The Classic Wanderer

What Does Valence Mean In Psychology?

• Sabrina Sarro
• 05.05.2023
• 0
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Emotional Valence in Human Perception and Processing – Emotional stimuli are highly relevant for human behavior since humans have to process such stimuli very quickly in order to detect and react to events important for our survival. General effects of emotionality i.e., a processing advantage for emotional as compared to neutral information, have been shown for various types of stimuli, including words (e.g., Kanske and Kotz, 2007 ; Kousta et al., 2009 ; Yap and Seow, 2013 ; Goh et al., 2016 ) and faces ( Johansson et al., 2004 ; Groß and Schwarzer, 2010 ).

1. In general, the emotional significance of a stimulus enhances its processing ( Zeelenberg et al., 2006 ).
2. One very basic feature of emotional stimuli is their hedonic valence.
3. Valence refers to the pleasantness or unpleasantness of an emotional stimulus.
4. Nearly all events and experiences, such as faces, sounds, music, art, pictures, written or spoken language, and many others can be classified along this dimension as more or less positive or negative.

Given that valence is such a crucial factor for the representation and categorization of human experience, efforts have been made to investigate whether the proximity of stimuli toward either end of this dimension’s continuum (positive or negative) leads to preferential processing, and if so, which one.

In the literature, a marked difference in responding to negative or positive stimuli has been labeled as “bias.” Although there is empirical evidence for existing asymmetries in the way humans use positive and negative information ( Vaish et al., 2008 ), the precise direction of such valence biases is still unclear.

In cases when either positive or negative stimuli are perceived and processed differentially, the response to the preferred category is described with terms like “advantage,” “preference,” “superiority,” “enhancement,” or “facilitation,” while the other category, accordingly, shows a “disadvantage” or “delay.” Evidence for a bias is usually associated with a behavioral advantage or facilitation in experimental tasks, in particular with higher accuracy scores or faster response times in the context of stimuli with a specific valence.

• Thus, “bias” can be defined as a behavioral advantage reflected by better or faster reactions toward positive or negative stimuli.
• In this regard, empirical findings are heterogeneous: Apart from studies that demonstrated a behavioral preference for either positive or negative stimuli, other studies found no asymmetries.

These inconsistent results may be due to methodological factors such as age, stimulus modality, or task. For example, Bayer and Schacht (2014) found that adults process positive words faster than negative ones, but show the reverse pattern for emotional pictures and facial expressions.

What does valence refer to in psychology?

N.1. in the field theory of Kurt Lewin, the subjective value of an event, object, person, or other entity in the life space of the individual. An entity that attracts the individual has positive valence, whereas one that repels has negative valence.

What is an example of valence in psychology?

In psychological terms, valence indicates the emotional value that is associated with a stimulus. For instance, the sight of a loved one will have a great emotional valence while seeing a neighbor from across the street might have only a minimal emotional valence. Add flashcard Cite Random

What does valence mean in relationship?

Cognitive valence theory ( CVT ) is a theoretical framework that describes and explains the process of intimacy exchange within a dyad relationship. Peter A. Andersen, PhD created the cognitive valence theory to answer questions regarding intimacy relationships among colleagues, close friends and intimate friends, married couples and family members.

1. Intimacy or immediacy behavior is that behavior that provides closeness or distance within a dyad relationship.
2. Closeness projects a positive feeling in a relationship, and distance projects a negative feeling within a relationship.
3. Intimacy or immediacy behavior can be negatively valenced or positively valenced,

Valence, associated with physics, is used here to describe the degree of negativity or positivity in expected information. If your partner perceives your actions as negative, then the interaction may repel your partner away from you. If your partner perceives your actions as positive, then the interaction may be accepted and may encourage closeness.

What is the meaning of personal valence?

The importance that somebody assigns to something, whether it is considered to be personally relevant or not. Published in Chapter: Factors Influencing Gossiping Behavior in Social Chatting Platforms Adilla Anggraeni (Bina Nusantara University, Indonesia) Copyright: © 2019 | Pages: 12 DOI: 10.4018/978-1-5225-8535-0.ch003 Abstract This chapter discusses the need for drama, interpersonal closeness, informational susceptibility, and compassion for others and their influence towards gossiping behavior via social chatting applications.

Technological advancements have enabled people to communicate with each other at the convenience of their homes and in real time. This change, however, also means the changes in human behaviors, such as computer-mediated communication, can be shaped by the richness of the media that people can use to convey their thoughts and opinions.

The existence of different chatting applications has fulfilled the needs of human beings to be connected and to interact with each other, and the interactions that take place can be in the form of gossiping and spreading information that may not necessarily be accurate.

Is valence the same as arousal?

Introduction – Emotions pervade every aspect of our life by influencing our physiology, perceptions and behaviours. Despite the prominence and effects of emotions on even basic sensory perception we know relatively little about how emotions are represented in the brain.

The two most widely cited theories suggest that emotions are either characterised as discrete entities (i.e. basic emotion categories; Panksepp, 1982 ; Ekman, 1992 ) or as independent dimensions, arousal and valence ( Russell, 1980 ). Arousal (or intensity) is the level of autonomic activation that an event creates, and ranges from calm (or low) to excited (or high).

Valence, on the other hand, is the level of pleasantness that an event generates and is defined along a continuum from negative to positive. Basic emotion theories propose that humans are evolutionarily endowed with a limited set of emotions. Thus the behavioural and physiological expression of each emotion should be separate and arise from unique neural substrates ( Ekman, 1992 ).

While popular, this theory has had limited scientific support from animal, developmental, cross-cultural and physiological studies ( Carroll et al,, 1999 ). Neuroimaging studies using traditional univariate analyses have generally not supported the existence of consistent and specifically localised, distinct brain regions for the perception of different emotion categories (e.g.

see Phan et al,, 2002 ; Lindquist et al,, 2012 for meta-analyses, and Hamann, 2012 for review). More recently, any studies confirming unique and consistent neural signatures using multivariate pattern analysis approaches have been fiercely criticised based on a misunderstanding of the statistics and of basic emotion theory ( Clark-Polner et al,, 2017, but see e.g., Kotz et al,, 2013 for a cautious approach).

Dimensional theories, on the other hand, posit that any affective experience can be defined along at least two dimensions ( Wundt, 1924 ). The essence of these theories is that emotions are multicomponent phenomena. Early support for a multi-dimensional organisation of the emotional space comes from psychophysiological studies.

For example, startle reflex amplitudes are greatest for negatively valenced photos but decrease with positive emotional content in photos ( Anders et al,, 2004 ; Adank et al,, 2012 ). Similarly, skin conductance amplitudes co-vary with increasing arousal ratings and electromyographic activity correlates with valence ratings ( Lang et al,, 1993 ).

Additional support for a multicomponent view of emotion comes from pupillometry whereby pupil size is larger during emotionally negative and positive sounds than during the presentation of neutral sounds ( Partala and Surakka, 2003 ). While there is support for dimensional theories of emotion, the exact number and labelling of the dimensions as well as the relationship between these dimensions is less clear.

Russell (1980) proposed that the dimensions of valence and arousal are independent and linear, i.e. how pleasant one is feeling gives no information about how calm or activated one feels. Intuitively, however, valence and arousal often go together (e.g.

Very negative events are also typically high in arousal), which is perhaps the reason why arousal has frequently been interpreted as reflecting the intensity of an emotion ( Schlosberg, 1954 ; Schachter and Singer, 1962 ; Otten and Moskowitz, 2000 ). A relationship of this kind would imply valence and arousal being quadratically related and this weak but consistent relationship between the two dimensions has been confirmed in behavioural studies (for words: e.g.

Bradley and Lang, 1994 ; Redondo et al,, 2007 ; Vo et al,, 2009 ; Kanske and Kotz, 2010 and pictures: e.g. Cuthbert et al,, 1996 ; Bradley and Lang, 1999 ; see also Kuppens et al,, 2013 ). Due to this relationship it is difficult to manipulate the two dimensions independently and find the neural underpinnings of each dimension separately.

• Several neuroimaging studies have attempted to describe the independent neural networks underlying valence and arousal using very different designs, analyses, tasks and sensory stimuli and often without awareness of the findings in the behavioural literature.
• Thus many neuroimaging researchers have assumed a linear and orthogonal relationship between the two dimensions and designed their studies accordingly.

While most studies report that arousal and valence are coded by distinct subsystems ( Anderson et al,, 2003 ; Anders et al,, 2004 ; Dolcos et al,, 2004 ; Lewis et al,, 2007 ; Nielen et al,, 2009 ; Colibazzi et al,, 2010 ; Sieger et al,, 2015 ) there is little consensus in the literature, which neural systems serve these functions.

Nevertheless, lesion ( Öngür and Price, 2000 ) and neuroimaging studies ( Zuckerman et al,, 1990 ; Viinikainen et al,, 2010 ) tend to agree on a central role of the medial or orbital prefrontal cortex in the processing of emotional valence with both positive and negative valence coded in overlapping neural regions ( Lindquist et al,, 2012 ).

Reports frequently, but not consistently, cite the amygdala as the region that underpins the processing of arousal ( Anderson et al,, 2003 ; Small et al,, 2003 ; Lewis et al,, 2007 ). In addition to difficulties in replicating certain regions, another recent complication is that BOLD signal may not actually vary linearly with linear modulations of the valence spectrum ( Viinikainen et al,, 2010, 2012 ).

1. We were interested in localising the overlap between valence and arousal as well as examining the effects specific to each affective dimension on neural activity when processing non-linguistic, vocal expressions.
2. To this end we created vocal morphs between neutral and various emotional expressions to broadly sample the valence and arousal space.

We then computed two parametric models whereby each of the regressors of interest (i.e. ratings of valence and arousal) was orthogonalised with respect to the other in the design matrix. These two models allowed us to examine any overlap between valence and arousal activations as well as any areas specific to each dimension.

What is valence perception?

When grabbing a coffee mug out of a cluttered cabinet or choosing a pen to quickly sign a document, what brain processes guide your choices? New research from Carnegie Mellon University’s Center for the Neural Basis of Cognition (CNBC) shows that the brain’s visual perception system automatically and unconsciously guides decision-making through what is known as valence perception.

Published in the journal ” Frontiers in Psychology,” the review hypothesizes that valence — which can be defined as the positive or negative information automatically perceived in the majority of visual information — integrates visual features and associations from experience with similar objects or features.

In other words, it’s the process that allows our brains to rapidly make choices between similar objects. The findings offer important insights into consumer behavior in ways that traditional consumer marketing focus groups cannot address. For example, asking individuals to react to package designs, ads or logos is simply ineffective.

Instead, companies can use this type of brain science to more effectively assess how unconscious visual valence perception contributes to consumer behavior. To transfer the research’s scientific application to the online video market, the CMU research team is in the process of founding the startup company neonlabs through the support of the National Science Foundation (NSF) Innovation Corps (I-Corps).

Michael J. Tarr, the George A. and Helen Dunham Cowan Professor of Cognitive Neuroscience and co-director of the CNBC, said that the NSF I-Corps program has been instrumental in helping the neonlabs’ team take this basic idea and teaching them how to turn it into a viable company.

1. The I-Corps program gave us unprecedented access to highly successful, experienced entrepreneurs and venture capitalists who provided incredibly valuable feedback throughout the development process,” he said.
2. NSF established I-Corps for the sole purpose of assessing the readiness of transitioning new scientific opportunities into valuable products through a public-private partnership.

A $50,000, six-month grant to investigate how understanding valence perception could be used to make better consumer marketing decisions was awarded to the CMU team of Tarr; Sophie Lebrecht, a CNBC and Tepper School of Business postdoctoral fellow; Babs Carryer, an embedded entrepreneur at CMU’s Project Olympus ; and Thomas Kubilius, president of Pittsburgh-based Bright Innovation and adjunct professor of design at CMU.

They are launching neonlabs to apply their model of visual preference to increase click rates on online videos, by identifying the most visually appealing thumbnail from a stream of video. “Everything you see, you automatically dislike or like, prefer or don’t prefer, in part, because of valence perception,” said Lebrecht, lead author of the study and the entrepreneurial lead for the I-Corps grant.

“Valence links what we see in the world to how we make decisions.” Lebrecht continued, “Talking with companies such as YouTube and Hulu, we realized that they are looking for ways to keep users on their sites longer by clicking to watch more videos. Thumbnails are a huge problem for any online video publisher, and our research fits perfectly with this problem.

1. Our approach streamlines the process and chooses the screenshot that is the most visually appealing based on science, which will in the end result in more user clicks.” CMU is well known for its entrepreneurial culture.
2. The university’s Greenlighting Startups initiative is uniquely designed to support new business creation at CMU.

“CMU has been an amazing place to build neonlabs,” Lebrecht said. “There’s a great intellectual community and facilities here as well as people unbelievably experienced in tech transfer and startups who have been so incredibly generous with their time.” Greenlighting Startups is uniquely designed to support CMU’s culture of entrepreneurship and new business creation. Learn more »

What does valence mean in motivation?

Valence – Valence refers to the emotional orientations people hold with respect to outcomes, The depth of the want of an employee for extrinsic or intrinsic rewards). Management must discover what employees value.

What is positive valence behavior?

Positive Valence Systems Positive Valence Systems primarily responsible for responses to positive motivational situations or contexts, such as reward seeking, consummatory behavior, and reward/habit learning.

Why is valence important?

Valence Electrons – The Periodic Table Variations Of Chemical Properties With Group And Row – MCAT Content Valence electrons are the electrons in the highest occupied principal energy level of an atom. Elements are organized by period and group, with the period corresponding to the principal energy level and the group relating to the extent the subshells are filled.

The properties of an atom relate directly to the number of electrons in various orbitals. Valence electrons, the electrons in the outermost or valence shell, are important as they provide insight into an element’s chemical properties and are the ones gained, lost, or shared during a chemical reaction.

In general, atoms are most stable and least reactive when their outermost electron shell is full. Inner-shell electrons are not involved directly in the element’s reactivity or in the formation of compounds. Remember, elements are placed in order on the periodic table based on their atomic number.

1. In a neutral atom, the number of electrons will equal the number of protons.
2. In addition, the position of an element in the periodic table—its column, or group, and row, or period —provides useful information about how those electrons are arranged.
3. Considering the first three rows of the table, each row corresponds to the filling of a different electron shell: helium and hydrogen place their electrons in the 1n shell, while second-row elements like Li start filling the 2n shell, and third-row elements like Na continue with the 3n shell.

Similarly, an element’s column number gives information about its number of valence electrons and reactivity. In general, the number of valence electrons is the same within a column and increases from left to right within a row. Group 1 elements have just one valence electron while group 18 elements have eight (except for helium which has only two electrons total).

Practice Questions Khan Academy MCAT Official Prep (AAMC) Chemistry Question Pack Passage 19 Question 106 Key Points • Valence electrons are the outermost electron in an electron configuration.• Valence electrons govern many chemical properties, reactivity, and bonding• The group numbers (columns) of the periodic table indicate the total number of outer electrons in the valence shell• The periods (rows) of the periodic table indicate the number of shells that surround a nucleus. Key Terms Inner-shell electrons: Those electrons that are not in the outer shell and are not involved in the reactivity of the element. Valence electrons: The electrons in the highest occupied principal energy level of an atom.

: Valence Electrons – The Periodic Table Variations Of Chemical Properties With Group And Row – MCAT Content

What is negative valence personality?

Negative Valence Systems Negative Valence Systems are primarily responsible for responses to aversive situations or context, such as fear, anxiety, and loss.

What is the role of emotional valence?

Emotional Valence in Human Perception and Processing – Emotional stimuli are highly relevant for human behavior since humans have to process such stimuli very quickly in order to detect and react to events important for our survival. General effects of emotionality i.e., a processing advantage for emotional as compared to neutral information, have been shown for various types of stimuli, including words (e.g., Kanske and Kotz, 2007 ; Kousta et al., 2009 ; Yap and Seow, 2013 ; Goh et al., 2016 ) and faces (Johansson et al., 2004 ; Groß and Schwarzer, 2010 ).

In general, the emotional significance of a stimulus enhances its processing (Zeelenberg et al., 2006 ). One very basic feature of emotional stimuli is their hedonic valence. Valence refers to the pleasantness or unpleasantness of an emotional stimulus. Nearly all events and experiences, such as faces, sounds, music, art, pictures, written or spoken language, and many others can be classified along this dimension as more or less positive or negative.

Given that valence is such a crucial factor for the representation and categorization of human experience, efforts have been made to investigate whether the proximity of stimuli toward either end of this dimension’s continuum (positive or negative) leads to preferential processing, and if so, which one.

In the literature, a marked difference in responding to negative or positive stimuli has been labeled as “bias.” Although there is empirical evidence for existing asymmetries in the way humans use positive and negative information (Vaish et al., 2008 ), the precise direction of such valence biases is still unclear.

In cases when either positive or negative stimuli are perceived and processed differentially, the response to the preferred category is described with terms like “advantage,” “preference,” “superiority,” “enhancement,” or “facilitation,” while the other category, accordingly, shows a “disadvantage” or “delay.” Evidence for a bias is usually associated with a behavioral advantage or facilitation in experimental tasks, in particular with higher accuracy scores or faster response times in the context of stimuli with a specific valence.

Thus, “bias” can be defined as a behavioral advantage reflected by better or faster reactions toward positive or negative stimuli. In this regard, empirical findings are heterogeneous: Apart from studies that demonstrated a behavioral preference for either positive or negative stimuli, other studies found no asymmetries.

These inconsistent results may be due to methodological factors such as age, stimulus modality, or task. For example, Bayer and Schacht ( 2014 ) found that adults process positive words faster than negative ones, but show the reverse pattern for emotional pictures and facial expressions.

Is valency positive or negative?

Hint: Valency of any element is the number of electrons it has to lose or gain in order to become stable. If the atom has to lose electrons to become stable, then the valency will be positive and if the atom has to gain electrons to become stable, then the valency will be negative.

Complete answer: Let us start from the third as the first two are already given.3 – \ – Chlorine, it can also exhibit valency such as 1, 3, 5 and 7.4 – \ – Nickel has a valency of positive two.5 – \ – Chlorate has a valency of negative one.6 – \ – Carbonate has a valency of negative two.7 – \ – Barium has a valency of positive two.8 – \ – Bicarbonate has a valency of positive one.9 – \ – Nitrite has a valency of negative one.10 – \ – Sodium has a valency of positive one.11 – \ – Bromine has a valency of negative one.12 – \ – Zinc has a valency of positive two.13 – \ – Magnesium has a valency of positive two.14 – \ – Oxide has a valency of negative two.15 – \, \ – Cobalt has variable valency of positive two and three.16 – \ – Chromate has a valency of negative two.17 – \ – Hypochlorite has a valency of negative one.18 – \ – Permanganate has a valency of negative one.19 – \ – Lithium has a valency of positive one.20 – \ – Iodine has a valency of negative one.21 – \ – Hydroxide has a valency of negative one.22 – \ – Peroxide ion has valency of positive two.23 – \ – Zincate has a valency of positive two.24 – \ – Silicate has a valency of negative two.25 – \ – Nitrate ions have a valency of negative one.26 – \ – Sulphite has a valency of negative two.27 – \ – Sulphate has a valency of positive two.28 – \ – Phosphate has a valency of negative three.29 – \, \ – Nitrogen has a valency of either positive five or negative three.30 – \, \ – Carbon has a valency of either positive four or negative four.31 – \ – Phosphite has a valency of negative three.32 – \ – Aluminium has a valency of positive three.33 – \ – Calcium has a valency of positive two.34 – \ – Hydrogen has a valency of positive one.35 – \ – Plumbite has a valency of positive two.36 – \ – Bisulphite has a valency of positive one.37 – \ – Meta aluminate has a valency of negative one.38 – \ – Chromium has a valency of positive three.39 – \ – Bisulphate has a valency of negative one.40 – \ – Ammonium has a valency of positive one.

Note: The knowledge of the names of these compounds and the valence electrons of each of those elements has to be known, without that we cannot get the correct answer. Also, most elements have more than one valence, here the most common valence is written.

What is social valence?

Social valence (uncountable) The quality of a robot or other artificial entity to be perceived as more than an object, but as a social agent, like a pet or a person.

Which emotion has high arousal and a negative valence?

1. Introduction – The intense involvement in a work of art seems to make us lose track of time. Though our retrospective judgments of how long such an experience lasts seem to make us think that time has flown, it is also the case that emotion changes our encoding and reproduction of memories for time.

A general consensus of recent studies is that the arousal related to emotional stimuli increases the speed of an internal timing mechanism ( Droit-Volet et al., 2004, 2013 ; Droit-Volet & Gil, 2009 ; Droit-Volet & Meck, 2007 ; Grommet et al., 2011 ). Emotions can be thought of categorically, such as happy, sad, or angry, but they can also be conceptualized as in a two dimensional space in which valence is on one axis and arousal is on the other (Lang et al., 2003).

Valence refers to how pleasant or unpleasant a stimulus is. Arousal refers to levels of activation as reflected in physiological responses such as skin conductance, heart rate, and the startle response ( Bradley et al., 2001 ; Mikels et al., 2005 ). Specific emotions can be placed on this two-dimensional space.

• For example, happiness is considered to be moderately arousing with positive valence, excitement is highly arousing with positive valence, sadness is moderately arousing with negative valence, and anger is highly arousing with negative valence.
• In fact, people are more consistent in their ratings of arousal and valence than in applying categorical labels ( Bradley & Lang, 2007a ) suggesting that these dimensions accurately reflect emotional experience.

Thus, emotions might impact time perception through valence and/or arousal. Clock models of timing, such as Scalar Expectancy Theory (SET), propose that temporal information is processed using an internal mechanism for counting time. This clock-like structure (pacemaker/timer) emits pulses at a given rate that pass through a switch and get added to an accumulator ( Gibbon, 1977 ; Gibbon et al., 1984 ).

The accumulation is transferred to a short-term memory store and, depending on environmental conditions, to a long-term memory store when it is appropriate to encode the duration of an event. Decisions are made about the temporal information when accumulated stores are compared to representations in long-term memory.

Another component of SET is that estimates demonstrate the scalar property. The scalar property, a version of Weber’s law, stipulates that as target duration increases the variability of temporal estimates will increase proportionally such that the coefficient of variation (standard deviation divided by the mean) will remain constant.

1. Research has suggested that in general, increased arousal increases the speed of the internal clock ( Fetterman & Killeen, 1995 ; Meck, 1996 ; Penton-Voak et al., 1996 ; Treisman et al., 1990 ; Wearden & Penton-Voak, 1995 ).
2. The effects of increased clock speed on time estimation differ depending on whether the change in clock speed occurs during the encoding or reproduction of a temporal memory ( Meck, 1996 ; Penton-Voak et al., 1996 ).

If clock speed is increased only when subjects are encoding the time, more pulses accumulate during encoding than would accumulate at normal clock speed. When that time is later reproduced with a clock running at normal speed, it will take more time for the accumulating pulses to match the memory representation encoded with the fast clock and reproductions will overestimate the time.

On the other hand, if duration is encoded at a normal clock speed but clock speed is increased when subjects are reproducing the duration, estimates are shorter because an increase in clock speed during retrieval causes subjects to reach the target duration encoded in memory more quickly than normal.

These same effects are noted when arousal is induced by emotional stimuli. Watts & Sharrock (1984) required subjects with phobias to view images of phobic stimuli during encoding and then provide temporal estimates of the exposure duration. Phobic subjects overestimated the duration of the images more than non-phobic subjects, which is consistent with an increase in clock speed during the encoding of the duration of the fearful stimuli.

• Langer et al.
• 1961) found that subjects underestimated duration when they were fearful while reproducing a previously learned interval, which is consistent with an increase in clock speed during reproduction.
• More recently, Droit-Volet et al.
• 2004) demonstrated that estimates of the duration of emotional faces increased with increasing arousal level during encoding.

These results are all consistent with the interpretation that arousal induced by emotional stimuli increases clock speed. Much of the research that directly addresses the impact of emotions on temporal processing has utilized the International Affective Picture System (IAPS) ( Lang et al., 2008 ), a large set of photographic images that have been rated on levels of arousal and valence.

Using these images Angrilli et al. (1997) assessed differences in time perception due to valence and arousal, and found an interaction between them. When low arousal stimuli were presented during encoding, estimates of positive images were longer than estimates of negative images. However, the pattern of results reversed when high arousal images were presented: estimates of negative images were longer than estimates of positive ones.

One interpretation of these results is that clock speed slows for negative images at low arousal level but speeds up for highly arousing negative images relative to positive ones. Attention also influences time perception because attentional resources are limited and must be shared across timing and non-timing processes ( Brown, 1997, 2008 ; Macar et al., 1994 ; Penney et al., 2000 ; Zakay & Block, 1997 ).

In the attentional allocation model, attention to time modulates the accumulating pulses in SET ( Penney et al., 2000 ; Zakay & Block, 1997 ). When attention is distracted from time during encoding fewer pulses accumulate making encoded durations shorter. When attention is distracted during reproduction of a previously learned time the slower accumulation of pulses results in it taking longer to reach a criterion number of pulses and estimates are longer ( Macar et al., 1994 ; Penney et al., 2000 ).

Inversely, increasing attention to time during encoding lengthens later estimates and increased attention to time during reproduction shortens them ( Macar et al., 1994 ; Penney et al., 2000 ). Thus, increased attention to time generates the same pattern of results as increased clock speed.

This makes it difficult to separate clock speed changes from changes in attention allocation when working with processes that can affect both mechanisms, such as emotions. For example, rather than interpret their results as a clock speed effect, Angrilli et al. (1997) proposed that the longer estimates of positive than negative images when arousal was low might be caused by the negative images distracting attention away from timing during encoding, leading to shortened estimates.

Lejeune (1998) suggested an additional role of attention in time perception. Rather than attention affecting the accumulation of pulses, attention might affect the latency of the switch that allows pulses to enter the accumulator. An effect of attention on switch latency is distinguishable from an effect on the allocation of attention to temporal information.

1. If a condition continuously alters attention allocation towards time, the distortions in timing will be proportional to the cue duration (e.g., a 10% effect on pulse accumulation will result in a 10% change in subjective duration for all intervals — Droit-Volet et al., 2004 ; Effron et al., 2006 ).
2. In contrast, if switch latency were altered, this would affect all target durations by the same absolute amount ( Brown et al., 2007 ; Lejeune, 1998 ).

The purpose of the current experiments was to further explore the impact of emotional valence and arousal on temporal information processing. Since changes in clock speed or attention allocation have different behavioral effects depending on whether they are manipulated during encoding or reproduction, we manipulated emotional content during both stages of the timing task.

What are the levels of female arousal?

The Sexual Response Cycle Medically Reviewed by on November 11, 2022 The sexual response cycle refers to the sequence of physical and emotional changes that occur as a person becomes sexually aroused and participates in sexually stimulating activities, including intercourse and masturbation. The sexual response cycle has four phases: excitement, plateau, orgasm, and resolution. Both men and women experience these phases, although the timing usually is different. For example, it is unlikely that both partners will reach orgasm at the same time.

Muscle tension increases.Heart rate quickens and breathing is accelerated. may become flushed (blotches of redness appear on the chest and back).Nipples become hardened or erect. flow to the genitals increases, resulting in swelling of the woman’s clitoris and labia minora (inner lips), and erection of the man’s,Vaginal lubrication begins.The woman’s become fuller and the vaginal walls begin to swell.The man’s testicles swell, their scrotum tightens and begin secreting a lubricating liquid.

General characteristics of the plateau phase, which extends to the brink of orgasm, include the following:

The changes begun in phase 1 are intensified.The continues to swell from increased flow, and the vaginal walls turn a dark purple.The woman’s clitoris becomes highly sensitive (may even be painful to touch) and retracts under the clitoral hood to avoid direct stimulation from the,The man’s testicles tighten.Breathing, heart rate, and continue to increase. may begin in the feet, face, and hands.Muscle tension increases.

The orgasm is the climax of the sexual response cycle. It is the shortest of the phases and generally lasts only a few seconds. General characteristics of this phase include the following:

Involuntary muscle begin.Blood pressure, heart rate, and breathing are at their highest rates, with a rapid intake of oxygen.Muscles in the feet spasm.There is a sudden, forceful release of sexual tension.In women, the muscles of the contract. The uterus also undergoes rhythmic contractions.In men, rhythmic contractions of the muscles at the base of the penis result in the ejaculation of semen.A, or “sex flush” may appear over the entire body.

During resolution, the body slowly returns to its normal level of functioning, and swelled and erect body parts return to their previous size and color. This phase is marked by a general sense of well-being, enhanced and, often,, Some women are capable of a rapid return to the orgasm phase with further sexual stimulation and may experience multiple orgasms.

What does a low valence mean?

4. Low-valence, low-arousal. These are emotions that are both negative and calm —honestly, not a strategy that works often because it doesn’t spur action. As mentioned above, sad articles were shared the least of any emotion. However, that does not mean that there isn’t a use case.

What are emotionally valenced words?

Emotional valence: the extent to which the emotion evoked by a word is positive or negative. The distinction is between valenced words (whether positive or negative) and neutral words (e.g. echo). Polarity: here the distinction is between positive (e.g. success) and negative (e.g. accident) words.

What is empathic valence?

Positive-Valence Empathy and Anhedonia – It is proposed here that positive-valence empathy and anhedonia are antithetical constructs, and in fact, positive-valence empathy may be a useful means by which to work with patients who are anhedonic (i.e., learning to experience positive affect vicariously may be one route toward relieving anhedonia).

Positive-valence empathy, as a scientific construct, is based on the idea that humans have the capacity (and perhaps the propensity) to share in the positive affect of other people, and the intact presence of this ability may be protective psychologically. Furthermore, deficits in this ability may be reversible, and gains made in positive-valence empathy may contribute to reduction in overall anhedonia.

As the parsing of emotional processes becomes ever more refined, an investigation of the processes by which positive affect can be transmitted between people (i.e., how positive affect gets under the skin) could prove to be a useful endeavor for the purposes of developing treatments for various mood disorders – particularly Major Depressive Disorder and Persistent Depressive Disorder (i.e., dysthymia) – and other conditions that affect a person’s basic interest in life and/or their subjective experience of positive emotional states.

What is an example of valence and arousal?

Emotional valence and arousal effects on memory and hemispheric asymmetries , October 2010, Pages 10-17 Emotions have been conceptualized as action dispositions that vary along valence and arousal dimensions (Lang, Bradley, & Cuthbert, 1990). Valence refers to the pleasant–unpleasant quality of a stimulus and ranges from negative to positive, whereas arousal refers to the intensity of a stimulus and ranges from dull to arousing (Heilman, 1997).

Using this bi-dimensional or circumplex model, one can see how emotions are defined. For example, anger and sadness are both negative in valence, but anger is high in arousal, whereas sadness is low in arousal (Heilman, 1997; see Fig.1). Over the past several decades, neuropsychological research has produced two theories regarding the processing of emotion: the right hemisphere (RH) and valence–arousal models (Killgore & Yurgelun-Todd, 2007).

The RH model was based upon early experimental and clinical studies, which found that all emotions, regardless of their valence, were processed preferentially by RH systems (Borod et al., 1998, Lang et al., 1990). Alternatively, the valence–arousal model of emotion, an integration of the RH and approach–withdrawal (Davidson, 1992) models, proposes that RH prefrontal systems are biased toward negative valences, RH parietal systems are biased toward arousal, and left hemisphere (LH) prefrontal systems are biased toward positive valences (Heller, Nitschke, & Lindsay, 1997).

1. To date, findings in the literature regarding the processing of emotion are mixed, with some supporting the RH model (e.g., Borod et al., 1998, Nagae and Moscovitch, 2002), and some supporting the valence–arousal model (e.g., Ali and Cimino, 1997, Tamagni et al., 2009).
2. We propose that the inconsistencies in the literature regarding hemispheric asymmetries for emotional stimuli can be explained, at least in part, by methodological differences between studies that may have resulted in the recruitment of various hemispheric regions (Borod, 1993).

One aim of this study was to provide a well-controlled test of these two theories while also extending the literature. A possible explanation for the mixed findings in the literature pertaining to the hemispheric lateralization of emotional stimuli concerns theoretical considerations.

For example, it has been proposed that stage of processing affects hemispheric asymmetries such that the RH model holds primarily at the stage of perceptual identification, whereas the valence–arousal model holds primarily at the stage of response preparation, suggesting that hemispheric asymmetries may shift over time within one or more cognitive tasks (Root, Wong, & Kinsbourne, 2006).

Specifically, this hypothesis predicts that RH systems mediate perceptual identification tasks, but that the hemispheres diverge in their specializations at the response preparation stage, with RH systems mediating responses to negative emotional stimuli and LH systems mediating responses to positive emotional stimuli.

• Root et al.
• Have contended that tasks employed in previous studies in the literature may have conflated these two successive stages, resulting in an integration or additive effect of the RH and valence–arousal models.
• These hypotheses have been corroborated by several studies employing nonverbal stimuli (e.g., Maxwell and Davidson, 2007, Root et al., 2006) and form the basis of the approach-withdrawal model of emotion (Davidson, 1992), which states that RH motivational systems mediate withdrawal-related behaviors and LH motivational systems mediate approach-related behaviors.

In contrast to what one would expect given these findings, Nagae and Moscovitch (2002) found LH superiority during a perceptual identification task using both emotional and non-emotional words. Accordingly, it is not clear whether this stage-of-processing analysis can be applied to the processing of emotional verbal stimuli and what predictions would be made regarding memory tasks or processes such as encoding, retention, and retrieval (Root et al., 2006).

1. Several studies have examined the RH and valence–arousal models within the context of explicit emotional memory.
2. These studies too yielded conflicting findings.
3. Consistent with the RH model, Graves, Landis, and Goodglass (1981) found greater recognition accuracy for positive and negative words relative to neutral words presented to the left visual field (LVF)/RH.

Similarly, Nagae and Moscovitch (2002) found that positive and negative words presented to the LVF/RH were both recognized with greater accuracy than neutral words presented to the LVF/RH and that recognition was equal for positive, negative, and neutral words presented to the right visual field (RVF)/LH.

• More recently, using a lexical decision paradigm, Landis (2006) found a LVF/RH bias for the processing of emotional words.
• Like the study by Graves et al., however, the study by Landis did not examine differences in the processing of negative and positive words separately, and as such, did not provide sufficient data with which to evaluate the valence–arousal model.

Consistent with the valence–arousal model, Alfano and Cimino (2008) found that memory for consonant trigrams presented to the LVF/RH was best when preceded by a negative word prime and that memory for consonant trigrams presented to the RVF/LH was best when preceded by a positive word prime.

Ali and Cimino (1997) found that positive words presented to the RVF/LH were recognized with greater accuracy than neutral or negative words presented to the RVF/LH and that negative words presented to the LVF/RH were recognized with greater accuracy than positive or neutral words presented to the LVF/RH.

A separate, but related, literature has found enhanced memory for emotional stimuli relative to non-emotional stimuli (e.g., Buchanan et al., 2001, Talarico et al., 2004). The arousal properties of stimuli-to-be-remembered seem to play a role in this phenomenon, such that stimuli that are more arousing (e.g., taboo words) tend to be recalled with greater accuracy than stimuli that are less arousing (e.g., Buchanan et al., 2006, Talarico et al., 2004).

• Emotional arousal has been found to modulate the memory consolidation process such that patients with either amygdala surgically resectioned failed to show the same memory enhancement for emotionally arousing words as healthy controls (LaBar & Phelps, 1998).
• Propranolol, a beta-adrenergic antagonist that reduces sympathetic arousal, has been found to impair short- and long-term retention of emotionally arousing material (e.g., Maheu, Joober, Beaulieu, & Lupien, 2004) whereas epinephrine or glucocorticoids, stress hormones that increase sympathetic arousal, have been found to enhance long-term retention of emotionally arousing material (Kensinger and Corkin, 2003, Roozendaal et al., 2008) Further, arousal may play a greater role than valence in long-term retention of emotionally arousing material (Nielson & Powless, 2007).

Arousal has also been associated with hemispheric asymmetries. For example, individuals with relatively high levels of anxiety (associated with arousal – Lang et al., 1990) have been found to demonstrate altered hemispheric asymmetries (e.g., Heller et al., 1995, Shackman et al., 2006).

Heller et al., 1995, Heller et al., 1997 have found that individuals with severe anxiety, as measured by two anxiety scales, showed greater LVF/RH biases on a chimeric faces task compared with individuals with little or no anxiety. Shackman et al. found that a threat of shock resulted in poorer performance on a visuospatial working memory task (considered to be mediated preferentially by the RH) but no change in performance on a verbal working memory task (considered to be mediated preferentially by the LH) when compared to no threat of shock.

As such, a failure to control for normative arousal in previous studies may have recruited various hemispheric systems to help complete the tasks; for example, RH systems may have been recruited to a greater extent than LH systems (Heller et al., 1995, Heller et al., 1997).

To our knowledge, only one previous study has examined hemispheric asymmetries for emotional memory while systematically controlling for the normative arousal properties of the stimuli used (Alfano & Cimino, 2008). Although the positive, negative, and neutral words in that study were equated with respect to normative arousal, the emotion words were not the to-be-remembered-stimuli; instead, the to-be-remembered stimuli were consonant trigrams.

Additionally, replications employing basic emotional memory paradigms have not yet been made. The mixed findings in this literature may also be due, at least in part, to methodological differences. For example, differences in samples and paradigms may have produced the disparate findings (Borod, 1993).

• More specifically, previous studies have not equated emotion words-to-be-remembered with regard to normative arousal ratings.
• In order to examine the influence of emotional valence on memory, it has been proposed that normative arousal ratings be controlled systematically (Lang et al., 1990).
• In previous studies, negative words might have been higher in arousal than positive words if investigators did not equate them on normative arousal, resulting in better memory for the negative words.

Traditionally, studies in this literature have controlled for other characteristics of their words-to-be-remembered (e.g., word length, concreteness, frequency with which the word is used in everyday situations) but have not considered the effects of normative arousal ratings on subsequent memory.

Although Ali and Cimino (1997) and Nagae and Moscovitch (2002) used emotion (i.e., positive and negative) and neutral words in their studies, neither controlled for the normative arousal ratings of the words, and these studies produced support for the valence–arousal model and for the RH model, respectively.

The findings in the literature may also suggest that the neural system underlying the processing of emotional verbal stimuli is more complex than indicated by either the RH or valence–arousal models. Using functional magnetic resonance imaging (fMRI), Killgore and Yurgelun-Todd (2007) tested the RH and valence–arousal models and, after finding partial support for both models, proposed that the neural systems specified by the two models operate concurrently or in parallel.

According to this proposal (herein referred to as the “integrated model”), the RH is biased toward the processing of all emotions, regardless of their valence, and especially for the processing of negative emotions. Specifically, the model predicts that, during LVF/RH presentations, positive and negative stimuli will both activate posterior RH systems relative to baseline, but that negative stimuli will activate these systems to a greater extent relative to LH systems because of a RH bias for the processing of negative emotion.

This integrated model merges the RH model, which proposes that the RH is biased toward all emotions, with one prediction of the valence–arousal model, that RH systems are biased toward negative emotions. It does not, however, include the other prediction of the valence–arousal model, that LH systems are biased toward the processing of positive emotions.

This integrated model is partially consistent with the proposal that some tasks may conflate early and late stages of processing, thus affecting hemispheric asymmetries and data interpretation (Root et al., 2006). Because participants in the study by Killgore and Yurgelun-Todd were tested using a facial emotion recognition task, it is uncertain whether this integrated model will generalize to the processing of emotional verbal stimuli.

Another aim of this study was to provide an exploratory test of this model of emotion processing using emotional verbal stimuli. In this study, we examined predictions based upon the RH model, the valence–arousal model, and an integrated model (Killgore & Yurgelun-Todd, 2007) of emotion processing by building upon the methodologies of previous studies in the literature.

To examine whether the inconsistencies in the literature concerning hemispheric asymmetries for emotional verbal stimuli are due to valence and arousal effects, we tested recall and recognition for positive, negative, and neutral verbal stimuli presented, one at a time, to either visual field. The positive and negative words in this study were equated on arousal ratings, and both emotion word lists were designed so that emotion words were higher in mean arousal ratings than were the neutral words.

Following the presentation of all the words, participants were asked to write down as many words as they could remember (i.e., free recall test). Then they were given a standard old–new recognition test, in which they were asked to identify whether each word occurred in the original presentation.

• If the RH model is correct and RH systems are biased toward the processing of all emotions, regardless of their valence, then we would expect to find more accurate recall and recognition of negative and positive words presented to the LVF/RH than neutral words presented to the LVF/RH.
• In addition, we would expect to find equal or more accurate recall and recognition of neutral than negative and positive words presented to the RVF/LH.

If the valence–arousal model is correct and LH systems are biased toward positive valences and RH systems are biased toward negative valences, then we would expect to find greater recall and recognition of negative words presented to the LVF/RH than of neutral and positive words presented to the LVF/RH and greater recall and recognition of positive words presented to the RVF/LH than of neutral and negative words presented to the RVF/LH.

If the integrated model of emotion processing (Killgore & Yurgelun-Todd, 2007) is correct and RH systems are biased toward the processing of all emotions, and especially negative emotions, then we would expect to find more accurate recall and recognition of emotional (i.e., negative and positive) words presented to the LVF/RH than of neutral words presented to the LVF/RH.

Further, we would also expect to find greater recall and recognition of negative words presented to the LVF/RH than of neutral and positive words presented to the LVF/RH. Participants in this study included 39 right-handed female undergraduates who participated for course credit.

1. Males were excluded from this study based on research that suggests that asymmetries for emotional processing differ between men and women (Canli et al., 2002, Graves et al., 1981, Proverbio et al., 2006).
2. Females, rather than males, were chosen because they comprised the majority of the participant pool.

Only those scoring between 13 and 17 on the Chapman Handedness Inventory (Chapman Participants’ eye movements were counted post hoc by two independent raters. Inter-rater reliability was high, intraclass r = 0.99. Across raters, the average number of eye movements was 33.26, with a range of 3–88 eye movements (out of a possible total of 94 eye movements).

Accordingly, across participants, eye movements occurred on 35% of all trials. Rather than exclude participants who made a certain percentage of eye movements (Ali & Cimino, 1997), we chose to examine frequency of eye In this study, we examined the RH model, the valence–arousal model, and a recently proposed integrated model of emotion processing within the context of explicit memory for emotional verbal stimuli.

Some of our findings were consistent with one or more of the three models, some were not consistent with any of the models, and some provided evidence against the valence–arousal and integrated models. As such, our findings best support an integrated model of emotion processing that is more complex In sum, we built upon methodologies of previous studies examining explicit memory for emotional verbal stimuli and found support for both the RH and valence–arousal models of emotion processing.

S. Windmann et al. C. Tamagni et al. M. Spokas et al. L.C. Robertson et al. L.C. Robertson et al. K.A. Nielson et al. S. Nagae et al. D.L. Na et al. J. Levy et al. T. Landis

R. Graves et al. R.J. Davidson L. Cohen et al. L.J. Chapman et al. T.W. Buchanan et al. M. Brázdil et al. K.M. Alfano et al. N. Ali et al. L.F. Barrett et al. K.J. Bellamy et al. J.C. Borod J.C. Borod et al. V.J. Bourne M.M. Bradley et al.

The risk of sudden cardiac death is remarkably high in patients with bipolar disorder. The risk is especially elevated, 32 times greater than in the general population, in patients younger than 30 years old. Early atherosclerosis and general lifestyle risk factors might play a role but are insufficient to account for this greatly inflated risk of sudden cardiac death. A recent mechanistic model of the pathophysiology of bipolar disorder highlights dysfunctional changes in functional hemispheric lateralization that are proposed to arise as a consequence of aberrant midbrain signaling at the level of the paraventricular nucleus of the hypothalamus. The paraventricular nucleus is also a key region involved in regulating bodily physiology, including the balanced autonomic control of cardiac function. Further, the sympathetic innervation of the heart is configured anatomically such that exaggerated imbalanced right-left stimulation of the heart can predispose to fatal arrhythmia. Correspondingly, lateralization of cortical activity related to emotional arousal and acute stress, if not otherwise corrected, may drive asymmetric sympathetic effects on the heart. In cardiac patients, asymmetric midbrain lateralization is associated with increased proarrhythmic changes during mental and physical stress challenges, predisposing vulnerable patients to lethal arrhythmias. Here, we propose that similar mechanisms involving lateralized brain–heart interactions account for the increased risk of sudden cardiac death in patients with bipolar disorder. We explore the evidence for, and implications of, this model. Approximately 45 million people worldwide are diagnosed with bipolar disorder (BD). While there are many known risk factors and models of the pathologic processes influencing BD, the exact neurologic underpinnings of BD are unknown. We attempt to integrate the existing literature and create a unifying hypothesis regarding the pathophysiology of BD with the hope that a concrete model may potentially facilitate more specific diagnosis, prevention, and treatment of BD in the future. We hypothesize that dysfunctional signaling from the parvocellular neurons of the paraventricular hypothalamic nucleus (PVN) results in the clinical presentation of BD. Functional damage to this nucleus and its signaling pathways may be mediated by myriad factors (e.g. immune dysregulation and auto-immune processes, polygenetic variation, dysfunctional interhemispheric connections, and impaired or overactivated hypothalamic axes) which could help explain the wide variety of clinical presentations along the BD spectrum. The neurons of the PVN regulate ultradian rhythms, which are observed in cyclic variations in healthy individuals, and mediate changes in functional hemispheric lateralization. Theoretically, dysfunctional PVN signaling results in prolonged functional hemispheric dominance. In this model, prolonged right hemispheric dominance leads to depressive symptoms, whereas left hemispheric dominance correlated to the clinical picture of mania. Subsequently, physiologic processes that increase signaling through the PVN (hypothalamic-pituitaryadrenal axis, hypothalamic- pituitary-gonadal axis, and hypothalamic-pituitary-thyroid axis activity, suprachiasmatic nucleus pathways) as well as, neuro-endocrine induced excito-toxicity, auto-immune and inflammatory flairs may induce mood episodes in susceptible individuals. Potentially, ultradian rhythms slowing with age, in combination with changes in hypothalamic axes and maturation of neural circuitry, accounts for BD clinically presenting more frequently in young adulthood than later in life. Previous research has attributed to the right hemisphere (RH) a key role in eliciting false memories to visual emotional stimuli. These results have been explained in terms of two right-hemisphere properties: (i) that emotional stimuli are preferentially processed in the RH and (ii) that visual stimuli are represented more coarsely in the RH. According to this account, false emotional memories are preferentially produced in the RH because emotional stimuli are both more strongly and more diffusely activated during encoding, leaving a memory trace that can be erroneously reactivated by similar but unstudied emotional items at test. If this right-hemisphere hypothesis is correct, then RH damage should result in a reduction in false memories to emotional stimuli relative to left-hemisphere lesions. To investigate this possibility, groups of right-brain-damaged (RBD, N = 15), left-brain-damaged (LBD, N = 15) and healthy (HC, N = 30) participants took part in a recognition memory experiment with emotional (negative and positive) and non-emotional pictures. False memories were operationalized as incorrect responses to unstudied pictures that were similar to studied ones. Both RBD and LBD participants showed similar reductions in false memories for negative pictures relative to controls. For positive pictures, however, false memories were reduced only in RBD patients. The results provide only partial support for the right-hemisphere hypothesis and suggest that inter-hemispheric cooperation models may be necessary to fully account for false emotional memories. This study examined the effects of right brain-damage (RBD) on oral discourse production using a multi-layered discourse processing model. Narrative and procedural discourse samples from participants with RBD and no brain damage were analysed in terms of seven broad areas corresponding to the processing levels of the model. Participants also completed attention, cognitive, general communication and RBD assessments. Despite their normal performance on all assessments (except those on attention), the participants with RBD demonstrated statistically significant differences in syntactic complexity, clarity disruptors and dysfluencies, as well as in discourse grammar and clausal structure in the narratives and in cohesion in the procedures. A model-based theoretical explanation accounting for the deficits noted in participants with RBD, together with clinical guidelines, is provided. Recent neuropsychological studies have attempted to distinguish between different types of anxiety by contrasting patterns of brain organisation or activation; however, lateralisation for processing emotional stimuli has received relatively little attention. This study examines the relationship between strength of lateralisation for the processing of facial expressions of emotion and three measures of anxiety: state anxiety, trait anxiety and social anxiety. Across all six of the basic emotions (anger, disgust, fear, happiness, sadness, surprise) the same patterns of association were found. Participants with high levels of trait anxiety were more strongly lateralised to the right hemisphere for processing facial emotion. In contrast, participants with high levels of self-reported physiological arousal in response to social anxiety were more weakly lateralised to the right hemisphere, or even lateralised to the left hemisphere, for the processing of facial emotion. There were also sex differences in these associations: the relationships were evident for males only. The finding of distinct patterns of lateralisation for trait anxiety and self-reported physiological arousal suggests different neural circuitry for trait and social anxiety.

Despite the important theoretical and applied implications, there is limited experimental research investigating the influence of emotional valence on young children’s verbal recall of everyday emotional experiences. This issue was addressed in the current study. Specifically, we investigated young children’s (5–6 years) recall of emotional experiences presented in six brief stories. To address methodological limitations of the small body of existing literature, we adopted a within participants design in which story content was matched and valence (positive, negative, neutral) was counterbalanced across stories. Fifty-four children were presented the six stories via narrated slideshow, and recall was assessed after delay. Results showed that emotional stories were better recalled than neutral stories and negatively valenced stories were better recalled than positively valenced stories. The recall advantage of negatively valenced information was found for all aspects of each story, suggesting that negative valence renders events particularly memorable. Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%. Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal–valence (AV) dimensional space. The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories. Emotion recognition can be achieved by speech recognition, the judgment of limb movements, analysis of Electrooculogram (EOG) or capturing of facial expressions. However, those types of emotion recognition methods cannot detect human emotion well, because humankind can use fake body movement and words to hide real emotions. In this paper, we proposed an EEG-based emotion classification method based on Bidirectional Long Short-Term Memory Network (BiLSTM). Electroencephalogram (EEG) signal can detect human emotion correctly because human represent their real emotions in their mind and cannot hide emotions there. Meanwhile, EEG is a time sequence signal which needs a model which can deal with this type of data. Therefore, we chose Long Short-term Memory Network to process the EEG signal. In particular, we used an improvement version of LSTM model BiLSTM to manage the signals. BiLSTM can processes input data from front to back and back to front. Meanwhile, BiLSTM can store important information and forget unnecessary information; therefore, this process increases the accuracy of the model. Our method classifies four discrete classifications (happy, sad, fear, and neutral) for emotion classification, which achieves competitive performance compared with other conventional emotion classification methods. The final experimental results show that we can achieve an accuracy of 84.21% for four emotional states classification by using our method. Human computer interaction is increasingly utilized in smart home, industry 4.0 and personal health. Communication between human and computer can benefit by a flawless exchange of emotions. As emotions have substantial influence on cognitive processes of the human brain such as learning, memory, perception and problem solving, emotional interactions benefit different applications. It can further be relevant in modern health care especially in interaction with patients suffering from stress or depression. Additionally rehabilitation applications, guiding patients through their rehabilitation training while adapting to the patients emotional state, would be highly motivating and might lead to a faster recovery. Depending on the application area, different systems for emotion recognition suit different purposes. The aim of this work is to give an overview of methods to recognize emotions and to compare their applicability based on existing studies. This review paper should enable practitioners, researchers and engineers to find a system most suitable for certain applications. An entirely contact-less method is to analyze facial features with the help of a video camera. This is useful when computers, smart-phones or tablets with integrated cameras are included in the task. Smart wearables provide contact with the skin and physiological parameters such as electro-dermal activity and heart related signals can be recorded unobtrusively also during dynamical tasks. Next to unimodal solutions, multimodal affective computing systems are analyzed since they promise higher classification accuracy. Accuracy varies based on the amount of detected emotions, extracted features, classification method and the quality of the database. Electroencephalography achieves 88.86 % accuracy for four emotions, multimodal measurements (Electrocardiography, Electromyography and bio-signals) 79.3 % for four emotive states, facial recognition 89 % for seven states and speech recognition 80.46 % for happiness and sadness. Looking forward, heart-related parameters might be an option to measure emotions accurately and unobtrusive with the help of smart wearables. This can be used in dynamic or outdoor tasks. Facial recognition on the other hand is a useful contact-less tool when it comes to emotion recognition during computer interaction. In this study, electroencephalography-based data for emotion recognition analysis are introduced. EEG signals were collected from 28 different subjects with a wearable and portable EEG device called the 14-channel EMOTIV EPOC+. Subjects played 4 different computer games that captured emotions (boring, calm, horror and funny) for 5 min, and the EEG data available for each subject consisted of 20 min in total. The subjects rated each computer game based on the scale of arousal and valence by applying the SAM form. We provide both raw and preprocessed EEG data with.csv and. mat format in our data repository. Each subject’s rating score and SAM form are also available. With this work, we aim to provide an emotion dataset based on computer games, which is a new method in terms of collecting brain signals. Additionally, we want to determine the success of the portable EEG device and compare the success of this device with classical EEG devices. Finally, we perform pattern recognition and signal-processing methods to observe the performance of our dataset and to classify EEG signals based on the arousal-valence emotion dimension and positive/negative emotions. The database will be publicly available, and researchers can use the dataset for analyzing signals for their own proposed method in the literature.

: Emotional valence and arousal effects on memory and hemispheric asymmetries

What are the examples of valence?

Valence electrons are the electrons in the outermost shell, or energy level, of an atom. For example, oxygen has six valence electrons, two in the 2s subshell and four in the 2p subshell. We can write the configuration of oxygen’s valence electrons as 2s²2p⁴.

What is an example of valence attitude?

Attitude and Social Cognition (Questions & Answers) 1. What are the Properties of Attitude?

• Ans.
• The properties of attitude are described below:
• (i) Valence:

Valence of an attitude tells us whether an attitude is positive or negative towards the attitude object. For example, an attitude towards a nuclear research is expressed on a 5-point scale ranging from 1 (Very Bad), 2 (Bad), 3 (Neutral), 4 (Good) and 5 (Very Good).

A rating of 4 or 5 indicates a positive attitude towards nuclear research while a rating of 1 or 2 indicates a negative attitude and a rating of 3 indicates a neutral attitude. (ii) Extremeness: The extremeness of an attitude indicates how positive or negative an attitude is. A rating of 1 or 5 indicates extreme attitudes.

(iii) Simplicity or Complexity (Multiplicity): An attitude system is said to be ‘simple’ if it contains one or a few attitudes and complex if it is made of many attitudes. For example, an attitude towards a person is a simple attitude while an attitude towards health and well-being is a complex attitude consisting of attitude towards physical and mental health, views about happiness and well-being etc.

1. 2. Describe the factors that influence attitude formation:
2. Ans.
3. The factors influencing attitude formation are as follows:
4. (i) Family and school Environment:
5. Learning of attitudes within the family and school usually takes place by association, through reward and punishment and through modelling.
6. (ii) Reference Groups:

Attitudes towards various topics such as political, religious and social groups, occupations, national and other issues are developed through reference groups. This is learning by reward and punishment. (iii) Personal Experiences: Personal experience can bring a drastic change in our attitude.

1. Here is a real-life example.
2. A driver in the army went through a personal experience that transformed his life.
3. On one mission, he narrowly escaped death although all his companions got killed.
4. He gave up his job in the army and worked actively as a community leader.
5. Through a purely personal experience the individual evolved a strong positive attitude towards community enlistment.

(iv) Media-related Influences: Media can exert both good and bad influences on attitudes. On one hand, the media and internet make people better informed Attitude and Social Cognition than other modes- of communication while on the other hand it can create negative attitudes in people.3.

How did Fritz Heider propose the Process of Attitude Change? Ans. The concept of balance proposed by Fritz Heider is described in the form of P-O-X triangle. Attitude changes if there is a state of imbalance between P-O attitude and O-X attitude and P-X attitude. For example, in the study of attitude towards dowry (X), a person (P) has a positive attitude towards dowry (P-X positive).

P is planning to get his son married to the daughter of some person (O) who has a negative attitude towards dowry (O-X negative). Here P-X is positive, O-P is positive but O-X is negative. That is, there are 2 positives and 1 negative in the triangle. This is a situation of imbalance.

• Imbalance on POX triangle is found when:
• (i) All three sides of the POX triangle are negative or
• (ii) Two sides are positive and one side negative.
• Balance is found when:
• (i) All three sides are positive or
• (ii) Two sides are negative and one side is positive.
• 4. Discuss the theory of Cognitive Dissonance:
• Ans.

The concept of cognitive dissonance was proposed by Leon Festinger. If an individual finds that two cognitions in an attitude are dissonant, then one of them will be changed in the direction of consonance.

1. For example, consider the case of the following two ideas or cognitions:
2. Cognition 1:
3. Pan masala causes mouth cancer which is fatal.
4. Cognition 2:
5. I eat pan masala.

Here, the two ideas are dissonant in the attitude towards pan masala. Therefore, one of these ideas will have to be changed so that consonance can be attained. Thus, to remove or reduce the dissonance, change Cognition 2. Thus Cognition 2 will become: I will stop eating pan masala.

• Both balance and cognitive dissonance are examples of cognitive consistency.
• Cognitive consistency means that two components, aspects or elements of the attitude or attitude system must be in the same direction.5.
• Discuss two-step Theory of Attitude Change: Ans.
• The two-step concept of attitude change was proposed by an Indian psychologist, S.M.

Mohsin:

• Step 1:
• The target changes his attitude by identifying with the source.
• Step 2:

The source shows an attitude change towards the attitude object. The target also shows an attitude change. This is a kind of imitation or observational learning. For example, Step 1: Preeti reads in newspaper that a particular soft drink she enjoys is harmful.

1. 6. Examine four ways through which People bring Consistency in their Attitudes:
2. Ans.
3. There is consistency between attitudes and behaviour in the following cases:
4. (i) The attitude is strong, and occupies a central place in the attitude system.

(ii) The person is aware of his attitude, and there is no external pressure for the person to behave in a particular way. For example, there is no group pressure to follow the norm.

• (iii) The person’s behaviour is not being watched or evaluated by others.
• (iv) The person thinks that the behaviour would have a positive consequence.
• 7. Discuss the sources of Prejudice:
• Ans.
• The sources of prejudice are:
• (i) Learning:

Prejudices are learned through association, reward and punishment, observing others, group or cultural norms and exposure to information. Family, reference groups, personal experiences and media play a role in the learning of prejudices. Prejudiced persons show low adjusting capacity, anxiety and feelings of hostility against out-group.

1. (ii) A strong social Identity and In-group Bias:
2. Prejudiced individuals who have a strong social identity and have a very positive attitude towards their own group boost this attitude by holding negative attitudes towards other groups.
3. (iii) Scapegoating:

The majority group places the blame on a minority out-group for its social, economic and political problems. Scapegoating is a group- based way of expressing frustration and it often results in negative attitudes or prejudices against the weaker group.

• (iv) Kernel of truth Concept:
• People continue to hold stereotypes because they think that after all, there must be some truth or ‘kernel of truth’ in what everyone says about the other group.
• (v) Self-fulfilling Prophecy:

The target group may behave in ways that justify the prejudice, i.e. conform the negative expectations which may thus strengthen the existing prejudice.

1. 8. State the Strategies for overcoming Prejudice:
2. Ans.
3. The strategies for handling prejudice would be effective if they aim at:
4. (i) Minimising opportunities for learning prejudices;
5. (ii) Changing such attitudes;
6. (iii) De-emphasising a narrow social identity based on in-group; and
7. (iv) Discouraging the tendency towards self-fulfilling prophecy among the victims of prejudice.
8. These goals can be accomplished through:
9. (i) Education and information dissemination, for correcting stereotypes related to specific target groups and tackling the problem of strong in-group bias;
10. (ii) Increasing inter-group contact allows for direct communication, removal of mistrust between the groups and discovery of positive qualities in the out-group. This strategy is successful if,
11. (a) The two groups meet in a cooperative rather than competitive context;
12. (b) Close interactions between the groups help them to know each other better; and
13. (c) The two groups are not different in power or status.
14. (iii) Highlighting individual identity rather than group identity.
15. 9. Discuss the Concept of Impression Formation with the help of Examples:
16. Ans.

When we meet people, we make inferences about their personal qualities. This is impression formation. For example, if a person is good looking we form impressions that the person would be sincere and hard-working. The person who forms the impression is called the perceiver. The individual about whom the impression is formed is called the target.

• Impression Formation Consists of the following Sub-processes:
• (i) Selection:
• Information is collected about target person.
• (ii) Organisation:
• Information is combined.
• (iii) Inference:
• A conclusion is drawn about the kind of person the target is.
• 10. Explain the factors that Influence Impression Formation:
• Ans.
• Impression formation is Influenced by:
• (i) Nature of information available to the perceiver;
• (ii) Social schemas in the perceiver (including stereotypes);
• (iii) Personality characteristics of the perceiver; and
• (iv) Situational factors.

11. Why do Individuals show better Performance in the Presence of Others?

1. Ans.
2. Individuals show better performance in the presence of others because of the following reasons:
3. (i) The individual experiences arousal in the presence of others which makes him react in a more intense manner.

(ii) The person feels that he would be evaluated. The idea of evaluation apprehension makes him to perform well and avoid mistakes. (iii) The nature of task affects performance in the presence of others. In case of a simple or familiar task, the person is more sure of performing well than in case of a complex or new task, the person may be afraid of making mistakes.

(iv) If others are performing the same task, there is a situation of co-action where there is social comparison and competition and hence performance is better. (v) If individuals are working together in a large group, a phenomenon of social loafing occurs based on the diffusion of responsibility in which there is a reduction of individual effort when working on a collective task.

: Attitude and Social Cognition (Questions & Answers)

What is an example of valence in motivation?

Vroom expectancy motivation theory Whereas Maslow and Herzberg look at the relationship between internal needs and the resulting effort expended to fulfil them, Vroom’s expectancy theory separates effort (which arises from motivation), performance, and outcomes.

Vroom’s expectancy theory assumes that behavior results from conscious choices among alternatives whose purpose it is to maximize pleasure and to minimize pain. Vroom realized that an employee’s performance is based on individual factors such as personality, skills, knowledge, experience and abilities.

He stated that effort, performance and motivation are linked in a person’s motivation. He uses the variables Expectancy, Instrumentality and Valence to account for this. Expectancy is the belief that increased effort will lead to increased performance i.e.

1. Having the right resources available (e.g. raw materials, time)
2. Having the right skills to do the job
3. Having the necessary support to get the job done (e.g. supervisor support, or correct information on the job)

Instrumentality is the belief that if you perform well that a valued outcome will be received. The degree to which a first level outcome will lead to the second level outcome.i.e. if I do a good job, there is something in it for me. This is affected by such things as:

1. Clear understanding of the relationship between performance and outcomes – e.g. the rules of the reward ‘game’
2. Trust in the people who will take the decisions on who gets what outcome
3. Transparency of the process that decides who gets what outcome

Valence is the importance that the individual places upon the expected outcome. For the valence to be positive, the person must prefer attaining the outcome to not attaining it. For example, if someone is mainly motivated by money, he or she might not value offers of additional time off.

• The three elements are important behind choosing one element over another because they are clearly defined: effort-performance expectancy (E>P expectancy) and performance-outcome expectancy (P>O expectancy).
• E>P expectancy: our assessment of the probability that our efforts will lead to the required performance level.
• P>O expectancy: our assessment of the probability that our successful performance will lead to certain outcomes.
• Crucially, Vroom’s expectancy theory works on perceptions – so even if an employer thinks they have provided everything appropriate for motivation, and even if this works with most people in that organisation, it doesn’t mean that someone won’t perceive that it doesn’t work for them.
• At first glance expectancy theory would seem most applicable to a traditional-attitude work situation where how motivated the employee is depends on whether they want the reward on offer for doing a good job and whether they believe more effort will lead to that reward.
• However, it could equally apply to any situation where someone does something because they expect a certain outcome. For example, I recycle paper because I think it’s important to conserve resources and take a stand on environmental issues (valence); I think that the more effort I put into recycling the more paper I will recycle (expectancy); and I think that the more paper I recycle then less resources will be used (instrumentality)
• Thus, Vroom’s expectancy theory of motivation is not about self-interest in rewards but about the associations people make towards expected outcomes and the contribution they feel they can make towards those outcomes.

: Vroom expectancy motivation theory

What is an example of valence and arousal?

Emotional valence and arousal effects on memory and hemispheric asymmetries , October 2010, Pages 10-17 Emotions have been conceptualized as action dispositions that vary along valence and arousal dimensions (Lang, Bradley, & Cuthbert, 1990). Valence refers to the pleasant–unpleasant quality of a stimulus and ranges from negative to positive, whereas arousal refers to the intensity of a stimulus and ranges from dull to arousing (Heilman, 1997).

• Using this bi-dimensional or circumplex model, one can see how emotions are defined.
• For example, anger and sadness are both negative in valence, but anger is high in arousal, whereas sadness is low in arousal (Heilman, 1997; see Fig.1).
• Over the past several decades, neuropsychological research has produced two theories regarding the processing of emotion: the right hemisphere (RH) and valence–arousal models (Killgore & Yurgelun-Todd, 2007).

The RH model was based upon early experimental and clinical studies, which found that all emotions, regardless of their valence, were processed preferentially by RH systems (Borod et al., 1998, Lang et al., 1990). Alternatively, the valence–arousal model of emotion, an integration of the RH and approach–withdrawal (Davidson, 1992) models, proposes that RH prefrontal systems are biased toward negative valences, RH parietal systems are biased toward arousal, and left hemisphere (LH) prefrontal systems are biased toward positive valences (Heller, Nitschke, & Lindsay, 1997).

• To date, findings in the literature regarding the processing of emotion are mixed, with some supporting the RH model (e.g., Borod et al., 1998, Nagae and Moscovitch, 2002), and some supporting the valence–arousal model (e.g., Ali and Cimino, 1997, Tamagni et al., 2009).
• We propose that the inconsistencies in the literature regarding hemispheric asymmetries for emotional stimuli can be explained, at least in part, by methodological differences between studies that may have resulted in the recruitment of various hemispheric regions (Borod, 1993).

One aim of this study was to provide a well-controlled test of these two theories while also extending the literature. A possible explanation for the mixed findings in the literature pertaining to the hemispheric lateralization of emotional stimuli concerns theoretical considerations.

For example, it has been proposed that stage of processing affects hemispheric asymmetries such that the RH model holds primarily at the stage of perceptual identification, whereas the valence–arousal model holds primarily at the stage of response preparation, suggesting that hemispheric asymmetries may shift over time within one or more cognitive tasks (Root, Wong, & Kinsbourne, 2006).

Specifically, this hypothesis predicts that RH systems mediate perceptual identification tasks, but that the hemispheres diverge in their specializations at the response preparation stage, with RH systems mediating responses to negative emotional stimuli and LH systems mediating responses to positive emotional stimuli.

1. Root et al.
2. Have contended that tasks employed in previous studies in the literature may have conflated these two successive stages, resulting in an integration or additive effect of the RH and valence–arousal models.
3. These hypotheses have been corroborated by several studies employing nonverbal stimuli (e.g., Maxwell and Davidson, 2007, Root et al., 2006) and form the basis of the approach-withdrawal model of emotion (Davidson, 1992), which states that RH motivational systems mediate withdrawal-related behaviors and LH motivational systems mediate approach-related behaviors.

In contrast to what one would expect given these findings, Nagae and Moscovitch (2002) found LH superiority during a perceptual identification task using both emotional and non-emotional words. Accordingly, it is not clear whether this stage-of-processing analysis can be applied to the processing of emotional verbal stimuli and what predictions would be made regarding memory tasks or processes such as encoding, retention, and retrieval (Root et al., 2006).

Several studies have examined the RH and valence–arousal models within the context of explicit emotional memory. These studies too yielded conflicting findings. Consistent with the RH model, Graves, Landis, and Goodglass (1981) found greater recognition accuracy for positive and negative words relative to neutral words presented to the left visual field (LVF)/RH.

Similarly, Nagae and Moscovitch (2002) found that positive and negative words presented to the LVF/RH were both recognized with greater accuracy than neutral words presented to the LVF/RH and that recognition was equal for positive, negative, and neutral words presented to the right visual field (RVF)/LH.

More recently, using a lexical decision paradigm, Landis (2006) found a LVF/RH bias for the processing of emotional words. Like the study by Graves et al., however, the study by Landis did not examine differences in the processing of negative and positive words separately, and as such, did not provide sufficient data with which to evaluate the valence–arousal model.

Consistent with the valence–arousal model, Alfano and Cimino (2008) found that memory for consonant trigrams presented to the LVF/RH was best when preceded by a negative word prime and that memory for consonant trigrams presented to the RVF/LH was best when preceded by a positive word prime.

Ali and Cimino (1997) found that positive words presented to the RVF/LH were recognized with greater accuracy than neutral or negative words presented to the RVF/LH and that negative words presented to the LVF/RH were recognized with greater accuracy than positive or neutral words presented to the LVF/RH.

A separate, but related, literature has found enhanced memory for emotional stimuli relative to non-emotional stimuli (e.g., Buchanan et al., 2001, Talarico et al., 2004). The arousal properties of stimuli-to-be-remembered seem to play a role in this phenomenon, such that stimuli that are more arousing (e.g., taboo words) tend to be recalled with greater accuracy than stimuli that are less arousing (e.g., Buchanan et al., 2006, Talarico et al., 2004).

• Emotional arousal has been found to modulate the memory consolidation process such that patients with either amygdala surgically resectioned failed to show the same memory enhancement for emotionally arousing words as healthy controls (LaBar & Phelps, 1998).
• Propranolol, a beta-adrenergic antagonist that reduces sympathetic arousal, has been found to impair short- and long-term retention of emotionally arousing material (e.g., Maheu, Joober, Beaulieu, & Lupien, 2004) whereas epinephrine or glucocorticoids, stress hormones that increase sympathetic arousal, have been found to enhance long-term retention of emotionally arousing material (Kensinger and Corkin, 2003, Roozendaal et al., 2008) Further, arousal may play a greater role than valence in long-term retention of emotionally arousing material (Nielson & Powless, 2007).

Arousal has also been associated with hemispheric asymmetries. For example, individuals with relatively high levels of anxiety (associated with arousal – Lang et al., 1990) have been found to demonstrate altered hemispheric asymmetries (e.g., Heller et al., 1995, Shackman et al., 2006).

• Heller et al., 1995, Heller et al., 1997 have found that individuals with severe anxiety, as measured by two anxiety scales, showed greater LVF/RH biases on a chimeric faces task compared with individuals with little or no anxiety.
• Shackman et al.
• Found that a threat of shock resulted in poorer performance on a visuospatial working memory task (considered to be mediated preferentially by the RH) but no change in performance on a verbal working memory task (considered to be mediated preferentially by the LH) when compared to no threat of shock.

As such, a failure to control for normative arousal in previous studies may have recruited various hemispheric systems to help complete the tasks; for example, RH systems may have been recruited to a greater extent than LH systems (Heller et al., 1995, Heller et al., 1997).

• To our knowledge, only one previous study has examined hemispheric asymmetries for emotional memory while systematically controlling for the normative arousal properties of the stimuli used (Alfano & Cimino, 2008).
• Although the positive, negative, and neutral words in that study were equated with respect to normative arousal, the emotion words were not the to-be-remembered-stimuli; instead, the to-be-remembered stimuli were consonant trigrams.

Additionally, replications employing basic emotional memory paradigms have not yet been made. The mixed findings in this literature may also be due, at least in part, to methodological differences. For example, differences in samples and paradigms may have produced the disparate findings (Borod, 1993).

1. More specifically, previous studies have not equated emotion words-to-be-remembered with regard to normative arousal ratings.
2. In order to examine the influence of emotional valence on memory, it has been proposed that normative arousal ratings be controlled systematically (Lang et al., 1990).
3. In previous studies, negative words might have been higher in arousal than positive words if investigators did not equate them on normative arousal, resulting in better memory for the negative words.

Traditionally, studies in this literature have controlled for other characteristics of their words-to-be-remembered (e.g., word length, concreteness, frequency with which the word is used in everyday situations) but have not considered the effects of normative arousal ratings on subsequent memory.

Although Ali and Cimino (1997) and Nagae and Moscovitch (2002) used emotion (i.e., positive and negative) and neutral words in their studies, neither controlled for the normative arousal ratings of the words, and these studies produced support for the valence–arousal model and for the RH model, respectively.

The findings in the literature may also suggest that the neural system underlying the processing of emotional verbal stimuli is more complex than indicated by either the RH or valence–arousal models. Using functional magnetic resonance imaging (fMRI), Killgore and Yurgelun-Todd (2007) tested the RH and valence–arousal models and, after finding partial support for both models, proposed that the neural systems specified by the two models operate concurrently or in parallel.

According to this proposal (herein referred to as the “integrated model”), the RH is biased toward the processing of all emotions, regardless of their valence, and especially for the processing of negative emotions. Specifically, the model predicts that, during LVF/RH presentations, positive and negative stimuli will both activate posterior RH systems relative to baseline, but that negative stimuli will activate these systems to a greater extent relative to LH systems because of a RH bias for the processing of negative emotion.

This integrated model merges the RH model, which proposes that the RH is biased toward all emotions, with one prediction of the valence–arousal model, that RH systems are biased toward negative emotions. It does not, however, include the other prediction of the valence–arousal model, that LH systems are biased toward the processing of positive emotions.

This integrated model is partially consistent with the proposal that some tasks may conflate early and late stages of processing, thus affecting hemispheric asymmetries and data interpretation (Root et al., 2006). Because participants in the study by Killgore and Yurgelun-Todd were tested using a facial emotion recognition task, it is uncertain whether this integrated model will generalize to the processing of emotional verbal stimuli.

Another aim of this study was to provide an exploratory test of this model of emotion processing using emotional verbal stimuli. In this study, we examined predictions based upon the RH model, the valence–arousal model, and an integrated model (Killgore & Yurgelun-Todd, 2007) of emotion processing by building upon the methodologies of previous studies in the literature.

To examine whether the inconsistencies in the literature concerning hemispheric asymmetries for emotional verbal stimuli are due to valence and arousal effects, we tested recall and recognition for positive, negative, and neutral verbal stimuli presented, one at a time, to either visual field. The positive and negative words in this study were equated on arousal ratings, and both emotion word lists were designed so that emotion words were higher in mean arousal ratings than were the neutral words.

Following the presentation of all the words, participants were asked to write down as many words as they could remember (i.e., free recall test). Then they were given a standard old–new recognition test, in which they were asked to identify whether each word occurred in the original presentation.

If the RH model is correct and RH systems are biased toward the processing of all emotions, regardless of their valence, then we would expect to find more accurate recall and recognition of negative and positive words presented to the LVF/RH than neutral words presented to the LVF/RH. In addition, we would expect to find equal or more accurate recall and recognition of neutral than negative and positive words presented to the RVF/LH.

If the valence–arousal model is correct and LH systems are biased toward positive valences and RH systems are biased toward negative valences, then we would expect to find greater recall and recognition of negative words presented to the LVF/RH than of neutral and positive words presented to the LVF/RH and greater recall and recognition of positive words presented to the RVF/LH than of neutral and negative words presented to the RVF/LH.

If the integrated model of emotion processing (Killgore & Yurgelun-Todd, 2007) is correct and RH systems are biased toward the processing of all emotions, and especially negative emotions, then we would expect to find more accurate recall and recognition of emotional (i.e., negative and positive) words presented to the LVF/RH than of neutral words presented to the LVF/RH.

Further, we would also expect to find greater recall and recognition of negative words presented to the LVF/RH than of neutral and positive words presented to the LVF/RH. Participants in this study included 39 right-handed female undergraduates who participated for course credit.

Males were excluded from this study based on research that suggests that asymmetries for emotional processing differ between men and women (Canli et al., 2002, Graves et al., 1981, Proverbio et al., 2006). Females, rather than males, were chosen because they comprised the majority of the participant pool.

Only those scoring between 13 and 17 on the Chapman Handedness Inventory (Chapman Participants’ eye movements were counted post hoc by two independent raters. Inter-rater reliability was high, intraclass r = 0.99. Across raters, the average number of eye movements was 33.26, with a range of 3–88 eye movements (out of a possible total of 94 eye movements).

• Accordingly, across participants, eye movements occurred on 35% of all trials.
• Rather than exclude participants who made a certain percentage of eye movements (Ali & Cimino, 1997), we chose to examine frequency of eye In this study, we examined the RH model, the valence–arousal model, and a recently proposed integrated model of emotion processing within the context of explicit memory for emotional verbal stimuli.

Some of our findings were consistent with one or more of the three models, some were not consistent with any of the models, and some provided evidence against the valence–arousal and integrated models. As such, our findings best support an integrated model of emotion processing that is more complex In sum, we built upon methodologies of previous studies examining explicit memory for emotional verbal stimuli and found support for both the RH and valence–arousal models of emotion processing.

S. Windmann et al. C. Tamagni et al. M. Spokas et al. L.C. Robertson et al. L.C. Robertson et al. K.A. Nielson et al. S. Nagae et al. D.L. Na et al. J. Levy et al. T. Landis

R. Graves et al. R.J. Davidson L. Cohen et al. L.J. Chapman et al. T.W. Buchanan et al. M. Brázdil et al. K.M. Alfano et al. N. Ali et al. L.F. Barrett et al. K.J. Bellamy et al. J.C. Borod J.C. Borod et al. V.J. Bourne M.M. Bradley et al.

The risk of sudden cardiac death is remarkably high in patients with bipolar disorder. The risk is especially elevated, 32 times greater than in the general population, in patients younger than 30 years old. Early atherosclerosis and general lifestyle risk factors might play a role but are insufficient to account for this greatly inflated risk of sudden cardiac death. A recent mechanistic model of the pathophysiology of bipolar disorder highlights dysfunctional changes in functional hemispheric lateralization that are proposed to arise as a consequence of aberrant midbrain signaling at the level of the paraventricular nucleus of the hypothalamus. The paraventricular nucleus is also a key region involved in regulating bodily physiology, including the balanced autonomic control of cardiac function. Further, the sympathetic innervation of the heart is configured anatomically such that exaggerated imbalanced right-left stimulation of the heart can predispose to fatal arrhythmia. Correspondingly, lateralization of cortical activity related to emotional arousal and acute stress, if not otherwise corrected, may drive asymmetric sympathetic effects on the heart. In cardiac patients, asymmetric midbrain lateralization is associated with increased proarrhythmic changes during mental and physical stress challenges, predisposing vulnerable patients to lethal arrhythmias. Here, we propose that similar mechanisms involving lateralized brain–heart interactions account for the increased risk of sudden cardiac death in patients with bipolar disorder. We explore the evidence for, and implications of, this model. Approximately 45 million people worldwide are diagnosed with bipolar disorder (BD). While there are many known risk factors and models of the pathologic processes influencing BD, the exact neurologic underpinnings of BD are unknown. We attempt to integrate the existing literature and create a unifying hypothesis regarding the pathophysiology of BD with the hope that a concrete model may potentially facilitate more specific diagnosis, prevention, and treatment of BD in the future. We hypothesize that dysfunctional signaling from the parvocellular neurons of the paraventricular hypothalamic nucleus (PVN) results in the clinical presentation of BD. Functional damage to this nucleus and its signaling pathways may be mediated by myriad factors (e.g. immune dysregulation and auto-immune processes, polygenetic variation, dysfunctional interhemispheric connections, and impaired or overactivated hypothalamic axes) which could help explain the wide variety of clinical presentations along the BD spectrum. The neurons of the PVN regulate ultradian rhythms, which are observed in cyclic variations in healthy individuals, and mediate changes in functional hemispheric lateralization. Theoretically, dysfunctional PVN signaling results in prolonged functional hemispheric dominance. In this model, prolonged right hemispheric dominance leads to depressive symptoms, whereas left hemispheric dominance correlated to the clinical picture of mania. Subsequently, physiologic processes that increase signaling through the PVN (hypothalamic-pituitaryadrenal axis, hypothalamic- pituitary-gonadal axis, and hypothalamic-pituitary-thyroid axis activity, suprachiasmatic nucleus pathways) as well as, neuro-endocrine induced excito-toxicity, auto-immune and inflammatory flairs may induce mood episodes in susceptible individuals. Potentially, ultradian rhythms slowing with age, in combination with changes in hypothalamic axes and maturation of neural circuitry, accounts for BD clinically presenting more frequently in young adulthood than later in life. Previous research has attributed to the right hemisphere (RH) a key role in eliciting false memories to visual emotional stimuli. These results have been explained in terms of two right-hemisphere properties: (i) that emotional stimuli are preferentially processed in the RH and (ii) that visual stimuli are represented more coarsely in the RH. According to this account, false emotional memories are preferentially produced in the RH because emotional stimuli are both more strongly and more diffusely activated during encoding, leaving a memory trace that can be erroneously reactivated by similar but unstudied emotional items at test. If this right-hemisphere hypothesis is correct, then RH damage should result in a reduction in false memories to emotional stimuli relative to left-hemisphere lesions. To investigate this possibility, groups of right-brain-damaged (RBD, N = 15), left-brain-damaged (LBD, N = 15) and healthy (HC, N = 30) participants took part in a recognition memory experiment with emotional (negative and positive) and non-emotional pictures. False memories were operationalized as incorrect responses to unstudied pictures that were similar to studied ones. Both RBD and LBD participants showed similar reductions in false memories for negative pictures relative to controls. For positive pictures, however, false memories were reduced only in RBD patients. The results provide only partial support for the right-hemisphere hypothesis and suggest that inter-hemispheric cooperation models may be necessary to fully account for false emotional memories. This study examined the effects of right brain-damage (RBD) on oral discourse production using a multi-layered discourse processing model. Narrative and procedural discourse samples from participants with RBD and no brain damage were analysed in terms of seven broad areas corresponding to the processing levels of the model. Participants also completed attention, cognitive, general communication and RBD assessments. Despite their normal performance on all assessments (except those on attention), the participants with RBD demonstrated statistically significant differences in syntactic complexity, clarity disruptors and dysfluencies, as well as in discourse grammar and clausal structure in the narratives and in cohesion in the procedures. A model-based theoretical explanation accounting for the deficits noted in participants with RBD, together with clinical guidelines, is provided. Recent neuropsychological studies have attempted to distinguish between different types of anxiety by contrasting patterns of brain organisation or activation; however, lateralisation for processing emotional stimuli has received relatively little attention. This study examines the relationship between strength of lateralisation for the processing of facial expressions of emotion and three measures of anxiety: state anxiety, trait anxiety and social anxiety. Across all six of the basic emotions (anger, disgust, fear, happiness, sadness, surprise) the same patterns of association were found. Participants with high levels of trait anxiety were more strongly lateralised to the right hemisphere for processing facial emotion. In contrast, participants with high levels of self-reported physiological arousal in response to social anxiety were more weakly lateralised to the right hemisphere, or even lateralised to the left hemisphere, for the processing of facial emotion. There were also sex differences in these associations: the relationships were evident for males only. The finding of distinct patterns of lateralisation for trait anxiety and self-reported physiological arousal suggests different neural circuitry for trait and social anxiety.

Despite the important theoretical and applied implications, there is limited experimental research investigating the influence of emotional valence on young children’s verbal recall of everyday emotional experiences. This issue was addressed in the current study. Specifically, we investigated young children’s (5–6 years) recall of emotional experiences presented in six brief stories. To address methodological limitations of the small body of existing literature, we adopted a within participants design in which story content was matched and valence (positive, negative, neutral) was counterbalanced across stories. Fifty-four children were presented the six stories via narrated slideshow, and recall was assessed after delay. Results showed that emotional stories were better recalled than neutral stories and negatively valenced stories were better recalled than positively valenced stories. The recall advantage of negatively valenced information was found for all aspects of each story, suggesting that negative valence renders events particularly memorable. Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%. Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal–valence (AV) dimensional space. The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories. Emotion recognition can be achieved by speech recognition, the judgment of limb movements, analysis of Electrooculogram (EOG) or capturing of facial expressions. However, those types of emotion recognition methods cannot detect human emotion well, because humankind can use fake body movement and words to hide real emotions. In this paper, we proposed an EEG-based emotion classification method based on Bidirectional Long Short-Term Memory Network (BiLSTM). Electroencephalogram (EEG) signal can detect human emotion correctly because human represent their real emotions in their mind and cannot hide emotions there. Meanwhile, EEG is a time sequence signal which needs a model which can deal with this type of data. Therefore, we chose Long Short-term Memory Network to process the EEG signal. In particular, we used an improvement version of LSTM model BiLSTM to manage the signals. BiLSTM can processes input data from front to back and back to front. Meanwhile, BiLSTM can store important information and forget unnecessary information; therefore, this process increases the accuracy of the model. Our method classifies four discrete classifications (happy, sad, fear, and neutral) for emotion classification, which achieves competitive performance compared with other conventional emotion classification methods. The final experimental results show that we can achieve an accuracy of 84.21% for four emotional states classification by using our method. Human computer interaction is increasingly utilized in smart home, industry 4.0 and personal health. Communication between human and computer can benefit by a flawless exchange of emotions. As emotions have substantial influence on cognitive processes of the human brain such as learning, memory, perception and problem solving, emotional interactions benefit different applications. It can further be relevant in modern health care especially in interaction with patients suffering from stress or depression. Additionally rehabilitation applications, guiding patients through their rehabilitation training while adapting to the patients emotional state, would be highly motivating and might lead to a faster recovery. Depending on the application area, different systems for emotion recognition suit different purposes. The aim of this work is to give an overview of methods to recognize emotions and to compare their applicability based on existing studies. This review paper should enable practitioners, researchers and engineers to find a system most suitable for certain applications. An entirely contact-less method is to analyze facial features with the help of a video camera. This is useful when computers, smart-phones or tablets with integrated cameras are included in the task. Smart wearables provide contact with the skin and physiological parameters such as electro-dermal activity and heart related signals can be recorded unobtrusively also during dynamical tasks. Next to unimodal solutions, multimodal affective computing systems are analyzed since they promise higher classification accuracy. Accuracy varies based on the amount of detected emotions, extracted features, classification method and the quality of the database. Electroencephalography achieves 88.86 % accuracy for four emotions, multimodal measurements (Electrocardiography, Electromyography and bio-signals) 79.3 % for four emotive states, facial recognition 89 % for seven states and speech recognition 80.46 % for happiness and sadness. Looking forward, heart-related parameters might be an option to measure emotions accurately and unobtrusive with the help of smart wearables. This can be used in dynamic or outdoor tasks. Facial recognition on the other hand is a useful contact-less tool when it comes to emotion recognition during computer interaction. In this study, electroencephalography-based data for emotion recognition analysis are introduced. EEG signals were collected from 28 different subjects with a wearable and portable EEG device called the 14-channel EMOTIV EPOC+. Subjects played 4 different computer games that captured emotions (boring, calm, horror and funny) for 5 min, and the EEG data available for each subject consisted of 20 min in total. The subjects rated each computer game based on the scale of arousal and valence by applying the SAM form. We provide both raw and preprocessed EEG data with.csv and. mat format in our data repository. Each subject’s rating score and SAM form are also available. With this work, we aim to provide an emotion dataset based on computer games, which is a new method in terms of collecting brain signals. Additionally, we want to determine the success of the portable EEG device and compare the success of this device with classical EEG devices. Finally, we perform pattern recognition and signal-processing methods to observe the performance of our dataset and to classify EEG signals based on the arousal-valence emotion dimension and positive/negative emotions. The database will be publicly available, and researchers can use the dataset for analyzing signals for their own proposed method in the literature.

: Emotional valence and arousal effects on memory and hemispheric asymmetries