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The influence of intuition and emotions on decision making

Using insights from behavioral economics, psychology and neuroscience

Term Paper 2016 62 Pages

Business economics - Miscellaneous

Excerpt

Table of Contents

List of Abbreviations

1. Introduction
1.1. Problem Statement
1.2. Objectives and Structure

2. Decision Making
2.1. Disambiguation
2.2. Historical Classification
2.2.1. Chronological Development
2.2.2. Content Development
2.3. Meaning of Heuristics
2.4. Impact and Compensation of Biases and Information
2.5. Behavioral Economics, Decision Theory and Evaluation of Decisions

3. Neurological Insights
3.1. Human Nervous System
3.2. Limbic System
3.3. Neurological Localization of Decision Making

4. Emotions
4.1. Demarcation of Feelings
4.2. Disambiguation of Emotion
4.3. Influence on Decision Making

5. Meaning of Motivation in a Decision Making Context

6. Intuition
6.1. Disambiguation
6.2. Neurological Localization of Intuition
6.3. Explanatory Approaches and Models
6.3.1. Experiential Knowledge
6.3.2. Subliminal Perception
6.3.3. Somatic Marker, Embodiment and Focusing
6.3.4. Affect-Logic
6.3.5. Mirror Neurons and Empathy
6.3.6. Enteric Nervous System
6.4. Influence on Decision Making
6.5. Limitations and Risks

7. Effects and Exceptions in Decision Making
7.1. Genetic Influences
7.2. Group Decisions
7.3. Social Explorer Approach
7.4. Authenticity in Decisions
7.5. Sudden Inspirations and Problem-Solving Skills

8. Conclusion and Outlook

References

List of Abbreviations

illustration not visible in this excerpt

1. Introduction

1.1. Problem Statement

“Indecisiveness is a frequently encountered weakness of leaders.”1 Fredmund Malik blames managers for their non-decisions: actual decision makers consult advisers, commission studies, ask experts and do all these things again and again. Managers behave like this to cover their own aversion to decide.2 The permanent and recurring analysis of all possible options and contingencies delay, impede or even prevent necessary decisions. Anyway, the consequences of decisions or non-decisions of any kind, the resulting developments and possible risks are difficult to predict and in some cases incalculable.

The fact that permanent change has become a normal condition in organizations3 means that previous decisions are short-lived and decision cycles are getting shorter. Constant transformations which are determined by increasing complexity4 reduce the overview of the potential impact of the decisions taken. This complexity within the society and intra-company increases the uncertainty regarding actions, knowledge and decision making.5 Consequences of complexity in relation with decisions are amongst other things the non- analysability and unpredictability6 that both limits the decisiveness in general.

Coming from the military strategy analysis, the VUCA7 model and the LFP8 strategy take into account increasing complexity and perceived uncertainty in the business environment.9 Derived from that, Franz Kühmayer calls for a new economics of decision making: overstimulation, the vast amount of data and their incredible speed represent a difficulty for decision making. A cognitive loss of control can result e.g. in postponing decisions. A recent study of the Economic Intelligence Unit shows that executives rely mainly on their gut feeling in complicated decision situations: 90% of the executives ignore provided data if these data contradict their own intuition.10 Based on that, the question is what priority has to be given to one’s own intuition in times of a complex decision making process and what are the consequences.

1.2. Objectives and Structure

Gideon Keren and George Wu “feel that there is a high level of agreement among judgement and decision making researches as to the main developments that have shaped the field.”11 This mentioned broad consensus in scientific terms is in relation to the basic knowledge how decisions are made. The influencing factors on decision making, however, are rated very different. This study aims to answer where the similarities and differences within the decision making approaches are and what influence intuition and emotions have.

Starting with a definition to classify decision making, afterwards, a historical classification is done. Thereby, heuristics is given a special significance and the classification of heuristics and biases is explained in detail. Following, it is shown how decisions can be evaluated. The following section explains basics in terms of the human nervous system, the relevant brain regions with the limbic system (LS) and an attempt to locate an anatomical decision making center in the brain. In the next chapter emotions are focused: After an analysis what the difference between emotion and feeling is and the influence of emotions on decisions is shown. Afterwards, basics of human motivation and its influence on decisions is discussed. Subsequently, there is a definition of the concept of intuition and afterwards the dealing with various explanatory approaches is explained. After the impact to decisions has been shown, an analysis of limitations and risks of intuition is made. Furthermore, selected parameters and special cases of decision making are presented. Finally, a brief conclusion is made and an outlook on possible future developments is given.

2. Decision Making

2.1. Disambiguation

“Any human behavior, decision making processes not excluded, always involves three basic components: motivation, emotion and cognition.”12 Motivation and emotion are the main factors associated with intuition. In contrast to these two, cognitive processes are attributed to the head decisions, respectively the (assumed) rational decisions.13 Cognition is seen as “a knowledge, opinion or belief of the environment, by oneself or by the own behavior.”14

Decision making is not only a single decision, it is a process with different phases. The process of decision making15 is defined in five steps:

1. problem definition: a decision making process starts by noticing certain symptoms such as changes in the professional environment (e.g. loss of profit, quality defect). The decision maker will be aware that the current trend is not satisfying and there is a need to improve the situation. Usually, this knowledge is followed by a problem definition.
2. specification of the target system: basically, it is important that the goals are clear. Only a defined target system makes it possible to evaluate decision alternatives.
3. searching for alternatives: the search for alternative ways consists of three steps: Firstly, restrictions for alternatives have to be created. Restrictions may arise inter alia from financial, legal or social circumstances. Secondly, the actual search for alternatives starts. And in the end, there has to be an estimation of the possible consequences. Due to an incomplete level of information, future prospects are more or less a judgement of probability.16
4. selection of an option: the selection of a good or (if possible) the best alternative is made with the help of several kind of decision models17.
5. decision making during implementation: as part of the implementation of the alternative made, further decisions have to be made. Within the decision making process a large number of single decisions will be made in all phases of the process:18 “The decision making process is thus a process of solving numerous problems of single decisions.”19

To choose the best alternative is very individual and has to be relativized: “This is the problem of relativity - we look at our decisions in a relative way and compare them locally to the available alternative.”20 Xiao-Jing Wang defines decision making as “a cognitive process of choosing an opinion or an action among a set of two or more alternatives, with several defining characteristics.”21 Wang explains the decision making process in his model of recurrent neuronal circuits.22

In a similar way, Robin M. Hogarth divides the decision making process into three stages: specification, inputs and aggregation: Specifications means to take a decision between the possible alternatives. Inputs define the information that is used by the decider to characterize alternatives. Aggregation is determined by finding the most achievable value in terms of the decision taken.23

The triad of decision making in connection with intuition is seen by Gerald Traufetter as the alertness and the willingness to act (presence of mind) followed by using the treasure trove of experience (knowledge) and in the end, the mood and emotion (decisiveness) to act.24

2.2. Historical Classification

2.2.1. Chronological Development

The development of decisions research refers on the one hand to a time dimension: Chronologically, Keren and Wu define four main milestones: The years between 1954 and 1972 are characterized by the beginning of a systematic research in the field of judgement and decision making which produced the general dichotomy between the descriptive and normative approaches. The next period between 1972 and 1986 is determined by researches in terms of heuristics and biases and the introduction of the prospect theory. The initiation of the third period was the focus on psychological influences like emotion and motivation in the years 1986 to 2002. Within the latest period in the years 2002 and 2014 the multidisciplinary approach became more and more important.25 And in the coming years there will be a focus on neuroscientific approaches and “a larger role (…) in aiding and shaping real-world decisions.”26

2.2.2. Content Development

On the other hand, different streams in decision making exist that focus on content. Basically, three scientific schools of thought are distinguished:

In the 1950s, the decision analysis27 as a prescriptive approach 28 became more importance, based on mathematical and statistical methods, experts tried to develop systematic and standard procedures that allow to generate reliable decisions in uncertainty. John von Neumann29 established the term of the expected utility in the context of game theory: A quantitative basis for decisions arises from the fact that the benefits of an incident is multiplied by its probability of occurrence. The theory of expected utility was used in relation to economic relations to explain the rational behaving of the homo economicus.30 Thereby, how (a level of) new information changes probabilities and what rules can be defined for these probability adjustments, play a central role. In general, this approach is called bayes-statistic 31 and is based on and mainly affected by Pierre-Simon Laplace at the end of the 18th and the beginning of the 19th century and later in the 1950s by Leonard Jimmie Savage.32 This “bayesian updating can be understood that the expected probability of a particular incident is changing due to a particular made observation.”33

Also in the mid-1950s Ward Edwards began to analyze the psychological component of the by von Neumann postulated rational choice theory. Edwards “concluded that human reasoning did not accord with Bayes’s rule”34. Starting from that, he developed his theory of decision making. Based on these findings, Daniel Kahneman and Amos Tversky explored how people make predictions. They came to the basic conclusion that decision makers do not decide according to randomized rules or statistical projections. Rather, their decisions are based on heuristics and that heuristics can effect reasonable estimates or serious systematic errors. In addition to that, Kahneman and Tversky concentrated on analysing biases within decision making.35 This type of proceeding (how ordinary individuals) decide is called behavioral decision making and provides a descriptive approach in psychological analysis.36

It has been shown that the model of the rational agent who decides systematically and is based on probabilities, rarely correspond to reality. Herbert Simon introduced the concept of the bounded rationality. From the start, Simon has used the concept of heuristics in a positive context. He believed that ordinary decision- makers have neither the intellectual conditions nor the time available, to proceed decisions analytically - how the decision analysis provides, actually.37 He defines heuristics as “strategies that guide information search and modify problem representations to facilitate solutions”38. Regarding the decision making approaches and the game theories, the importance of information retrieval was focused by Reinhard Selten39 and John C. Harsanyi: “In the last twenty years we have seen the fruitfulness of modeling a wide variety of economic situations as non-cooperative games, with or without some form of incomplete information” and “the great multiplicity and diversity of equilibrium points (…) has become a serious problem.”40

Gerd Gigerenzer warns to underestimate heuristics and assigns heuristics an irreplaceable importance in decision making. Gigerenzer’s theses are in parts an alternative to Kahneman and Tversky and represent the third academic approach. Meanwhile, the leading decision theorists Dan Goldstein and Gary Klein can be described as moderates who stands more or less between the two extreme positions.41 Originally, “heuristics have even become associated with inevitable cognitive illusions and irrationality”42, but meanwhile, there is consensus that heuristics can be an important part in making decisions.

2.3. Meaning of Heuristics

Constantly, people have to make decisions under uncertainty. Thereby, probabilities have to be estimated and heuristics are often used for it.43 Gigerenzer defines a heuristic as a rule of thumb which allows people to come to a decision very quickly.44 Together with Goldstein he argues, that “heuristics has been used to refer to useful and indispensable cognitive processes for solving problems that cannot handled by logic and probability theory.”45

Kahneman describes a heuristic as “a simple procedure that helps find adequate, though often imperfect, answers to difficult questions”46. Choice heuristics are divided into two classes, depending on the degree of the state of knowledge with respect to probabilities: “outcome heuristics draw solely on information about outcomes and ignore probabilities. (…) Dual heuristics, in contrast, use at least rudimentary probability information.”47 In general, heuristics provide proposals for problems. A special form is the judgmental heuristic (also called mental shortcut), which is of significance in terms of decision making: The mental shortcut is a simplification and shortening of mental processes and is resulting in a higher speed in making decisions.48

2.4. Impact and Compensation of Biases and Information

Richard H. Thaler and Cass R. Sunstein explain the two systems working in the human brain like that: “The approach involves a distinction between two kinds of thinking, one that is intuitive and automatic, and another that is reflective and rational.”49 In literature, the automatic system is often referred to as system one and the reflective one as system two.50 The intuitive system “is rapid and is or feels instinctive, and it does not involve what we usually associate with the word thinking.”51 The reflective system is slower and “is more deliberate and self- conscious.”52 The system one, respectively intuitions or “gut feelings, can be quite accurate, but we often make mistakes because we rely too much on our Automatic System.”53

These mistakes or blunders are due to biases: Kahneman and Tversky defined three original biases: anchoring, availability and representativeness54 . Anchoring means that deciders (if they have to decide between alternatives) start with an anchor in their mind (e.g. a price or a number) and “adjust in the direction (…) (their) think is appropriate. (…) The bias occurs because the adjustments are typically insufficient”55. Availability means that information, experiences or examples that are available at the moment of decision, have a greater influence on the decision. In other words, decision makers assess the occurrence of risks which experiences just come to their mind.56 Kahneman defines representativeness as “the similarity of the description to the stereotypes (…) ignoring both the base rates and the doubts about the veracity of the description”.57 “Judging probability by representativeness has important virtues: the intuitive impressions that it produces are often - indeed, usually - more accurate than guesses would be.”58

Kahneman et al. assume that biases can be methodically identified and minimized. This is done by using a three-step procedure in (management) decisions: The basic requirement is that the decision maker understands the facts and all information that is available. Next, the correct view of these facts has to be checked.59 Finally, the decider has to use his “own experience, own knowledge and own rationality (…) to decide”.60 In the context with the evaluation of information, Max H. Bazerman “identifies what information we do not see or notice, and describes how we can use this knowledge to seek the information that will be most useful for making great decisions.”61 Based on the Bayesian reasoning, Kahneman propagates to “anchor your judgement of the probability of an outcome on a plausible base rate” and to “question the diagnosticity of your evidence”62.

Andreas Zeuch emphasis the meaning of information concerning the quality of decision making: “The ratio of amount of information to decision quality is not linear or proportional. Rather, it is a bell-shaped curve, which rises only significantly, then to decline again after reaching the optimum.”63 From a certain point, the amount of data is overwhelming and is obstructive for decision making. And moreover, it may be that information is missing, contradictory, incomprehensible or untrustworthy. Zeuch calls this data status the pentagon of nescience.64 Ute Reichert and Dietrich Dörner demonstrate in a systemic perspective, “that the knowledge about a system does not lead to a better control of this system”65. In his five-step process of planning and action, Dörner emphasis in the second step the acquisition of information and thereby, he focusses on the time constraints concerning the information retrieval.66

2.5. Behavioral Economics, Decision Theory and Evaluation of Decisions

“The behavioral economics movement has shown that it is often possible to incorporate slightly richer assumptions about individual behavior into economic models without losing the fundamental tractability and purpose of those models.”67 Thaler titles the seemingly inconsistent behavior as misbehaving because it contradicts the idealized model of behavior based on the economic theory.68

Hugh Courtney et al. see the increasing complexity and uncertainty in the business environment as a challenge for decision makers, especially, the question which methods should be used in management decisions. Usually, conventional methods for decision making require reliable and complete information but just this data is not available in a complex environment. For Courtney et al., the solution is to find the most suitable method for a decision making situation and defines three crucial points to recognize: which success factors of a decision are known, how can possible results are considered and how information can be procured.69

Phil M. Rosenzweig recommends two skills to all deciders: On the one hand the decision-maker must be able to recognize the nature of the decision and on the other hand respond appropriately to it.70 Rosenzweig classifies decisions in four fields71 and evaluates these decisions by using a matrix regarding performance and control. The impact on these two dimensions determines the decision procedure.72

In dealing with decision making, the question arises, what good or better decisions mean. For this reason, it is to define what is meant by good or better and what criteria can be applied. Florian Becker defines four main criteria that can be used for evaluation of (team) decisions: costs, speed, acceptance and quality. Costs are not only a negative parameter, more invest can be necessary to empower people or teams to come to sustainable decisions. The time factor is depending on the amount of team members: the more participants the lower the speed. In most cases (and in some cultural environment) team decisions have a higher acceptance in organizations and can be better enforced. Quality refers to the used input, for instance experts who are consulted.73

Hogarth argues that the quality of decisions is not only measurable by outcomes because some factors (e.g. luck) cannot be influenced by the decision maker.74 In principle, he believes “that making a decision involves processing information in order to select one of several choice alternatives.”75 The alternative choice is based on two dimensions of quality that Hogarth calls procedural and ecological: procedural is focused on the decision maker to ensure to achieve the set goals. The ecological dimension creates a match between the selection made and the characteristics of the relevant environment.76

3. Neurological Insights

Neuroscience has an influence on judgment and decision making and will get much more importance in the next years77. In the context of this study, an overview of the most important neurological principles is given and only the parts of the brain that are mainly related to the decision making process, intuition and emotions are discussed in detail.

3.1. Human Nervous System

Generally, the human nervous system can be defined by two criteria: The anatomical classification distinguishes between the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS consists of the brain and the spinal cord which are connected through the brainstem and work together as a common system. The nerve fibers of the PNS penetrate in each area of the body: on the one hand, these nerve fibers include information of the physical senses and transfer these with sensory fibers - called afferent nerve fibers - to the CNS, and on the other hand, they transmit impulses from the CNS via efferent nerve fibers to the target organs.78

The second possible classification is the functional approach to the somatic nervous system (SNS) and autonomic nervous system (ANS). The SNS controls all conscious experience, controls the skeletal muscles for the musculoskeletal system and transmits conscious sensations such as pain. In contrast, the ANS is the involuntary and unconscious control system and therefore the work of the ANS is rarely perceived. The ANS is among other things responsible for the hormonal balance, monitoring the condition of the organs, the control of digestion and blood pressure. For the sake of completeness, it is to be mentioned, that the ANS is divided into the sympathetic nervous system (SyNS) and the parasympathetic nervous system (PSyNS). Both have the task to regulate the internal organs, the SyNS has a driving, activating role and the PSyNS has a reducing, calming role.79 These two parts of the ANS differ indeed fundamentally from their nerve tracts and neurotransmitter systems, but act still parallel. The ANS as a whole has an impact on almost every part of the body.80

Next to the SNS and ANS the human nervous system is complemented through the enteric nervous system (ENS)81: The ENS is located in the abdomen and the nervous system of the intestine and can be controlled via the SyNS and the PSyNS.82 The ENS has roughly as many neurons as the spinal cord, but it is unlike the brain not completely autonomous because it receives signals via axons of the SyNS and PSyNS in an indirect way.83 “But beyond that, it also exerts completely independent functions and evades the control of the peripheral nervous system. For this reason, the enteric nervous system is often called as third nervous system of the body (…).”84

3.2. Limbic System

There are inconsistent assignments and definitions in certain brain regions in the scientific debate as others in the limbic system85. In this study, these differing views are neither opposed nor rated because for the purpose of clarification of the interrelationships of the various brain regions and the functionality, these differences are of minor importance. Henning Beck et. al define following areas of the brain as parts of the LS86: Hippocampus87, cingulate cortex88, amygdala, mammillary body and parts of the diencephalon (such as thalamus). The LS is often called an area of emotions or an emotional center because it is involved in the development of memory contents and largely controls our emotions.

[...]


1 Malik (2014), p. 214 (German citation translated by author)

2 cf. Malik (2014), p. 214

3 cf. Schiessler (2013), p. 590

4 cf. Malik (2014), p. 404

5 cf. Döring-Seipel/Lantermann (2015), p. 1

6 cf. Malik (2014), p. 405

7 the acronym VUCA means Volatility, Uncertainty, Complexity, Ambiguity. These four terms represent the analysis of the prevailing situation (cf. Vieweg (2015), p. 37).

8 the acronym LFP means Light FootPrint and is a military strategy that tries to minimize risks and collateral damages by using small special task forces (cf. Vieweg (2015), p. 37).

9 cf. Vieweg (2015), p. 37 - 38

10 cf. Kühmayer (2016), p. 42

11 Keren/Wu (2015), p. 1

12 Reuter (2015), p. 38 (German citation translated by author)

13 cf. Reuter (2015), p. 38

14 Festinger (2012), p. 17 (German citation translated by author)

15 this is only one defined process in the scientific research and is an example (cf. Laux/Gillenkirch/Schenk-Mathes (2014), p. 12 - 15)

16 cf. Laux/Gillenkirch/Schenk-Mathes (2014), p. 12 - 14

17 decision models are explained in the chapter 2.5.

18 cf. Laux/Gillenkirch/Schenk-Mathes (2014), p. 15

19 Laux/Gillenkirch/Schenk-Mathes (2014), p. 15 (German citation translated by author)

20 Ariely (2010), p. 20

21 Wang (2008), p. 215

22 cf. Abbott/Fusi/Miller (2013), p. 1612 - 1616

23 cf. Hogarth (2015), p. 953

24 cf. Traufetter (2007), p. 37 - 38

25 cf. Keren/Wu (2015), p. 2

26 Keren/Wu (2015), p. 25

27 this provides in its simplest form a three-stage approach: express a problem, list alternatives and evaluate options in a systematic way (cf. Fox (2015), p. 48).

28 is defined as a decision making process by analytically inclined individuals (cf. Raiffa/Richardson/Metcalfe (2007), p. XI).

29 the scientific work of von Neumann is often communicated with the name of Oskar Morgenstern. In this paper, is only referred to von Neumann who provided the more important content (cf. Behnke (2013), p. 9)

30 cf. Fox (2015), p. 49

31 this term refers to Thomas Bayes (cf. Behnke (2013), p. 186).

32 cf. Fox (2015), p. 48

33 Behnke (2013), p. 187 (German citation translated by author)

34 Goldstein/Gigerenzer (2002), p. 75

35 cf. Fox (2015), p. 49 - 50

36 cf. Raiffa/Richardson/Metcalfe (2007), p. XI

37 cf. Fox (2015), p. 51

38 Goldstein/Gigerenzer (2002), p. 75

39 first German Nobel prize laureate in economics, together with John Nash and John Harsanyi (cf. Heuser (2016), p. 24).

40 Harsanyi/Selten (1992), p. 341

41 cf. Fox (2015), p. 52

42 Goldstein/Gigerenzer (2002), p. 75

43 cf. Beck (2014), p. 25 - 26

44 cf. Gigerenzer (2013), p. 44

45 Goldstein/Gigerenzer (2002), p. 75

46 Kahneman (2013), p. 98

47 Hertwig (2015), p. 253

48 cf. Cialdini (2013), p. 28

49 Thaler/Sunstein (2009) p. 21

50 cf. Kahneman (2013), p. 13 and cf. Thaler/Sunstein (2009), p. 21

51 Thaler/Sunstein (2009) p. 21

52 Thaler/Sunstein (2009) p. 22

53 Thaler/Sunstein (2009) p. 23

54 as a result of representativeness, a wrong assessment of composite events can occur. This heuristic is called conjunction bias (cf. Beck (2014) p. 35).

55 Thaler/Sunstein (2009) p. 25 - 26

56 cf. Thaler/Sunstein (2009) p. 27

57 Kahneman (2013), p. 149

58 Kahneman (2013), p. 151

59 cf. Kahneman/Lovallo/Sibony (2011), p. 20

60 Kahneman/Lovallo/Sibony (2011), p. 20 (German citation translated by author)

61 Bazerman (2014), p. XX

62 Kahneman (2013), p. 154

63 Zeuch (2010), p. 31 (German citation translated by author)

64 cf. Zeuch (2010), p. 31 and 45

65 Zeuch (2010), p. 23 (German citation translated by author)

66 cf. Dörner (2001), p. 68 - 69

67 Pope/Sydnor (2015), p. 801

68 cf. Thaler (2016), p. 4

69 cf. Courtney/Lovallo/Clarke (2014), p. 41 - 42

70 cf. Rosenzweig (2014), p. 31

71 the four decision levels are: execution of routine jobs, influence on results, benchmarking in relation to competitors, focus on strategic success (cf. Rosenzweig (2014), p. 26 - 30)

72 cf. Rosenzweig (2014), p. 26 - 30

73 cf. Becker (2016), p. 37 - 38

74 cf. Hogarth (2015), p. 953

75 Hogarth (2015), p. 953

76 cf. Hogarth (2015), p. 952 - 953

77 cf. Keren/Wu (2015), p. 25

78 cf. Beck/Anastasiadou/Meyer zu Reckendorf (2016), p. 2

79 cf. Beck/Anastasiadou/Meyer zu Reckendorf (2016), p. 4 - 5

80 cf. Bear/Connors/Paradiso (2016), p. 550

81 the ENS is also seen as the third part of the ANS (cf. Bear/Connors/Paradiso (2016) p. 552).

82 cf. Beck/Anastasiadou/Meyer zu Reckendorf (2016), p. 6 - 7

83 cf. Bear/Connors/Paradiso (2016), p. 553 - 554

84 Beck/Anastasiadou/Meyer zu Reckendorf (2016), p. 6 (German citation translated by author)

85 cf. Beck (2013), p. 24 and cf. Peters/Ghadiri (2013) p. 29

86 is part of the cerebrum and is like a fringe along the diencephalon (cf. Beck/Anastasiadou/Meyer zu Reckendorf (2016), p. 42).

87 together with the prefrontal cortex, the hippocampus is responsible “(…) for encoding and storing explicit memories. (…) The prefrontal cortex mediates working memory (…). The hippocampus stores declarative information in a more stable form (…)” (Siegelbaum/Kandel (2013), p. 1487).

88 in detail: nucleus accumbens, ventromedial frontal cortex, orbitofrontal cortex and anterior cingulate cortex (cf. Sanfey/Stallen (2015), p. 272)

Details

Pages
62
Year
2016
ISBN (eBook)
9783668434004
ISBN (Book)
9783668434011
File size
631 KB
Language
English
Catalog Number
v358978
Institution / College
University of Applied Sciences Rosenheim
Grade
Tags
intuition emotion neuroscience decision making behavioural economics

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Title: The influence of intuition and emotions on decision making