Conductive polymer

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Chemical structures of some conductive polymers. From top left clockwise: polyacetylene; polyphenylene vinylene; polypyrrole (X = NH) and polythiophene (X = S); and polyaniline (X = NH) and polyphenylene sulfide (X = S).

Conductive polymers or, more precisely, intrinsically conducting polymers (ICPs) are organic polymers that conduct electricity. Such compounds may have metallic conductivity or can be semiconductors. The biggest advantage of conductive polymers is their processability, mainly by dispersion. Conductive polymers are generally not thermoplastics, i.e., they are not thermoformable. But, like insulating polymers, they are organic materials. They can offer high electrical conductivity but do not show similar mechanical properties to other commercially available polymers. The electrical properties can be fine-tuned using the methods of organic synthesis and by advanced dispersion techniques.

History

Polyaniline was first described in the mid-19th century by Henry Letheby, who investigated the electrochemical and chemical oxidation products of aniline in acidic media. He noted that reduced form was colourless but the oxidized forms were deep blue.

The first highly-conductive organic compounds were the charge transfer complexes. In the 1950s, researchers reported that polycyclic aromatic compounds formed semi-conducting charge-transfer complex salts with halogens. In 1954, researchers at Bell Labs and elsewhere reported organic charge transfer complexes with resistivities as low as 8 ohms-cm. In the early 1970s, researchers demonstrated salts of tetrathiafulvalene show almost metallic conductivity, while superconductivity was demonstrated in 1980. Broad research on charge transfer salts continues today. While these compounds were technically not polymers, this indicated that organic compounds can carry current. While organic conductors were previously intermittently discussed, the field was particularly energized by the prediction of superconductivity following the discovery of BCS theory.

In 1963 Australians B.A. Bolto, D.E. Weiss, and coworkers reported derivatives of polypyrrole with resistivities as low as 1 ohm·cm. References cite multiple reports of similar high-conductivity oxidized polyacetylenes. With the notable exception of charge transfer complexes (some of which are even superconductors), organic molecules were previously considered insulators or at best weakly conducting semiconductors. Subsequently, DeSurville and coworkers reported high conductivity in a polyaniline. Likewise, in 1980, Diaz and Logan reported films of polyaniline that can serve as electrodes.

While mostly operating in the quantum realm of less than 100 nanometers, "molecular" electronic processes can collectively manifest on a macro scale. Examples include quantum tunneling, negative resistance, phonon-assisted hopping and polarons. In 1977, Alan J. Heeger, Alan MacDiarmid and Hideki Shirakawa reported similar high conductivity in oxidized iodine-doped polyacetylene. For this research, they were awarded the 2000 Nobel Prize in Chemistry "for the discovery and development of conductive polymers." Polyacetylene itself did not find practical applications, but drew the attention of scientists and encouraged the rapid growth of the field. Since the late 1980s, organic light-emitting diodes (OLEDs) have emerged as an important application of conducting polymers.

Types

Linear-backbone "polymer blacks" (polyacetylene, polypyrrole, polyindole and polyaniline) and their copolymers are the main class of conductive polymers. Poly(p-phenylene vinylene) (PPV) and its soluble derivatives have emerged as the prototypical electroluminescent semiconducting polymers. Today, poly(3-alkylthiophenes) are the archetypical materials for solar cells and transistors.

The following table presents some organic conductive polymers according to their composition. The well-studied classes are written in bold and the less well studied ones are in italic.

The main chain contains	No heteroatom	Heteroatoms present
		Nitrogen-containing	Sulfur-containing
Aromatic cycles	Poly(fluorene)s polyphenylenes polypyrenes polyazulenes polynaphthalenes	The N is in the aromatic cycle: poly(pyrrole)s (PPY) polycarbazoles polyindoles polyazepines The N is outside the aromatic cycle: polyanilines (PANI)	The S is in the aromatic cycle: poly(thiophene)s (PT) poly(3,4-ethylenedioxythiophene) (PEDOT) The S is outside the aromatic cycle: poly(p-phenylene sulfide) (PPS)
Double bonds	Poly(acetylene)s (PAC)
Aromatic cycles and double bonds	Poly(p-phenylene vinylene) (PPV)

Synthesis

Conductive polymers are prepared by many methods. Most conductive polymers are prepared by oxidative coupling of monocyclic precursors. Such reactions entail dehydrogenation:

n H–[X]–H → H–[X]_n–H + 2(n–1) H⁺ + 2(n–1) e⁻

The low solubility of most polymers presents challenges. Some researchers add solubilizing functional groups to some or all monomers to increase solubility. Others address this through the formation of nanostructures and surfactant-stabilized conducting polymer dispersions in water. These include polyaniline nanofibers and PEDOT:PSS. In many cases, the molecular weights of conductive polymers are lower than conventional polymers such as polyethylene. However, in some cases, the molecular weight need not be high to achieve the desired properties.

There are two main methods used to synthesize conductive polymers, chemical synthesis and electro (co)polymerization. The chemical synthesis means connecting carbon-carbon bond of monomers by placing the simple monomers under various condition, such as heating, pressing, light exposure and catalyst. The advantage is high yield. However, there are many impurities plausible in the end product. The electro (co)polymerization means inserting three electrodes (reference electrode, counter electrode and working electrode) into solution including reactors or monomers. By applying voltage to electrodes, redox reaction to synthesize polymer is promoted. Electro (co)polymerization can also be divided into Cyclic Voltammetry and Potentiostatic method by applying cyclic voltage and constant voltage. The advantage of Electro (co)polymerization are the high purity of products. But the method can only synthesize a few products at a time.

Molecular basis of electrical conductivity

The conductivity of such polymers is the result of several processes. For example, in traditional polymers such as polyethylenes, the valence electrons are bound in sp³ hybridized covalent bonds. Such "sigma-bonding electrons" have low mobility and do not contribute to the electrical conductivity of the material. However, in conjugated materials, the situation is completely different. Conducting polymers have backbones of contiguous sp² hybridized carbon centers. One valence electron on each center resides in a p_z orbital, which is orthogonal to the other three sigma-bonds. All the p_z orbitals combine with each other to a molecule wide delocalized set of orbitals. The electrons in these delocalized orbitals have high mobility when the material is "doped" by oxidation, which removes some of these delocalized electrons. Thus, the conjugated p-orbitals form a one-dimensional electronic band, and the electrons within this band become mobile when it is partially emptied. The band structures of conductive polymers can easily be calculated with a tight binding model. In principle, these same materials can be doped by reduction, which adds electrons to an otherwise unfilled band. In practice, most organic conductors are doped oxidatively to give p-type materials. The redox doping of organic conductors is analogous to the doping of silicon semiconductors, whereby a small fraction of silicon atoms are replaced by electron-rich, e.g., phosphorus, or electron-poor, e.g., boron, atoms to create n-type and p-type semiconductors, respectively.

Although typically "doping" conductive polymers involves oxidizing or reducing the material, conductive organic polymers associated with a protic solvent may also be "self-doped."

Undoped conjugated polymers are semiconductors or insulators. In such compounds, the energy gap can be > 2 eV, which is too great for thermally activated conduction. Therefore, undoped conjugated polymers, such as polythiophenes, polyacetylenes only have a low electrical conductivity of around 10⁻¹⁰ to 10⁻⁸ S/cm. Even at a very low level of doping (< 1%), electrical conductivity increases several orders of magnitude up to values of around 0.1 S/cm. Subsequent doping of the conducting polymers will result in a saturation of the conductivity at values around 0.1–10 kS/cm for different polymers. Highest values reported up to now are for the conductivity of stretch oriented polyacetylene with confirmed values of about 80 kS/cm. Although the pi-electrons in polyacetylene are delocalized along the chain, pristine polyacetylene is not a metal. Polyacetylene has alternating single and double bonds which have lengths of 1.44 and 1.36 Å, respectively. Upon doping, the bond alteration is diminished in conductivity increases. Non-doping increases in conductivity can also be accomplished in a field effect transistor (organic FET or OFET) and by irradiation. Some materials also exhibit negative differential resistance and voltage-controlled "switching" analogous to that seen in inorganic amorphous semiconductors.

Despite intensive research, the relationship between morphology, chain structure and conductivity is still poorly understood. Generally, it is assumed that conductivity should be higher for the higher degree of crystallinity and better alignment of the chains, however this could not be confirmed for polyaniline and was only recently confirmed for PEDOT, which are largely amorphous.

Properties and applications

Conductive polymers show promise in antistatic materials and they have been incorporated into commercial displays and batteries. Literature suggests they are also promising in organic solar cells, printed electronic circuits, organic light-emitting diodes, actuators, electrochromism, supercapacitors, chemical sensors, chemical sensor arrays, and biosensors, flexible transparent displays, electromagnetic shielding and possibly replacement for the popular transparent conductor indium tin oxide. Another use is for microwave-absorbent coatings, particularly radar-absorptive coatings on stealth aircraft. Conducting polymers are rapidly gaining attraction in new applications with increasingly processable materials with better electrical and physical properties and lower costs. The new nano-structured forms of conducting polymers particularly, augment this field with their higher surface area and better dispersability. Research reports showed that nanostructured conducting polymers in the form of nanofibers and nanosponges, showed significantly improved capacitance values as compared to their non-nanostructured counterparts.

With the availability of stable and reproducible dispersions, PEDOT and polyaniline have gained some large-scale applications. While PEDOT (poly(3,4-ethylenedioxythiophene)) is mainly used in antistatic applications and as a transparent conductive layer in form of PEDOT:PSS dispersions (PSS=polystyrene sulfonic acid), polyaniline is widely used for printed circuit board manufacturing – in the final finish, for protecting copper from corrosion and preventing its solderability. Moreover, Polyindole is also starting to gain attention for various applications due to its high redox activity, thermal stability, and slow degradation properties than competitors polyaniline and polypyrrole.

Electroluminescence

Electroluminescence is light emission stimulated by electric current. In organic compounds, electroluminescence has been known since the early 1950s, when Bernanose and coworkers first produced electroluminescence in crystalline thin films of acridine orange and quinacrine. In 1960, researchers at Dow Chemical developed AC-driven electroluminescent cells using doping. In some cases, similar light emission is observed when a voltage is applied to a thin layer of a conductive organic polymer film. While electroluminescence was originally mostly of academic interest, the increased conductivity of modern conductive polymers means enough power can be put through the device at low voltages to generate practical amounts of light. This property has led to the development of flat panel displays using organic LEDs, solar panels, and optical amplifiers.

Barriers to applications

Since most conductive polymers require oxidative doping, the properties of the resulting state are crucial. Such materials are salt-like (polymer salt), which diminishes their solubility in organic solvents and water and hence their processability. Furthermore, the charged organic backbone is often unstable towards atmospheric moisture. The poor processability for many polymers requires the introduction of solubilizing or substituents, which can further complicate the synthesis.

Experimental and theoretical thermodynamical evidence suggests that conductive polymers may even be completely and principally insoluble so that they can only be processed by dispersion.

Trends

Most recent emphasis is on organic light emitting diodes and organic polymer solar cells. The Organic Electronics Association is an international platform to promote applications of organic semiconductors. Conductive polymer products with embedded and improved electromagnetic interference (EMI) and electrostatic discharge (ESD) protection have led to both prototypes and products. For example, Polymer Electronics Research Center at University of Auckland is developing a range of novel DNA sensor technologies based on conducting polymers, photoluminescent polymers and inorganic nanocrystals (quantum dots) for simple, rapid and sensitive gene detection. Typical conductive polymers must be "doped" to produce high conductivity. As of 2001, there remains to be discovered an organic polymer that is intrinsically electrically conducting. Recently (as of 2020), researchers from IMDEA Nanoscience Institute reported experimental demonstration of the rational engineering of 1D polymers that are located near the quantum phase transition from the topologically trivial to non-trivial class, thus featuring a narrow bandgap.

Electron transport chain

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https://en.wikipedia.org/wiki/Electron_transport_chain 

 

The electron transport chain in the mitochondrion is the site of oxidative phosphorylation in eukaryotes. The NADH and succinate generated in the citric acid cycle are oxidized, which releases the energy of oxygen to power ATP synthase.

 

Photosynthetic electron transport chain of the thylakoid membrane.

An electron transport chain (ETC) is a series of protein complexes and other molecules that transfer electrons from electron donors to electron acceptors via redox reactions (both reduction and oxidation occurring simultaneously) and couples this electron transfer with the transfer of protons (H⁺ ions) across a membrane. Many of the enzymes in the electron transport chain are membrane-bound.

The flow of electrons through the electron transport chain is an exergonic process. The energy from the redox reactions creates an electrochemical proton gradient that drives the synthesis of adenosine triphosphate (ATP). In aerobic respiration, the flow of electrons terminates with molecular oxygen as the final electron acceptor that provides most of the energy. In anaerobic respiration, other, lower-energy electron acceptors are used, such as sulfate.

In an electron transport chain, the redox reactions are driven by the difference in the Gibbs free energy of reactants and products. The free energy released when a higher-energy electron donor and acceptor convert to lower-energy products, while electrons are transferred from a lower to a higher redox potential, is used by the complexes in the electron transport chain to create an electrochemical gradient of ions. It is this electrochemical gradient that drives the synthesis of ATP via coupling with oxidative phosphorylation with ATP synthase.

In eukaryotic organisms the electron transport chain, and site of oxidative phosphorylation, is found on the inner mitochondrial membrane. The energy of oxygen released in its reaction with reduced compounds such as cytochrome c and (indirectly) NADH and FADH is used by the electron transport chain to pump protons into the intermembrane space, generating the electrochemical gradient over the inner mitochondrial membrane. In photosynthetic eukaryotes, the electron transport chain is found on the thylakoid membrane. Here, light energy drives electron transport through a proton pump and the resulting proton gradient causes subsequent synthesis of ATP. In bacteria, the electron transport chain can vary between species but it always constitutes a set of redox reactions that are coupled to the synthesis of ATP through the generation of an electrochemical gradient and oxidative phosphorylation through ATP synthase.

Mitochondrial electron transport chains

Most eukaryotic cells have mitochondria, which produce ATP from reactions of oxygen with products of the citric acid cycle, fatty acid metabolism, and amino acid metabolism. At the inner mitochondrial membrane, electrons from NADH and FADH₂ pass through the electron transport chain to oxygen, which provides the energy driving the process as it is reduced to water. The electron transport chain comprises an enzymatic series of electron donors and acceptors. Each electron donor will pass electrons to an acceptor of higher redox potential, which in turn donates these electrons to another acceptor, a process that continues down the series until electrons are passed to oxygen, the most energy-rich and terminal electron acceptor in the chain. Each reaction releases energy because a higher-energy donor and acceptor convert to lower-energy products. Via the transferred electrons, this energy is used to generate a proton gradient across the mitochondrial membrane by "pumping" protons into the intermembrane space, producing a state of higher free energy that has the potential to do work. This entire process is called oxidative phosphorylation since ADP is phosphorylated to ATP by using the electrochemical gradient that the redox reactions of the electron transport chain have established driven by the energy of oxygen.

Mitochondrial redox carriers

Energy associated with the transfer of electrons down the electron transport chain is used to pump protons from the mitochondrial matrix into the intermembrane space, creating an electrochemical proton gradient (ΔpH) across the inner mitochondrial membrane. This proton gradient is largely but not exclusively responsible for the mitochondrial membrane potential (ΔΨ_M). It allows ATP synthase to use the flow of H⁺ through the enzyme back into the matrix to generate ATP from adenosine diphosphate (ADP) and inorganic phosphate. Complex I (NADH coenzyme Q reductase; labeled I) accepts electrons from the Krebs cycle electron carrier nicotinamide adenine dinucleotide (NADH), and passes them to coenzyme Q (ubiquinone; labeled Q), which also receives electrons from Complex II (succinate dehydrogenase; labeled II). Q passes electrons to Complex III (cytochrome bc₁ complex; labeled III), which passes them to cytochrome c (cyt c). Cyt c passes electrons to Complex IV (cytochrome c oxidase; labeled IV), which uses the electrons and hydrogen ions to release the energy of molecular oxygen as it is reduced to water.

Four membrane-bound complexes have been identified in mitochondria. Each is an extremely complex transmembrane structure that is embedded in the inner membrane. Three of them are proton pumps. The structures are electrically connected by lipid-soluble electron carriers and water-soluble electron carriers. The overall electron transport chain can be summarized as follows:

NADH+H+ → Complex I → Q                     
   
            ↑
    Complex II 
            ↑
    Succinate                                → Complex III → cytochrome c → Complex IV  → H2O
                       ↑
                   Complex II 
                       ↑
                   Succinate 

Complex I

Further information: Respiratory complex I

In Complex I (NADH ubiquinone oxidoreductase, Type I NADH dehydrogenase, or mitochondrial complex I; EC 1.6.5.3), two electrons are removed from NADH and transferred to a lipid-soluble carrier, ubiquinone (Q). The reduced product, ubiquinol (QH₂), freely diffuses within the membrane, and Complex I translocates four protons (H⁺) across the membrane, thus producing a proton gradient. Complex I is one of the main sites at which premature electron leakage to oxygen occurs, thus being one of the main sites of production of superoxide.

The pathway of electrons is as follows:

NADH is oxidized to NAD⁺, by reducing flavin mononucleotide to FMNH₂ in one two-electron step. FMNH₂ is then oxidized in two one-electron steps, through a semiquinone intermediate. Each electron thus transfers from the FMNH₂ to an Fe–S cluster, from the Fe-S cluster to ubiquinone (Q). Transfer of the first electron results in the free-radical (semiquinone) form of Q, and transfer of the second electron reduces the semiquinone form to the ubiquinol form, QH₂. During this process, four protons are translocated from the mitochondrial matrix to the intermembrane space. As the electrons move through the complex an electron current is produced along the 180 Angstrom width of the complex within the membrane. This current powers the active transport of four protons to the intermembrane space per two electrons from NADH.

Complex II

In Complex II (succinate dehydrogenase or succinate-CoQ reductase; EC 1.3.5.1) additional electrons are delivered into the quinone pool (Q) originating from succinate and transferred (via flavin adenine dinucleotide (FAD)) to Q. Complex II consists of four protein subunits: succinate dehydrogenase, (SDHA); succinate dehydrogenase [ubiquinone] iron–sulfur subunit, mitochondrial, (SDHB); succinate dehydrogenase complex subunit C, (SDHC) and succinate dehydrogenase complex, subunit D, (SDHD). Other electron donors (e.g., fatty acids and glycerol 3-phosphate) also direct electrons into Q (via FAD). Complex II is a parallel electron transport pathway to complex 1, but unlike Complex I, no protons are transported to the intermembrane space in this pathway. Therefore, the pathway through Complex II contributes less energy to the overall electron transport chain process.

Complex III

In Complex III (cytochrome bc₁ complex or CoQH₂-cytochrome c reductase; EC 1.10.2.2), the Q-cycle contributes to the proton gradient by an asymmetric absorption/release of protons. Two electrons are removed from QH₂ at the Q_O site and sequentially transferred to two molecules of cytochrome c, a water-soluble electron carrier located within the intermembrane space. The two other electrons sequentially pass across the protein to the Q_i site where the quinone part of ubiquinone is reduced to quinol. A proton gradient is formed by one quinol (${\displaystyle {\ce {2H+2e-}}}$) oxidations at the Q_o site to form one quinone (${\displaystyle {\ce {2H+2e-}}}$) at the Q_i site. (In total, four protons are translocated: two protons reduce quinone to quinol and two protons are released from two ubiquinol molecules.)

${\displaystyle {\ce {QH2 + 2}}}$${\displaystyle {\text{ cytochrome }}c}$${\displaystyle {\ce {(Fe^{III}) + 2 H}}}$${\displaystyle _{\text{in}}^{+}}$${\displaystyle {\ce {-> Q + 2}}}$${\displaystyle {\text{ cytochrome }}c}$${\displaystyle {\ce {(Fe^{II}) + 4 H}}}$${\displaystyle _{\text{out}}^{+}}$

When electron transfer is reduced (by a high membrane potential or respiratory inhibitors such as antimycin A), Complex III may leak electrons to molecular oxygen, resulting in superoxide formation.

This complex is inhibited by dimercaprol (British Antilewisite, BAL), Napthoquinone and Antimycin.

Complex IV

In Complex IV (cytochrome c oxidase; EC 1.9.3.1), sometimes called cytochrome AA3, four electrons are removed from four molecules of cytochrome c and transferred to molecular oxygen (O₂) and four protons, producing two molecules of water. The complex contains coordinated copper ions and several heme groups. At the same time, the energy of O₂ is used to remove eight protons from the mitochondrial matrix (although only four are translocated across the membrane), contributing to the proton gradient. The exact details of proton pumping in Complex IV are still under study. Cyanide is an inhibitor of Complex IV.

Coupling with oxidative phosphorylation

Depiction of ATP synthase, the site of oxidative phosphorylation to generate ATP.

According to the chemiosmotic coupling hypothesis, proposed by Nobel Prize in Chemistry winner Peter D. Mitchell, the electron transport chain and oxidative phosphorylation are coupled by a proton gradient across the inner mitochondrial membrane. The efflux of protons from the mitochondrial matrix creates an electrochemical gradient (proton gradient). This gradient is used by the F_OF₁ ATP synthase complex to make ATP via oxidative phosphorylation. ATP synthase is sometimes described as Complex V of the electron transport chain. The F_O component of ATP synthase acts as an ion channel that provides for a proton flux back into the mitochondrial matrix. It is composed of a, b and c subunits. Protons in the inter-membrane space of mitochondria first enter the ATP synthase complex through an a subunit channel. Then protons move to the c subunits. The number of c subunits determines how many protons are required to make the F_O turn one full revolution. For example, in humans, there are 8 c subunits, thus 8 protons are required. After c subunits, protons finally enter the matrix through an a subunit channel that opens into the mitochondrial matrix. This reflux releases free energy produced during the generation of the oxidized forms of the electron carriers (NAD⁺ and Q) with energy provided by O₂. The free energy is used to drive ATP synthesis, catalyzed by the F₁ component of the complex.
Coupling with oxidative phosphorylation is a key step for ATP production. However, in specific cases, uncoupling the two processes may be biologically useful. The uncoupling protein, thermogenin—present in the inner mitochondrial membrane of brown adipose tissue—provides for an alternative flow of protons back to the inner mitochondrial matrix. Thyroxine is also a natural uncoupler. This alternative flow results in thermogenesis rather than ATP production.

Reverse electron flow

Reverse electron flow is the transfer of electrons through the electron transport chain through the reverse redox reactions. Usually requiring a significant amount of energy to be used, this can reduce the oxidized forms of electron donors. For example, NAD⁺ can be reduced to NADH by Complex I. There are several factors that have been shown to induce reverse electron flow. However, more work needs to be done to confirm this. One example is blockage of ATP synthase, resulting in a build-up of protons and therefore a higher proton-motive force, inducing reverse electron flow.

Bacterial electron transport chains

In eukaryotes, NADH is the most important electron donor. The associated electron transport chain is NADH → Complex I → Q → Complex III → cytochrome c → Complex IV → O₂ where Complexes I, III and IV are proton pumps, while Q and cytochrome c are mobile electron carriers. The electron acceptor providing the energy for this process is molecular oxygen.

In prokaryotes (bacteria and archaea) the situation is more complicated, because there are several different electron donors and several different electron acceptors. The generalized electron transport chain in bacteria is:

                     Donor            Donor                    Donor
                       ↓                ↓                        ↓
                 dehydrogenase   →   quinone   →   bc1   →   cytochrome
                                        ↓                        ↓
                                oxidase(reductase)       oxidase(reductase)
                                        ↓                        ↓
                                     Acceptor                 Acceptor

Electrons can enter the chain at three levels: at the level of a dehydrogenase, at the level of the quinone pool, or at the level of a mobile cytochrome electron carrier. These levels correspond to successively more positive redox potentials, or to successively decreased potential differences relative to the terminal electron acceptor. In other words, they correspond to successively smaller Gibbs free energy changes for the overall redox reaction.

Individual bacteria use multiple electron transport chains, often simultaneously. Bacteria can use a number of different electron donors, a number of different dehydrogenases, a number of different oxidases and reductases, and a number of different electron acceptors. For example, E. coli (when growing aerobically using glucose and oxygen as an energy source) uses two different NADH dehydrogenases and two different quinol oxidases, for a total of four different electron transport chains operating simultaneously.

A common feature of all electron transport chains is the presence of a proton pump to create an electrochemical gradient over a membrane. Bacterial electron transport chains may contain as many as three proton pumps, like mitochondria, or they may contain two or at least one.

Electron donors

In the current biosphere, the most common electron donors are organic molecules. Organisms that use organic molecules as an electron source are called organotrophs. Chemoorganotrophs (animals, fungi, protists) and photolithotrophs (plants and algae) constitute the vast majority of all familiar life forms.

Some prokaryotes can use inorganic matter as an electron source. Such an organism is called a (chemo)lithotroph ("rock-eater"). Inorganic electron donors include hydrogen, carbon monoxide, ammonia, nitrite, sulfur, sulfide, manganese oxide, and ferrous iron. Lithotrophs have been found growing in rock formations thousands of meters below the surface of Earth. Because of their volume of distribution, lithotrophs may actually outnumber organotrophs and phototrophs in our biosphere.

The use of inorganic electron donors such as hydrogen as an energy source is of particular interest in the study of evolution. This type of metabolism must logically have preceded the use of organic molecules and oxygen as an energy source.

Complexes I and II

Bacteria can use several different electron donors. When organic matter is the electron source, the donor may be NADH or succinate, in which case electrons enter the electron transport chain via NADH dehydrogenase (similar to Complex I in mitochondria) or succinate dehydrogenase (similar to Complex II). Other dehydrogenases may be used to process different energy sources: formate dehydrogenase, lactate dehydrogenase, glyceraldehyde-3-phosphate dehydrogenase, H₂ dehydrogenase (hydrogenase), electron transport chain. Some dehydrogenases are also proton pumps, while others funnel electrons into the quinone pool. Most dehydrogenases show induced expression in the bacterial cell in response to metabolic needs triggered by the environment in which the cells grow. In the case of lactate dehydrogenase in E. coli, the enzyme is used aerobically and in combination with other dehydrogenases. It is inducible and is expressed when the concentration of DL-lactate in the cell is high.

Quinone carriers

Quinones are mobile, lipid-soluble carriers that shuttle electrons (and protons) between large, relatively immobile macromolecular complexes embedded in the membrane. Bacteria use ubiquinone (Coenzyme Q, the same quinone that mitochondria use) and related quinones such as menaquinone (Vitamin K<₂). Archaea in the genus Sulfolobus use caldariellaquinone. The use of different quinones is due to slight changes in redox potentials caused by changes in structure. The change in redox potentials of these quinones may be suited to changes in the electron acceptors or variations of redox potentials in bacterial complexes.

Proton pumps

A proton pump is any process that creates a proton gradient across a membrane. Protons can be physically moved across a membrane, as seen in mitochondrial Complexes I and IV. The same effect can be produced by moving electrons in the opposite direction. The result is the disappearance of a proton from the cytoplasm and the appearance of a proton in the periplasm. Mitochondrial Complex III uses this second type of proton pump, which is mediated by a quinone (the Q cycle).

Some dehydrogenases are proton pumps, while others are not. Most oxidases and reductases are proton pumps, but some are not. Cytochrome bc₁ is a proton pump found in many, but not all, bacteria (not in E. coli). As the name implies, bacterial bc₁ is similar to mitochondrial bc₁ (Complex III).

Cytochrome electron carriers

Cytochromes are proteins that contain iron. They are found in two very different environments.

Some cytochromes are water-soluble carriers that shuttle electrons to and from large, immobile macromolecular structures imbedded in the membrane. The mobile cytochrome electron carrier in mitochondria is cytochrome c. Bacteria use a number of different mobile cytochrome electron carriers.

Other cytochromes are found within macromolecules such as Complex III and Complex IV. They also function as electron carriers, but in a very different, intramolecular, solid-state environment.

Electrons may enter an electron transport chain at the level of a mobile cytochrome or quinone carrier. For example, electrons from inorganic electron donors (nitrite, ferrous iron, electron transport chain) enter the electron transport chain at the cytochrome level. When electrons enter at a redox level greater than NADH, the electron transport chain must operate in reverse to produce this necessary, higher-energy molecule.

Terminal oxidases and reductases

When bacteria grow in aerobic environments, the terminal electron acceptor (O₂) is reduced to water by an enzyme called an oxidase. When bacteria grow in anaerobic environments, the terminal electron acceptor is reduced by an enzyme called a reductase. In mitochondria the terminal membrane complex (Complex IV) is cytochrome oxidase. Aerobic bacteria use a number of different terminal oxidases. For example, E. coli (a facultative anaerobe) does not have a cytochrome oxidase or a bc₁ complex. Under aerobic conditions, it uses two different terminal quinol oxidases (both proton pumps) to reduce oxygen to water.

Bacterial Complex IV can be split into classes according to the molecules act as terminal electron acceptors. Class I oxidases are cytochrome oxidases and use oxygen as the terminal electron acceptor. Class II oxidases are quinol oxidases and can use a variety of terminal electron acceptors. Both of these classes can be subdivided into categories based on what redox-active components they contain. E.g. Heme aa3 Class 1 terminal oxidases are much more efficient than Class 2 terminal oxidases.

Anaerobic bacteria, which do not use oxygen as a terminal electron acceptor, have terminal reductases individualized to their terminal acceptor. For example, E. coli can use fumarate reductase, nitrate reductase, nitrite reductase, DMSO reductase, or trimethylamine-N-oxide reductase, depending on the availability of these acceptors in the environment.

Most terminal oxidases and reductases are inducible. They are synthesized by the organism as needed, in response to specific environmental conditions.

Electron acceptors

Just as there are a number of different electron donors (organic matter in organotrophs, inorganic matter in lithotrophs), there are a number of different electron acceptors, both organic and inorganic. If oxygen is available, it is invariably used as the terminal electron acceptor in aerobic bacteria and facultative anaerobes, because it generates the greatest Gibbs free energy change and produces the most energy.

In anaerobic environments, different, lower-energy electron acceptors are used, including nitrate, nitrite, ferric iron, sulfate, carbon dioxide, and small organic molecules such as fumarate.

Photosynthetic

Further information: Light-dependent reaction and Photosynthetic reaction center

In oxidative phosphorylation, electrons are transferred from a moderate-energy electron donor such as NADH to a high-energy acceptor such as O₂ through an electron transport chain, releasing the energy. In photophosphorylation, the energy of sunlight is used to create a high-energy electron donor which can subsequently reduce oxidized components and couple to ATP synthesis via proton translocation by the electron transport chain.

Photosynthetic electron transport chains, like the mitochondrial chain, can be considered as a special case of the bacterial systems. They use mobile, lipid-soluble quinone carriers (phylloquinone and plastoquinone) and mobile, water-soluble carriers (cytochromes). They also contain a proton pump. The proton pump in all photosynthetic chains resembles mitochondrial Complex III. The commonly-held theory of symbiogenesis proposes that both organelles descended from bacteria.

Heuristic (psychology)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Heuristic_(psychology) 

Heuristics is the process by which humans use mental short cuts to arrive at decisions. Heuristics are simple strategies that humans, animals, organizations, and even machines use to quickly form judgments, make decisions, and find solutions to complex problems. Often this involves focusing on the most relevant aspects of a problem or situation to formulate a solution. While heuristic processes are used to find the answers and solutions that are most likely to work or be correct, they are not always right or the most accurate. Judgments and decisions based on heuristics are simply good enough to satisfy a pressing need in situations of uncertainty, where information is incomplete. In that sense they can differ from answers given by logic and probability.

The economist and cognitive psychologist Herbert A. Simon introduced the concept of heuristics in the 1950s, suggesting there were limitations to rational decision making. In the 1970s, psychologists Amos Tversky and Daniel Kahneman added to the field with their research on cognitive bias. It was their work that introduced specific heuristic models, a field which has only expanded since. While some argue that pure laziness is behind the heuristics process, others argue that it can be more accurate than decisions based on every known factor and consequence, the less-is-more effect.

History

Herbert A. Simon formulated one of the first models of heuristics, known as satisficing. His more general research program posed the question of how humans make decisions when the conditions for rational choice theory are not met, that is how people decide under uncertainty. Simon is also known as the father of bounded rationality, which he understood as the study of the match (or mismatch) between heuristics and decision environments. This program was later extended into the study of ecological rationality.

In the early 1970s, psychologists Amos Tversky and Daniel Kahneman took a different approach, linking heuristics to cognitive biases. Their typical experimental setup consisted of a rule of logic or probability, embedded in a verbal description of a judgement problem, and demonstrated that people's intuitive judgement deviated from the rule. The "Linda problem" below gives an example. The deviation is then explained by a heuristic. This research, called the heuristics-and-biases program, challenged the idea that human beings are rational actors and first gained worldwide attention in 1974 with the Science paper "Judgment Under Uncertainty: Heuristics and Biases" and although the originally proposed heuristics have been refined over time, this research program has changed the field by permanently setting the research questions.

The original ideas by Herbert Simon were taken up in the 1990s by Gerd Gigerenzer and others. According to their perspective, the study of heuristics requires formal models that allow predictions of behavior to be made ex ante. Their program has three aspects:

1. What are the heuristics humans use? (the descriptive study of the "adaptive toolbox")
2. Under what conditions should humans rely on a given heuristic? (the prescriptive study of ecological rationality)
3. How to design heuristic decision aids that are easy to understand and execute? (the engineering study of intuitive design)

Among others, this program has shown that heuristics can lead to fast, frugal, and accurate decisions in many real-world situations that are characterized by uncertainty.

These two different research programs have led to two kinds of models of heuristics, formal models and informal ones. Formal models describe the decision process in terms of an algorithm, which allows for mathematical proofs and computer simulations. In contrast, informal models are verbal descriptions.

Formal models of heuristics

Simon's satisficing strategy

Main article: Satisficing

Herbert Simon's satisficing heuristic can be used to choose one alternative from a set of alternatives in situations of uncertainty. Here, uncertainty means that the total set of alternatives and their consequences is not known or knowable. For instance, professional real-estate entrepreneurs rely on satisficing to decide in which location to invest to develop new commercial areas: "If I believe I can get at least x return within y years, then I take the option." In general, satisficing is defined as:

• Step 1: Set an aspiration level α
• Step 2: Choose the first alternative that satisfies α

If no alternative is found, then the aspiration level can be adapted.

• Step 3: If after time β no alternative has satisfied α, then decrease α by some amount δ and return to step 1.

Satisficing has been reported across many domains, for instance as a heuristic car dealers use to price used BMWs.

Elimination by aspects

Unlike satisficing, Amos Tversky's elimination-by-aspect heuristic can be used when all alternatives are simultaneously available. The decision-maker gradually reduces the number of alternatives by eliminating alternatives that do not meet the aspiration level of a specific attribute (or aspect). During a series of selections, people tend to experience uncertainty and exhibit inconsistency. Elimination by aspects could be used when facing selections. In general, the process of elimination by aspects is as follows:

·Step 1: Select one attribute related to decision making

·Step 2: Eliminate all alternatives that exclude this specific attribute

·Step 3: Use another attribute in order to further eliminate alternatives

·Step 4: Repeat step 3 until only one option is left, a decision has then been made

Elimination by aspects does not speculate that choosing alternatives could help consumers to maximize utility, on the contrary, it holds that selection is the result of a probabilistic process that gradually eliminates alternatives. A simple example is given by Amos Tversky: when someone wants to purchase a new car, the first aspect they will take into account might be the automatic transmission, this will eliminate all alternatives that do not contain such an aspect. Then when all the alternatives that do not have this feature are eliminated, another aspect will be given such as a 3000$ price limit. The process of elimination continues to occur until all alternatives are eliminated.

Elimination by aspects is well used in the early stage of business angels’ decision-making process since it facilitates a fast-decision-making tool - alternatives will be eliminated when investors find a critical defect of the potential opportunities. Another research also demonstrated that elimination by aspects has widely been used in electricity contract choice. The logic behind these two examples is that elimination by aspects helps to make decisions when facing a series of complicated choices. One may need to make a decision among all alternatives while he or she only has limited intuitive computational facilities and time. However, elimination by aspects as a compensatory model could help to make such complex decisions since it is easier to apply and involves nonnumerical computations.

Recognition heuristic

Main article: Recognition heuristic

The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. For two alternatives, the heuristic is:

If one of two alternatives is recognized and the other not, then infer that the recognized alternative has the higher value with respect to the criterion.

For example, in the 2003 Wimbledon tennis tournament, Andy Roddick played Tommy Robredo. If one has heard of Roddick but not of Robredo, the recognition heuristic leads to the prediction that Roddick will win. The recognition heuristic exploits partial ignorance, if one has heard of both or no player, a different strategy is needed. Studies of Wimbledon 2003 and 2005 have shown that the recognition heuristic applied by semi-ignorant amateur players predicted the outcomes of all gentlemen single games as well and better than the seedings of the Wimbledon experts (who had heard of all players), as well as the ATP rankings. The recognition heuristic is ecologically rational (that is, it predicts well) when the recognition validity is substantially above chance. In the present case, recognition of players' names is highly correlated with their chances of winning.

Take-the-best

Main article: Take-the-best heuristic

The take-the-best heuristic exploits the basic psychological capacity for retrieving cues from memory in the order of their validity. Based on the cue values, it infers which of two alternatives has a higher value on a criterion. Unlike the recognition heuristic, it requires that all alternatives are recognized, and it thus can be applied when the recognition heuristic cannot. For binary cues (where 1 indicates the higher criterion value), the heuristic is defined as:

Search rule: Search cues in the order of their validity v. Stopping rule: Stop search on finding the first cue that discriminates between the two alternatives (i.e., one cue values are 0 and 1). Decision rule: Infer that the alternative with the positive cue value (1) has the higher criterion value).

The validity v_i of a cue i is defined as the proportion of correct decisions c_i:

v_i = c_i / t_i

where ti is the number of cases the values of the two alternatives differ on cue i. The validity of each cue can be estimated from samples of observation.

Take-the-best has remarkable properties. In comparison with complex machine learning models, it has been shown that it can often predict better than regression models, classification-and-regression trees, neural networks, and support vector machines. [Brighton & Gigerenzer, 2015]

Similarly, psychological studies have shown that in situations where take-the-best is ecologically rational, a large proportion of people tend to rely on it. This includes decision making by airport custom officers, professional burglars and police officers and student populations. The conditions under which take-the-best is ecologically rational are mostly known. Take-the-best shows that the previous view that ignoring part of the information would be generally irrational is incorrect. Less can be more.

Fast-and-frugal trees

Main article: Fast-and-frugal trees

A fast-and-frugal tree is a heuristic that allows to make a classifications, such as whether a patient with severe chest pain is likely to have a heart attack or not, or whether a car approaching a checkpoint is likely to be a terrorist or a civilian. It is called “fast and frugal” because, just like take-the-best, it allows for quick decisions with only few cues or attributes. It is called a “tree” because it can be represented like a decision tree in which one asks a sequence of questions. Unlike a full decision tree, however, it is an incomplete tree – to save time and reduce the danger of overfitting.

Figure 1: Screening for HIV in the general public follows the logic of a fast-and-frugal tree. If the first enzyme immunoassay (ELISA) is negative, the diagnosis is “no HIV.” If not, a second ELISA is performed; if it is negative, the diagnosis is “no HIV.” Otherwise, a Western blot test is performed, which determines the final classification

Figure 1 shows a fast-and-frugal tree used for screening for HIV (human immunodeficiency virus). Just like take-the-best, the tree has a search rule, stopping rule, and decision rule:

Search rule: Search through cues in a specified order. Stopping rule: Stop search if an exit is reached. Decision rule: Classify the person according to the exit (here: No HIV or HIV).

In the HIV tree, an ELISA (enzyme-linked immunosorbent assay) test is conducted first. If the outcome is negative, then the testing procedure stops and the client is informed of the good news, that is, “no HIV.” If, however, the result is positive, a second ELISA test is performed, preferably from a different manufacturer. If the second ELISA is negative, then the procedure stops and the client is informed of having “no HIV.” However, if the result is positive, a final test, the Western blot, is conducted.

In general, for n binary cues, a fast-and-frugal tree has exactly n + 1 exits – one for each cue and two for the final cue. A full decision tree, in contrast, requires 2ⁿ exits. The order of cues (tests) in a fast-and-frugal tree is determined by the sensitivity and specificity of the cues, or by other considerations such as the costs of the tests. In the case of the HIV tree, the ELISA is ranked first because it produces fewer misses than the Western blot test, and also is less expensive. The Western blot test, in contrast, produces fewer false alarms. In a full tree, in contrast, order does not matter for the accuracy of the classifications.

Fast-and-frugal trees are descriptive or prescriptive models of decision making under uncertainty. For instance, an analysis or court decisions reported that the best model of how London magistrates make bail decisions is a fast and frugal tree. The HIV tree is both prescriptive– physicians are taught the procedure – and a descriptive model, that is, most physicians actually follow the procedure.

Informal models of heuristics

In their initial research, Tversky and Kahneman proposed three heuristics—availability, representativeness, and anchoring and adjustment. Subsequent work has identified many more. Heuristics that underlie judgment are called "judgment heuristics". Another type, called "evaluation heuristics", are used to judge the desirability of possible choices.

Availability

Main article: Availability heuristic

In psychology, availability is the ease with which a particular idea can be brought to mind. When people estimate how likely or how frequent an event is on the basis of its availability, they are using the availability heuristic. When an infrequent event can be brought easily and vividly to mind, this heuristic overestimates its likelihood. For example, people overestimate their likelihood of dying in a dramatic event such as a tornado or terrorism. Dramatic, violent deaths are usually more highly publicised and therefore have a higher availability. On the other hand, common but mundane events are hard to bring to mind, so their likelihoods tend to be underestimated. These include deaths from suicides, strokes, and diabetes. This heuristic is one of the reasons why people are more easily swayed by a single, vivid story than by a large body of statistical evidence. It may also play a role in the appeal of lotteries: to someone buying a ticket, the well-publicised, jubilant winners are more available than the millions of people who have won nothing.

When people judge whether more English words begin with T or with K , the availability heuristic gives a quick way to answer the question. Words that begin with T come more readily to mind, and so subjects give a correct answer without counting out large numbers of words. However, this heuristic can also produce errors. When people are asked whether there are more English words with K in the first position or with K in the third position, they use the same process. It is easy to think of words that begin with K, such as kangaroo, kitchen, or kept. It is harder to think of words with K as the third letter, such as lake, or acknowledge, although objectively these are three times more common. This leads people to the incorrect conclusion that K is more common at the start of words. In another experiment, subjects heard the names of many celebrities, roughly equal numbers of whom were male and female. The subjects were then asked whether the list of names included more men or more women. When the men in the list were more famous, a great majority of subjects incorrectly thought there were more of them, and vice versa for women. Tversky and Kahneman's interpretation of these results is that judgments of proportion are based on availability, which is higher for the names of better-known people.

In one experiment that occurred before the 1976 U.S. Presidential election, some participants were asked to imagine Gerald Ford winning, while others did the same for a Jimmy Carter victory. Each group subsequently viewed their allocated candidate as significantly more likely to win. The researchers found a similar effect when students imagined a good or a bad season for a college football team. The effect of imagination on subjective likelihood has been replicated by several other researchers.

A concept's availability can be affected by how recently and how frequently it has been brought to mind. In one study, subjects were given partial sentences to complete. The words were selected to activate the concept either of hostility or of kindness: a process known as priming. They then had to interpret the behavior of a man described in a short, ambiguous story. Their interpretation was biased towards the emotion they had been primed with: the more priming, the greater the effect. A greater interval between the initial task and the judgment decreased the effect.

Tversky and Kahneman offered the availability heuristic as an explanation for illusory correlations in which people wrongly judge two events to be associated with each other. They explained that people judge correlation on the basis of the ease of imagining or recalling the two events together.

Representativeness

Main article: Representativeness heuristic

 

Snap judgement of whether novel object fits an existing category

The representativeness heuristic is seen when people use categories, for example when deciding whether or not a person is a criminal. An individual thing has a high representativeness for a category if it is very similar to a prototype of that category. When people categorise things on the basis of representativeness, they are using the representativeness heuristic. "Representative" is here meant in two different senses: the prototype used for comparison is representative of its category, and representativeness is also a relation between that prototype and the thing being categorised. While it is effective for some problems, this heuristic involves attending to the particular characteristics of the individual, ignoring how common those categories are in the population (called the base rates). Thus, people can overestimate the likelihood that something has a very rare property, or underestimate the likelihood of a very common property. This is called the base rate fallacy. Representativeness explains this and several other ways in which human judgments break the laws of probability.

The representativeness heuristic is also an explanation of how people judge cause and effect: when they make these judgements on the basis of similarity, they are also said to be using the representativeness heuristic. This can lead to a bias, incorrectly finding causal relationships between things that resemble one another and missing them when the cause and effect are very different. Examples of this include both the belief that "emotionally relevant events ought to have emotionally relevant causes", and magical associative thinking.

Representativeness of base rates

Main article: Base rate fallacy

A 1973 experiment used a psychological profile of Tom W., a fictional graduate student. One group of subjects had to rate Tom's similarity to a typical student in each of nine academic areas (including Law, Engineering and Library Science). Another group had to rate how likely it is that Tom specialised in each area. If these ratings of likelihood are governed by probability, then they should resemble the base rates, i.e. the proportion of students in each of the nine areas (which had been separately estimated by a third group). If people based their judgments on probability, they would say that Tom is more likely to study Humanities than Library Science, because there are many more Humanities students, and the additional information in the profile is vague and unreliable. Instead, the ratings of likelihood matched the ratings of similarity almost perfectly, both in this study and a similar one where subjects judged the likelihood of a fictional woman taking different careers. This suggests that rather than estimating probability using base rates, subjects had substituted the more accessible attribute of similarity.

Conjunction fallacy

Main article: Conjunction fallacy

When people rely on representativeness, they can fall into an error which breaks a fundamental law of probability. Tversky and Kahneman gave subjects a short character sketch of a woman called Linda, describing her as, "31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations". People reading this description then ranked the likelihood of different statements about Linda. Amongst others, these included "Linda is a bank teller", and, "Linda is a bank teller and is active in the feminist movement". People showed a strong tendency to rate the latter, more specific statement as more likely, even though a conjunction of the form "Linda is both X and Y" can never be more probable than the more general statement "Linda is X". The explanation in terms of heuristics is that the judgment was distorted because, for the readers, the character sketch was representative of the sort of person who might be an active feminist but not of someone who works in a bank. A similar exercise concerned Bill, described as "intelligent but unimaginative". A great majority of people reading this character sketch rated "Bill is an accountant who plays jazz for a hobby", as more likely than "Bill plays jazz for a hobby".

Without success, Tversky and Kahneman used what they described as "a series of increasingly desperate manipulations" to get their subjects to recognise the logical error. In one variation, subjects had to choose between a logical explanation of why "Linda is a bank teller" is more likely, and a deliberately illogical argument which said that "Linda is a feminist bank teller" is more likely "because she resembles an active feminist more than she resembles a bank teller". Sixty-five percent of subjects found the illogical argument more convincing. Other researchers also carried out variations of this study, exploring the possibility that people had misunderstood the question. They did not eliminate the error. It has been shown that individuals with high CRT scores are significantly less likely to be subject to the conjunction fallacy. The error disappears when the question is posed in terms of frequencies. Everyone in these versions of the study recognised that out of 100 people fitting an outline description, the conjunction statement ("She is X and Y") cannot apply to more people than the general statement ("She is X").

Ignorance of sample size

Main article: Insensitivity to sample size

Tversky and Kahneman asked subjects to consider a problem about random variation. Imagining for simplicity that exactly half of the babies born in a hospital are male, the ratio will not be exactly half in every time period. On some days, more girls will be born and on others, more boys. The question was, does the likelihood of deviating from exactly half depend on whether there are many or few births per day? It is a well-established consequence of sampling theory that proportions will vary much more day-to-day when the typical number of births per day is small. However, people's answers to the problem do not reflect this fact. They typically reply that the number of births in the hospital makes no difference to the likelihood of more than 60% male babies in one day. The explanation in terms of the heuristic is that people consider only how representative the figure of 60% is of the previously given average of 50%.

Dilution effect

Richard E. Nisbett and colleagues suggest that representativeness explains the dilution effect, in which irrelevant information weakens the effect of a stereotype. Subjects in one study were asked whether "Paul" or "Susan" was more likely to be assertive, given no other information than their first names. They rated Paul as more assertive, apparently basing their judgment on a gender stereotype. Another group, told that Paul's and Susan's mothers each commute to work in a bank, did not show this stereotype effect; they rated Paul and Susan as equally assertive. The explanation is that the additional information about Paul and Susan made them less representative of men or women in general, and so the subjects' expectations about men and women had a weaker effect. This means unrelated and non-diagnostic information about certain issue can make relative information less powerful to the issue when people understand the phenomenon.

Misperception of randomness

Representativeness explains systematic errors that people make when judging the probability of random events. For example, in a sequence of coin tosses, each of which comes up heads (H) or tails (T), people reliably tend to judge a clearly patterned sequence such as HHHTTT as less likely than a less patterned sequence such as HTHTTH. These sequences have exactly the same probability, but people tend to see the more clearly patterned sequences as less representative of randomness, and so less likely to result from a random process. Tversky and Kahneman argued that this effect underlies the gambler's fallacy; a tendency to expect outcomes to even out over the short run, like expecting a roulette wheel to come up black because the last several throws came up red. They emphasised that even experts in statistics were susceptible to this illusion: in a 1971 survey of professional psychologists, they found that respondents expected samples to be overly representative of the population they were drawn from. As a result, the psychologists systematically overestimated the statistical power of their tests, and underestimated the sample size needed for a meaningful test of their hypotheses.

Anchoring and adjustment

Main article: Anchoring (cognitive bias)

Anchoring and adjustment is a heuristic used in many situations where people estimate a number. According to Tversky and Kahneman's original description, it involves starting from a readily available number—the "anchor"—and shifting either up or down to reach an answer that seems plausible. In Tversky and Kahneman's experiments, people did not shift far enough away from the anchor. Hence the anchor contaminates the estimate, even if it is clearly irrelevant. In one experiment, subjects watched a number being selected from a spinning "wheel of fortune". They had to say whether a given quantity was larger or smaller than that number. For instance, they might be asked, "Is the percentage of African countries which are members of the United Nations larger or smaller than 65%?" They then tried to guess the true percentage. Their answers correlated well with the arbitrary number they had been given. Insufficient adjustment from an anchor is not the only explanation for this effect. An alternative theory is that people form their estimates on evidence which is selectively brought to mind by the anchor.

The amount of money people will pay in an auction for a bottle of wine can be influenced by considering an arbitrary two-digit number.

The anchoring effect has been demonstrated by a wide variety of experiments both in laboratories and in the real world. It remains when the subjects are offered money as an incentive to be accurate, or when they are explicitly told not to base their judgment on the anchor. The effect is stronger when people have to make their judgments quickly. Subjects in these experiments lack introspective awareness of the heuristic, denying that the anchor affected their estimates.

Even when the anchor value is obviously random or extreme, it can still contaminate estimates. One experiment asked subjects to estimate the year of Albert Einstein's first visit to the United States. Anchors of 1215 and 1992 contaminated the answers just as much as more sensible anchor years. Other experiments asked subjects if the average temperature in San Francisco is more or less than 558 degrees, or whether there had been more or fewer than 100,025 top ten albums by The Beatles. These deliberately absurd anchors still affected estimates of the true numbers.

Anchoring results in a particularly strong bias when estimates are stated in the form of a confidence interval. An example is where people predict the value of a stock market index on a particular day by defining an upper and lower bound so that they are 98% confident the true value will fall in that range. A reliable finding is that people anchor their upper and lower bounds too close to their best estimate. This leads to an overconfidence effect. One much-replicated finding is that when people are 98% certain that a number is in a particular range, they are wrong about thirty to forty percent of the time.

Anchoring also causes particular difficulty when many numbers are combined into a composite judgment. Tversky and Kahneman demonstrated this by asking a group of people to rapidly estimate the product 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1. Another group had to estimate the same product in reverse order; 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8. Both groups underestimated the answer by a wide margin, but the latter group's average estimate was significantly smaller. The explanation in terms of anchoring is that people multiply the first few terms of each product and anchor on that figure. A less abstract task is to estimate the probability that an aircraft will crash, given that there are numerous possible faults each with a likelihood of one in a million. A common finding from studies of these tasks is that people anchor on the small component probabilities and so underestimate the total. A corresponding effect happens when people estimate the probability of multiple events happening in sequence, such as an accumulator bet in horse racing. For this kind of judgment, anchoring on the individual probabilities results in an overestimation of the combined probability.

Examples

People's valuation of goods, and the quantities they buy, respond to anchoring effects. In one experiment, people wrote down the last two digits of their social security numbers. They were then asked to consider whether they would pay this number of dollars for items whose value they did not know, such as wine, chocolate, and computer equipment. They then entered an auction to bid for these items. Those with the highest two-digit numbers submitted bids that were many times higher than those with the lowest numbers. When a stack of soup cans in a supermarket was labelled, "Limit 12 per customer", the label influenced customers to buy more cans. In another experiment, real estate agents appraised the value of houses on the basis of a tour and extensive documentation. Different agents were shown different listing prices, and these affected their valuations. For one house, the appraised value ranged from US$114,204 to $128,754.

Anchoring and adjustment has also been shown to affect grades given to students. In one experiment, 48 teachers were given bundles of student essays, each of which had to be graded and returned. They were also given a fictional list of the students' previous grades. The mean of these grades affected the grades that teachers awarded for the essay.

One study showed that anchoring affected the sentences in a fictional rape trial. The subjects were trial judges with, on average, more than fifteen years of experience. They read documents including witness testimony, expert statements, the relevant penal code, and the final pleas from the prosecution and defence. The two conditions of this experiment differed in just one respect: the prosecutor demanded a 34-month sentence in one condition and 12 months in the other; there was an eight-month difference between the average sentences handed out in these two conditions. In a similar mock trial, the subjects took the role of jurors in a civil case. They were either asked to award damages "in the range from $15 million to $50 million" or "in the range from $50 million to $150 million". Although the facts of the case were the same each time, jurors given the higher range decided on an award that was about three times higher. This happened even though the subjects were explicitly warned not to treat the requests as evidence.

Assessments can also be influenced by the stimuli provided. In one review, researchers found that if a stimulus is perceived to be important or carry "weight" to a situation, that people were more likely to attribute that stimulus as heavier physically.

Affect heuristic

Main article: Affect heuristic

"Affect", in this context, is a feeling such as fear, pleasure or surprise. It is shorter in duration than a mood, occurring rapidly and involuntarily in response to a stimulus. While reading the words "lung cancer" might generate an affect of dread, the words "mother's love" can create an affect of affection and comfort. When people use affect ("gut responses") to judge benefits or risks, they are using the affect heuristic. The affect heuristic has been used to explain why messages framed to activate emotions are more persuasive than those framed in a purely factual way.

Others

See also: Category:Cognitive biases and Category:Heuristics

 

• Control heuristic
• Contagion heuristic
• Effort heuristic
• Familiarity heuristic
• Fluency heuristic
• Gaze heuristic
• Hot-hand fallacy
• Naive diversification
• Peak–end rule
• Recognition heuristic
• Scarcity heuristic
• Similarity heuristic
• Simulation heuristic
• Social proof

Theories

There are competing theories of human judgment, which differ on whether the use of heuristics is irrational. A cognitive laziness approach argues that heuristics are inevitable shortcuts given the limitations of the human brain. According to the natural assessments approach, some complex calculations are already done rapidly and automatically by the brain, and other judgments make use of these processes rather than calculating from scratch. This has led to a theory called "attribute substitution", which says that people often handle a complicated question by answering a different, related question, without being aware that this is what they are doing. A third approach argues that heuristics perform just as well as more complicated decision-making procedures, but more quickly and with less information. This perspective emphasises the "fast and frugal" nature of heuristics.

Cognitive laziness

See also: Cognitive miser

An effort-reduction framework proposed by Anuj K. Shah and Daniel M. Oppenheimer states that people use a variety of techniques to reduce the effort of making decisions.

Attribute substitution

Main article: Attribute substitution

 

A visual example of attribute substitution. This illusion works because the 2D size of parts of the scene is judged on the basis of 3D (perspective) size, which is rapidly calculated by the visual system.

In 2002 Daniel Kahneman and Shane Frederick proposed a process called attribute substitution which happens without conscious awareness. According to this theory, when somebody makes a judgment (of a target attribute) which is computationally complex, a rather more easily calculated heuristic attribute is substituted. In effect, a difficult problem is dealt with by answering a rather simpler problem, without the person being aware this is happening. This explains why individuals can be unaware of their own biases, and why biases persist even when the subject is made aware of them. It also explains why human judgments often fail to show regression toward the mean.

This substitution is thought of as taking place in the automatic intuitive judgment system, rather than the more self-aware reflective system. Hence, when someone tries to answer a difficult question, they may actually answer a related but different question, without realizing that a substitution has taken place.

In 1975, psychologist Stanley Smith Stevens proposed that the strength of a stimulus (e.g. the brightness of a light, the severity of a crime) is encoded by brain cells in a way that is independent of modality. Kahneman and Frederick built on this idea, arguing that the target attribute and heuristic attribute could be very different in nature.

[P]eople are not accustomed to thinking hard, and are often content to trust a plausible judgment that comes to mind.

Daniel Kahneman, American Economic Review 93 (5) December 2003, p. 1450

Kahneman and Frederick propose three conditions for attribute substitution:

1. The target attribute is relatively inaccessible.
   Substitution is not expected to take place in answering factual questions that can be retrieved directly from memory ("What is your birthday?") or about current experience ("Do you feel thirsty now?).
2. An associated attribute is highly accessible.
   This might be because it is evaluated automatically in normal perception or because it has been primed. For example, someone who has been thinking about their love life and is then asked how happy they are might substitute how happy they are with their love life rather than other areas.
3. The substitution is not detected and corrected by the reflective system.
   For example, when asked "A bat and a ball together cost $1.10. The bat costs $1 more than the ball. How much does the ball cost?" many subjects incorrectly answer $0.10. An explanation in terms of attribute substitution is that, rather than work out the sum, subjects parse the sum of $1.10 into a large amount and a small amount, which is easy to do. Whether they feel that is the right answer will depend on whether they check the calculation with their reflective system.

Kahneman gives an example where some Americans were offered insurance against their own death in a terrorist attack while on a trip to Europe, while another group were offered insurance that would cover death of any kind on the trip. Even though "death of any kind" includes "death in a terrorist attack", the former group were willing to pay more than the latter. Kahneman suggests that the attribute of fear is being substituted for a calculation of the total risks of travel. Fear of terrorism for these subjects was stronger than a general fear of dying on a foreign trip.

Fast and frugal

Gerd Gigerenzer and colleagues have argued that heuristics can be used to make judgments that are accurate rather than biased. According to them, heuristics are "fast and frugal" alternatives to more complicated procedures, giving answers that are just as good.

Consequences

Efficient decision heuristics

Warren Thorngate, a social psychologist, implemented ten simple decision rules or heuristics in a computer program. He determined how often each heuristic selected alternatives with highest-through-lowest expected value in a series of randomly-generated decision situations. He found that most of the simulated heuristics selected alternatives with highest expected value and almost never selected alternatives with lowest expected value.

"Beautiful-is-familiar" effect

Psychologist Benoît Monin reports a series of experiments in which subjects, looking at photographs of faces, have to judge whether they have seen those faces before. It is repeatedly found that attractive faces are more likely to be mistakenly labeled as familiar. Monin interprets this result in terms of attribute substitution. The heuristic attribute in this case is a "warm glow"; a positive feeling towards someone that might either be due to their being familiar or being attractive. This interpretation has been criticised, because not all the variance in familiarity is accounted for by the attractiveness of the photograph.

Judgments of morality and fairness

Legal scholar Cass Sunstein has argued that attribute substitution is pervasive when people reason about moral, political or legal matters. Given a difficult, novel problem in these areas, people search for a more familiar, related problem (a "prototypical case") and apply its solution as the solution to the harder problem. According to Sunstein, the opinions of trusted political or religious authorities can serve as heuristic attributes when people are asked their own opinions on a matter. Another source of heuristic attributes is emotion: people's moral opinions on sensitive subjects like sexuality and human cloning may be driven by reactions such as disgust, rather than by reasoned principles. Sunstein has been challenged as not providing enough evidence that attribute substitution, rather than other processes, is at work in these cases.

Persuasion

An example of how persuasion plays a role in heuristic processing can be explained through the heuristic-systematic model. This explains how there are often two ways we are able to process information from persuasive messages, one being heuristically and the other systematically. A heuristic is when we make a quick short judgement into our decision making. On the other hand, systematic processing involves more analytical and inquisitive cognitive thinking. Individuals looks further than their own prior knowledge for the answers. An example of this model could be used when watching an advertisement about a specific medication. One without prior knowledge would see the person in the proper pharmaceutical attire and assume that they know what they are talking about. Therefore, that person automatically has more credibility and is more likely to trust the content of the messages than they deliver. While another who is also in that field of work or already has prior knowledge of the medication will not be persuaded by the ad because of their systematic way of thinking. This was also formally demonstrated in an experiment conducted my Chaiken and Maheswaran (1994). In addition to these examples, the fluency heuristic ties in perfectly with the topic of persuasion. It is described as how we all easily make "the most of an automatic by-product of retrieval from memory". An example would be a friend asking about good books to read. Many could come to mind, but you name the first book recalled from your memory. Since it was the first thought, therefore you value it as better than any other book one could suggest. The effort heuristic is almost identical to fluency. The one distinction would be that objects that take longer to produce are seen with more value. One may conclude that a glass vase is more valuable than a drawing, merely because it may take longer for the vase. These two varieties of heuristics confirms how we may be influenced easily our mental shortcuts, or what may come quickest to our mind.