossref PubMed Scopus (127) Google Scholar], including Broca's area in the left hemisphere. Friederici and colleagues hypothesized that the function of the frontal operculum might be evolutionarily conserved, serving a homologous role in processing adjacent sequencing relationships in humans and extant nonhuman primates (frontal operculum hypothesis [39Friederici A.D. et al.The brain differentiates human and non-human grammars: functional localization and structural connectivity.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 2458-2463Crossref PubMed Scopus (455) Google Scholar, 46Friederici A.D. Processing local transitions versus long-distance syntactic hierarchies.Trends. Cogn. Sci. 2004; 8: 245-247Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar]). By contrast, BA 44 and BA 45 appear (at least in part) to be functionally specialized for linguistic operations not present in nonhuman primates, even though area 44 in monkeys is known to be cytoarchitectonically comparable to BA 44 in humans [47Petrides M. et al.Orofacial somatomotor responses in the macaque monkey homologue of Broca's area.Nature. 2005; 435: 1235-1238Crossref PubMed Scopus (271) Google Scholar]. A second, complementary evolutionary hypothesis suggests that human language and communication are supported by two distinct but interacting neurobiological systems [48Bozic M. et al.Bihemispheric foundations for human speech comprehension.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 17439-17444Crossref PubMed Scopus (114) Google Scholar]. Specializations for core syntactic language functions are thought to depend on a left-lateralized frontotemporal system. This system is functionally integrated with an evolutionarily older, more bilaterally distributed network, which is proposed to have more general language-related communicative functions and is shared with nonhuman primates (dual neurobiological language systems hypothesis [49Marslen-Wilson W.D. et al.Morphological systems in their neurobiological contexts.in: Gazzaniga M.S. Cognitive Neuroscience. 5th ed. MIT Press, 2014: 639-647Google Scholar]). Recent comparative neuroimaging studies are beginning to test these hypotheses and to ask whether similar sequence-processing behaviors are supported by the same or different neural substrates in humans and other primates. We first consider oddball sound detection paradigms, which provide an important point of reference for the sequence-ordering operations that we consider later. The neural processing of oddball sounds has been investigated in depth in both human and nonhuman animals (e.g., [50Ulanovsky N. et al.Processing of low-probability sounds by cortical neurons.Nat. Neurosci. 2003; 6: 391-398Crossref PubMed Scopus (732) Google Scholar, 51Fishman Y.I. Steinschneider M. Searching for the mismatch negativity in primary auditory cortex of the awake monkey: deviance detection or stimulus specific adaptation?.J. Neurosci. 2012; 32: 15747-15758Crossref PubMed Scopus (117) Google Scholar]). Two recent studies used deviance detection paradigms to determine how the brains of rhesus macaque monkeys respond either to unexpected oddball sounds or to the absence of an expected sound in a sequence [10Wang L. et al.Representation of numerical and sequential patterns in macaque and human brains.Curr. Biol. 2015; 25: 1966-1974Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar, 20Uhrig L. et al.A hierarchy of responses to auditory regularities in the macaque brain.J. Neurosci. 2014; 34: 1127-1132Crossref PubMed Scopus (53) Google Scholar]. In this paradigm, a standard sequence of 'A' tones is infrequently interrupted by a deviant 'B' tone at a different pitch (i.e., 'AAAB'). In both humans and monkeys, fMRI studies show that oddball sound det