Heavy metals limits for soil improvers, mulch and organic constituents of growing media [13].
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
Soil pollution is commonly associated with industry, mines and road transport. In the case of water contamination, the significant role of agriculture is primarily indicated. Horticulture can also adversely affect the soil environment. In this case, the basic problem is the use of very high doses of fertilizers and the heavy metals contained in some fertilizers. Contaminants can also occur in waste materials used to improve the properties of soil and horticultural substrates. Besides mineral impurities, toxic organic substances, which are metabolites of plants and micro-organisms or substances of anthropogenic origin used for the control of pests, pathogens and weeds can be released into soil and substrates. The ability to suppress other plants through the release of toxic substances from living parts or dead plant tissues during their decomposition is called allelopathy. Understanding of the causes and consequences of risks outlined above determines for rational decision-making in horticulture.
\nHorticulture is the branch of agriculture dealing mainly with the cultivation of vegetables, medicinal plants, ornamental plants and fruit trees and bushes. Horticultural plants are an important part of the human diet. For this reason, attention is paid to factors affecting the quantity and quality of yield.
\nCrop yield depends on many factors including variety, control of diseases and insects, and weather conditions. However, the essential role is played by the physical and chemical properties of the soil or growing medium. To improve soil quality, farmers use organic and mineral fertilizers. Unfortunately, fertilizers can be contaminated by substances that can potentially pose a risk to human and animal health and the environment. In the case of mineral fertilizers, this problem concerns mainly cadmium compounds.
\nThe presence of cadmium in topsoil is a consequence of the use of phosphate fertilizers contaminated with this element [1–4]. Cadmium uptake by plants depends on pH of soil or growing medium. Under acidic conditions, cadmium solubility increases. In these conditions, the adsorption of cadmium by soil colloids, hydrous oxides, and organic matter is very low. However, zinc can reduce cadmium’s availability to plants, by inhibiting calcium uptake and preventing it from moving from the roots to the shoots of the plants [5]. Lime fertilizers, as well as waste materials rich in calcium and/or magnesium, can also be contaminated by heavy metals [6–8]. Moreover, in this case, the list of potentially toxic elements is much longer: Cd, Cr, Co, Cu, Pb, Mo, Ni, Zn, As and Hg [8–11]. Essential and beneficial elements can become toxic only at high concentrations. In many countries, the use of fertilizers or waste materials contaminated with heavy metals is limited by the introduction of a maximum permissible content of these elements. However, the rules of individual countries are not unified [4]. For example, in Poland, the maximum permissible concentrations of heavy metals in fertilizers are as follows:
in organic and organic mineral fertilizer (in 1 kg of dry weight of the fertilizer): Cr—100 mg, Cd—5 mg, Ni—60 mg, Pb—140 mg, Hg—2 mg,
in agricultural lime (expressed per 1 kg CaO): Cd—8 mg, Pb—200 mg,
in agricultural lime containing magnesium (calculated per 1 kg of the sum CaO + MgO): Cd—15 mg, Pb—600 mg, and
in other mineral fertilizers (in 1 kg of dry weight of the fertilizer): As—50 mg, Cd—50 mg, Pb—140 mg, Hg—2 mg [12].
Heavy metals may be introduced into the soil and substrates also with soil improvers or mulches. ‘Soil improver’ is defined as a material added to soil in situ whose main function is to maintain or improve its physical and/or chemical and/or biological properties, with the exception of liming materials. ‘Mulch’ means a type of soil improver used as protective covering placed around plants on the topsoil whose specific functions are to prevent the loss of moisture, control weed growth and reduce soil erosion. According to a regulation of the European Union, the maximum content of heavy metals in the final product or constituent may not exceed the values shown in Table 1.
\nElement | \nMaximum content in the product (mg kg dw) | \n
---|---|
Cadmium (Cd) | \n1 | \n
Chromium total (Cr) | \n100 | \n
Copper (Cu) | \n100 | \n
Mercury (Hg) | \n1 | \n
Nickel (Ni) | \n50 | \n
Lead (Pb) | \n100 | \n
Zinc (Zn) | \n300 | \n
Heavy metals limits for soil improvers, mulch and organic constituents of growing media [13].
In organic and mineral growing media, the content of heavy metals in the final product may not exceed the values shown in Table 2.
\nElement | \nMaximum content in the product (mg kg dw) | \n
---|---|
Cadmium (Cd) | \n3 | \n
Chromium total (Cr) | \n150 | \n
Copper (Cu) | \n100 | \n
Mercury (Hg) | \n1 | \n
Nickel (Ni) | \n90 | \n
Lead (Pb) | \n150 | \n
Zinc (Zn) | \n300 | \n
Heavy metal limits for growing media, including mineral growing media [13].
The source of heavy metals may also be sewage sludge from municipal sewage treatment plants used to fertilize soil or compost from sewage sludge. The use of these materials in the EU is subject to a number of strict requirements. The most important are the Water Framework Directive 2000/60/EC on water protection, Directive 91/271/EEC on urban waste water treatment, Directive 96/61/EC concerning integrated pollution prevention and control, Directive 99/31/EC on the landfill of waste and Directive 86/278/EEC on the use of sludge in agriculture [13–17]. The limit values for heavy metals in sludge or in composts are defined in national regulations. The regulatory framework prevents harmful effects on soil, vegetation, animals and humans [18–20].
\nTraditional cultivation of plants in greenhouses or plastic tunnels is based on intensive organic and mineral fertilization of soil. Manure and compost are commonly used organic fertilizers. In temperate climate of central Europe, the cultivation of plants in greenhouses and tunnels is uneconomic due to short days and low light intensity as well as high heating costs from November to March. The gardening season begins in early spring and ends in late autumn. In this relatively short period, intensive fertilization is carried out. The doses of fertilizers used in greenhouses and plastic tunnels are much higher than the doses used in field crops. For example, for wheat, 230–360 kg NPK/ha is recommended, while for early varieties of cauliflower grown in the greenhouse, 450–580 kg NPK/ha is recommended. Moreover, due to the greenhouse effect, the average day and night temperatures in greenhouses and tunnels are significantly higher than the temperatures in the field. Plants grow faster and produce greater biomass. For this reason, the watering of plants is more intense, and therefore, the elution of components into the soil is stronger. A detailed documentation of this problem was presented by Breś and Roszyk [21, 22]. The authors selected five horticultural farms near Poznan (Poland) in which the plants were grown for 20–40 years. In the middle of the growing season, the authors took soil samples from the layers 0–20, 20–40, 40–60, 60–80, 80–100 and 100–120 cm. For the sake of comparison, the studies also included samples taken near the greenhouse from occasionally fertilized lawn. To evaluate the effect of long-term fertilization on the distribution of nutrients in the profile of soils, chemical analysis of samples was performed. For nutrient extraction, 0.03 M CH3COOH was used. This method allows one to assess the amount of components readily available for plants. As an example, the content of N-NO3, P, K, Ca, Mg, Cl and S–SO4 in soil samples collected in two of the five test farms is given below. In Table 3, data refer to a greenhouse where vegetables and ornamental plants were grown for 40 years, while Table 4 presents the results of analyses of soil samples from a greenhouse in which for 40 years only vegetables were cultivated. Most of the nitrogen, phosphorus, calcium, magnesium, chlorides and sulphates were found in a layer 0–40 cm deep. In extreme cases, the greenhouse in soil nitrogen content was 60 times, phosphorus 3 times and potassium 15 times higher compared to the soil next to the greenhouse. Greenhouse soils were very rich, even at a depth of 80 cm. The significant amount of sulphates in the soil in greenhouses is a result of more frequent use of potassium sulphate than potassium chloride. This practice is very common in horticulture. Based on the scale of pollution, it can be assumed that in these farms, the evaluation of fertilization requirements based on the chemical analysis of soil or substrate was conducted infrequently or not at all. The authors found that the range of changes in the chemical properties of the investigated soils depended most on the length of greenhouse utilization. Moreover, the soil of the farms where ornamental plants were grown exclusively contains more nutrients than the soil from farms specializing in the cultivation of vegetables. Soil texture had the least impact on the chemical composition of soils. Similar trends were observed by examining the content of micronutrients. The results of these studies clearly indicated strong leaching of nutrients and the threat of groundwater contamination. The soil contamination in the greenhouse reported in this study was so high that it became necessary to rapidly introduce new technologies friendly for the environment. As a method to reduce leaching of nutrients, wider use of slow-release, controlled-release and inhibitor-stabilized fertilizers was proposed. Another solution to the problem was soilless cultures and fertigation.
\nLayer of soil (cm) | \nN–NO3 | \nP | \nK | \nCa | \nMg | \nCl | \nS–SO4 | \n
---|---|---|---|---|---|---|---|
Content in the soil (mg/dm3) | \n|||||||
\n | \n|||||||
0–20 | \n314 | \n248 | \n491 | \n4013 | \n256 | \n306 | \n497 | \n
20–40 | \n297 | \n244 | \n484 | \n4600 | \n230 | \n346 | \n342 | \n
40–60 | \n77 | \n261 | \n376 | \n1123 | \n106 | \n224 | \n94 | \n
60–80 | \n76 | \n231 | \n517 | \n1322 | \n12 | \n215 | \n69 | \n
80–100 | \n65 | \n152 | \n676 | \n752 | \n136 | \n93 | \n55 | \n
100–120 | \n89 | \n138 | \n586 | \n556 | \n108 | \n151 | \n93 | \n
\n | \n|||||||
0–20 | \n5 | \n77 | \n29 | \n2408 | \n99 | \n22 | \n0 | \n
20–40 | \n4 | \n78 | \n16 | \n2624 | \n111 | \n21 | \n0 | \n
40–60 | \n5 | \n89 | \n12 | \n2949 | \n102 | \n23 | \n1 | \n
60–80 | \n4 | \n80 | \n12 | \n2793 | \n94 | \n20 | \n0 | \n
80–100 | \n4 | \n71 | \n16 | \n2255 | \n91 | \n22 | \n0 | \n
100–120 | \n3 | \n70 | \n14 | \n2140 | \n80 | \n20 | \n0 | \n
Effect of long-term fertilization on the distribution of nutrients in profile of greenhouse soil—farm Ogrody [21].
Soilless culture is the cultivation of plants in systems other than soil in situ, including hydroponics and another growing media or substrates. The main advantage of soilless culture is a pathogen-free root environment at the beginning of the crop cycle. Thanks to that fact, one can avoid costly and time-consuming soil replacement or sterilization [23]. An essential element of this technology is fertigation, that is the process in which fertilizers are applied with the irrigation. Fertigation can be carried out in an open or closed system. In the open system, an excess of the applied nutrient solution leaks into the soil. In the closed system, the excess of nutrient solution after disinfection returns to the fertigation system (recirculation of nutrient solution). In this system, drainage water does not contaminate the environment [24]. Fertigation would also provide less water and fertilizer utilization. In soilless cultures as a growing medium expanded clay aggregates, growstones, perlite, pumice, sand and wood fibre are used. However, the most commonly used substrates in soilless cultures are rockwool and coconut fibres. The described cultivation technology requires high-quality water and very good water-soluble fertilizers [25, 26]. According to the recommendations, in order to stabilize the concentration and the pH value of the solution in the root zone and in order to adjust the substrate moisture, the volume of nutrient solution must be higher than the nutritional requirements of plants [27]. For most soilless cultures, 30–50% overflow is recommended [28]. As an effect of open systems, the excess nutrient solution leaks from the growing medium and pollutes the soil. This process was documented by Breś [25]. The author measured the volume of leaking solution and analysed the chemical composition of leakage during the growth of cherry tomato in coconut fibre, as well as gerbera, rose, tomato and cucumber growing in rockwool. Concentrations of nutrients found in the drainage from soilless cultures were many times higher than the mean concentrations of components in the nutrient solution supplied to plants. This suggests that the basic cause of the increase in ion concentrations is a predominance of transpiration over nutrient uptake by plants [29]. The monthly deposition of elements transferred with drainage waters to the soil was also calculated. Some details from the publications of Breś [25] are given in Table 5. Notable is deposition of K (up to 413 kg/month/ha), N–NO3 (up to 230 kg/month/ha), Ca (up to 220 kg/month/ha) and S–SO4 (up to 101 kg/month/ha). Leaching of Na (up to 62 kg/month/ha) and Cl (up to 34 kg/month/ha) was lower. A similar trend was found for
Layer of soil (cm) | \nN–NO3 | \nP | \nK | \nCa | \nMg | \nCl | \nS–SO4 | \n
---|---|---|---|---|---|---|---|
Content in the soil (mg/dm3) | \n|||||||
\n | \n|||||||
0–20 | \n159 | \n255 | \n309 | \n2445 | \n229 | \n151 | \n891 | \n
20–40 | \n111 | \n238 | \n326 | \n1379 | \n160 | \n126 | \n779 | \n
40–60 | \n90 | \n160 | \n431 | \n728 | \n95 | \n56 | \n284 | \n
60–80 | \n69 | \n99 | \n541 | \n1463 | \n125 | \n45 | \n441 | \n
80–100 | \n79 | \n97 | \n420 | \n1491 | \n91 | \n33 | \n296 | \n
100–120 | \n49 | \n66 | \n476 | \n2599 | \n78 | \n54 | \n149 | \n
\n | \n|||||||
0–20 | \n13 | \n54 | \n155 | \n3101 | \n77 | \n124 | \n84 | \n
20–40 | \n9 | \n71 | \n103 | \n1493 | \n87 | \n105 | \n141 | \n
40–60 | \n13 | \n54 | \n125 | \n2340 | \n72 | \n80 | \n43 | \n
60–80 | \n14 | \n50 | \n110 | \n1538 | \n61 | \n57 | \n7 | \n
80–100 | \n12 | \n34 | \n102 | \n1103 | \n49 | \n49 | \n40 | \n
100–120 | \n11 | \n28 | \n119 | \n1062 | \n57 | \n47 | \n38 | \n
Effect of long-term fertilization on the distribution of nutrients in profile of greenhouse soil—farm Marcelin [21].
In research conducted by Uronen [31] during the cultivation of cucumbers grown in rockwool, phosphorus leakage was 35–47% while nitrate leakage amounted to 33–43% of the applied nutrients. Cultivation in organic substrates is characterized by a smaller run-off than in rockwool [25, 31]. Thus environmental pollution is reduced. The amount of nutrients leaking from 1 ha of agricultural field crops is distinctly smaller. For example, nitrogen seldom exceeds 140 kg N/ha/year [32, 33].
\nBesides the amount of fertilizers leaking from open fertigation systems, the vertical distribution of nutrients accumulating in the soil profile (mean content in subsequent soil layer), in relation to the duration of greenhouse operation, is also important. Such investigations were conducted in the years 2004–2011 in horticultural farms specializing in soilless plant cultivation [34]. The greenhouses were located in the Wielkopolska province (Poland). Every year, from February to November tomatoes were grown in rockwool. Before the first crop culture, soil samples were collected for chemical analyses at every 20 cm layer to the depth of 1 m. Successive samples were taken in autumn after the completion of 1, 2, 3 and 7 growing cycles. For nutrient extraction from soil, 0.03 M CH3COOH was used. The amount of components readily available for plants was determined. Significant changes in the chemical properties of soils were detectable already after the first growth cycle of plants. Figure 1 shows the dynamics of changes in electrical conductivity measured in soil layers. The degradation rate of the soil environment as a result of application of an open fertigation system depended primarily on the duration of greenhouse operation. The increase of nutrient contents in the soil profile during seven years of monitoring was very high: Ca 283%, Mg 325%, N–NO3 326%, K 666%, P 684% and S–SO4 2164%. Once again, it proved that the previously reported benefits of fertigation apply only for recirculating systems. Only in closed systems, it is possible to reduce water consumption by 15–35% and to limit losses of nutrient solution by 15–67% [35, 36].
\n\nNutrient | \nTomato in rockwool | \nCherry tomato in coconut fibres | \nRose in rockwool | \n
---|---|---|---|
N–NO3 | \n30–230 | \n23–177 | \n13–83 | \n
P | \n7–54 | \n2–18 | \n3–16 | \n
K | \n53–413 | \n36–282 | \n17–106 | \n
Ca | \n23–178 | \n28–220 | \n9–54 | \n
Mg | \n7–57 | \n5–38 | \n3–21 | \n
Na | \n4–33 | \n8–62 | \n1–6 | \n
Cl | \n1–10 | \n4–30 | \n0.1–0.3 | \n
S–SO4 | \n13–101 | \n12–90 | \n4–24 | \n
Ranges (kg/ha) of monthly losses of nutrients during plant cultivation in soilless culture with the application of open fertigation systems [25].
Relationship between duration of greenhouse operation (0—before first growth season and after 1, 2, 3…7 growth cycles), depth of soil sampling (cm) and electrical conductivity (EC mS/cm).
There are many plant species that possess the ability to suppress other plants through the release of toxic substances from living parts or dead plant tissues. This phenomenon is called allelopathy. Allelopathy is a chemical interaction between plants defined as any direct or indirect, beneficial or harmful effects of one plant (donor plant) on another (recipient plant) through the production of chemical compounds that are released into the environment through root exudation, leaching, volatilization and decomposition of plant residues. A wide variety of phytotoxic substances exists in plant residues. Microbial decay of plant residues releases the toxic metabolites into the soil where they may adversely affect the growth and development of plants. In agro-ecosystems, decaying post-harvest residues are the main source of phytotoxic compounds, and they can provide a serious problem [37].
\nAllelopathic chemicals are generally secondary metabolites, and most of them have been identified as volatile terpenes and phenolic compounds [38]. Allelochemicals can be synthesized in every part of the plant. They can be found in seeds, flowers, fruits, pollen, leaves, stems and roots. Their content depends on the developmental stage of the plant or plant part. It was found that significantly larger amounts of them occur in young plants [39]. Different stress factors can enhance the production and release of allelochemicals by plants [40].
\nSome plant species with a high allelopathic potential release into the environment particularly high amounts of allelopathic compounds. These include crop plants from the families Fabaceae and Brassicaceae. Perennial crops and monocultures of these families are common in many parts of the world, and they cause a number of problems due to soil sickness, regeneration failure and replant problems. Allelochemicals from legumes are mainly polyphenols and propanoids [41]. Crops from the family Brassicaceae contain compounds called glucosinolates, which break down during the decomposition of post-harvest residues into powerful volatile allelochemicals—isothiocyanates, which can affect plant growth and microbial activity [42–44]. Also, plants belonging to the group of the world’s worst weeds displaying great expansion and invasiveness properties such as quackgrass (
Allelopathic chemicals act in many ways. Some retard plant growth or inhibit seed germination by disrupting cell division. Some interfere with respiration and other physiological process. Many affect plant nutrition by reducing the water and nutrient uptake. Biological activity of phytotoxic substances depends on their chemical nature and concentration—at lower concentrations, they may exert stimulatory effects, whereas at higher concentrations, they may exert inhibitory effects [49].
\nThe decomposition of crop residues is the result of complex microbial processes controlled by numerous environmental factors influencing the activity of microflora such as temperature, moisture, aeration, inorganic ions and pH [50, 51]. Allelochemicals released into the soil are also continuously removed from the soil solution by plant uptake, immobilized due to adsorption to soil particles and degraded by micro-organisms [52–55]. Moreover, allelopathic compounds are subjected to degradation by oxidation and photolysis as well as processes of removal by volatilization or leaching [53]. The type of soil is important in the accumulation of allelochemicals, for example, in poorly drained, clay soils, the allelochemicals are not leached easily. By contrast, in well-drained sandy soils, the allelochemicals have a tendency to leach. The difference between the speed of allelochemicals’ release into the environment and the speed of their degradation will decide whether they will accumulate in the soil to a toxic level [49]. A low concentration of allelochemicals at a given point in time is not an argument against their allelopathic role or evidence of their activity at low concentrations, because the allelopathic effects depend on many factors interacting with them in the soil and may not be directly related to the actual concentrations. Soil factors and their interactions with microflora need to be considered in assessing the factors that determine the presence and stability of allelochemicals [56–58].
\nThe phenomenon of soil sickness is defined as a decrease in soil fertility as a result of the prolonged growth of the same plant species, in spite of its intensive cultivation and fertilization. Delayed development of plants and a significant reduction in yield are symptoms of soil sickness. It is widely assumed that soil sickness is a phenomenon caused by a complex combination of biotic and abiotic factors disturbing the biological balance in soil, that is deficiencies or imbalance of plant nutrients, degradation of soil properties, disproportionate development of various groups of micro-organisms in soil, increased infestation of pathogens, pests and weeds and accumulation of phytotoxic compounds [59]. The intensive modern agriculture with mechanization, indiscriminate use of fertilizers and pesticides and with an emphasis on reduced crop diversity has led to serious changes in the physical, chemical and biological properties of soil, which have adversely influenced plant development and crop yields. Soil sickness in modern agriculture is mainly due to specialized single crop based limited rotations. These systems do not follow the scientific principles of crop rotations. In horticulture, soil sickness concerns mainly monoculture and perennial crops with limited rotation, such as nurseries, orchards, plantations of berries and asparagus, lawns as well as greenhouse cultivations, where the same substrate is used many times [54, 60–62]. One of the main causes of soil sickness is the accumulation of phytotoxic compounds, that is plant and microbial phytotoxins, as well as remains of pesticides [59].
\nAs a result of long-term growth of the same plant species, there occurs in the soil accumulation of homogeneous compounds secreted from plants and the products of microbial decomposition of plant post-harvest residues. The living plants can secrete allelochemicals and the decaying plant residues can release toxic metabolites into the soil. In soil sickness, the release of toxic substances from the dead plant tissues during their decomposition plays a greater role than their active secretion from the living plants. A specific kind of soil sickness is autotoxicity, which manifests when a plant species releases chemical substances that inhibit or delay the germination and growth of the same plant species. Many crop plants exhibit autotoxicity, i.e. self-destruction of a plant species through the production of metabolites that escape into the environment and directly inhibit the growth of that species [63]. Autotoxicity is a cause of soil sickness in the cropping of such vegetables as asparagus, carrot, cucumber, eggplant, pea and tomato [64–66]. This phenomenon is also observed in orchards and then is called the replantation problem. Cutting down an old, non-productive orchard and establishing a new one in the same place is associated with the replantation problem. It occurs most frequently in apple, peach, sour cherry and sweet cherry orchards. When an old orchard is removed, large amounts of root residues remain in the soil. They are a rich source of phytotoxic substances. For example, peach root bark contains two glycosides—amygdalin and prunasin—that under enzymatic hydrolysis in soil produce hydrogen cyanide, a powerful inhibitor of respiration [67]. The main cause of soil sickness in apple orchards is accumulation of the toxic dihydrochalcone—phlorizin, large amounts of which occur in the bark of apple roots. The release into the soil of these compounds from the decaying residues of tree roots after the liquidation of old trees prevents the normal growth of young trees in the replanted orchard [68].
\nMonoculture and perennial crops with limited rotation favour the proliferation of pathogenic fungi, which produce mycotoxins.
Pesticides are toxic chemicals used to control weeds, pests and pathogens in crops. It is normal practice to apply several different pesticides to a single crop in any given growing season. In intensive agriculture, the application of pesticides is frequently inappropriate or excessive. Although each pesticide is meant to kill a certain pest, pathogen or weed, a very large percentage of pesticides reach other destinations than their target. Instead, they enter the air, water and soil [70]. Some of these pesticides or their remains can act as toxins to plants when found in soil at sufficient concentrations. Accumulation refers to the build-up of pesticides resulting from repeated use. Excessive use of pesticides is one of the main factors causing soil pollution and can lead to several unintended, harmful effects on the environment, adversely affecting the soil micro-organisms and generally causing a decrease of soil fertility. The toxicity level of a pesticide depends on the kind of chemical, the dose, the length of exposure and the route of entry or absorption by the plant. The accumulation of pesticides in the soil can kill or reduce the populations of essential soil macro- and micro-organisms, including earthworms, insects, spiders, mites, fungi and bacteria, thus reducing or stopping important nutrient cycles [71, 72]. The fate of pesticides in soils varies greatly depending on their chemical nature, the type of soil, the climate conditions and the agricultural practices. In the soil, they are decomposed by soil micro-organisms, leached from the root zone, or they are adsorbed and accumulated by soil particles [73]. The amount of pesticide adsorbed to the soil varies with the type of pesticide, soil moisture, pH and texture. Pesticides are strongly adsorbed to soils that are rich in clay or organic matter, whereas they are not as strongly adsorbed to sandy soils. Pesticide degradation in soil generally results in a reduction in toxicity; however, breakdown products of some pesticides are sometimes more toxic than the substrate. Plant injury can be a problem resulting from adsorption of pesticides to soil particles. Injury can result when a pesticide used for one crop is later released from the soil particles in amounts great enough to cause injury to a sensitive rotational crop. It is also hard to predict the long-term effects of such changes in the soil microbial communities, which may lead to the occurrence of soil-borne pathogens [73].
\nMulching is a popular form of soil care, especially in green areas. A mulch is a layer of material applied to the surface of soil. It limits weeding, improves soil moisture, stabilizes soil temperature, reduces soil compaction and increases soil nutrition, which indirectly contribute to better plant growth. For the preparation of mulches, various organic and inorganic materials are used. Natural materials such as bark, sawdust, straw, shredded or chipped wood, leaves, coniferous needles or dried grass clippings are used as organic mulches. Plant residues from a crop may also be used to form a mulch [43, 47]. However, most of these materials are not suitable in green belts because of poor aesthetic appeal [74].
\nAlthough mulches are multifunctional and in green areas, they are applied mainly for aesthetic purposes, mulching is one of the most effective methods for non-herbicide weed control [75]. Mulches can act only as a physical barrier that limits access of light to germinated weeds and reduces their ability to photosynthesis. Certain organic materials, especially shredded and chipped bark or wood, may control weeds chemically through the leaching of allelopathic compounds. Bark and wood mulches are often used for weed suppression in urban landscapes and gardens where herbicides are prohibited or unwanted [74]. Biological activity of phytotoxic substances depends on their chemical nature and the tree species from which they are derived. The results obtained by Rathinasabapathi and co-workers [76] showed the phytotoxic activity of wood chips from deciduous trees and conifers (
Most commonly, the branches of various tree species are used as mulch material, fresh and without composting, because composting is a time- and cost-consuming process. Thus, the use of these wood wastes for the preparation of mulches is a simple way of recycling them. However, although the wood chips are easy to obtain and one of the cheapest organic materials for mulching, especially in green areas, their application may be associated with the release into the soil of phytotoxic substances. The use of wood chips for mulching the soil contributed to an increase in the content of phenolic compounds [77]. It was found that the strongly lignified wood wastes decomposed in the soil by micro-organisms are a rich source of phenolic compounds, even small amounts of which may adversely affect the growth and development of plants [77, 78]. According to Krasutsky [79], the bark of
In recent years, interest has grown in mulches from a variety of wood wastes, which are crushed and coloured. Wood chips are durable and easy to use as an organic material for mulching. Their sources are sawmill wastes and wastes arising from logging or cutting trees and shrubs [81]. Sometimes processed wood is also used, for example manufactured product debris, discarded pallets and wood reclaimed from constructions and demolitions [82]. Depending on the source of the wood chips, they may contain toxic chemicals, which pollute soil and ground water. It has been found that some of the recycled waste wood used for making landscape mulch products is contaminated with various chemicals, such as creosote, chromium copper arsenate or lead-based paints used for wood preservation against fungi and insects [83–85].
\nSome problems can develop when hardwood bark is stored in overlarge or waterlogged piles, which creates anaerobic conditions. Then, anaerobic micro-organisms carry out fermentation and in the pile such products as acetic acid, methanol, ammonia and hydrogen sulphide accumulate. Application of such bark as mulch can cause direct plant injury. Damage symptoms including leaf scorch, bleached leaves and defoliation occur very quickly, and in the case of sensitive herbaceous plants, even plant death may occur [86].
\nA new intellectual movement in the field of cognitive science1 has been developed, above all, in the last two decades of the current century, starting from debates that took place, mainly, in the philosophy of science at the end of the twentieth century. This movement has been described more broadly by many authors as a “new mechanistic philosophy” [4, 5, 6, 7]. Strongly influenced by recent advances in computer science, neuroscience, and artificial intelligence, the theoretical framework developed by some of the movement’s most prominent authors offers a new physicalist (or materialist) and mechanistic view of human cognition2 [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].
The theory formulated from the application of the neo-mechanistic philosophy to cognitive science and, specifically, to human cognition, can be called the
One of the most central elements present in the framework of MTHC is a “model of human cognitive computation” [9, 10, 11, 13, 15], which is also part of the attempt made by several influential authors to provide some type of unification or integration for the field of cognitive science [9, 10, 23, 24, 25]. However, some complex cognitive capacities and some particular aspects of human cognition still present a challenge for explanations constructed by using this theoretical structure [22].
My central goal in this chapter, therefore, is to present an argument to show that human cognition cannot yet be completely understood and explained in terms of mechanistic computation and that this view indeed presents many substantial limitations.
To develop my argument, I present, firstly, some of the central elements of this neo-mechanistic framework and its application to cognitive science. Secondly, I present the mechanistic model of human cognitive computation, as it is currently framed, and, based on the specialized literature, I show in what dimensions it helps our understanding of some aspects of human cognitive capacities, such as visual perception and memory consolidation. Thirdly, I show that to understand and explain some human cognitive capacities, such as self-consciousness and conscious informal reasoning and decision making, the neo-mechanistic framework shows substantial limitations. I conclude the chapter by suggesting that the notion of human artificial cognitive computation can be useful for several projects, but to fully understand natural human cognition we will most certainly have to consider theories that go beyond the current neo-mechanistic model of human cognitive computation.
The contemporary movement of neo-mechanistic philosophy has been historically associated with ideas already present in the period of Ancient Philosophy. Philosophers, such as Democritus, Leucippus, Aristotle, Epicurus, and Lucretius [9, 14, 26], for example, have been mentioned in the specialized literature as precursors. Although there is no unity of thought regarding this philosophical tradition, these thinkers would arguably have launched, in Western philosophical thought, the first notions linked to mechanistic reflections. In other words, these philosophers would have proposed the general idea that many phenomena in nature must be explained through their basic components, their forms of movement, their properties, and their interactions since these phenomena are also composed of these basic elements.
In Modern Philosophy, the history of what might be called “mechanistic philosophy” is quite complex, given the many debates over definitions of the term and the variety of positions that can be considered within a more general view of what the term means in this period. In any case, many authors consider that the movement of mechanistic philosophy in the seventeenth century is a reaction to Aristotelian natural philosophy and various natural philosophies of the Renaissance period [27]. The French philosopher René Descartes (1596–1650), for example, is considered one of the main figures who laid the foundations of modern mechanistic philosophy, especially with regard to explanations of biological natural phenomena [9, 27, 28, 29, 30, 31]. Des Chene [30] argues that Descartes united a mechanistic ontology, on the one hand, with a method of mechanistic explanation, on the other, applying these ideas to numerous biological phenomena, including the behavior of non-human animals and the human body.
Shortly thereafter, this reasoning would also be applied quite influentially to human beings and their mental capacities. One of the most prominent advocates of this view was the French philosopher and physician Julien Offray de La Mettrie (1709–1751), who published
At the beginning of the twentieth century, the debate about the best explanation for the complex phenomenon of “life” was still quite strong [32]. The controversy was over whether or not this phenomenon could be explained in mechanistic terms. In this context, a very influential work was that of the German-born American physiologist and biologist Jacques Loeb (1859–1924), published in 1912,
In the second half of the twentieth century, philosophers of science sought to analyze, in a more precise way, this mechanistic explanatory strategy. One of the most influential analyzes is present in the work of the American philosopher Ernest Nagel (1901–1985),
It was also during this period that some philosophers of science working in the field of biology began the task of elaborating, in an even more robust and systematic way, notions related to mechanistic explanations in science – mainly in biology. Along these lines, some pioneering works were the following: Herbert Simon,
Within this line of philosophical thinking, the work of William Bechtel and Robert Richardson,
It is also important to point out that in the development of the neo-mechanist movement, at the end of the twentieth century, we can distinguish, more generally, two main trends [5]. One of them focuses more on metaphysical and ontological directions. Authors who work in this line seek, above all, to answer what mechanisms are as real things in the world. The other strand followed in the direction of a greater elaboration of the philosophy of science with epistemological and methodological discussions about scientific explanations, mainly in the area of biology. They seek to explain how something works and not make claims about the ultimate reality of things. These two strands of the new mechanism have been elaborated in an enormous specialized literature that covers several scientific and philosophical areas, dominating a great part of the central debates. Despite being two dimensions that can be separated in the debate, ontological and epistemological discussions are deeply related in many works, both directly and indirectly.
The neo-mechanistic philosophy began to be applied with greater emphasis to cognitive science since the decade of 1990 – with this application becoming stronger in the first decade of the twenty-first century – and it has been better elaborated since then until the present days in central works of very influential authors [9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 35, 36, 37, 38, 39, 40, 41, 42, 43]. According to this view, human cognition, specifically, as well as biological cognition, in general, can be understood and explained through complex models of multilevel neurocognitive mechanisms. At these levels, there are causal processes related to cognitive information processing, cognitive representation, cognitive computing, as well as processes related to chemical and physical reactions that can be used to explain a given cognitive phenomenon. These are, in fact, autonomous processes of causation, which take place at all these different levels and are relevant to the explanation of the phenomenon of interest [44]. According to this theory of human cognition, namely, MTHC, all these causal levels and processes, although autonomous, can be related in a pluralistic mechanistic explanation, where the relevant scientific theories are integrated. As a result, MTHC includes not only a theory of human cognition but also a theory of the human neurocognitive relationship; that is, the theoretical framework suggests a possible solution to the problem of how we are to understand and explain the connection between human neural and cognitive phenomena, thus attempting to relate neuroscience and cognitive science.
The main objective of a mechanistic scientific explanation in scientific areas, such as biology, cognitive neuroscience, and cognitive science, is to identify the parts of a mechanism, its operations, its organization, and thus show how these elements constitute the system’s relationship with the phenomenon that must be explained [9, 10, 45]. Particularly, in cognitive science, the central idea present in the theory is that human neurocognitive processes are a type of information processing performed by neural systems (mechanisms). These processes and the components that carry them out can be decomposed into subparts, and these subparts are decomposed again, as far as necessary for the understanding of the investigated phenomenon. After that, these components and activities have to be located in the brain as spatiotemporal parts of a complex multilevel neurobiological mechanism. As a result, there may be multiple levels of mechanistic composition in a human neurocognitive mechanism.
Another important feature of MTHC is that it was developed within a broad physicalist context that is present in a vast amount of work in contemporary cognitive science, philosophy of cognitive science, and philosophy of mind. In this physicalist context, the theory tries to combine central ideas present in traditional cognitive science with the main ideas present in certain fields of neuroscience that investigate human cognition. In this sense, some authors argue that this mechanistic physicalist framework can provide a consistent way to build a unified science of cognition and integrate cognitive science and neuroscience [23, 24, 25, 40].
Indeed, integrating and unifying, from a physicalist background, traditional cognitive science and traditional neuroscience to understand and investigate human cognition is an old dream held by many authors. Patricia Churchland, in 1986, calls for the unification of cognitive research and neural research in her book
It is possible to argue that MTHC was articulated with the objective of providing this integration and unification in a more precise theoretical way and within a clear physicalist background. The influential version of MTHC by William Bechtel is a clear example. He considers the human phenomenon “mind-brain” as “a set of mechanisms for controlling behavior” [9], and he explains that cognitive phenomena (e.g., perception, attention, memory, problem solving, and language) can be characterized as “information-processing mechanisms” [9]. Bechtel [9] states that scientific disciplines that aim to explain cognitive activities recognize that “in some way, these activities depend upon our brain.” Or, to put it in another way: “Psychological phenomena are realized in brains comprised of neurons” [45]. This means that cognitive phenomena are physical and need to be explained in some physical (neural) way.
Craver and Tabery [47] describe the physicalist commitment quite clearly—“many mechanists opt for some form of explanatory anti-reductionism, emphasizing the importance of multilevel and upward-looking explanations, without rejecting the central ideas that motivate a broad physicalist world-picture.” Therefore, in this approach, there is no space for any form of dualism, pluralism, or non-physicalism of any kind in relation to the ontology of human cognition. There is, indeed, a clear commitment to a form of ontological monism, namely, physicalism, that underlies the neo-mechanistic theory of human cognition.
Neo-mechanistic ideas about human cognitive phenomena are becoming increasingly dominant in fields related to theoretical cognitive science and cognitive neuroscience [48]. Consequently, the neo-mechanistic framework is often presented as one of the main theories, or the main theory, to explain human cognition in the twenty-first century.
Formulations of the idea that human cognition can be considered in computational terms can already arguably be found in the works of Thomas Hobbes (1588–1679) and Gottfried Leibniz (1646–1716). However, it is in the first half of the twentieth century that new developments in this tradition made the thesis gain great strength [49]. Alan Turing (1912–1954), with his work on computation, made a solid mathematical contribution to advances in the attempt to build machines capable of thinking like humans. And with the development of the computer and the emergence of studies in computer science and artificial intelligence, there was an even greater push for the acceptance of these ideas in the period. Indeed, these were crucial factors in the development of cognitive psychology in the 1950s and cognitive science (in the specific sense) in the 1970s. In discussing the foundations of cognitive science, Gardner [3] states that “there is the faith that central to any understanding of the human mind is the electronic computer.” Furthermore, according to him: “Involvement with computers, and belief in their relevance as a model of human thought, is pervasive in cognitive science” [3].
The first formulations of the philosophical foundations and the most central bases of the “computational theory of cognition” were presented, above all, in central works by Hilary Putnam (1926–2016) and Jerry Fodor (1935–2017). It is mainly based on works like these that the “classical model of cognitive computation” was formulated [49]. According to this proposal, the human mind is a computational system similar in important respects to a “Turing machine,” which works through “Turing-style computations.” In this view, cognitive processes, such as problem solving, decision making, and formal reasoning, are performed through computations similar to those of a Turing machine.
Another line of work, however, developed an alternative notion of cognitive computation. Inspired by research in the field of neurophysiology, some authors in the 1980s proposed that cognitive computation was something very different from Turing-style computation [50]. The correct format of cognitive computation for them was that of neural networks, in which, very briefly, data nodes are connected in a particular way so that when the network is activated through an input, it can provide an output. This framework became known as connectionism, and it has been developed in numerous works since then. Many cognitive models of different phenomena were built based on this view, such as object recognition, speech perception, and sentence comprehension.
The notion of “cognitive mechanistic computation” is part of this tradition, and it is especially related to the model of neural networks. Craver [10], for example, writes about the “computational properties of brain regions” and “computational properties of neural systems,” without giving much detail about what exactly this means. In any case, it is clear that the supposed computation is much more related to concrete properties of neural systems than to abstract functional properties of psychological capacities considered in terms of Turing computation or something similar. Milkowski [11], in turn, presents a proposal that holds that neurocognitive processing occurs over states that contain information, but he does not elaborate much on the content and the semantic dimension of cognitive information or of putative cognitive computations.
Bechtel [9, 19] considers mental mechanisms as information-processing mechanisms that operate through neural representations and neural computations about vehicles and content. In his view, the “control theory of dynamical systems” shows how content is placed in this context. And Thagard [14, 15] thinks that mental mechanisms operate through computations that take place on representations at the cognitive level and computations that take place at the neural and molecular levels. In Thagard’s work, there is also recourse to the “theory of dynamical systems” (as in Bechtel’s); however, just in his version of the mechanistic theory, there is a definite number of mechanistic levels and extensive discussion about the “semantic pointers theory” of Chris Eliasmith.
Finally, there is the work of Piccinini [12, 13, 51], which is one of the most theoretically sophisticated and detailed among neo-mechanists regarding such issues. The author defends a mechanistic neurocomputational theory of human cognition. In his view, the human nervous system is a functional mechanism that produces computations through the activation of neurons, while the processing occurs in vehicles according to rules. Cognitive capacities are explained then by multilevel neurocognitive mechanisms that perform neural computations over neural representations. Besides, he thinks that neural computation (i.e., computations defined on the functionally relevant elements of neural activity) is not purely digital, as classically understood, nor purely analog, as alternatively understood; in his view, neural computation is
One does not need to enter so deep into these individual theories to see that they differ significantly. Craver mentions computations but does not offer an elaborated account. Thagard is the only one mentioning semantic pointers as central to the account. Milkowski and Piccinini attempt to avoid the problems with content, by means of focusing on formal properties. And Bechtel uses control theory to deal with the issue of content. As a result, it is not possible to derive from those accounts a single theory, as each author develops his/her own point of view with its significant particularities. There is, therefore, no theoretical substantial unity among these proponents.
However, one can try to find common aspects to evaluate at least the most basic and important tenets. To do that, an analysis of two cases where this mechanistic view on human cognitive computation can be applied will be helpful.
One of the best examples found in the specialized literature of a concrete application of this view to particular cognitive phenomena is related to memory, which, indeed, has been traditionally an object of study in the field of psychology [9, 10]. Functional analyses of the human memory capacity reveal the existence of many sub-capacities, such as short-term memory, long-term memory, phonological memory, visuospatial memory, semantic memory, episodic memory, and memory consolidation. In mechanistic terms, one of the best-understood phenomena in this memory system is memory consolidation. Roughly put, this is the phenomenon of transforming short-term memories (which are liable and easy to disrupt) into long-term memories, which are robust and enduring, when consolidation takes place and permits the organism to remember important events for a longer period of time and modify its behavior accordingly [52]. To explain this phenomenon, all the relevant regions in the brain responsible for the functions that compose the neuro-cognitive mechanism of memory consolidation, including all relevant mechanistic levels of decomposition, must be identified, that is, all the particular component parts and component operations of the whole mechanism must be determined, as shown on Figure 1. Finally, the causal processes and causal interactions within the mechanism functions need also to be understood, that is, the general organization of the mechanism.
An example of a simple model of a neuro-cognitive biological mechanism (M1). In this model, M1 is composed, at the level L1, by its component parts C1, C2, and C3, which perform the functions (or activities) f1, f2, and f3. The component parts can be decomposed into smaller components, as it happens with C3, which is composed, at level L2, of the sub-components SC1, SC2, SC3, and SC4. The component SC3 can be further decomposed, at level L3, into its subcomponents ssc1, ssc2, and ssc3.
The explanation starts at the highest level of the whole mechanism. At this level, it is necessary to correctly identify all the large neural network that is responsible for memory consolidation. Secondly, it must be established whether this large neural system is indeed all that is relevant for the explanation of the phenomenon. The mechanistic explanation at this level also needs to clarify how the neural network process information about new memory episodes through
Once this has been clarified, the explanation turns to the second level of description in which the large neural system is decomposed into particular sub-neural systems localized in more specific regions. Here the goal is to understand the information processing and computational operations (e.g., spiking patterns in populations of neurons) of these smaller neural networks and how they contribute to the performance of the whole mechanism composed of such neural nets.
Moreover, a further stage of decomposition must be reached that concerns the processes underlying memory at an intercellular level. The explanation at this particular level aims at describing the components of a particular neural network and at understanding how a small number of neurons operate (e.g., how they depolarize and fire in the process of propagation of action potentials, or how they are responsible for synaptic processes, neurotransmitters being released, and so on). Here it is possible to measure spiking rates of neurons, or spiking frequency and record neural activity in general.
Finally, the explanation can go even to another lower mechanistic level—the intracellular and molecular level. At this level, the description is in terms of the activity of relevant proteins, molecules, and ions. As one can see, this kind of explanation “exhibits a progression from the behavioral-level characterization of memory consolidation to the identification of important components in the process at progressively lower levels.” [52]. All levels are equally important to achieve the complete multilevel mechanistic explanation of the particular phenomenon in the end.
Another example is related to human visual perception [9, 13, 40], which is roughly understood as the capacity to acquire and process visual information from objects and events in the environment. In the biological mechanism related to human visual perception, the occipital lobe is central, since many studies on humans show deficits in visual processing due to damage in the occipital lobe. The mechanism also includes a projection of the optic tract going from the eye, passing by the lateral geniculate nucleus (LGN), which is an area of the thalamus, and achieving the occipital lobe. Besides, it includes the eyes, optic nerves, and other brain areas responsible for visual perception. All these areas can be decomposed in working components and their operations, and each decomposition is considered to be a lower level in the entire constitution of the mechanism. The occipital lobe, for instance, can be itself decomposed in areas responsible for particular visual functions, such as the striate cortex, also known as Brodmann area 17, or V1 (primary visual cortex, or visual area 1).
The same procedure can be done for all the other areas in the brain that are also part of the mechanism responsible for visual perception; for instance, V2, V3, V4, and V5/MT. It is necessary to identify also the cells (including visual receptor cells in the retina of the eye, such as cones and rods), networks of cells, or larger neural systems in these areas that are responsible for
As one can observe by looking at these two examples, the notion of “computation” in the mechanistic framework stands for some causal interactions within the nervous system and this is how different brain regions “compute” different information. Each brain region “stands for” some kind of particular information—related to perception, sensation, memory, language, reasoning, emotion, etc. The substantial problems with such an account of human cognition will be analyzed in what follows.
A great deal of criticism has arisen in the specialized literature concerning the notion of human cognitive computation. It is nearly impossible to review all of the works, but I will make some considerations of some of the most influential critics.
Fodor [53, 54, 55], for instance, claims that many mental representations (e.g., beliefs) and mental processes (e.g., abductive reasoning) are sensitive to global properties (i.e., properties that beliefs, for instance, have so that they are determined by a set of other beliefs which they are members of). For example, a belief about a tennis racket being broken may complicate the plan of playing tennis on the weekend, but not the plan of playing soccer. This means that a mental representation, such as an intention to play tennis, will depend on the context at the moment—whether there is a racket available for the game or not. Fodor argues, though, that classical symbolic computing models are only sensitive to local properties, and neural network models cannot handle this feature of human cognition.
Dreyfus [56], in turn, claims that much human knowledge cannot be captured by symbolic manipulation and formal rules, since this knowledge is constructed through direct contact and practicing in the world. Nagel [57] brings attention to the problem of phenomenal consciousness—roughly, the issue of what it feels like to experience something subjectively. Following this line of thinking, we can also say that a computer cannot know (if it can know anything) what it feels like to taste the flavor of chocolate. It has no idea of what it is like to eat chocolate, something that is quite basic for any child that does it. More than that, computers do not feel pain or pleasure, which is quite basic for human beings. Furthermore, Searle [58] brings attention to the difficulties related to intentionality, understanding, and meaning, with his famous “Chinese room argument.” And, additionally, Putnam [59] develops the idea that mental states cannot be identified with computational states, consequently arguing vigorously against computational reductionism3.
The case of Bruner’s critics is also very interesting. One of the names most frequently mentioned in influential works of historical reconstruction of the events and studies that contributed to the beginning and development of the cognitive movement in psychology is the American psychologist Jerome Bruner (1915–2016) [1, 2, 3, 60, 61]. He is recognized for having founded, together with George Miller (1920–2012), the Center for Cognitive Studies at Harvard University, in 1960. In addition, Bruner published, together with colleagues, in 1956,
One of the most interesting points in Bruner’s work, however, is his strong criticism of the very cognitive movement he helped to develop. He has presented this criticism in key works, such as
In
However, in Bruner’s view, this is not the way forward. In
One of the major problems pointed out by Bruner in the computationalist approach is that the production of meaning is often extremely complex, sensitive to the context, and involves the difficulty of clear and precise understanding [64]. This is not the same as establishing computational procedures for the processing of input and output information to the system, whether this is computational processing in digital format or the form of neural networks. For Bruner, meaning making is not merely information processing; it is something more profound and more complex. Culture, in his view, has a fundamental role in human life and it is only through it and in it that certain processes and mental structures are formed and used.
The human being, in Bruner’s view, was able to develop a way of life in which reality is represented by a symbolism shared by members of a cultural community, and human life is organized and built from this symbolism that is conserved, elaborated, and transmitted through successive generations [64]. Although meaning is in the mind and is produced by it, it also has its origins in culture and has its importance within the culture in which it was generated. And for the production of meanings, the human mind creates and makes use of symbolic cultural systems. Thus, in this view, thinking and learning are always situated in a cultural context [64]. Computer systems, however, are not capable of producing meanings. They only deal with a certain set of formalized and operationalized meanings, but they do not make interpretations of human and cultural phenomena.
Furthermore, there is no very clear reason to suppose that processes and relationships between all mental phenomena are literally computational in nature, nor that all mental representations have this same character. The application of the concept of computation to these phenomena investigated in the tradition of psychological research is based only on a working hypothesis present in a certain particular theoretical system. Nevertheless, there is as yet no concrete proof that all human cognition works according to a type of computational processing x, y, or z. In fact, finding out what kind of computational processing is related to the human mind has become an extremely debated issue internally by adherents of any computational model of human cognition [49]. It is no accident that comprehensive theoretical systems were developed precisely with the intention of questioning the computational model of cognition.
Now, to illustrate more concretely some of the difficulties mentioned with the notion of human cognitive computation, let us consider some cases involving conscious complex informal reasoning and conscious complex decision making where explanations for human behavior might be required [22].
Consider, firstly, a case where a person is dissatisfied with her marriage and is thinking about getting a divorce. To make such a decision, she has been consciously reflecting for months on the current state of the marriage, her beliefs about the relationship, her emotions about her partner, her desires and expectations in life, the beliefs of her family and closest friends about the issue and what are the reasons to take action in this regard. After thinking carefully for a very long time, being aware that she really does not feel comfortable and happy at all, she decides to go for a divorce.
Consider also a second example. A person needs to decide which candidate she will vote for as president of her country. To make this decision, she needs to use her conscious informal reasoning ability. Thus, she reflects on the arguments put forward by politicians running for the election, the arguments put forward by commentators, scientists, and political analysts, as well as journalists writing on the subject, and the arguments of friends and family she finds relevant and credible. After three months of thinking, she has not decided yet but is rather still in doubt concerning her vote in the major candidates A and B. When someone asks her which candidate she is going to vote, she says: “I still don’t know.” Then, some surprising news arises in a serious newspaper with charges of corruption against candidate A, and she is a frequent reader of this newspaper, so she becomes immediately aware of this. Upon reflection on the matter and related issues, she takes the new information seriously and she finally decides that voting for candidate B is the best option. The major reason is that there is no charge whatsoever of corruption against him. When she is asked now which candidate she is going to vote for, she answers immediately: “candidate B.” After she made up her mind, she finally goes to the appropriate place on the proper day and time to cast her vote.
A third example is the case of a college student who suffers from difficulties related to his excessive anxiety. Through a general psychological assessment, it can be seen that the factors related to student anxiety are financial difficulties, difficulties in family life where physical and psychological violence occurs, difficulties in finding leisure time to relax and have fun (since they need to work and study at the same time) and difficulties with excessive concerns about the uncertain future, as he believes that it will not be easy to find a job when he graduates. All of these factors seem to contribute to generate in the student’s mind distorted and dysfunctional negative thoughts about himself and his life, and it seems very plausible that these distorted thoughts are strongly associated with his excessive anxiety. This interpretation is, indeed, supported by numerous works in the specialized literature in clinical psychology. Thus, we observe that the most relevant causal factors to explain this psychological phenomenon are not merely computational, but psychological, social, and environmental.
Psychological scientific explanations, in these cases, need considerations that go beyond the investigation of computations being performed in nervous systems or even in any abstract functional system. What explains the psychological phenomenon of belief formation and decision making in the first example and the excessive anxiety in the third example is the meaning formation and interaction of beliefs, desires, and intentions to act (according to logical rules, practical rules, and interpretation of reality), which are strongly affected by emotions, physical environment, and social factors.
In the second example, evidently, an informative explanation would have to mention an important causal factor—the event of the corruption charges against candidate A, appearing in a serious newspaper. Moreover, the explanation would have to mention that the person becomes aware of this event, accepts it as reliable, accepts the charges as true and accurate, and now this content is present in one or some of her beliefs. In possession of this content, she can rationally justify herself when engaging in discussions about the topic with family, friends, and other people, providing reasons for her related beliefs and her related behaviors. Thus, the influence of the event on her is external and affects the internal logic and content of her systems of beliefs, emotions, desires, and intentions. This explanation involves then particular properties of human cognitive systems, present for instance in belief and intention systems. These properties are clearly different than those involved in merely describing supposed automatic computational activities in her neural networks or describing what is happening in terms of physical and chemical neural processes. The explanation for this phenomenon of belief formation, therefore, would also have to account for how this new information could change a particular belief given her system of beliefs about the topic.
In the examples above, there are cognitive processes that often necessitate consciousness and complex informal reasoning about belief systems that are often linked to particular perceptions, sensations, emotions, desires, intentions, attitudes, as well as related to each other and the external environment. Some of these beliefs have great value, such as some moral beliefs, which makes this whole dynamic even more complex. In these cases, blind computation might even occur at some level, but what is most relevant are environmental, social, cultural, historical, and psychological factors (such as beliefs, emotions, desires, and intentions) that acquire meaning in a given cognitive system.
The relevant explanation of the actions in such cases is made through considerations—(1) about the creation and alteration of the content of perceptions, beliefs, sensations, emotions, maxims, wills, desires, intentions, etc.; (2) about their internal relationships; and (3) about their external relationships with the physical, social, historical, and cultural context. Rigorous empirical scientific research can aid in discovering strong and systematic (stable) regularities in human behavior explained in such terms without the need for the notion of computation. Statistical tools and analysis, through the mathematical application, can bring greater objectivity, avoiding both an extremely subjective and confusing vocabulary, as well as unproductive speculation and mere common sense.
Moreover, self-consciousness here is crucial, since we humans have the ability to
Therefore, human beings have the ability to form original belief systems and relate them according to logical and interpretative rules, building arguments to support their point of view, which often influences their behavior. Human beings are also able to think about different types of relevant information for months or years to make an important and complex decision. To make a difficult decision, a human being can take into account information related to plans for the very distant future, in which many scenarios are considered. A human may wonder what happened in the very distant past, or what might have happened, even if he or she knew what really happened. And complex informal reasoning and complex decision making are things that humans do naturally and often in their daily lives.
Thus, in cognitive science, it is necessary to deal with extremely complex phenomena, given that human beings show great differences when compared to other animals in nature. Human beings have a cumulative, complex, dynamic, and elaborate culture that is passed on through generations. Humans are also involved in understanding and writing their own history. They have natural languages with enormous, complex, and refined expressive power and sophisticated grammar. Human beings practice and appreciate art, such as literature, painting, cinema, and music. They engage in purely formal or very abstract thoughts when they do mathematics, logic, and engage in certain religious thoughts. They create legal laws for their societies and think about morality, building moral systems. They build artificial intelligence machines that are able to learn with a certain level of autonomy and are able to explore other planets. Furthermore, humans are involved in politics, science, and philosophy.
Computers, by contrast, so far, do not form beliefs on their own, they do not have the capacity to evaluate and improve them by themselves, and they do not interact in the social environment neither using natural language with a huge degree of sophistication as humans do nor engaging in social and cultural practices. If we look at the problem from a very concrete and objective point of view, we observe that even the most advanced computer systems, the most advanced robots, and the most advanced artificial neural and cognitive architectures today are still very far from behaving like human beings in relation to language and actions that involve consciousness and informal rationality. Humans are capable of playing chess, cooking pizza, making coffee, having a conversation about politics, creating a new song on a guitar, and playing tennis on the same day. No computational artificial system is currently capable of this generality in cognition. So, as a matter of current fact, computational artificial cognition cannot be used to fully explain the major capacities of human cognition and intelligence.
It is no surprise, then, that mechanistic accounts of psychological capacities usually suggest only
Difficulties with the notion of cognitive computation are recognized by influential neo-mechanists themselves. Milkowski [21], for instance, concludes his work by admitting that we “still don’t know how to model consciousness mechanistically.” Additionally, there are several alternative models of cognitive computation in cognitive science nowadays—syntactic computation; algorithmic computation; causal computation; and semantic computation [65]. None of the models has gained significant prominence over the others concerning the understanding and explanation of human cognition. Finally, there is strong criticism even of the attempt by neo-mechanists to propose that good computational explanations in cognitive science must be also mechanistic explanations [66, 67].
Therefore, if we think about the issue from the point of view of current facts, we need to recognize that the neo-mechanistic proposal for human cognition is still far from being able to be considered the best or most plausible understanding and explanation of human cognition. It is just one view among many.
The mechanistic framework has been offering significant contributions to the field of cognitive science, on the one hand. One of its best contributions is the promotion of debates on the issue of human cognitive computation. In this sense, there is a search for a better understanding of what this notion actually means. All this effort is very worthwhile and welcome. More generally, the theoretical debate about fundamental questions in cognitive science promoted by new mechanists is also very important, as well as their effort to clarify what a “biological mechanism” and a “cognitive mechanism” are and what a “mechanistic explanation” in cognitive science is. Furthermore, another contribution of the new mechanistic philosophy is to encourage historical research and current debate, in cognitive science and beyond, about the relationship among “mechanism,” “materialism,” “reductionism” and “computationalism”, so that these concepts are not confused and that the positions adopted by the authors, as well as the different dimensions of the debate, are appreciated in a fair and correct way. Finally, the new mechanistic philosophy applied to cognitive science is also contributing to the important debate concerning the unity, integration, and plurality in the field.
On the other hand, however, many of the current promises of the new mechanism for cognitive science are quite difficult to fulfill. Firstly, neo-mechanistic philosophy is a philosophy of science built primarily from examples from the biological sciences and neuroscience that is serving as the basis for building a philosophy of the science of mind. We live in a period in which neuroscience and artificial intelligence research have gained great prestige and recognition. A great deal of economic investment has been made in these areas and this is very attractive. In part, this also influences “the new wave of mechanism,” and the necessity of some authors to expand the framework. However, numerous particularities related to psychology and human cognition are being neglected in this theoretical structure, as I tried to show.
Secondly, there is considerable disagreement among leading neo-mechanists over the most plausible formulation of MTHC regarding fundamental issues, such as the idea of human cognitive computation. Thus, there is a considerable difficulty related to the internal articulation and unification of the theory. Furthermore, many alternative major theories, and the research programs based on them, strongly threaten the neo-mechanistic framework in current cognitive science, since they are also seeking predominance in the field, or just for having more space and recognition.
Given this, we can conclude that the mechanistic model of human cognitive computation cannot provide substantial theoretical or explanatory unification or integration to the field of cognitive science today, since there is no unification between the proponents themselves. Moreover, their different proposals are often unclear on many important aspects concerning traditional problems of intentionality, consciousness, and self-consciousness. The accounts are sometimes internally not well-articulated; and, externally, there is serious criticism of them, with countless debates and controversies on several fundamental questions. In addition, there are several alternative models competing for predominance on this particular issue. And it is yet by no means clear whether the explanatory power of any of them is greater than the explanatory power of the others.
This analysis shows, therefore, that the neo-mechanistic proposal concerning human cognitive computation has serious weaknesses. But the problem is not to use the idea of cognitive computing to advance models of biological and artificial cognitive architectures, since many human cognitive abilities can already be simulated. Indeed, it is very interesting to see that our science has advanced to the point where a computer can win against the best chess and go game players in the world. In fact, advancements within computational artificial systems and robotics could well be applied to improve our educational and health systems. For example, inspired by scientific developments in the field of cognitive science, artificial cognitive systems could possibly be developed to help children with the learning process of mathematics, natural language, or history at schools, or even at the university level. Artificial systems could possibly be developed to help people with excessive anxiety symptoms, as well. This could be extremely worthwhile. Moreover, better and more advanced artificial cognitive systems and robotic systems can contribute to improving theories of human cognition, as much as better and more correct theories of human cognition can help in faster advancements of cognitive artificial systems and robotic systems. But there is good reason to keep these efforts separated and to consider human cognition as a very complex and particular phenomenon in nature.
The problem arises only with the untenable suggestion that we already have, or that we are very close to getting, the complete and definitive understanding and explanation of all the major capacities of human cognition in computational terms. This, yes, is a mistake.
The author declares no conflict of interest.
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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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