**Contents**

### **About the Editor**

**Victor Borovkov** (Prof. Dr.) received a Ph.D. degree from the Moscow Institute of Fine Chemical Technology (Russia) in 1988. Following a postdoctoral period at Osaka University, he worked at different research organizations in Japan for over twenty years, at the Tallinn University of Technology (Estonia) as a senior research scientist, and at the South-Central University for Nationalities (China) as a full professor. He is a member of the editorial boards of several scientific journals, an author of more than 145 publications, including research papers, reviews, and books, with an h-index of 29 (WOS and Scopus) and 30 (Google Scholar), and 10 patents, and serves as an external peer reviewer of various international journals and scientific foundations. He has headed and participated in different international and industrial collaboration projects and attended numerous international and national conferences as an invited speaker and a member of advisory committees. His current research interests include the chemistry of porphyrins and related macrocycles, supramolecular chemistry, nanotechnology, chirality science, functional chiral materials, and asymmetric catalysis.

### **Preface to "Chiral Auxiliaries and Chirogenesis II"**

Chirality is an inevitable property of our universe, having an enormous impact in different fields ranging from nuclear physics and astronomy to living organisms and human beings. Furthermore, chirality has important applications in various technological processes including pharmaceutical and agrochemical manufacturing. Therefore, in order to highlight the importance of this phenomenon in chemical science, the first Special Issue on "Chiral Auxiliaries and Chirogenesis" was launched in 2017 [1]. This Special Issue has attracted much attention from the scientific community and resulted in a high scientific impact, on the basis of the articles' access statistics and citation indexes (with up to 25 citations as of April 16th 2021 [2]).

This success prompted us to initiate a subsequent Special Issue entitled "Chiral Auxiliaries and Chirogenesis II" on the same subject in 2019, which was completed in 2021. The aim of this second Special Issue, which consisted of one review paper and six research articles, was to address some particular aspects of chiral auxiliary and chirogenesis which were not covered by the first Special Issue.

In particular, Trubitson˜ and Kanger, in their review [3], described new methodologies for the synthesis of chiral N-functionalized indoles. The synthesis of enantioenriched indole derivatives is of great importance in organic chemistry, especially in light of their potential application in the pharmaceutical industry. The review illustrated efficient applications of organocatalytic and organometallic strategies for the construction of chiral α-N-branched indoles. Both the direct functionalization of the indole core and indirect methods based on asymmetric N-alkylation of indolines, isatins, and 4,7-dihydroindoles were discussed.

Another important class of chiral molecules is macrocycle compounds. Recently, the chirality of a novel group of emerging host molecules, pillar[n]arenes, has attracted increasing attention due to their potential applications for chiral induction, molecular recognition, and asymmetric catalysis. In the research communication [4], Xiao et al. reported the synthesis and successful resolution of planar (P*R*)- and (P*S*)-enantiomeric Boc-protected pillar[4]arene[1]diaminobenzene. Their racemization kinetics were studied. It was shown that hexane and CH2Cl2 can maintain the enantiomeric forms for long periods, because of the complexation of the solvent molecules with the cavity of pillar[4]arene[1]diaminobenzene. The racemization process was accelerated by increasing the temperature or the use of solvents that cannot thread into the cavity of these molecules or can destroy intramolecular hydrogen bonds. This study has provided, for the first time, thermodynamic parameters of the pillararenes in different solvents that will serve as an important guideline in studying the conformational properties of pillar[n]arenes.

A crucial problem of self-induced diastereomeric anisochronism (SIDA) in NMR has been addressed by Baumann et al. in their feature article [5]. This phenomenon may occur when chiral molecules that associate in solution in a dynamic equilibrium that is fast on the NMR timescale have significant condition-dependent NMR chemical shifts. This study was carried out by using alcohol and ester derivatives and highlighted the potential problems that SIDA can cause. Additionally, scalemic samples of both the alcohol and ester compounds were firstly reported to exhibit the self-disproportionation of enantiomers phenomenon by preparative TLC.

Chiral porphyrinoids have important implementations in the fields of chiral sensors, biomimetic functions, asymmetric catalysis, and other applications. Setsune et al. [6] described the synthesis and magnetic properties of linear trinuclear CuII and NiII complexes of porphyrin analogs embedded with chiral binaphthol units. It was found that the observed paramagnetic shifts in the pyrrolic ligand and the binaphthyl ligand could be used to estimate spin delocalization from the terminal metal and the central metal, respectively, and these paramagnetic 1H NMR data were consistent with the spin densities calculated. Additionally, the strong antiferromagnetic coupling observed for both CuII3 and NiII3 complexes could be ascribed to the unique coordination geometry that was also responsible for the reversible ligation of butylamine only at the central metal ion without decomposition of the trinuclear core. The reported multinuclear complexes of an enantiomerically pure helical porphyrin analog are expected to lead to further exploration of the helical multinuclear complexes.

Further expansion of supramolecular chirogenesis was examined by Osadchuk et al. in [7]. In particular, a comprehensive study on the complexation of ethane-bridged bis(zinc octaethylporphyrin), as a host, with a chiral guanidine derivative, as a guest, was carried out by means of ultraviolet–visible and circular dichroism absorption spectroscopies, single crystal X-ray diffraction, and computational simulation. The formation of a 1:2 host–guest complex was established by X-ray diffraction and spectroscopic titration studies. Such supramolecular organization of the complex results in a screw arrangement of the two porphyrin subunits, inducing a strong circular dichroism signal in the porphyrin Soret band region. The corresponding computational studies were in a good agreement with the experimental results. This study was one of the rare examples of comprehensive circular dichroism analysis of chirality induction in bis-porphyrins caused by external chiral ligands, which can be a benchmark approach for the rationalization of supramolecular chirogenesis in bis-porphyrins. Furthermore, the obtained results demonstrate the necessity of careful consideration of all external and internal factors that influence the supramolecular organization of a complex to attain the best match between experimental and simulated circular dichroism spectra.

In a related chirogenic study, Mądry et al. reported a new sensitive stereodynamic reporter for primary amines operating on the basis of the point-to-axial chirality mechanism [8]. The throughspace inductor–reporter interactions forced a change in the chromophore conformation toward one of the diastereomeric forms. The structure of the reporter, with the terminal flipping biphenyl groups, led to generating Cotton effects in both lower- and higher-energy regions of the circular dichroism spectrum. The reporter system turned out to be sensitive to the subtle differences in the inductor structure. Despite the size of the chiral substituent, the molecular structure of the inductor– reporter systems in the solid state showed many similarities. The most important one was the tendency of the core part of the molecules to adopt a pseudocentrosymmetric conformation. Supported by a weak dispersion and Van der Waals interactions, the face-to-face and edge-to-face interactions between the π-electron systems present in the molecule were found to be responsible for the molecular arrangement in the crystal.

The last article in this Special Issue, by Fujiki et al., was devoted to one of the most fundamental questions of chirogenesis [9]. It is known that non-charged semi-flexible and rod-like helical copolymers and π–π molecular stacks reveal sergeants-and-soldiers (Ser-Sol) and majority-rule (Maj) effects in dilute solutions and as a suspension in fluidic liquids. A question remained unanswered as to whether the Ser-Sol and Maj effects between non-charged rod-like helical polysilane copolymers and non-charged, non-helical π-conjugated homopolymers occur when these polysilane copolymers encounter the π-polymer in the co-colloidal systems. Based on different types of chiral polysilane copolymers and detailed analyses of circular dichroism and circularly polarized luminescence results, this paper discussed the origins of noticeable non-mirror-symmetrical Ser-Sol and Maj effects in terms of macroscopic parity violation that differed from rigorous criteria of a molecular parity violation hypothesis. The comprehensive helicity/chirality transfer experiments in the artificial

helical/non-helical polymer co-colloids in the tuned refractive index optofluidic media may lead to possible answers to several unresolved questions in the realms of molecular biology, stereochemistry, supramolecular chemistry, and polymer chemistry: (i) whether mirror symmetry on macroscopic levels is rigorously conserved, (ii) why nature chose L-amino acids and five-membered D-furanose (not six-membered D-pyranose) in DNA/RNA?

While this Special Issue has just been completed, the scientific interest in the published articles is constantly increasing, on the basis of the articles' statistics (with up to four citations as of April 16th 2021 [10]). This may result in a new Special Issue covering additional chirality topics to be launched in *Symmetry* in the near future.


The vascular permeability factor assay is a dermal test wherein the activity of CT or LT is determined by the neutralization of standardized amount of specific antisera against CT. Here 20–30 supernatants can be injected per rabbit. A cell-free supernatant is injected intradermally into the rabbit, following which Evans blue dye is injected intravenously. The increased capillary permeability mediated by CT leads to perfusion of the dye in the skin causing a blue coloration with localized induration at the site of injection. The diameter of discoloration and

Tissue culture methods are widely used in laboratories nowadays for the toxin assay. It requires skilled professionals and equipment in the laboratory with existing tissue culture facilities. It is a very sensitive assay with high reproducibility. In addition to detecting toxins,

The Chinese hamster ovary (CHO) and the Y1 mouse adrenal (Y1) cell lines are used for the assay of CT and LT, although Vero monkey kidney cells are also used. The toxin if present in the cell-free supernatant increases the intracellular production of cAMP in the cell lines. This can be made out by the morphological changes in the cell lines (Y1 cells round and CHO cells

As CT and LT are immunogenic to both humans and animals, many immunogenic assays have been developed to detect the presence of toxins in vitro. As discussed earlier, the GM1 ganglioside is the natural receptor for CT. In ELISA method, this GM1 ganglioside receptor is purified and is used for the preparation of ganglioside-capture enzyme-linked immunosorbent assay. The cell-free supernatant in which the toxicity is to be tested is added to the microtiter wells coated with GM1 ganglioside. CT antiserum is then added on the plate to detect the binding of the toxin with the bound receptor on the plate, followed by the addition

In this assay, highly purified specific anti-CT is bound to latex particles. A commercial version of kit is available in the market which allows easy detection of cholera toxins in the laboratory.

induration is measured with a negative control [1, 12].

of enzyme-conjugated antiglobulin antibody [12].

these assays are also useful in detecting toxin-neutralizing antibodies.

*<IP_ADDRESS>. Tissue culture methods*

140 Bacterial Pathogenesis and Antibacterial Control

elongate) [7, 12].

*<IP_ADDRESS>. Elisa*

*5.4.2. Immunoassays*

*<IP_ADDRESS>. Latex agglutination*

sample.

#### *<IP_ADDRESS>. Polymerase chain reaction (PCR)*

PCR is a molecular approach based on specific DNA sequences. In PCR, DNA polymerase enzyme is used to amplify certain specific sequences of DNA (amplicons) which can be detected on an agarose gel or using specific DNA probes [23]. The toxigenicity of *V. cholerae* can be detected by using PCR and primers that specifically amplify only CT genes. The advantage of PCR is that it does not require a pure culture or even viable strains. The toxigenicity of the organism can be detected from the infected stool samples, contaminated water or food samples, thus determining the presence of *Vibrio* even without culturing it. The PCR method also has the advantage of detecting CT toxins that is produced in very low levels by certain strains, by identifying ctx genes possessed by them [12, 23]. The technical problems associated with radioisotopic labels have been solved by non-radioactive DNA labels such as biotin and digoxigenin. Apart from the fact that DNA-based assays require skilled professionals and sophisticated laboratories, these methods provide reliable, specific and rapid results unlike the conventional methods.

**Table 4.** Commonly used assays for the detection of cholera toxin.

#### **6. Conclusion**

*V. cholerae* is one among the most successful emerging and reemerging pathogens that has both human and environmental components in its life cycle. The epidemic and pandemic occurrence of *V. cholerae* especially *V. cholerae* O139 has been a turning point in the history of the most dreaded diarrhoeal disease—cholera. The conventional methods of screening and identification of these Gram-negative, motile bacilli are not sufficient enough for the rapid diagnosis of cholera. Although molecular methods have evolved for the identification of most of the serotypes and biotypes of *V. cholerae,* the most important factor to control the epidemic and pandemic of the dreaded, fatal, life-threatening disease is to sanitize the food and water environment as the most important prophylactic measure. India, Bangladesh and Sri Lanka have been victims of cholera epidemics, and now it is affecting a multitude of countries. Being developing countries, the concerned governments along with medical professionals should come up with certain safety measures and evoke the necessity of sanitization and cleanliness of the food and water systems to all people. Along with the above-said preventive measures, researchers and medical professionals are on the verge of producing an effective vaccine which will enable to boost the immune system of the individuals. As mentioned in the chapter, the antigenic variation and shift of the organism are the hindrances in producing an effective vaccine. If people are immunized broadly, herd immunity results, with a decrease in the extent of contamination in the environment.

**References**

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[4] Nelson EJ, Harris JB, Morris JG Jr, et al. Cholera transmission: The host, pathogen and

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[6] Centers for Disease Control and Prevention (CDC). Update: outbreak of cholera—Haiti,

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[8] Harris JB, LaRocque RC, Chowdhury F, et al. Susceptibility to vibrio cholerae infection in a cohort of household contacts of patients with cholera in Bangladesh. PLoS Neglected

[9] Levine M, Nalin DR, Rennels MB, Hornick RB, Sotman S, Hughes TP, O'Donnell, Barua D. Genetic susceptibility to cholera. Annals of Human Biology. 2009;**6**(4):369-374. DOI:

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#### **6.1. Future perspectives of the chapter**

The Centers for Disease Control and Prevention (CDC) is always one step ahead in investigating any cholera outbreaks across the globe. They have trained professionals and sophisticated laboratory protocols for identification of toxigenic vibrios and emergence of any new antigenically modified strain as the Haiti outbreak. The WHO in collaboration with the CDC also provides information to the public and the health officials regarding the necessity of proper sanitization and preventive control measures to overcome the dreaded disease. The antigenic variation of the *V. cholerae* strains is one of the major limitations and hindrances in developing a 100% protective vaccination against it. The successful accomplishment of an effective vaccine will pave the way for a cholera-free globe so that the developed and the developing countries will not be able to face any more epidemics like the Haiti's and the West Bengal O139.

#### **Author details**

Tirumale Sharmila\* and Tessy Anu Thomas

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Microbiology and Biotechnology, Jnana Bharathi Campus, Bangalore University, Bangalore, India

### **References**

**6. Conclusion**

142 Bacterial Pathogenesis and Antibacterial Control

extent of contamination in the environment.

Tirumale Sharmila\* and Tessy Anu Thomas

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Microbiology and Biotechnology, Jnana Bharathi Campus, Bangalore

**6.1. Future perspectives of the chapter**

West Bengal O139.

**Author details**

University, Bangalore, India

*V. cholerae* is one among the most successful emerging and reemerging pathogens that has both human and environmental components in its life cycle. The epidemic and pandemic occurrence of *V. cholerae* especially *V. cholerae* O139 has been a turning point in the history of the most dreaded diarrhoeal disease—cholera. The conventional methods of screening and identification of these Gram-negative, motile bacilli are not sufficient enough for the rapid diagnosis of cholera. Although molecular methods have evolved for the identification of most of the serotypes and biotypes of *V. cholerae,* the most important factor to control the epidemic and pandemic of the dreaded, fatal, life-threatening disease is to sanitize the food and water environment as the most important prophylactic measure. India, Bangladesh and Sri Lanka have been victims of cholera epidemics, and now it is affecting a multitude of countries. Being developing countries, the concerned governments along with medical professionals should come up with certain safety measures and evoke the necessity of sanitization and cleanliness of the food and water systems to all people. Along with the above-said preventive measures, researchers and medical professionals are on the verge of producing an effective vaccine which will enable to boost the immune system of the individuals. As mentioned in the chapter, the antigenic variation and shift of the organism are the hindrances in producing an effective vaccine. If people are immunized broadly, herd immunity results, with a decrease in the

The Centers for Disease Control and Prevention (CDC) is always one step ahead in investigating any cholera outbreaks across the globe. They have trained professionals and sophisticated laboratory protocols for identification of toxigenic vibrios and emergence of any new antigenically modified strain as the Haiti outbreak. The WHO in collaboration with the CDC also provides information to the public and the health officials regarding the necessity of proper sanitization and preventive control measures to overcome the dreaded disease. The antigenic variation of the *V. cholerae* strains is one of the major limitations and hindrances in developing a 100% protective vaccination against it. The successful accomplishment of an effective vaccine will pave the way for a cholera-free globe so that the developed and the developing countries will not be able to face any more epidemics like the Haiti's and the

[15] Todar K. *Vibrio cholerae* and Asiatic cholera. Todar's Online Textbook of Bacteriology.

[16] Farmer P, Almazor CP, Bahnsen ET. Meeting cholera's challenge to Haiti and the world: A joint statement on cholera prevention and care. PLoS Neglected Tropical Diseases.

[17] Merrell DS, Butler SM, Qadri F. Host-induced epidemic spread of the cholera bacterium.

[19] Reidl J, Klose KE. Vibrio cholerae and cholera: Out of the water and into the host. FEMS

[20] Sehdev PS. The origin of quarantine. Clinical Infectious Diseases. 2002;**35**(9):1071-1072.

[21] Sinclair D, Abba K, Zaman K, Qadri F, Graves PM. Oral vaccines for preventing cholera. Cochrane Database of Systematic Reviews. 2007;**3**:CD008603. DOI: 10.1002/14651858.

[22] King AA, Ionides EL, Luckhurst J, Bouma MJ. Inapparent infections and cholera dynam-

[23] Graves PM, Deeks JJ, Demicheli V, Jefferson T. Vaccines for preventing cholera: Killed whole cell or other subunit vaccines (injected). Cochrane Database of Systematic

Microbiology Reviews. 2002;**26**(2):125-139. DOI: 10.1111/j.1574-6976.2002

http://www.textbookofbacteriology.net/cholera.html. Retrieved 2010-12-20

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ics. Nature. 2008;**454**(7206):877-880. DOI: 10.1038/nature07084

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[18] Cholera's seven pandemics. CBC News. October 22, 2010.

2011:5e1145

144 Bacterial Pathogenesis and Antibacterial Control

DOI: 10.1086/344062

CD008603.pub2

## *Edited by Sahra Kırmusaoğlu*

Bacterial pathogens have been becoming the main problem in hospital and communityacquired infections. It is hard to treat the strains that are resistant to antibiotics, due to the causing recurrent and untreatable infections. In recent years, the combination treatments and the novel technologies have been preferred to overcome the emergence of antibacterial resistance of pathogens. In this book, examples of pathogenesis by clinical cases, control by antibiotics and bioactive antimicrobials, control by novel technologies with the collection of up-to-date researches and reviews are presented. This book can be useful for researchers interested in antibacterials, bioactive compounds, and novel technologies.

Published in London, UK © 2018 IntechOpen © Sinhyu / iStock

Bacterial Pathogenesis and Antibacterial Control

Bacterial Pathogenesis

and Antibacterial Control


**8. Final Conclusions**

The theoretical part of this article presents the meaning of sporting events for tourism industry and indicates the negative and positive effects this kind of tourism brings to host cities. The whole refers to the theoretical foundations of the term of "overtourism." The second part presents results of empirical research was conducted by the method of diagnostic surveys during two sporting events of different sporting rank, which took place in Poland and represented various sports disciplines—running and horse riding. The case study is the city of Poznan and two, well-known events in this agglomeration. The first one is Poznan Half Marathon—mass sports event, the second one is Cavaliada—elite equestrian event. A total of 774 respondents took part in the study, resident hosts who took part in the studied events. The main goal of this study was to investigate the impact of non-mega sporting events on quality of the host's life. The detailed aim of the study was to examine whether the inhabitants of the city feel the negative effects of organizing sporting events (communication problems or inappropriate behavior of supporters) and do they believe that these events increase the level of crime in the city or, despite these inconveniences, they are satisfied with the organization of sporting events in their place of residence. This phenomenon can also be referred to the social exchange theory (SET) to analyze the perceptions of residents. Many authors [135,137–139] have continuously drawn on this theory. For example, Homans [137], the author of SET has been applied it to variety of leisure disciplines, to understand the views of local residents with regard to tourism. Harrill [139] states that SET involves the trading and sharing of resources between individuals and groups. Ap [140] things that not only highlights the exchange of resources but has also been expanded to include the mutual benefits that all exchange participants can get. In applying this to tourism, research by Teye et al. [141] indicates that perceived benefits associated with host community improvement led to support of residents. Moreover, Waitt [142] has found that enthusiasm of the residents and support varied according to how tourism events were perceived either positively according to the benefits derived or negatively with respect to any costs incurred from what they supplied. It is essential that event organizers and affiliates consider the local voices about the sport tourism event.

The results of the presented research show that both athletes as well as fans of the Half Marathon said that the Poznan Half Marathon causes bothersome communication problems in the city. Cavaliada, as an international equestrian event, causes onerous communication difficulties only in the opinion of 31.5% of researched respondents. Therefore, the Half Marathon made public transport much more difficult than Cavaliada situated in one place—Poznan Trade Centre. It also turned out that in the opinion of most of the researched fans and athletes, the surveyed events did not cause inadequate behavior of fans in the city or an increase in crime in the city and people are satisfied with the organization of these events. It turns out that the inhabitants, despite minor inconveniences that are felt as a result of organizing sporting events in the city, must also see the benefits. The negative impact of Cavaliada was very low. For checking the difference between the two examined groups of

respondents: Half Marathon fans and Cavaliada fans, Chi-square test and Mann Whitney's test was used. The participants feel bothersome communication problems that cause the Half Marathon and have an average level of dissatisfaction higher than the average level of dissatisfaction of Cavaliada participants. Moreover, the participants of the half marathon have an average level of satisfaction with the organization of sports events in Poznan significantly lower than the average level of satisfaction of Cavaliada participants. Therefore, an elite sporting event is less burdensome for its residents and gives them more satisfaction. They do not think the event will harm them. However, they are aware of social exchange and profits for the city. The conclusions and reflections resulting from these studies can be used by organizers of non-sporting events and be their inspiration. Importantly, the inhabitants play an important role in the development of sustainable tourism, because they are cultural agents and the social group in which tourism is provided and local hospitality is a key element of the tourism product [143].

The paper provides data that may be useful for support marketing events such as half marathons. The popularity of sporting events participation fulfills a number of important sociocultural functions in the modern world. The most important include enabling sports tourists to build a sense of connection and integration with other people, thanks to which sports events become a postmodern form of participation in social life. Further research should go towards recognizing the importance and impact of small sporting events on people's lives and their environment. These types of events are definitely less recognized but their growing popularity indicates great importance for the development of cities and regions, tourism and economics.

**Author Contributions:** Conceptualization, E.M.-M.; methodology, E.M.-M.; software, J.P.; validation, E.M.-M.; formal analysis, J.P., E.M.-M.; investigation, E.M.-M.; resources, J.P., E.M.-M., A.D., M.K., F.G.; data curation, J.P., E.M.-M.; writing—original draft preparation, J.P.; writing—review and editing, J.P.; visualization, J.P.; supervision, E.M.-M., J.P.; project administration, J.P., E.M.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.


These frameworks indicate that human behaviors and mental or physical structures can be treated as combinatorial objects. Boeckx (2017) claims that the neural basis of recursion is realized from the pairing of the fronto-parietal and fronto-temporal networks. He takes it that although both networks may be of the finite-state variety, pairing two finite-state devices could have the effect of boosting computational possibilities. Instead of operating on one-dimensional sequences, one now operates on two-dimensional tree representations. The fronto-parietal network may have the role of the global workspace as proposed by Dehaene et al. (1998). The global workspace is inherently hierarchical: It sits on top of modular networks of other cognitive domains and acts as a chunking device in a sequence producer. If this device is to be integrated with another sequencing machine, sequences of sequences would naturally emerge. Once this network is established, a variety of cognitive domains coopt and account for other aspects of human-specific cognition (Boeckx, 2017). In this paper, the domain-general characteristics of discrete object combination and recursion are focused upon these.

When do object recognition and its recursive manipulation advance? Recursive combination has been observed in the object manipulation of animals and has also been researched in a cup-combining experiment with human infant participants (Greenfield et al., 1972). Greenfield posited the notion of a grammar of action, or in other words, a set of syntactic rules for behaviors such as object manipulation. Sequential behaviors are classified into two strategies in the framework of action grammar: the pot strategy and the sub-assembly strategy, visualized below (**Figure 3**) with the manipulation of cups used to illustrate object manipulation.

It has been noted that sub-assembly strategy or other equally similar behavior such as tool-making is rarely observed in animal behavior (Greenfield, 1991; Conway and Christiansen, 2001). Therefore, it is assumed to be a precursor of recursive combination in syntax (Maynard Smith and Szathmáry, 1995; Fujita, 2009, 2016). Although Chomsky and Berwick (2015) insist that the recursive combination, Merge, abruptly appeared at some time in human evolution, we assume that it was a gradual evolutionary scenario. We further presume that object manipulation of a physical entity was a pre-adaptation to recursive syntactic operation, and the target of manipulation was qualitatively generalized. This is a reasonable hypothesis derived from the following evidence in addition to the results of comparative cognitive experiments and analysis in archeology:

hierarchical structure of the action sequences. The Oldowan tool is generally produced by making stone flakes from a stone core. Making stone flakes from a stone core is called the flaking process. The Acheulian tool is produced by shaping a large stone flake in combination with this flaking process. This production method reflects hierarchically organized higher order intention and suggests that recursive combination of action sequences is followed. In addition, Stout (2011) illustrates stone tool-making using tree diagram. Stout shows that hominins used recursive combination in a production sequence with sub-goals when making stone tools. These are dated earlier than the appearance of symbolic behavior in human evolution (Mithen, 1996). It suggests that the recursive combination of objects pre-dated the recursive combination of lexical items.

These findings suggest that humans might have acquired recursive combination (that is a different evolutionary effect of language on communication such as sharing information) through an action sequencing process such as tool-making. Henceforth, we term the pot and sub-assembly strategies nonrecursive combination and recursive combination, respectively.

The hypothesis that social recognition and population size cause recursive mental structure is reasonable because it assumes an evolutionary continuity carried over from nonhuman animals (Dunbar, 2009; Oesch and Dunbar, 2017). According to this hypothesis, recursive thinking became the necessary cognitive scaffolding. Dunbar claims that recursion in the language structure is boot-strapped by a primitive mentalizing ability as evidenced by an experiment that investigated correlation between recursive syntax and intentionality. However, it must be noted that recursion, which is often assumed to be the subordinate clause in a sentence is not equal to "recursive combination" in this paper. Recursive combination means "combination of combined objects," thus this interpretation of recursion can also be applied to mental object manipulation like mind-reading. We will elaborate on this point later in the Discussion section.

It is most important that we answer the following questions. What is the evolutionary process of recursive combination? What does the adaptability of the recursive combination consist of, if the process is adaptive evolution?

According to Tinbergen (1963), adaptability (which is effectiveness in survival and reproduction) is an important aspect used to explain the characteristics of animals. Although the adaptability of the human ability of recursive combination has been investigated in comparative cognitive science; similar traits have not yet been discovered. Furthermore, the phenomenon of evolution can only be observed in living things that have rapid generation alternation. The evolution of higher cognitive ability is not that easily studied. This problem can be solved partially by using simulations (Hashimoto, 2001). The advantage of simulation is that it allows the elaboration of hypotheses and the consideration of evolutionary processes. This is enabled by repeating the experiments in a constructive environment on a phenomenon that is difficult to observe empirically. It is not possible to prove a hypothesis solely by using this method. However, we can explain the process of the generation of a system (in this research, capacity of agent and ecological environment) causing a specific phenomenon (the evolution of recursive combination) by reproducing the phenomenon by implementing and operating a model derived from the hypothesis.

In this paper, we study the evolutionary process and adaptability of recursive combination using evolutionary simulations. The objectives are (1) to demonstrate the conditions in which recursive combination could have evolved, and (2) the possible evolutionary processes by which recursive combination could have evolved. We will claim that recursive combination has two adaptabilities; the diversifiability of production methods that promotes the secure manufacturing of the target product and the diversifiability of products by the reuse of parts of manufacturing processes that are already acquired. Two factors promote these adaptabilities: (1) extending the time available for making products, and (2) decreasing the cost of object manipulation. As a possible evolutionary process, it is necessary to increase the opportunity for production and reduce the manipulation cost before the evolution of recursive combination.

The rest of this paper is organized as follows: (1) The simulation model to examine whether agents evolve to be capable of recursive combination is described in section Materials and Methods. (2) The simulation results and resulting considerations for the model are presented in section Results. (3) A discussion based on the simulation results in consideration with other results is delivered in section Discussion. (4) The conclusion is delivered in section Conclusion.

## MATERIALS AND METHODS

In this section, (1) the concepts and mechanisms of genetic algorithm (GA) and evolutionary simulation are introduced; (2) the model of object manipulation used in this paper is explained; (3) we describe how recursive and non-recursive combinations are modeled; (4) to illustrate evolutionary simulation of object manipulation, we describe the encoding of a state transition table onto a gene and also the simulation flow; and finally, (5) three fitness functions in the evolutionary simulation are posited.

## Evolutionary Simulation for Investigating Adaptability

Evolution has three basic factors; (1) Variation meaning that there are groups with different traits. (2) Selection meaning that variation causes differences of survival probability depending on the environment. (3) Inheritance meaning that the traits aiding in the survival of individuals will be passed on to the next generation. These mechanisms can be written as a sequential procedure that is the genetic algorithm.

The genetic algorithm is constructed from the following processes:

If the fitness function is presented as a problem, then the genes are the optimized solution to this problem by a cumulative process.

Typically, a genetic algorithm is used to search for (quasi-)optimal solutions according to a fitness function representing an optimization problem. However, we intend to identify fitness functions having recursive combination (as an abstract operation) as their solution. Therefore, we define the candidates for the fitness functions by considering the ecological meanings of recursive combination, i.e., the evolutionary processes and adaptability are examined by evolutionary simulations. It is not our intention to model biological evolution directly, and this simulation does not reproduce the process of human evolution.

## Model of the Object Combination Operation

#### Abstraction of Recursive Combination and Non-recursive Combination

Prior to designing the model, we considered the computational difference between recursive combination and non-recursive combination. The crafting of a stone spear from diverse materials such as wood for the shaft, a chiseled stone for the head and adhesives used to bind everything together is a good example. Such tools had been made in 0.2 mya (Wymer, 1984). When non-recursive combination is performed, one object is combined repeatedly, i.e., the builder attaches the base of the stone edge to the wooden shaft, and fixes it using an adhesive. Thus, this operation needs both a finite set of states that is expressed as an object and a transition function that is expressed as a combination. When recursive combination is performed, combined objects combine to form another object, i.e., the builder attaches the base of the stone edge to the part where the adhesive was applied beforehand on the wooden shaft. Therefore, this operation needs two finite sets of states (the state for combining and the state for storing) and the transition functions that are expressed as storing and retrieving.

#### Agent Performing Object Manipulation

An agent performing object manipulation to manufacture products is modeled using an automaton with a stack. The aim of the agent is to make products by combining the objects (hereinafter, an elemental object is represented by a letter such as A or B and a combined object by concatenating letters, such as AB or ABC). An agent is equipped with a workspace in which objects are combined and a stack in which objects are stored temporarily from the workspace. The objects correspond to the cups in the experiments of Greenfield (1991) and Matsuzawa (1986); two or more objects cannot exist in the workspace simultaneously, and this is true for the stack as well. There are any number of objects of the same type in a set of elemental objects; thus, it is possible to make a product including multiple instances of the same type of object, such as AAB or AAA. Once combined, the objects are treated as one object and cannot be separated into two objects.

In this simulation, in order to clarify the difference between recursive combination and non-recursive combination, both combinatorial operations can produce the same set of objects by assuming that a combined object has a linear structure with directionality. Therefore, an object is added at the end of another (elemental or combined) object.

The agent performs the following four actions, depending on the state of its workspace and stack:

If multiple actions are possible in a state, one action is randomly chosen.

The initial state for the agent features an empty workspace and stack. Product-making is the process of state transitions of combined objects from the initial state to the final state. If there is an object in the stack, the agent is accepted as being in the process of production; the stack must be empty at the final state. There are k types of elemental objects, and an agent can make products composed of any number of elemental objects up to the maximum length, l, hereinafter, the maximum length of the product. The two combining actions, Get and Pop, are limited to avoid producing a combined object longer than l. If an agent cannot perform the Stop action when the length of the combined objects in the workspace becomes l, this production process is a failure, and a new production process begins from the initial state. An agent can make any number of products within the upper limit of the number of manipulation steps, which sets the agent's lifetime.

In this model, two strategies, non-recursive combination and recursive combination, are formalized, respectively, as follows:

Note that the following operations are not recursive combinations:

#### State Transition Table

A state transition, effected by performing an action, is expressed as:

$$(\text{stack, workspace}) \stackrel{\text{action}}{\rightarrow} \quad \left(\text{stack}', \text{ workspace}'\right). \qquad (1)$$

The behavior of a particular agent is defined by the state transition table shown in **Figure 4**. The state transition table describes a transition of a finite number of states, in our paper, workspace and stack, of the agents. In **Figure 4**, the two columns on the left are the state of the stack and of the workspace, that is, the left-hand side of (1). The five columns on the right are the actions. The destination of the transition after each action, corresponding to the right-hand side of (1), is indicated in each box as the states of the stack and the workspace. The symbol "ε" signifies nothing in the stack or in the workspace; that is, it represents an empty state. An instance of "–" indicates that the agent cannot perform this transition, while "n/a" indicates that the transition is forbidden due to a non-empty stack. If more than one destination is provided, one is selected randomly. Both the number of workspace states and stack states are

1 + S ,

$$\text{The first-order coupling between the two-dimensional } \mathcal{N} \text{-matrices is the only possible } \mathcal{N} \text{-matrices with } \mathcal{N} = \{0, 1, 2, \dots, N\} \text{ and } \mathcal{N} = \{0, 1, 2, \dots, N\}.$$

$$S = \sum\_{l=1}^{l} k^{l}$$

is the size of the combinatorial space, and the number of actions is (k + 3). The number of n/a's is

$$\text{2S} \left( \text{l} + \text{S} \right) \text{ ...}$$

where

Therefore, the total size of the state transition table is

$$(k+\mathfrak{Z})\left(1+\mathbb{S}\right)^2 - 2\mathbb{S}\left(1+\mathbb{S}\right) = (1+\mathbb{S})\left\{\left(1+k\right)+k+\mathfrak{z}\right\}\dots$$

**Figure 5** provides examples of state transitions corresponding to the state transition table in **Figure 4**. For an example of the state transitions, when an agent has states where the workspace is ε and the stack is also ε (as seen in columns 1 and 2, row 1 in **Figure 4**; as at the top of **Figure 5**), if the agent performs Get A, the agent will have a state where the workspace is A and the stack is ε (as seen in columns 1 and 2, row of workspace 2 and stack 1 in **Figure 4**; as seen at the left top of **Figure 5**). Then, if the agent performs Pop, the agent will have states where the workspace is ε and the stack is A (as seen in columns 1 and 2, row of workspace 1 and stack 2 in **Figure 4**; as seen under the top left of **Figure 5**). The same product can be manufactured either by using or by not using stacks, but production using stacks require more steps than the latter process.

#### Model for Evolutionary Simulation Gene Encoding of Transition Table

The state transition table of the agent is encoded into a gene with a binary string, as shown in **Figure 4**. If a transition is possible, the corresponding box in the state transition table is filled; in such a case, the locus is one. If a transition is impossible, the box is "–" then the locus is zero. Boxes showing "n/a" are not encoded into a gene. There is a regulatory locus for stacks. If it is zero, agents cannot use any stacks even if loci for Push and Pop are on1 . As can be seen from the figure, for an agent to be equipped with a stack that can store all possible objects, all loci corresponding to Push and Pop and the regulatory loci must be turned on in the agent's gene.

#### Simulation Flow and Selection Mechanism

In an evolutionary simulation, the initial population's gene is generated as all loci are zero for all agents. Each agent performed production according to the state transition table encoded in its gene; the fitness of each agent is evaluated depending on the results of its production. The fitness function is defined in the following subsection.

For generation turnover, two parents are selected from the top 10% with a rank selection according to fitness values, and two offspring are produced using a one-point crossover. This process of selection and reproduction is repeated until the number of offspring reached a predefined population. Thereafter, bit inversions occur as mutations with a locus in each agent's gene in the next generation.

Although this is not a biologically plausible implementation, this design is adopted because the aim is to identify the role of recursive combinations.

#### Fitness Function

The evolutionary process and evolvability of recursive combinations under each fitness function were examined by evolutionary simulations. The following three fitness functions were set.

• Making any product:

$$F\_{\mathcal{I}}(t) = \sum\_{\text{all }x} n\_{\mathcal{X}}^i(t) \;, \tag{2}$$

where x represents a product composed of up to l elements and n i x (t) is the number of times the product x is produced by agent i at generation t. The fitness function FI is based on the expectation that recursive combination is used in making many products.

• Making a specific product:

$$F\_{\text{II}}\left(t\right) = n\_{\text{x}}^{i}\left(t\right),\tag{3}$$

where x represents a product which is the longest, that is, l, and consists of the most number of types of elemental objects, namely, k. This fitness function is based on the fact that human made products have become increasingly complex in structure (Stout et al., 2008; Arthur, 2009). We choose a target product such as ABAB (k = 2, l = 4) or ABCABC (k = 3, l = 6).

• Making products as diverse as possible:

$$F\_{\text{III}}\left(t\right) = \sum\_{\text{all }\chi} \delta\left(n\_{\chi}^{i}\left(t\right)\right),$$

$$\delta\left(n\_{\chi}^{i}\left(t\right)\right) = \begin{cases} 1, \ n\_{\chi}^{i}\left(t\right) \ge 1\\ 0, \ n\_{\chi}^{i}\left(t\right) = 0 \end{cases}.\tag{4}$$

This fitness function is based on the fact that humans make increasingly diverse products (Arthur, 2009). We expect that manufacturing many types of products encourages an agent's survival and reproduction, while manufacturing the same product does not.

Although the manipulation steps for making one product are not explicitly expressed in these fitness functions, they nevertheless indirectly influence agent fitness because an upper limit of the number of manipulation steps is set. Thus, when an agent requires a considerable number of manipulations to make one product, the number of products made decreases and the agent's fitness is reduced.

#### RESULTS

The purpose of this evolutionary simulation is to clarify the adaptability of recursive combination to demonstrate the conditions of the ecological environment and the process of evolution. In the first subsection, we show the simulation results in the three fitness functions introduced above, at first by setting

1The regulatory locus was introduced to reduce computational time. We confirmed that there is no change in simulation results using a model without the regulatory locus for stacks.

FIGURE 4 | Example of part of a state transition table. The number of the types of elemental object, k = 2. A corresponding gene code is shown above the table. The first bit of gene is a regulatory locus for stacks.

the number of types of elemental object k = 2 and the maximum length of product l = 6. Then, the dependencies of these results on the parameters, k and l, are illustrated. These analyses suggest that recursive combination has two kinds of adaptabilities. In the second subsection, considerations based on the adaptabilities are used to modify the fitness functions to add cost factors that may affect the evolution of recursive combination. It is expected that the cost of manipulation influences negatively the evolution of recursive combination because it requires a greater number of manipulation steps than non-recursive combination. We also investigated the influence of a possible failure of operation on the evolution of recursive combination. We considered the evolutionary mechanism of recursive combination only on the simulation in this section of the paper. The cognitive or linguistic interpretations about the simulation results are considered in the Discussion section.

The parameters are summarized as shown in **Table 1**. The population size is 100, and the upper limit of manipulation steps is set at 10,000, which does not influence the results unless it is too small. Simulation results were taking 200 runs in each parameter. In this section, hereinafter, recursive combination, non-recursive combination, and the agent using recursive combination are called RC, non-RC, and RC agent, respectively.

#### The Fitness Function for Which the Recursive Combination Is Adaptive Making Any Products

With the fitness function FI , RC agents did not evolve in all the 200 runs as shown in **Figure 6A**. Since the fitness function FI encourages the act of making any product, agents gained fitness by repeatedly making specific simple products over many production trials. The average fitness is 5,000 with the upper limit of manipulation steps set at 10,000. This fitness value indicates that agents make products containing only one element such as A or B, using Get and Stop actions, that is, two manipulations, and RC is not used as shown. The number of types of product is one with slight fluctuations. This means that the population is mostly occupied by agents making products with one elemental object. This result suggests one reason that RC is observed only in humans. In human activity, the typical case of product manufacturing is tool-making for resource acquisition. This notable human behavior requires the combination of elemental objects or units made from elemental objects. In contrast, animals other than humans develop survival strategies without tool-making, in which object combination is not necessary.

#### TABLE 1 | List of simulation parameters.

#### Making a Specific Product

With the fitness function FII, **Figure 6B** demonstrates that the RC has appeared; it increased the average fitness when it appeared. It disappeared, however, with increasing the average fitness as shown in **Figure 7** that depicts an example of the transition of the population share of RC agents in a typical run under FII. This phenomenon implies that RC makes it easier to discover a specific product than non-RC (a detailed explanation of this point is in the next paragraph). An agent using non-RC for a product obtains more fitness value than an agent using RC for the same product because RC requires longer manipulation steps than non-RC; and the opportunity for making products is limited by the upper limit of manipulation steps. Therefore, after the product is discovered, RC agents are taken over by non-RC agents. When the length of the gene (which is determined by k and l) is too long, it is hard for non-RC agents to take over from RC agents because the mutation is one locus per agent per generation. For example, converting an agent that performs a state transition shown in column 2 row 3 in **Figure A1** in Appendix (RC) to one shown in column 1 row 1 (non-RC) needs to switch four loci.

The fitness landscape of FII makes hill-climbing evolution virtually impossible and makes it hard to discover a specific product x for earning fitness. We employed the adaptability of

FIGURE 6 | Transitions of the population share of RC agents in (A) FI , (B) FII, and (C) FIII (average of 200 runs). The x axes denote generation. The y axes on the left denote the population share of RC agents (red line), and those on the right denote average fitness over the population (green line).

RC by providing it with multiple routes to increase the discovery rate of a specific product. When the agent makes a specific product ABABAB (if only non-RC agents without stacks exist) the production of this specific product is unique because the elements must be obtained in exactly the same order from left to right of the specific product, as shown in the top left of **Figure A1** in Appendix. Therefore, the discovery rate of making a specific product is very low. In contrast, if RCs are possible, at most 25 methods for making the product are available. Thus, the discovery rate greatly increases. Additionally, multiple methods to make a specific product promote robustness against failure in making processes (for which a detailed explanation is in section Effect of Failure Rate of Combination on Recursive Combination). In summation, the first adaptability of RC is diversifiability of production methods.

The number of production methods using RC depends on the size of the combinatorial space. **Figure 8** shows the population share of RC agents in a combinatorial space parametrized by the number of types of elemental objects k (vertical axis) and the maximum length of products per product l (horizontal axis). When k = 2, the combinatorial space is larger than when k = 1, the RC agents evolve more frequently than when k = 1; however, if the combinatorial space is too large, the agents cannot discover the production process of a specific product until the 100,000th generation.

#### Making Products as Diversified as Possible

In an environment fostering diversified products, RC evolves most in the three fitness functions as shown in **Figure 6C** compared with other cases (**Figures 6A,B**). **Figure 9** shows typical examples of the transition of the population share of RC agents in two runs with FIII. In this fitness function, the maximum fitness depends on the size of combinatorial space. If

lines is the point where simulation results are not available due to limited

the upper limit of manipulation steps is sufficient for making all types of products, both RC and non-RC can earn the maximum fitness. Therefore, the RC agents or the non-RC agents can be maintained once either achieved the maximum fitness.

RC agents more frequently appears than other fitness functions because the production method using RC to make new products can evolve by less loci change than that using only non-RC. We explain this difference using **Figure 10**. For example, when an agent can already make BABAB as shown by solid arrows in the left branch, the agent evolves to make ABABAB by three loci changes represented by the broken arrows which depict the RC production method. These changes are much fewer than evolving to make the product only with the non-RC making method as shown in the right branch (6 loci changes). Therefore, agents to make new products using RC method are more easily attainable than those using non-RC method in evolutionary process. Further those that make new products earn more fitness than their ancestral agents. Thus, RC agents can appear and spread more rapidly than non-RC agents with FIII. The second adaptability of RC is diversifiability of product.

The effect of the size of combinatorial space was investigated. Since the RC production method is more effective in searching production space than non-RC, the RC agents are more likely to evolve when the combinatorial space is large enough as shown in the center part of **Figure 11**. However, if the combinatorial space is very large, such as k = 3 and l = 6, the making processes of products are difficult to find, and the RC agents are not likely to appear by the 100,000th generation.

## Factors Affecting the Evolution of Recursive Combination

In the previous settings of the fitness functions, we identified two adaptabilities of RC: the diversifiability of production methods and the diversifiability of product. From these results, in this section, several factors that may affect the evolution of RC are introduced. The factors are the cost of manipulation and the failure of combination. RC exhibits a disadvantage when tool-making requires energy. In contrast, the diversification of production methods is useful for failure in object combination. As a result, these factors affect the evolution of RC. The evolutionary scenario of RC is expected from these effects.

#### Effect of Manipulation Cost on Recursive Combination

RC requires more manipulation steps than non-RC. We did not consider the cost incurred to perform operations in the simulation described in the previous section. If RC is costlier than non-RC, how does their evolution change? In order to find answers, we modified the fitness functions FII and FIII as follows:

$$F\_{\text{II}}^{'} = \sum\_{\text{x}} \frac{n\_{\text{x}}^{i}(t)}{m\_{\text{x}}^{i}(t)^{\varepsilon}},\tag{5}$$

$$F\_{\text{III}}^{'} = \sum\_{\mathbf{x}} \frac{\delta\left(n\_{\mathbf{x}}^{i}(t)\right)}{m\_{\mathbf{x}}^{i}(t)^{\varepsilon}},\tag{6}$$

computational power.

FIGURE 9 | Examples of the transition of population share of RC agents in FIII. (A) A case where the RC agents are maintained, and (B) a case where the RC agents do not appear. The x axis is generation. The y axis is the population share of RC agents.

where mi x (t) is the manipulation steps required to make the product x at each production for (5) and at its first production for (6) and the parameter c regulates the effect of the cost.

The agents incur the manipulation cost when they perform Get, Push, and Pop actions. **Figure 12A** illustrates the effect of the manipulation cost on the population share of RC agents. It is naturally understandable that increasing the manipulation cost made the evolution of RC more difficult with F ′ III since RC requires more manipulation steps than non-RC. Even if an agent makes many types of products, the fitness is discounted at the cost of production depending on manipulation steps. However, with F ′ II, the manipulation cost does not influence the evolution of RC. Since the fitness landscape of F ′ II, and FII as well, is not a hill-climb type but discrete, the difference of fitness values of the fitted traits is hard to affect the possibility of takeover from RC to non-RC agents (A detailed explanation is provided in section Making a Specific Product, paragraph 1).

#### Effect of Failure Rate of Combination on Recursive Combination

In the fitness function FII, we expected that the multiple production methods by RC would promote robustness against failures in production processes. We introduced the failure of combination action into the model to confirm this expectation. With a constant probability, the agents fail to combine objects using Get or Pop action, and the state of the workspace becomes empty. This modeling expresses that a product is broken due to a failure of combination. The fitness functions are the same as the Equations (3) and (4). **Figure 12B** shows that the probability of appearance of RC increase gradually with increase in the failure rate of the fitness function FII. In FIII, the population share of RC agents rise when the failure rate is not zero but decrease slightly with a larger rate of failure. These increases are explained by the function of stack to keep a combined object. If an agent fails to make a product on the way of production, the agent does not have to return to the initial state but can restart from a production step when a partial product is kept in the stack. This function of stack

arrows) and using stack actions (Push and Pop, red arrows). The notation (x, y) is that x is the stack state and y is the workspace state. The broken arrows are actions whose corresponding loci are not turned on. The vertical arrows represent Get actions, and the horizontal arrows Push (rightward) or Pop (leftward) actions.

realizes the diversification of production methods, but is not so strongly effectual for robustness. Actually, it is not so successful for higher failure rate in FIII, the higher is the failure rate and the longer is the manipulation steps for a product, the more difficult to complete the production process of the product. Thus, the population share of RC agents decreases with larger failure rate in FIII.

## DISCUSSION

In this section, we mainly discuss the implication of each simulation result and its application to human evolution and language from the viewpoint of producing action sequences such as making tools. First, from the simulation results of FII and FIII , the adaptability and evolvability of recursive combination are considered. Next, a possible evolutionary scenario of recursive combination in human history is provided and supported with evidence from anthropology and archeology. Then, we speculate that recursive combination realizes flexibility of interpretation (that corresponds to diversifiability of production methods and of language products or expressions) and a driving force to diversify concepts and culture. Finally, we discuss the origin of recursive combination and recursive syntax by comparing two hypotheses (1) evolution of recursive combination via action control and (2) boot-strapping of recursive syntax via recursive intentionality.

FIGURE 11 | Distribution of the population share of RC agents with FIII in the combinatorial space parametrized by l and k at the 100,000th generation. The horizontal axis is the maximum length of products, and the vertical axis is the number of types of elemental objects, and the brightness is the population share of RC agents (average of 200 runs). The part masked by the red oblique lines is the point where simulation results are not available due to limited computational power.

## Adaptability of Recursive Combination

In an environment in which making a specific product with a complicated sequence is adaptive, production methods using recursive combination are discovered frequently (section Making a Specific Product). Additionally, the availability of multiple production methods for one tool is a workaround for inaccurate and/or irreversible manipulation. The greater access an agent has to multiple production methods the better that agent can make tools with increased stability (section Making a Specific Product); therefore, agents using recursive combination evolve faster than those that do not. When an agent must use many types of objects for product-making or must undergo a long process to make products (section Effect of Failure Rate of Combination on Recursive Combination), the frequency of failure derives from increase in inaccurate or irreversible manipulation; thus, diversifiability of production methods using recursive combination is effective.

In an environment in which making products as diversified as possible is adaptive, an agent searching for a production method that reuses existing methods can obtain relatively larger fitness than those who search for an all-new production method (section Making Products as Diversified as Possible). Therefore, the agent using recursive combination passes on its gene more easily than others. This adaptability is the diversifiability of products. In other words, recursive combination may have diversified the types of product in material culture beginning from stone tools. Human beings have diversified and complexified technology from the early stone age to the present. Arthur and Polak (2006) show that recursive combination of modularized technologies helped to identify more complex structures in a vast searching space. If the agents incur high manipulation costs, the adaptability of the diversifiability of products does not work (section Effect of Manipulation Cost on Recursive Combination).

Although we have already attempted other variants of this model, the approximate results of simulation (adaptability of recursive combination) did not change. The adaptability of

recursive combination will not be altered by adopting a learning algorithm such as a neural network instead of GA, since learning algorithms do not influence the size of the learning space for the production procedure. This expectation, however, has to be checked in future research.

## The Evolution of Recursive Combination in Human History

How is a condition formed in which recursive combination is adaptive? From the results of the simulation with F ′ II and F ′ III (**Figure 12A**), when the manipulation cost is applied, recursive combination is used more easily and with a lower cost. As we introduced in section Results, recursive combination is not common in animal behavior; we assume that this strategy is costly and not adaptive in most environments. Consequently, we must identify the environmental conditions that promote the evolution of recursive combination while considering the existence of manipulation cost. Manual dexterity may be a key factor to performing significant object manipulations with decreased cost. Development of dexterity can lower manipulation cost at product-making.

Is there any archeological evidence in human evolutionary history corresponding to our proposal? In fact, the morphology of the early hominin's hand 3.00 mya acquired forceful opposition of the thumb, that is, an opposable thumb with the ability to exert forceful precision and power "squeeze" gripping (Skinner et al., 2015). Moreover, by 1.42 mya the hominin's hand had essentially evolved into the form of the modern human hand (Ward et al., 2013), in particular in terms of the distinctively human arrangement of the wrist associated with enhanced hand function when making and using tools. This evidence implies that early hominins might have been able to use their hands as dexterously as modern humans. According to other archeological evidence, tool use started around 3.39 mya (McPherron et al., 2010); tool-making around 2.60 mya (Plummer, 2004); and the recursive combination of objects around 0.28 mya (Moore, 2010). When the cost of object manipulation was high, recursive combinations could not have been maintained (section Effect of Manipulation Cost on Recursive Combination); this parallels the reasons that recursive combination is difficult to observe in animals, that is, its disadvantages (energy loss, manipulation injuries due to mistakes, etc.) are greater than its benefits.

Based on this account, we speculate on the possible evolutionary process of recursive combination. First, hominins came to use stone tools more frequently. This led to the evolution of hands and fingers to become dexterous enough to make superior tools that could survive repeated use. This dexterity helped decrease the cost of object manipulation and increase the chance of tool-making by reducing the steps to make each tool. When certain complicated tools were produced, recursive combination emerged as an adaptability to avoid failure in making these tools through diversification of production methods. Finally, these agents used their developing ability of recursive combination to develop various new tools, showing adaptability by diversifiability of products.

Diverse products can be made without recursion, and the recursive and non-recursive combinations can produce the same set of products. We argue, however, that recursive combination can increase the efficiency of product-making. If agents use non-recursive combination only, they make products through specific procedures. If they use recursive combination as well, they can create a variety of products from the combination of partial modules, and the creation procedure becomes flexible; thus, the success and discovery rates of production are improved. We showed that improving the success and discovery rates contributes to the successful diversification of products. Hominins could create a variety of products from the combination of partial modules or procedures in actual behavior of making stone tools (Moore, 2010, 2011; Stout, 2011).

### Recursive Combination in Language

Let us now consider whether the adaptability of recursive combination (shown by this simulation and explained by the speculative evolutionary account above) can also be demonstrated in language. Recursive combination in language, that is, a syntactic operation, is used to generate hierarchically structured symbol sequences. In our simulation, object manipulation and product manufacturing are modeled on the lines of an agent combining elemental objects represented by a letter such as A and B, or a combined object by concatenating letters, such as AB or ABC. If this model applies to language, elemental objects are lexical items, and products are sentences. For instance, when non-recursive combination is performed, words are combined repeatedly, e.g., the agent combines a word book and a word club to a word child. When recursive combination is performed, combined words (phrase) combine to form another word or phrase, e.g., the agent combines words child and book and then combines it with club to form child book club.

Diversifiability of production methods by recursive combination in language is presumed to encompass the making of multiple hierarchical structures, because various combination procedures can be of utility. This diversifiability assists plentiful interpretations to one expression. In linguistic communication, the interpretations of a sentence depend not only on sequential order but also on hierarchical structures that are not directly disclosed to receivers. The multiple hierarchical structures may cause ambiguity in meaning sharing when hierarchical structures represent meanings as the notable characteristic of human language, which is known as structural dependency.

Diversifiability of products by recursive combination in language then entails generating various expressions or ideas, because various possible combinations of lexical items can be assumed by this adaptability. In this way, recursive combination enables and requires the creation of new expressions and concepts by combining symbols.

Taking together the two types of diversifiability described above, we introduce a concept called co-creation. Making a hierarchical structure by combining symbols does not merely produce an internal expression but constructs a hierarchically structured concept that leads to the creation of a new, sometimes fictitious, concept that can attain a socially shared reality via linguistic communication. At the same time, however, the interpretation of these hierarchically structured sequences

remains potentially ambiguous, enabling message receivers (as well as senders) to produce personal, sometimes creative, conceptual structures. In short, the interaction between senders and receivers promotes creativity in both parties. Our premise is that the adaptability of language is in co-creation. Co-creation is not necessarily a creative activity through actual collaboration. The viewpoint of co-creation, integrating two different functions (communication and thinking) can explain the reality and nature of humans and the human cultures they have cumulatively created (or, that have cumulatively evolved). Humans create and share new concepts via linguistic communication and produce higher-level concepts. Money, a symbolic concept socially created and shared, is a good example. We mutually believe that it mediates exchange among us, measures value, and makes it possible to store wealth—and so it does, based on this belief and on the new conceptual structures supported by this belief, such as banks, bonds, capital markets, and the global economy. In this way, novel concepts emerge and are realized through the interaction of the thinking function and the communication function. The cultural explosion and the spread of mankind all over the world around 50–100 Kya (Mithen, 1996) can be considered as having been brought about by co-creation through linguistic communication.

On the other hand, if new concepts and expressions continue to be created only in a certain group, cultural isolation may occur between that group and other groups. In particular, higherlevel, abstract concepts that do not have concrete existence and are not grounded in any physical object, are often very difficult to interpret and share due to lack of appropriate underlying concepts and linguistic means to convey their meaning. This difficulty of mutual understanding is probably a major cause of cultural conflict.

## Origin of Recursive Combination and Recursive Syntax

In the introduction, we mentioned two reasonable hypotheses, origin of recursive combination via action control (Fujita, 2009, 2016) and boot-strapping of recursive syntax via recursive intentionality (Stiller and Dunbar, 2007; Oesch and Dunbar, 2017). In this subsection, the possibility of integrating these two hypotheses will be discussed as a future research. Recursive combination in object manipulation is to combine combined objects. Recursive intentionality has a structure that embeds a subject into a subject. It might be that these two hypotheses describe similar evolutionary scenarios of two different abilities.

In our simulation model, recursive combination needs a stack to store an object temporarily. In human cognition, this function for temporal storing is implemented by working memory (Baddeley, 2000, 2007). Working memory is an important faculty for higher order general cognition and behavior in humans, i.e., complicated action planning, presence of intentionality, and generation or recognition of other physical or conceptual structures. Therefore, we should consider an evolutionary process of working memory in human history.

Stout (2011) analyzed the production methods of stone tools that required complicated action planning, both the Oldowan and Acheulian types, and illustrated the methods using a tree diagram (Stout, 2011, **Figure 1**). The analyses of stone toolmaking in Moore (2010, 2011) and Stout (2011) are almost the same. The notable point of Stout's (2011) analysis is using a tree diagram with dominance relationship in hierarchical structures. According to this analysis, the process of production of Oldowan tools required several steps of action: procurement of materials (for stone core and hammer stone) of appropriate size, shape, and composition; examination of the core; selection of target point to strike; positioning and fixing of the core; selection of hammerstone grip; and finally, accurate striking. These manipulations can be expressed by a tree diagram that has sixth order nesting. Unlike Moore (2010, 2011), Stout (2011) argued that the production method of Oldowan tools has discrete infinity that leads to the hierarchical structure of language. In the production method of the Acheulian type, Stout pointed out that the action sequence for achieving sub-goals was incorporated recursively into a higher order goal since the process of making a stone flake was included in the higher order intention of making stone flakes.

Arbib (2011) simplified the analysis of Stout's (2011) tree diagram from the viewpoint of working memory and reinterpreted the sixth order tree diagram in the production of Oldowan tools to five working processes. The five processes correspond to the following questions that stone tool-makers must answer: (1) Do I have a hammerstone? (2) Do I have a core? (3) Is there an available affordance for flake detachment? (4) If so, proceed with flake detachment. (5) If not, back up as far as needed. For Acheulian tools, Arbib insisted that automatization of the action sequence (working memory becomes needless) was essential because a complicated action sequence for stone flaking was incorporated into a subordinate component of the production of a stone tool. These studies argued that maintaining and combining sub-goals or sub-ordinate processes were essential for goal-directed action sequences that was a remarkable feature of Acheulian stone tools. Therefore, it is highly possible that the ability of recursive combination appeared in the age of Acheulian at the latest. In our simulation, a learning process such as Arbib's "automatization" is not implemented. We will clarify the relation of recursive combination and automatization as a future work by employing simulations with learning algorithms.

Mentalizing also needs working memory to maintain the mental state of others who have intentionality, such as Simon believes that Martin thinks that Charlotte supposes that Jane knows that Simon thinks . . . . Some studies show that mentalizing is limited to around the fourth or fifth order by working memory requirements (Stiller and Dunbar, 2007; Oesch and Dunbar, 2017). Oesch and Dunbar (2017) experimentally suggest that from first to fifth-order intentionality is necessary to assist the processing of simpler syntactic structures, but beyond fifth-order intentionality the cognitive scaffolding is provided by recursive syntax. We may apply this suggestion to the hypothesis of the origin of recursive combination via action control. Namely, lower-order recursive combination is necessary to assist the processing of simpler syntactic structures, but for more complicated action planning the cognitive scaffolding is provided by recursive syntax. It is assumed that two cognitive abilities, recursive combination and inference of intentionality, evolved separately then they were integrated to create diverse and complicated hierarchical structures.

We do not claim to know the origin of recursive syntax. However, we argue that, if diversity, novelty, and robustness of production are required to survive or reproduce, recursive combination has adaptability in the various domains, and the ability of recursive combination needs working memory. It does not matter whether it originates from action control, social cognition, or others.

## CONCLUSION

Adopting the hypothesis that recursive combination of object manipulation is the precursor of the syntactic ability intrinsic to human language, we developed an evolutionary simulation of product-making to clarify the adaptability of recursive combination in human evolution. In our study, a recursive combination, which is considered as a unique human ability, was modeled as a recursive combination in action grammar.

The main finding reported by this study, as evidenced by an evolutionary simulation, is that the adaptability of recursive combination increased the rate of discovery and success at product making by diversifying production methods and therein increased fitness by diversifying products. We argue that recursive combination may have evolved to become a consistent feature of human nature, through the production and use of tools that was

#### REFERENCES

later generalized to many aspects of human cognition, including human language. Effectually, this may be part of the explanation as to how and why recursive combination evolved to become a consistent feature of human language, and not of other animal communication systems.

### AUTHOR CONTRIBUTIONS

GT designed the study, analysis of data, and wrote the initial draft of the manuscript. TH contributed to designing of the study, interpretation of data, critically reviewed and assisted in the preparation of the manuscript. All authors approved the final version of the manuscript, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

## FUNDING

This work was supported by MEXT/JSPS Grant-in-Aid for Scientific Research on Innovative Areas #4903 (Evolinguistics) Grant Number JP17H06383 and Grant-in-Aid for JSPS Research Fellow Grant Number JP16J07821.

### ACKNOWLEDGMENTS

The authors would like to thank K. Fujita and R. Asano for their valuable discussions and useful comments.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Toya and Hashimoto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## APPENDIX

**Figure A1** is a summary of state transitions of workspace and stack to make a specific product ABABAB.

FIGURE A1A | Examples (portion) of state transitions to make a product ABABAB using non-RCs (blue arrows) and RCs (red arrows). The notation (x, y) means that x is the stack state and y is the workspace state. Vertical arrows represent Get actions, and horizontal arrows represent Push (Rightward) or Pop (Leftward) actions. The table has no particular order. In case of other product than ABABAB, the ratio of state transition using RC changes. State transitions using the stack more than once are omitted.

FIGURE A1B | Examples (portion) of state transitions using the stack more than once to make a product ABABAB using non-RCs (blue arrows) and RCs (red arrows). The notation (x, y) means that x is the stack state and y is the workspace state. Vertical arrows represent Get actions, and horizontal arrows represent Push or Pop actions. The table has no particular order. In case of other product than ABABAB, the ratio of state transition using RC changes.


*5.2. In Vitro Dissolution and Kinetics of Drug Release*

When NCM is used for delivery of API, the release of the API is evaluated over time in order to determine the availability of API for absorption and ultimately at target sites thereby influencing therapeutic outcomes [72]. Three possible mechanisms of release from NCM can be envisaged viz., desorption of adsorbed API (A), API diffusion from a polymer/surfactant matrix (B), and/or release following polymer/surfactant erosion (C) as depicted in Figure 1. *Crystals* **2021**, *11*, x FOR PEER REVIEW 17 of 26

**Figure 1.** Possible mechanisms of drug release from NCM; desorption-controlled drug release (**A**), diffusion-controlled drug release (**B**) and erosion-controlled drug release (**C**). **Figure 1.** Possible mechanisms of drug release from NCM; desorption-controlled drug release (**A**), diffusion-controlled drug release (**B**) and erosion-controlled drug release (**C**).

In the case of matrix-type polymer/surfactant NCM in which the crystalline API is uniformly distributed or embedded in the matrix, drug release would be predominantly controlled by diffusion through the matrix and/or erosion of the matrix. Depending on the API and matrix physicochemical properties, either one of these mechanisms can be the dominant one. If diffusion occurs more rapidly than matrix/surfactant degradation, diffusion is likely to be the main mechanism of release. In many instances, an initial rapid initial release is noticed and attributable to the crystalline material fraction adsorbed or weakly bound to the surface(s) or that is not entirely embedded in the system (Figure 1 A) In the case of matrix-type polymer/surfactant NCM in which the crystalline API is uniformly distributed or embedded in the matrix, drug release would be predominantly controlled by diffusion through the matrix and/or erosion of the matrix. Depending on the API and matrix physicochemical properties, either one of these mechanisms can be the dominant one. If diffusion occurs more rapidly than matrix/surfactant degradation, diffusion is likely to be the main mechanism of release. In many instances, an initial rapid initial release is noticed and attributable to the crystalline material fraction adsorbed or weakly bound to the surface(s) or that is not entirely embedded in the system (Figure 1A) [171].

[171]. API release from NCM is investigated in a number of ways. These are by dialysis membrane diffusion, membrane-less diffusion, sampling and separation, or the use of an in situ analytical technique [172]. When using the sampling and separation technique, API API release from NCM is investigated in a number of ways. These are by dialysis membrane diffusion, membrane-less diffusion, sampling and separation, or the use of an in situ analytical technique [172]. When using the sampling and separation technique, API release is determined by separating the released API from the sample by filtration,

release is determined by separating the released API from the sample by filtration, centrifugation, or centrifugal filtration, and subsequent quantification using an appropriate

this method having the capability of being performed with a small sample size and simple analytical equipment, several drawbacks exist: the separation methods is slow, tedious, and inefficient, making it inappropriate for studying rapid/immediate release NCM. In addition, the force of centrifugation or shear stress during filtration required for NCM separation tends to increase with a reduction in NCM PS. This can ultimately alter the release kinetics of the API. The main advantage of the dialysis membrane diffusion technique is that he diffusion of the NCM is continuous across the dialysis membrane and not subject to destructive separation processes, making sample acquisition simple and rapid

The use of dialysis membranes may attenuate API release as it is a diffusion barrier that may behave as an adsorptive surface. Thus, this approach should be conducted with control experiments in which free API is used to assess membrane effects. The dialysis membrane diffusion method typically makes use of large volumes of dissolution media. While large volumes maintain sink conditions for release, API analysis may be hindered due to the low concentrations to be tested. An in situ analytical technique is useful for studying NCM, which are prepared almost exclusively of an API. This technique is used to analyse the properties of NCM in situ to indirectly determine the quantity of API released. Various analytical techniques, including electrochemical analysis, solution calo-

[163,173,174].

centrifugation, or centrifugal filtration, and subsequent quantification using an appropriate analytical method such as (U)HPLC or UV spectroscopy.

The NCM are augmented with fresh release medium, preferably simulated bodily fluids and resuspended and incubated further until the next sampling interval. Despite this method having the capability of being performed with a small sample size and simple analytical equipment, several drawbacks exist: the separation methods is slow, tedious, and inefficient, making it inappropriate for studying rapid/immediate release NCM. In addition, the force of centrifugation or shear stress during filtration required for NCM separation tends to increase with a reduction in NCM PS. This can ultimately alter the release kinetics of the API. The main advantage of the dialysis membrane diffusion technique is that he diffusion of the NCM is continuous across the dialysis membrane and not subject to destructive separation processes, making sample acquisition simple and rapid [163,173,174].

The use of dialysis membranes may attenuate API release as it is a diffusion barrier that may behave as an adsorptive surface. Thus, this approach should be conducted with control experiments in which free API is used to assess membrane effects. The dialysis membrane diffusion method typically makes use of large volumes of dissolution media. While large volumes maintain sink conditions for release, API analysis may be hindered due to the low concentrations to be tested. An in situ analytical technique is useful for studying NCM, which are prepared almost exclusively of an API. This technique is used to analyse the properties of NCM in situ to indirectly determine the quantity of API released. Various analytical techniques, including electrochemical analysis, solution calorimetry, or turbidometric and the light scattering techniques have been used for this purpose [172]. These approaches do not need separation of NCM and enable real-time assessment of the release kinetics of API.

While the aforementioned characterizations may be generally applicable, it may not always be applicable to use all of them. The role of PS, PDI, ZP, and polymorphism on NCM performance with regards to processability, content uniformity, and stability of a drug product is recognizable. The possible effect of PS and polymorphism on solubility, dissolution, and bioavailability parameters being strictly related to each other is one of the primary concerns. Definitions of specifications are required particularly when the product performance is affected by PS and polymorphism. We provide an appropriate decision tree in Figure 2, that is closely adapted to that in the ICH Q6A guidelines [175], on what characterization is required and to which performance parameter it relates.

characterization is required and to which performance parameter it relates.

rimetry, or turbidometric and the light scattering techniques have been used for this purpose [172]. These approaches do not need separation of NCM and enable real-time assess-

While the aforementioned characterizations may be generally applicable, it may not always be applicable to use all of them. The role of PS, PDI, ZP, and polymorphism on NCM performance with regards to processability, content uniformity, and stability of a drug product is recognizable. The possible effect of PS and polymorphism on solubility, dissolution, and bioavailability parameters being strictly related to each other is one of the primary concerns. Definitions of specifications are required particularly when the product performance is affected by PS and polymorphism. We provide an appropriate decision tree in Figure 2, that is closely adapted to that in the ICH Q6A guidelines [175], on what

ment of the release kinetics of API.

**Figure 2.** Decision tree relating to NCM characterization. **Figure 2.** Decision tree relating to NCM characterization.

#### **6. Conclusions 6. Conclusions**

NCM are non-toxic crystalline carriers composed almost entirely of API and very little excipients. NCM are non-toxic crystalline carriers composed almost entirely of API and very little excipients.

Unlike other nanoparticle technologies such as solid lipid nanoparticles (SLN), nano lipid carriers (NLC), and nanocapsules, NCM are suitable for the delivery of hydrophilic and hydrophobic compounds with very high efficiency. NCM are also relatively easy to prepare and synthesize when compared to other nanoparticle technologies such as liposomes and nanocapsules that require multiple steps and use of organic solvents. NCM have the added advantage of exhibiting better stability than other nanoparticles due to the crystalline state of the particles. In addition, NCM formulations prevent the accumulation of the payload in cells and tissues of healthy organs and often exhibit improved bioavailability for some API. The suitability of the carriers has been proven since they are already on the market (Rapamune® and Emend®). NCM fulfil the key prerequisites for the introduction of the technology to the clinic trial phases and the market, that is, they are in line with regulatory requirements and provide the possibility of qualified industrial large-scale production. Furthermore, NCM exhibit flexibility in terms of the route of ad-

ministration and have been used for oral, parenteral, ophthalmic, and topical delivery of a variety of compounds.

The characterization of NCM delivery systems is an area that requires the formulation development and production of a products of high quality. Ideally, NCM should be in the nano range with a PDI < 0.500, determined using DLS and Photon Correlation Spectroscopy (PCS) in combination with Laser Diffraction (LD). However, PCS and DLS cannot be used to characterize the shape, surface morphology, and elemental composition of NCM and additional analytical tools are required. Therefore, TEM, SEM, and EDX should be used to assess the shape and surface morphology of NCM in the solid-state and dispersions. The crystalline nature and polymorphic transition of NCM are characterised using DSC, TGA, PXRD, and FTIR/Raman spectroscopy to investigate potential interactions between the API and excipients to be used. Laser Doppler Anemometry (LDA) is used to establish electrophoretic mobility and the ZP of NCM, which should be > +30 mV or < −30 mV in dispersion.

Other characterisations perhaps not considered the most fundamental but worth additional exploration include two-dimensional (2D) nuclear magnetic resonance (NMR) techniques such as correlation spectroscopy (COSY), diffusion-ordered spectroscopy (DOSY), and cross-polarization magic-angle spinning (CPMAS). These are used to generate additional information in terms of the stability of the nanocarriers in solution and solid-state in addition to the presence of hydrogen bonding.

**Author Contributions:** Conceptualization, B.A.W.; writing—original draft preparation, B.A.W. and M.A.; writing—review and editing, B.A.W., M.A., L.L.M. and P.A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was not funded with an external research grant.

**Institutional Review Board Statement:** Not applicable

**Informed Consent Statement:** Not applicable

**Acknowledgments:** The authors acknowledge the Research Committee of Rhodes University (P.A.M.).

**Conflicts of Interest:** The authors declare no conflict of interest.