Emotions: Revealing Our Value Systems

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This modulation by affect-driven belief systems can be cast into a Bayesian learning framework, that is, an iterative dynamic probabilistic system, and can be used to provide a quantitative as well as a heuristic basis for measuring and explaining dysfunctions of decision-making in individuals with affective disorders. Decision-making is a process that unfolds over time. This temporal structure can be used to identify three component processes. Specifically, choosing among options initially involves the process of assessing the available options.

This is followed by the selection of an option based on the value that has been associated with the option.

Lastly, the outcome associated with the selected action is evaluated. The influences of emotions on these specific component processes have begun to be considered only recently. In part, this may be because of the difficulty to incorporate emotions into computational models, on the one hand, while retaining a connection to the rich literature of the phenomenology [ 19 ], physiology [ 20 ], and psychology of emotion [ 21 ], on the other. Traditionally, emotions have been conceptualized along valence and arousal dimensions for a review, see [ 21 ]. However, others have categorized emotions along different facial expressions [ 22 ] or appraisal dimensions [ 23 ].

These approaches acknowledge that emotions have both quantitative dimensions i. Within a dual-system framework of reasoning [ 24 ], emotions are thought to affect the relative extent to which an intuitive, heuristic System 1 and an analytic, deliberate System 2 contribute to decision-making. Moreover, based on a temporal perspective of delineating decision-making into component processes, emotions can be both factors that contribute to the modulation of assessment, selection, and outcome evaluation of options and consequences that emerge from these component processes.

For example, a sad mood may overvalue the negative attributes of an option during its assessment, but can also be the consequence of an undesired outcome. The ground-breaking work of Tversky and Kahneman [ 25 ] established that both value and the probability of an outcome are represented as nonlinear functions, such that higher values have decreasing marginal gains, losses are valued greater than gains, low probabilities are overweighted, and high probabilities are underweighted. Several investigators have developed models that conceptualize the effect of emotions as modulating these nonlinear value and probability functions [ 26 — 28 ] within a dual-system framework.

For example, Mukherjee [ 29 ] developed a model to account for individual differences and emotion influences on decision-making based on experimental findings by Hsee [ 6 ] and others [ 30 ]. In these experiments, individuals who were instructed to engage affective processes when processing value were less sensitive to magnitude changes of the available options and showed greater distortions of the S-shaped probability weight function [ 7 ].

In the computational model developed by Mukherjee [ 29 ], the value of an option is obtained from a mixture of System 1 and System 2 processes, which is parameterized by a mixture coefficient quantifying the degree to which affect influences decision-making. This mixture parameter can be conceptualized as individual differences in decision-making dispositions, context-dependent outcome processing, and affective construction of the decision-making situation.

Other investigators have departed more radically from the traditional utility model to explain differences in choices as they relate to emotion processing. Specifically, Kusev [ 31 ] has proposed that individuals do not calculate utilities explicitly. Instead, people construct preferences based on their experiences [ 32 ]. For example, people overweight small, medium-sized, and moderately large probabilities and they also exaggerate risks. However, neither of these findings is anticipated by prospect theory or experience-based decision research.

As a consequence, choices depend strongly on context, the type of options, implicitly the degree of affect associated with these options, and the nature of the presentation of the available options in the decision-making situation. Similarly, Vlaev and colleagues [ 34 , 35 ] have suggested that individuals do not have a common representation of value across different domains. For example, people will offer to pay more money when stakes are high in a pairing of low versus medium pain than when stakes are low, which is thought to be due to the fact that individuals are not able to compare qualitatively different options or outcomes on a single value dimension.

Although these investigators do not deny the evidence provided by descriptive theories of utilities associated with options in decision-making situations, they challenge the universality of the shape of the observed value and probability weight functions and propose a much more dynamic formalism to explain behavioral observations in decision-making situations. The experimental and theoretical accounts of the influence of emotion on decision-making we have discussed reveal that: Studies of decision-making in clinical populations are of immense value, because they can help to establish brain-behavior relationships, clarify the nature of dysfunctional process es in a disorder group, and point toward the development of potential treatments for disorders.

However, when studying decision-making in individuals with a particular disorder, it is important to note that the observed differences between the psychiatric target population and the comparison group are typically the result of a complex set of factors that include pre-disease characteristics, disease-related e. Moreover, most studies have utilized a limited number of decision-making paradigms to examine anxiety and mood related effects on decision-making.

Given these complexities and limitations, it should not be surprising that it can be difficult to link a particular mood or anxiety state to decision-making dysfunctions. Altered belief systems [ 36 — 38 ] for a review, see [ 39 ] play a critical role in the conceptualization of both anxiety and depression. In particular, individuals with anxiety disorders show an increased bias towards threat-related content [ 40 ] and an intolerance of uncertainty [ 41 ].

In comparison, depressed individuals show reduced responsiveness to reward [ 42 ] and an increased negative evaluation of self [ 43 ]. There is some consensus that anxiety makes individuals more sensitive, and thus more aversive, to options with large negative consequences [ 44 ]. This heightened sensitivity to options with large negative consequences can hurt them when an option occasionally associated with a highly negative outcome is actually on average the best option, but may help them on tasks with intermittent large punishments that signal the need for representational overhaul, such as the Iowa Gambling Task IGT.

In particular, individuals with generalized anxiety disorder learn to avoid decisions with high immediate gain but high long-term loss significantly faster than comparison subjects [ 45 ]. On this decision-making task, better performance is associated with poorer ability to regulate emotions [ 46 ], which may be due to a more prolonged visceral response [ 47 ]. However, other groups have found impaired performance on the IGT as a function of both high and low trait anxiety [ 48 , 49 ].

In particular, high trait anxious individuals generate an increased anticipatory physiological response prior to low reward, low punishment, or advantageous choices. These investigators have argued that anxiety may result in distraction from task-relevant processing, inefficient processing of relevant vs irrelevant cues, and interference from increased verbal processing ruminations.

Post-traumatic stress disorder subjects do not show an impairment of learning the contingencies of the IGT, but show reduced activation of reward-related areas [ 50 ], which is consistent with the finding that stress interferes with the acquisition of advantageous response selection on this task [ 51 ]. Patients with obsessive compulsive disorder show decision-making dysfunctions that have been attributed to their inability to appropriately process emotions [ 52 ]. On the whole, the effects of anxiety disorder on decision-making are complex and may be related to other, more anxiety-specific dysfunctions, such as avoidance of threat-relevant information [ 53 ], interference by negative distracters [ 54 ], or a general deficit of inhibitory processing in the presence of limited availability of controlled processing resources [ 55 ].

Anhedonia [ 56 , 57 ], that is, the inability to experience pleasure, and deficits in reward-related processing [ 58 ] have been considered to be the critical components that contribute to dysfunctions in decision-making in depressed individuals. Consequently, it is not surprising that individuals with depression show reduced responsiveness to reward [ 42 ]. On the IGT, individuals with major depressive disorder show poorer performance: Moreover, these individuals select more cards from decks with high frequency, low-magnitude punishment contingencies [ 61 ].

Surprisingly, other research has showed that acutely depressed individuals make better choices relative to controls or those recovering from depression [ 62 ], which is consistent with the finding that depressive individuals learn to avoid risky responses faster than control participants [ 60 ]. In general, depressed individuals appear to experience an increase in decisional conflict in a number of different decision-making situations [ 63 , 64 ], attenuated processing of counterfactual outcomes [ 65 ], and a prolonged attenuation of temporal discounting of rewards [ 66 ].

In addition, individuals with depressive symptoms fail to develop a response bias towards rewarded stimuli [ 67 — 70 ], in tasks in which which subjects must categorize a briefly presented stimulus as belonging to category A or B. In comparison, manic, depressed, and euthymic bipolar subjects select significantly more cards from risky decks and prefer decks that yield infrequent penalties over those yielding frequent penalties [ 71 ]. The percentage of trials on which subjects choose the more likely of two possible outcomes is also significantly impaired in depressed bipolar patients [ 72 ].

Others have reported that bipolar manic individuals show impaired [ 73 ] or erratic [ 74 ] decision-making ability, which has been termed suboptimal [ 75 ] decision-making. Depressed and manic individuals show slower deliberation times, a failure to accumulate as many points as controls, and suboptimal betting strategies [ 75 ]. These findings point towards both trait-dependent i. This dysfunction may be due to decision-specific alteration in value-related, probability-related, or temporal processing of available options.

In sum, anxiety, depression, and mood swings exert complex effects on decision-making as measured by performance on the IGT. Specifically, increased sensitivity to losses, attenuated processing of reward, and differential selection based on the history of rewards and punishment point towards emotion-related modulation of both value and probability in anxiety and depression. In the following section, we integrate these findings with a computational approach aimed at quantifying belief systems. In recent years, the understanding of the behavioral and neural processes underlying decision-making has benefited significantly from neuroeconomic models [ 2 , 76 , 77 ] and reinforcement learning models [ 13 , 78 , 79 ].

These approaches have provided insights into how individuals quantify the value of options, what brain systems play a key role in this process, as well as how the underlying neural substrates give rise to behavioral phenomena. However, one critical aspect of goal-directed action selection that does not typically receive explicit treatment in neuroeconomic or reinforcement learning models is the subjective uncertainty individuals have about both the state of the world and the eventual consequences associated with the different action choices in that state.

Uncertainty arises from a multitude of sources, for example, noisy sensing, imperfect motor control, neuronal communication errors, intrinsic stochasticity, and non-stationarity in the environment. There are two ways in which uncertainty complicates decision-making: In the past decade, significant progress has been made in understanding how the brain represents uncertain information with the help of Bayesian statistical models, which offer a mathematically precise language for describing probabilistic knowledge, as well as normative procedures for computation based on such knowledge.

In particular, Bayesian models provide a way of formalizing how beliefs are linked to observations to arrive at estimations of the status of the world, which guide the decision-making process. In the context of Bayesian models, emotions can be conceptualized as modulating the mathematical representation of these beliefs and the processing of observations.

As a generalization of the classical notion of an ideal observer in psychology [ 80 ], Bayesian models have helped to demonstrate that humans perform close to Bayesian ideal optimum in a number of simple experimental tasks e. However, even more revealing than when the brain performs optimally is when it does not. For example, it has been shown that the apparently irrational tendency for subjects to extract transient stimulus patterns in a truly random stimulus sequence may be due to the inappropriate, reflexive engagement of neural mechanisms necessary for adapting to changing environmental contingencies [ 88 ].

One approach to link conceptually the emotional state of the individual to his or her decision-making is to assume that feelings affect the way probabilities of gains or losses are transformed to weights and values see Figure 1 and its description for an example. In the context of Bayesian statistical models, this approach translates into modifications of belief prior probabilities and evidence likelihood as a function of mood states. In this approach, individuals do not have precise knowledge of the state in which they are; instead they maintain a probability distribution of being in a particular state, which is the result of previous experiences.

The probability distribution of belief states can be used to estimate the expected reward or cost associated with an action and can, thus, be used to determine how good or bad an action is. Observations or outcomes resulting from the selected action, in turn, update the probability distribution of the belief states via a so-called belief-state estimator. The optimal decision is determined by the distribution of the current belief states, which, in turn, is determined by the probability of transitioning from one state to another via a particular action and the probability of observing an outcome when selecting a particular action.

In addition, the optimal decision is determined by the nature of the costs and rewards, the degree to which the decision-maker looks ahead and estimates costs and rewards of subsequent actions, and the probability of experiencing a cost or reward when transitioning from one state to another. A rich computational [ 89 , 91 ], applied [ 92 ], and even neural-systems level [ 93 ] literature is emerging based on this heuristic scheme. This figure shows a simple gamble consisting of two options A and B with probable outcomes and shows the effect of modulating the value function left column , the probability weighting function middle column and the resulting utility of the two available options right column.

The dark grey lines signify a larger distortion due to presumed affectively driven modulation of objective value or probability. The bar graphs indicate the overall utility of option A or B; a relatively larger subjective utility of A over B is assumed to result in a preference for A over B. In the first two rows, examples are given such that alterations of the parameter determining the subjective weight, a , can reverse first line or not reverse second line the preference of the gamble.

In the third and fourth row a similar example is provided for alterations of the value parameter, b , that reverses or does not reverse the preference of the gamble. Taken together, these simple calculations show that alterations of probability and value in accordance with empirical and theoretical approaches to understanding the effect of emotion on decision-making can have significant preference reversal effects. We propose that the influence of emotion, in general, and of anxiety or depression, in particular, is that of changing the nonlinearity of the weighting and the value function, such that preference reversals occur.

These reversals help to explain performance differences on risk-related decision-making tasks.

Human Values – Human Systems – Medium

A schematic representation of partially observable Markov decision processes [ 89 , 90 ]. A POMDP consists of a belief state, which summarizes previous experiences, is represented in a probabilistic framework, and is updated by a belief-state estimator. Decision-making occurs as a consequence of a decision policy that maps the current belief state onto actions. Emotions can affect this process in two ways. First, the observed rewards are hypothesized to be transformed into values based on the subjective value function as shown in Figure 1.

Second, probabilities are hypothesized to be transformed into weights and can, therefore, affect the updating via the belief-state estimator. In other words, faulty updating by the belief-state estimator because of attenuated valuation or exaggerated representation of low probabilities can result in suboptimal estimation of the current state and, therefore, poor selection of a decision policy.

Emotion and decision-making: affect-driven belief systems in anxiety and depression

For example, greater weighting of threat-related states in anxiety may result in avoidance of action. Alternatively, attenuated representation of subjective value may result in diminished learning and updating of the belief systems that provide the basis for making optimal decisions in depression. This theoretical approach to the substrates of information representation and goal-directed decision-making can be fruitfully applied to understanding the influence of emotions on decision-making. Huys and Dayan [ 94 ] have developed layered notions of control in order to formalize dysfunctions of these processes in the context of decision-making for individuals with anxiety or depression.

In this framework, three layers of control are constructed to explain the relationship between emotional dysfunction and decision-making: Huys and Dayan suggest that controllability of reinforcement is a critical factor of decision-making dysfunction in depression. Within the context of Bayesian inference, POMDPs Figure 2 provide a particularly useful heuristic framework to examine alterations in decision processes due to altered affect-driven belief systems. The probabilistic formalism can support the general hypothesis that individuals with anxiety and depression represent information and action outcomes in such a way that probabilities are misrepresented and outcome values are biased.

Thus, in both disorders, transformation of probabilities into decision weights may result in greater weighting of low probability events, as suggested by Mukherjee [ 29 ]. Moreover, the altered valuation process that leads to attenuated sensitivity to magnitude changes of the value of available options [ 6 ] can contribute to inappropriate valuation of expected outcomes. As a consequence, altered reward perception affects the belief-state estimator processes. Consequently, an anxious or depressed individual may select options based on an altered representation of the current belief states, which can be the result of biased transition probabilities due to altered processing of costs and rewards.

One reason was that it is difficult to find spider phobics, because they usually avoid situations where they are confronted with spiders.

However, our control group was also small. The reason for that is that we initially also tested nine participants in the control group same number as in the experimental group but then we had to eliminate two participants due to response strategy, incomplete data recording and could not replace them by two new participants for technical reasons.

However, we do not think that this is a serious problem, because even with this small sample size our differences reached the level of statistical significance. Given these thoughts we think that our results reliably show that illness related tasks impair reasoning for anxiety patients. There are a couple of possible explanations of how positive and negative emotions impeded on reasoning performance. One explanation is that all kinds of emotions have negative effects on the motivation or effort of the participants e.

Other explanations are based on dual process models System or Type 1: A good overview on the different theories is provided in Blanchette However, we believe that the most reasonable explanation for the current findings is provided by the suppression theory Oaksford et al. This yields a strong emotional response resulting in a pre-load of working memory resources. Moreover, there is evidence that spider phobia could change reasoning patterns.

De Jong et al. While spider phobics performed worst on phobia relevant problems in our study, non-phobics revealed worst performance on problems with negative content. These results are in line with Blanchette and Richards and Blanchette Overall affirmation of consequence and denial of antecedent with spider phobia relevant and negative content resulted in more errors which is similar to findings of Blanchette and Richards This experiment was designed to investigate if the effect found in Experiment 3 extends to other anxiety related conditions such as exam-anxiety.

Therefore, participants were also selected based on their anxious state and some of the problems had an emotional content which was relevant to exam-anxiety while others were neutral or generally negative. The sample consisted of 17 students with exam anxiety and 17 students without exam-anxiety.

They were all female because exam-anxiety is more prevalent amongst women Zeidner and Safir, ; Chapell et al. The age range was 20—29 years mean age for participants with exam-anxiety: For remuneration they could choose to receive five Euro or a course credit. Psychology students and people who have already taken part in experiments about this topic were excluded. All participants were native German speakers and provided informed written consent.

Participants were assessed with the TAI-G Hodapp, , a measure for exam-anxiety, in order to differentiate between exam-anxious and non-anxious participants. The TAI-G consists of 30 statements which describe emotions and thoughts in exam situations. Participants are asked how well those statements describe them when they have to take exams. Scores of the TAI-G range from 30 to In order to be classified as exam-anxious a minimum score of 84 is necessary while a score below 54 is classified as non-exam-anxious.

Those limits were obtained in a study with students Wacker et al. Once participants finished the TAI-G, they were given the conditional inference problems. Presentation of the problems and recording of answers was identical to Experiments 2 and 3. The selection of exam-phobic and non-exam phobic groups of participants was successful. However, no significant interaction was found for content and group.

This means that both exam-anxious and non-exam-anxious participants performed similar across fear-relevant, negative, and neutral problems. Our results show that exam-anxious and non-exam-anxious participants performed similar across fear-relevant, negative and neutral problems. Inferences about exam-anxiety resulted in reduced performance in both groups.

This may be because all participants were currently enrolled at university and so can relate to exam-anxiety. Moreover, physiological changes have been observed in people who are high-exam-anxious as well as low-exam-anxious Holroyd et al. Therefore, associations to exam-situations can get triggered which reduce working memory resources and subsequently performance on reasoning problems Oaksford et al. In contrast to previous findings Lefford, ; De Jong et al. Even though these problems were emotional and negative e.

We conducted two experiments with participants who underwent a mood induction and two with participants that were either anxious about spiders or exams. Experiment 1 showed that the emotions of an individual have an effect on reasoning performance independent from task content. In Experiment 2, we found that reasoning performance can be affected either by the emotion of the individual or the content of the problem or the type of inference.

In Experiment 3, spider-phobic participants showed lower reasoning performance in spider-related inferences, but in Experiment 4, exam-anxious participants did not perform worse on inferences with an exam-related content. The results agree with some of our hypotheses but not with all of our initial assumptions. Our first hypothesis was that positive and negative emotion will result in a reduction of logical reasoning performance. This was confirmed as in the first and second experiment participants in a neutral emotional state outperformed those in negative or positive emotion independent of the task WST and conditionals.

These findings are consistent with previous research Channon and Baker, ; Melton, ; Oaksford et al. When a negative or positive emotional state has been induced in participants this results in a deterioration of performance on a Wason selection task compared to participants in a neutral emotional state Oaksford et al. An explanation that has been offered is that as emotionally congruent information gets retrieved and processed this takes away resources from working memory e.

In addition, positive emotional states also result in poorer performance Melton, , as it is assumed that people in a positive mood pursuit more global reasoning strategies, paying less attention, and are therefore more prone to errors than people in a negative, analytic mood. Our results concerning the second hypothesis predicting a detrimental effect on performance of positive and negative problem content are mixed.

It was confirmed by the third experiment in which non-phobic participants performed best when the content was neutral. On the other hand, the content had no effect on performance in the first experiment, and in the second experiment, best performance was measured with negative content, whereas most errors were committed with positive content. In the fourth experiment there was no difference between negative and neutral content and performance was worst with exam-anxiety related content.

These findings partially agree with previous research showing that performance is affected when the content is related to general threats because then participants tend to select threat-confirming and safety-falsification strategies in a Wason selection task De Jong et al. Furthermore, if the content is controversial, it can stir up emotions that result in a stereotypical reaction that negatively affects performance of a conditional reasoning task Lefford, In this study participants made more errors when the content was controversial e.

The third hypothesis stating there may be an effect on performance when positive and negative mood is combined with positive and negative problem content was only supported by Experiment 3, which found the expected interaction.

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Nonetheless, the absence of the suggested interaction in three of four experiments is in line with some previous findings e. Only in the third experiment participants who are afraid of spiders performed worse on problems with a spider phobia relevant content compared to a negative content which strengthens other findings De Jong et al.

A similar trend was observed for the performance on spider phobia relevant problems compared to neutral ones. Yet this difference was insignificant, maybe a bigger sample would have yielded clearer results. A previous study showed that, when reasoning about health-threats in a Wason selection task, health-anxiety patients have a threat-confirming strategy Smeets et al.

However, spider phobic patients compared to non-phobic controls performed worse when the content of the reasoning problem was specifically related to their phobia as well as when it contained general threat material De Jong et al. Why did we find no evidence showing that performance is improved when emotion and content are congruent? In Blanchette et al. In another study participants who had been primed to be angry or who remembered an incident when they had been cheated on performed better when the reasoning task involved detecting cheaters Chang and Wilson, We think that the ambiguity in previous findings Channon and Baker, ; Melton, ; Oaksford et al.

The first two experiments induced emotions in participants who were primarily sad and frustrated whereas the last two experiments' participants were anxious. Hence one is not comparing like with like. The latter two experiments can be further differentiated as the third experiment selected people for the control group who are not afraid of spiders. However, most students experience some form of exam-anxiety and the sample of the fourth experiment was entirely made up of students. This may explain why participants who reported exam anxiety as well as those who reported none both performed poorly when the content was exam anxiety related.

According to the suppression theory Oaksford et al. This means that emotional participants should perform worse than those in a neutral state. This has been confirmed in Experiments 1 and 2. Content may give rise to emotion and so similar results due to reduced working memory resources should also be found in experiments with emotional content.

In Experiment 3 best performance was with neutral content, possibly because spider-related content triggered a response that used resources of working memory that would otherwise have been used to solve the task e. Anxious content in Experiment 4 resulted in worst performance possibly for the same reason.

Thus far we focused on working memory resources, but it is also possible that attentional processes are of major relevance in this context. For example, correct decisions and decision times may be compromised during emotional especially negative processing, since emotional processing in addition to reasoning requires attentional resources see for instance the work of Harmon-Jones et al. However, we cannot fully dissolve this problem of working memory vs. The findings of Experiment 3 are in contrast to those of Experiments 1 and 2, where no content and interaction effect were found.

People with a phobia may perform worse on problems that have a content which is related to their phobia because they try to avoid stimuli that are anxiety-provoking American Psychiatric Association, This avoidance is not necessarily found in depressed participants as they tend to ruminate on depressive material American Psychiatric Association, While participants in Experiments 1 and 2 were not clinically depressed, the emotion that was induced had a depressive quality and therefore may explain why no interaction was found in these experiments.

In addition maybe only anxiogenic stimuli have a depleting effect on working memory and previous research was largely based on anxiety De Jong et al. In contrast, Lefford's material was not anxiogenic but he found an effect. He argued that this was due to a stereotypical response. However, if people do not relate to the content, then this will not result in a stereotypical response. The reason for why no effect was found in Experiments 1 and 2 might be that the material was not as personally relevant and therefore did not trigger sufficient emotions for an effect to show.

This does not explain why in Experiments 2 and 4 best performance was with negative content. One could argue that since this content is negative, participants are more deliberate in order to avoid negative consequences if personally relevant for them. Furthermore, a more analytic processing style has been proposed for depression Edwards and Weary, so that this content may have triggered such a processing style compared to a more global processing strategy with a positive emotion. Considering this one would have expected superior performance for negative emotion in Experiments 1 and 2 which was not the case.

Therefore, more clarity might be achieved if experiments compare personally relevant emotional content and emotional content that is not personally relevant. Content should also be differentiated according to it being anxious or depressive. Furthermore, anxious participants should be compared to depressed participants.

A distinction has to be made between avoidance caused by anxiety and rumination caused by depression. If a detrimental effect on performance is found in both groups it has to be investigated whether this has the same cause, namely depleted working memory resources or attentional resources. From a psychotherapeutic point of view our studies are interesting as they show that spider phobic patients do not only show inadequate emotional responses to spiders. They, in fact, also show a decrement in performing cognitive tasks, such as logical reasoning if they have to do with spiders. The study shows an apparent connection between reported fear on the SPQ Klorman et al.

Introduction

This has been the case for decades in some therapeutic approaches which have recognized that being freed from misery better equips one to deal with life's adversities Freud, People appear to find it easiest to process neutral non-emotional information Experiments 1 and 2 but ideally sessions work with hot cognitions and elicit key emotions and cognitions Safran and Greenberg, ; Beck, If neutral information becomes the focus of sessions, then sessions would elicit less key emotions and cognitions and turn into a nice chat which will be remembered pleasantly by the patient.

Thereby the patient does not get overwhelmed with emotional material which will have a detrimental effect on reasoning. Instead the emotional material can be introduced bit by bit e. It is worthwhile for patients to remember what has been discussed in sessions because new behaviors and alternative viewpoints which have been collaboratively developed in sessions may be easily forgotten, especially when the patient is suffering from a depression which often results in decreased concentration.

Some therapists recommend that their patients take notes during sessions Beck, but if only things that are easily remembered are discussed, this problem is circumvented. Therefore, if the patient wishes to get stabilized, non-emotional material may be best. If they want to work through distressing material however, it will not be possible to avoid emotional content. Hence emotions and cognitions are related and influence each other and one has to combine them according to what the goal is. Thus far the key finding is that emotional state and content may interact to modulate logical reasoning.

This is however only the case if mood state and task content are related Experiment 3; spider-related content among spider phobics. But, this does so far not generalize to other contexts, since it could for example not be found in a sample with exam anxiety Experiment 4; exam anxiety in combination with exam content.

These ambiguities, the role of working memory and attentional processes need to be addressed in future studies in order to explain the influence of emotional content and emotion on human reasoning performance. Nadine Jung did the statistical analysis and wrote the paper. Christina Wranke designed and conducted the experiments, and did the statistical analysis.

Kai Hamburger designed the experiments and wrote the paper. Markus Knauff designed the experiments and wrote the paper. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Luzie Jung and Nadja Hehr for carrying out some of the experiments. Finally, we thank the reviewers for their valuable comments. The distinction between emotion and mood will only be pointed out were necessary.

National Center for Biotechnology Information , U. Journal List Front Psychol v. Published online Jun This article was submitted to Emotion Science, a section of the journal Frontiers in Psychology. Received Oct 29; Accepted May The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms. This article has been cited by other articles in PMC.

Emotions, Integrity, and Wisdom

Abstract Recent experimental studies show that emotions can have a significant effect on the way we think, decide, and solve problems. Logical reasoning problems Logical reasoning goes back to the antique Greek philosopher Aristotle and is today considered to be essential for the success of people in school and daily life and all kinds of scientific discoveries Johnson-Laird, Previous studies and main hypotheses Several studies on logical reasoning found that participants' performance is modulated by their emotional state.

Based on this combination we formulated and tested the following hypotheses: Grappling with conflicts leads to unusual values. This diagram shows how a series of conflicts can lead via feelings to new and interesting values:. It tells the story of two conflicts:. In each case, the transition from old values to new remedied an error in thinking:. The importance of the old values was entirely captured by the new, more comprehensive value [ 1 ]. These powerful new values and perspectives come from negative feelings.

I believe all this—feelings, values, reconciliation—is just part of being human, like speech or gesture. But this process is unfamiliar to many people. If feelings are natural, why is the nature of feelings so obscure? It seems that our ability to feel through is under some kind of attack.

Broad cultural myths have been set up to confuse us about feelings and their relationship to integrity:. These myths are often supported and furthered in clinical psychology: As organizations, bureaucracies, and institutions grow more complex, they depend on predictable, incentives-aligned behavior from the people inside. So perhaps the subcultures which are most successful in fitting into organizations, bureaucracies, and institutions are those with cultural myths.

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