Trauma Reflection Paper

3 minutes read

Veneta Callpani, MA, BS, RDMS

When we talk about trauma, the neurobiology foundation is important to understand what happens to our brain and body when we are traumatized. With the advanced technology, scanning the brain with PET or fMRI helps scientists to track in real time the activity of the brain. Bessel Van der Kolk describes a couple experiments of how the brain responded during the re-introduction of the trauma memory months or even years after the traumatic event took place (van der Kolk, 2015). There were 3 regions of the brain that changed activity during the re-experience of trauma: limbic system, visual cortex, and Broca’s area. The surprising part of the scan was that Broca’s area went offline whenever flashbacks were triggered (van der Kolk, 2015). This explains why individuals have a difficult time describing what happened to them. Also, the difference between the right and left-brain sheds light on the idea that trauma shuts down part of the left brain (the rational part), therefore it is hard to make sense of time or understand that they are re-experiencing their trauma (van der Kolk, 2015).

One assumption of the socioecological model of trauma is that the majority of people won’t access clinical care after being traumatized. This can be explained by the lack of awareness individuals have about their traumatic experience. Even if at some point they do recognize their trauma, it is very hard for them to express it in words. This should be taken into consideration when thinking about the treatment or therapeutic approach. This is why I think EMDR can be a very good approach when targeting memories, and trauma that is “stuck” and unprocessed in the brain. Other alternatives can be somatic therapy, bodywork, or even acupuncture in cases when an individual’s response to trauma is numbness. Clients often share in therapy: “I wish I can cry. For some reason I just can’t. I feel numb”.

Neuroplasticity is one of the great features of our brain. It’s how we learn, how we create memories. But this very great feature that is necessary for our survival, our life and experience, can also work against us when it comes to trauma. As van der Kolk says, the repetition of the circuit firing can become a default, and if the repetition is trauma, then we get stuck and we keep experiencing the traumatic event/memory as if it’s happening now. The good news is that this allows our brain to “unlearn” the maladaptive coping mechanism by rewiring itself. Therefore, I would argue that when it comes to trauma, the experience is more important than the insights. So, the therapeutic relationship, and the experience of the therapy is more important than the talk and the insights that the client can get. I personally think that as a therapist, we should first focus on creating a positive experience, and a trusting therapeutic relationship before starting to work with trauma. This positive experience can be used as an anchor for the client to bring them back to the present and make them feel safe, once we start diving into the trauma work.

Something to keep in mind is that the therapist can also be a trigger of the client’s trauma and there might be manifestation of problems with trust (i.e., sexual abuse survivors), and the premature disclosure of trauma can induce re-traumatization, and leave the client feeling overexposed.

Reference

 Van der Kolk, B. (2015). The body keeps the score: Mind, brain and body in the transformation of trauma. Penguin Books.

https://mch.umn.edu/resources/mhecomodel/

WHY DO WE “AVOID” THERAPY

4 minutes read

“I don’t need therapy”, “I did three sessions and I didn’t see any changes”, “I would rather talk to a friend for free” “Who has time for that?” “I would rather go shopping” “I don’t trust the therapist” “It’s too expensive” “I have no problems; I am very happy”

The list can go on, and so can the reasons why therapy is good. However, I will focus on what I think is a good start to learn and give you some questions to reflect on.

First, let me start by validating all your feelings around therapy. Yes, it can be expensive, yes you can talk to a friend for free, yes, it is possible not to trust the therapist and whatever your priorities are, you have a right to prioritize them in whatever way you want. I will take you on a neuroscience perspective journey (in a simplified way) to help you make sense of this.

As human beings we all have emotions, feelings and a language to express (sometimes limited). Having a friend, a family, a partner, or community that supports you it’s something I as a therapist not only encourage, but will also help you built that support system, because it is very important for your well-being. We all go through difficult moments and everything we go through (no matter of its intensity) will have an impact in our system (mind-brain-body). Language, communication, the narrative you create around your experiences, the memories, the interactions and connections (internal and external) will deeply affect your decision-making and whether or not you seek help. Let’s take an example: Anxiety. Despite the additional circuits, one important part of the brain involved in anxiety is amygdala. Amygdala will be activated by many external or internal events, and by the meanings we perceive regarding our life situations. Let’s call this our defense system that it’s there to protect us. An interesting fact about this system is that it also operates under the unconscious level. So, fear reactions can be elicited without our awareness (and these have been measured in experiments, – see reference). That means sometimes you might not be able to tell why you are anxious or in fear. Furthermore, because the fear/anxious reaction doesn’t reach your conscious level, you might be unable to tell someone (and the therapist) that you are experiencing anxiety. This influences your behavior; and of course, it’s understandable now that you might want to avoid therapy, because that feeling which sometimes is unconscious, is trying to protect you. Going to therapy means you will be facing some difficult feelings, emotions, trauma and things you are not even aware of. And of course, trusting a therapist is not a given. It is something that takes time and needs to be built, and I understand the resistance there. That resistance has helped you a lot in your life before. And I understand that you haven’t seen any change after three sessions; you are right, you can’t see the change after only 3 sessions (I will write another blog about the change process). It is expensive sometimes (it goes back to priorities, and this is relative) but there are so many other resources you can take advantages of. Me and my team are currently offering affordable therapy (see the link below) and supporting groups.

Now ask yourself these questions: What am I really trying to avoid when I refuse to seek therapy? What are the long-term benefits of the therapy that is worth investing my time, money, and dedication? What are some myths or stigma around therapy and how has that affected my beliefs? How can a therapist be more helpful than my friend or my support system? How committed am I on working on myself and make this my priority? Why am I so afraid of change?

Write your reflections down and bring it to your therapy sessions when you decide to seek therapy. You already have a lot to bring to the table!

Reference

Grawe, K., & Beitman, B. (2007). Neuropsychotherapy: How the neurosciences inform effective psychotherapy. L. Erlbaum.

Links

https://www.psychologytoday.com/us/therapists/veneta-callpani-long-island-city-ny/1038567

https://www.allytherapy.org/shop

When meditation and stillness doesn’t work

2 minutes read

Meditation has been shown to have a great benefit in reducing stress, anxiety, and focusing in the present. This increases our awareness and helps us look at our problems from another perspective. Sometimes this means we can separate the problem from ourselves and see it for what it is: just a problem. Doing so, it allows us to maintain our core identity and avoid labeling ourselves as damaged people who need to be fixed.

What happens when we think we are damaged, and we need to be fixed?

Well, this thinking makes us believe we cannot help ourselves. Because we are damaged, only someone else can fix us, and that means it is their responsibility. We end up giving the power to someone else. And why is this dangerous?

Because we might end up giving this power to the wrong people and that will only deepen our trauma, anxiety or whatever it is we are going through. However, even if we find the right people; even if we seek therapy; holding the belief that the therapist is responsible to fix you will not help. The good news is that the therapist can identify this unhealthy belief and will help you work it out.

Meditation and relaxing techniques are one method therapists will use to help you cope with anxiety; and of course, as I mentioned, meditation is very beneficial. But this method is not always effective. Context is the most important thing we as therapists have to always be mindful of. Dr. Stephen Porges talks about what he calls polyvagal theory, which means that our body not only remembers a traumatic experience, but it can also get stuck in the trauma response mode. So, if we are treating someone who has complex trauma, meditation and stillness can be a trigger, rather than a tool. If you are in a state of relaxation in your body this is translated as vulnerability, which means it is dangerous, and will activate your trauma response. Your body wants to escape.

The solution? One solution can be body movements techniques.

Knowing techniques, tools and different therapeutic approaches is not enough. Context, individual needs, and knowing how to integrate this knowledge and tools, is what makes the difference.

New manipulations to test stability bias and ease of processing effect on metamemory

9 minutes read

Abstract

The present study is an expansion on prior research about metamemory. New manipulations of word frequency and study repetition were introduced to test the ease of process and stability bias. Thirty-nine college students, native and non-native English speakers participated in the study. The data and results are consistent with stability bias hypothesis: students underestimated the word repetition because of their beliefs of the stability of their memory; but not with the ease of process ( the high frequency words had no significant effect). Further studies can be made to determine whether the non-native English speaker had an effect on this different result and see how language might be related to metamemory.  

Introduction

Metamemory is the prediction people make about their memory based on their judgements and belief they have about how memory operates. We use this every day even when we are not aware of it. When we say “I will never forget the year of Covid-19 pandemic” we are making a judgment about that specific memory, or when we decide not to review for the test one day prior is due to our belief that we already know all the information.

Metamemory is important because it drives our everyday decisions on what we need to learn, how long we need to study and where to focus our attention.  These beliefs can create biases which can lead to bad decisions. Because of the important role metamemory plays in our life, many research and experiments have been conducted to determine how accurate metamemory is, are our beliefs justified or biased and what factors might influence that bias. A double dissociation between metamemory and memory performance has been shown when testing the perceptual fluency hypothesis (Beksen&Mulligan 2013). The hypotheses stated that easily perceived items are predicted to be remembered better regardless of the actual memory. To test this hypothesis, researchers manipulated perceptual fluency by inducing perceptual interference using backward masks. The results supported the theory: participants predicted they would recall better words that they perceived as fluent, but they recalled better words where perceptual interference was added.  Another study that supports this hypothesis and shows that fluency affects both metacognition and metamemory was conducted by Carpetner, Wilford, Kornell & Mullaney, (2013). They separated two groups of students and had them view two videos; one fluent lecture and one disfluent version of the same lecture by the same professor. The group of students who viewed the fluent lecture predicted that they learned more  compared to the other group, however, the actual memory performance did not differ as a function of the lecture fluency. Kornell, Rhodes, Caste, & Tauber (2011) suggested that people think their memories are much more stable than they really are. This is called stability bias and to test this, they conducted two experiments where participants had to memorize a list of words. The study design featured two variables: type size (small and large) and number of study trials (one or two). Participants were given instructions, then asked to estimate the chance of recalling each word. The results were as expected. Participants perceived the large type size words as fluent, easier to remember therefore they predicted a high percentage of recall, but the number of study trials only affected the actual recall. Next, they added 3 additional chances to study the words. The recall prediction was low compared with the actual recall. Participants underestimated the importance of additional study trials.

Based on previous studies, considering that the ease of process is correct, then anything that influences ease of process will influence the judgment of learning. If we perceive something as easy to process, then our belief that we have learned more increases. There is a way to expand this ease of processing hypothesis by testing a new manipulation. The goal of this experiment is to test how new manipulation such as word frequency affects the ease of process, and how study repetition manipulation is related to the stability bias. We expect that word frequency will be perceived as easy to remember, therefore participants will predict to recall more of those words, and they will not take in consideration the announcement of repetition words because they believe their memory is much more stable than it really is.

Methods

Participants

Thirty-nine undergraduate students in Cognitive Psychology class participated in a study designed to test metamemory. The academic background of the students is similar: they are psychology majors, and/or biology- brain science track. The average age = 25.9. There were 16 native English speakers and 23 were either bilingual or non-native speakers of English.

Materials

The study was conducted online. All participants had access to the Blackboard Collaborate Ultra, and they all had a demo worksheet in front of them to complete. This was done synchronously. The worksheet had two columns, one was for the number of trials and the other one was for the estimated recall.  A list of 36 words in capital was presented to the students to study. These words selected varied in word frequency. The study list was organized in two parts : ½ of the high frequency words were presented one time in each list, and ½ of the low frequency words were presented two times in each list. A counterbalancing method was used to control the order effect. So, the order of words in list one was reversed in list two. Each word that was studied one time in list one, was studied two times in list two. Each word that was studied two times in list one, was studied one time in list 2.Participants were told to memorize these words, make an estimated recall of the words, and during the estimation they will be told whether they will get a 1 or 2 total chances to study each word.

Procedure

The students were provided with the demo worksheet and with the instructions for the experiment. Thirty-six words were presented with a note of whether each word will be repeated a second time or not. During the first presentation, students were asked to make an estimated call for each word expressed in a percentage from 0-100% chance of recalling that word later.  After studying these words and writing down the percentage of the estimated recall, a second presentation of the words that they were told to have a second chance opportunity to study, was shown to the students. After this, they were asked to write down as many words as they can remember in two minutes.  Next they were instructed to write down next to each word their estimation recall. Words order in first trial: legislate, history, morning, travesty, charade, courier, voucher, distance, flannel, article, original, careful, mistake, navigator, literary, fiasco, wardrobe, people, word, window, jamboree, trouble, physical, tonight, friend, scorpion, medicine, birthday, mustang, festival, serious, question, seep, obscenity, intestine, number, epidemic.

Results

For all analyses, a paired samples t-test statistical procedure was used. Participants made judgments of learning for each word, and the average of words in each of four conditions- high frequency words presented once, low frequency words presented once, high frequency words presented twice, and low frequency words presented twice- was calculated. For each condition we calculated the percentage of those words recalled.  An analysis of word frequency on predicted recall was made. To determine the effect of the word frequency on predicted recall, the difference between predicted recall for high frequency M=55.7 and predicated recall for low frequency M=39.4 was calculated. To compare these numbers, a paired samples t- test method was used and the result was reliable ( t(38)= 5.2, p< .001). The effect of word frequency in actual recall was also reliable (t(38)= 3.1, p< .004).To calculate the number of study opportunities effect on predicted recall similar analysis was used. The difference was not reliable for the predicted recall ( t (38)= 1.4, p= .16) but it was reliable for the actual recall ( t(38)= 7.2, p< .001).To determine whether metamemory matches the actual recall for the word frequency, the average predicted recall difference for high and low frequency was calculated to be M=16.3 , and M=10.5 for the actual recall. The difference of these numbers, using the paired sample t-test, was not reliable (t (38)= 1.48, p= .15). We used the same method to find whether the metamemory matches actual memory for study repetition.  The average difference for the predicted recall was 3.3% versus 20.4% for the actual recall. This difference was reliable ( t(38)=-, 5.63, p < .001).

Discussion

We expanded the research about the ease of processing and the stability bias by testing new manipulations. It was expected that the high frequency words would be perceived as easier and more memorable, and the study repetition would be underestimated because of the belief that memory is more stable than it actually is. Therefore, the words shown twice had no effect on the students prediction but they had an effect in actual recall. The data is consistent with the stability bias hypothesis, where the average difference between the predicted and actual recall was reliable. This supports previous research (Kornell, Rodhes et al., 2011). However, our data is not consistent with the previous research (e.g., Carpenter et al., 2013) regarding the ease of processing.  The hypothesis was that ease of process would affect the predicted recall but not the actual performance. Our data shows that it actually had an effect on the actual performance. One reason for this new finding might be the presence of non-native English speakers. There is a probability that some words were new for the non-native speakers therefore their memory for those words wouldn’t be as good. This effect was not captured in our experiment due to the small sample size (which might be another reason for the inconsistent result compared to other research), but further studies can be done in the future to determine the difference between the non-native English speakers and native speakers. This would aid in the study of how language affects memory.

References

       Besken, M., & Mulligan, N. W. (2013). Easily perceived, easily remembered? Perceptual interference produces a double dissociation between metamemory and memory performance. Memory & cognition, 41(6), 897–903.

       Carpenter, S. K., Wilford, M. M., Kornell, N., & Mullaney, K. M. (2013). Appearances can be deceiving: instructor fluency increases perceptions of learning without increasing actual learning. Psychonomic bulletin & review, 20(6), 1350–1356.

       Kornell, N., Rhodes, M. G., Castel, A. D., & Tauber, S. K. (2011). The ease-of-processing heuristic and the stability bias: dissociating memory, memory beliefs, and memory judgments. Psychological science, 22(6), 787–794.

Research paper: Testing the Brain’s Decision-Making Process In Humans

5 minutes read

Underlying mechanisms of confidence and decision-making

Our data showed that anxiety and confidence have an important role in decision-making. The self-report of the amount of effort required to solve a problem doesn’t seem to have a big effect on decision-making. However our experiment showed a positive correlation (significant in almost – but not all cases) between effort and anxiety as demonstrated in table 1 and 2.  These results lead to the following questions: How is the brain making this decision? What cognitive processes are involved when the brain is  assessing to avoid or engage with a task? What specific areas of the brain are responsible for the decision-making?

A literature review of visual confidence in humans looks at the evidence related to the anatomical brain structures that might possibly be responsible for confidence and decision-making (Grimaldi., at al. 2015). Some of the experimental evidence in this review paper supports the hypothesis that confidence and decision-making are encoded in the same brain areas such as: the lateral intraparietal (LIP) area and superior colliculus. Other experimental evidence supports the hypothesis that confidence is performed by specialized circuits including the prefrontal cortex. This controversy might be due to the fact that  the experiments and studies were focusing mostly in the decision-making circuits when they also found information about the confidence, but as Grimaldi., at al 2005, argue is that this doesn’t necessarily mean that’s where the confidence is ultimately generated. They propose future research where imaging studies in humans and non-humans can translate to electrophysiological experiments which might help to understand how the brain encodes confidence.

Another literature review investigates the locus coeruleus-norepinephrine (LC-NE) system which recently has been found to play a specific role in behavior and decision-making(Aston-Jones & Cohen, D. J. 2005). This system is one of the several brain-stem neuromodulatory nuclei that regulates cortical functions (Aston-Jones & Cohen, D. J. 2005). The modeling studies in monkeys showed that the LC system consists of two modes: phasic and tonic. In the phasic mode, NE is quickly released and there is an increase in attentiveness to the task relevant which increased task performance. On the other hand, during the tonic mode the neurons are less excited, and the behavior is more distractable. Monkeys were responding more to the nontarget stimuli at this time which decreased the task performance. Aston-Jones & Cohen, D. J. 2005’theory of Locus Coeruleus -Norepinephrine proposes that the phasic mode optimizes task performances versus the tonic mode favors exploration. This happens due to the evaluation of the benefits and costs where there is an increasing evidence suggesting that the anterior cingulate (ACC) and the orbitofrontal cortices (OFC) play an important role in this evaluation (Aston-Jones & Cohen, D. J. 2005). One of the limitations of the study they reported was its inclusion  of animals only.

In real life, the switching between these two modes is important because it allows us to focus on relevant tasks but also to explore our environment, so we don’t miss out on opportunities that can change our life for the better. However, when it comes to learning and focusing while in the classroom, the tonic phase would be costly for students. The LC activity – is, however,-  adaptive and changes in the teaching methods and rewards might help to encourage both modes in students. This would allow them to engage in both effortful and effortless tasks.

Limitations and future research

Since we didn’t look at performance, continuing research and experiments can be done to look at the relationship between performance, anxiety, and effort. Our study had some limitations including conducting it online. It has been reported that environment plays an important role in students’ behavior and thinking, (Closs, L., Mahat, M. & Imms, W 2021) but due to the restrictions of the pandemic, for safety reasons we conducted the experiment online.  Another limitation is the language. We didn’t ask if English was the participant’s native language. It is possible that had an effect on the decision to choose the reading and comprehension task. Also, our scale for rating and measuring anxiety, effort, and confidence must be validated.

If the experiments were to be done in person or in classroom environment, it could be expanded by adding a second part where students instead of choosing the cognitive tasks they want, would be given a task that would most likely avoid. We could then look at their performance and measure their level of anxiety and their attention. We cannot always choose what we want or how we want to feel in life, so this could help us understand the behavior and the performance of students when facing academic challenges, they may want to avoid.

   References

Aston-Jones & Cohen, D. J. (2005). An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance.  Annual Review Neuroscience, 28, 403-50.

Cacioppo, T. J., Petty, E, R., & Kao, F. Ch. (1984).The Efficient Assessment of Need for Cognition. Journal of Personality Assessment, 48:3, 306-307.

Closs, L., Mahat, M. & Imms, W. Learning environments’ influence on students’ learning experience in an Australian Faculty of Business and Economics. Learning Environ Res (2021).

Grimaldi, P., Lau, H., & Basso, A. M. (2015). There are things that we know, and there are things that we don’t know we do not know: Confidence in decision-making. Neuroscience&Biobehavioral Reviews. 55, 88-97.

Maat, S. M., & Rosli, M. K. (2016).The Rasch Model Analysis for Statistical Anxiety Rating Scale (STARS). Create ive Education, 7, 2820-2828.

Inzlicht, M., Shenhav, A., Olivola, Y, Ch (2018). The Effort Paradox: Effort Is Both Costly and Valued. Trends Cogn Sci.  22(4):337-349.

Teman, E. D. (2013). A Rasch Analysis of the Statistical Anxiety Rating Scale. Journal of Applied Measurement, 14 (4), 414-434.

Székely, M., Michael, J, (2020). The Sense of Effort: A Cost-Benefit Theory of Phenomenology of Mental Effort. Rev.Phil.Psych. 

Shiffrin, M. R., Schneider, W. (1977).Controlled and Automatic Human Information Processing: II Perceptual learning, automatic Attending and a General Theory. Psychological Review, 84: 127-190.

Childhood Antisocial Behavior and Possible Solutions

Veneta Callpani | Department of Applied Psychology, New York University | Human Growth and Development

Introduction

The study of antisocial, criminal and aggressive behaviors has been long researched given that these behaviors often operate at a high cost to society (Frick et al., 2009). Antisocial behaviors (ASB) in children and adolescents include aggressive acts, theft, lying and a number of other symptoms that violate social rules (Kazdin et al., 1987). These behaviors have been studied from different perspectives such as anthropological, sociological, evolutionary, psychological and biological perspectives (Frick et al., 2009). However, there is limited research from a developmental psychopathology and neurodevelopmental perspective. Some level of antisocial and aggressive behavior is normal in children and adolescence (Frick et al., 2009), especially while they’re going through adolescence and identity formation (Berger, 2020). But when these behaviors persist, they might have a profound effect on their brain development, cognitive skills and social interactions. This is an issue that also affects parents who might get frustrated, angry and feeling helpless when facing their children’s behavior. 

There are many challenges in finding the right treatment for this ASB, and this is mostly due to the heterogeneity of the behaviors. Sometimes children/adolescents who perform one type of antisocial behavior are likely to perform others as well and then this becomes a syndrome: a conduct disorder (Kazdin, 1987). In order to find a treatment or intervention that helps both the children and the parents, it is necessary to look into the mechanisms of ASB from a neurodevelopmental perspective and also know more about risk factors, and parenting. Current clinical practice tends not to acknowledge the individual neuropsychological risk factors or target them for intervention (vanGoozen et al., 2022), therefore the aim of this study is to review the literature and find possible solutions and future considerations for treatment options.

Methods

Keyword searching of the Google Scholar articles included two groups. First group related to child behavior and included terms: antisocial behavior, problems, externalizing, aggression, child mental health, child neurodevelopment. The second group was related to interventions and parenting. This included: parent training, CBT for antisocial behavior, interventions for antisocial behavior, etiology and prevention.

Results and Discussion

Antisocial Behavior and Antisocial Behavior Subtypes

Antisocial behavior, which includes aggressive acts, thefts, lying etc., has a relatively high prevalence and clinical referral rates (Kazdin et al., 1987). Bullying is also considered part of conducting problems, however there is limited research on this topic (Golmaryami et al., 2015). The clinical significance of antisocial behavior is related to the high prevalence, stability and poor prognosis over the course of development and their continuity within families across generations (Kazdin et al., 1987). Because of its heterogeneity, a multiple level analysis (e.g., neurological, cognitive, social) is important. One new perspective added to the multiple level analysis is the neurodevelopment to understand the psychopathological conditions. However, one broad question that is raised when taking a developmental psychopathology perspective on ASB is whether or not this should be considered a psychopathological condition, psychiatric or mental illness (Frick et al., 2009). While the psychopathological approach cannot completely address these questions, because it depends on how the “mental disorder” is defined, it can help to define various causal pathways that might lead to antisocial behavior (Frick et al., 2009). When trying to classify ASB, one can rely on behavior differences, personality traits or neurodevelopmental differences, which are caused by many different factors. Therefore, Frick et al., (2009) emphasizes the concept of equifinality and multifinality when talking about the ASB subgroups. A number of reviews described from Frick et., (2009) have shown a distinction based on the different patterns of the onset: childhood onset versus adolescent onset; showing that the childhood onset group is more likely to show aggressive behavior in childhood, adolescence and most likely to continue and show antisocial and criminal behaviors into adulthood. So, the childhood and adolescence subtypes of ASB show different patterns of the onset but also the trajectory over time. 

Another subgroup division is based in Callous-unemotional traits, which supports both the equifinality and multifinality. Equifinality (equal in final form) proposes that one symptom can result from many different causes; versus multifinality is when one cause can have many final manifestations (Berger, 2020, p.948). The distinction is based on the presence of a callous and unemotional interpersonal style characterized by a lack of guilt and empathy and callous use of others (Frick et al., 2009). Youth with childhood onset of ABS tend to score higher on measures of CU traits than those who show adolescent onset (Frick et al., 2009). Furthermore, youth with CU traits (fearless, thrill seeking) show more severe and pervasive patterns of aggression, versus youth without CU traits tend to show less aggression overall, and when they do show it is largely reactive in nature (Frick et al., 2009). When it comes to multifinality, research has proposed that the same temperament of fearlessness can lead some children to become “bold” and others “mean” depending on presence of other traits or socializing environments (Frick et al., 2009).  

Another subtype division is based on the development of a conduct disorder, which includes children and adolescents who show severe and impairing levels of antisocial behavior but do not display the CU traits which suggests that different developmental processes may be underlying their aggressive and antisocial behavior. Frick et al., (2003) conducted research where they predicted that conduct problems, irrespective of the presence of CU traits, would be associated with measures of emotional and behavioral dysregulation; thus, the main effect of conduct problem would not be modified by the presence or absence of CU traits. The total number of participants was 100 and they were separated into 5 groups: control (n=25), conduct problems only (n=23), Callous-unemotional (CU) only (n=25) and combined (n=25).  The mean age of the participants was 12.4; the mean SES (Duncan’s socioeconomic index) was 46.6 and mean K-BIT (Composite Index from the Kaufman Brief Intelligence Test) was 104.8. For the independent variables an antisocial process screening device (APSD) was used for screening. APSD is a 20-item behavior rating scale that was completed by each child’s parent and teacher (Frank et al., 2003). For the dependent variables multiple measures were taken such as:  thrill and adventure seeking, reward dominance computer task, emotional lexical decision task, behavioral assessment system for children, diagnostic interview schedule for children -version 4, and why kids do things (an instrument which is a hypothetical situation procedure used to assess tendencies to attribute to attribute hostile intent and to react angrily in social situations) (Frank et al., 2003). In correlations with demographic characteristics, of significance was the emotional reactivity to peer provocation which tended to show negative correlation with age, SES, and intelligence. In the summary figure representation, it was shown that both groups of children with conduct problems scored higher in emotional and behavioral dysregulation (i.e., measures of anxiety and impulsivity-hyperactivity). In contrast, only children with both conduct problems and CU traits showed low behavioral inhibition (distress in new situations). Their findings showed that children with conduct problems show evidence of emotional and behavioral dysregulations, regardless of the presence or absence of CU traits. However, the dysregulation was more severe in children with both conduct problems and CU traits (Frank et al., 2003).

There were some limitations of the study, such as methodological limitations; the sample was not clinically-referred, and that diagnoses were not clinical, rather based on parents and teacher ratings. Nevertheless, the study suggests the importance of considering multiple casual pathways in development of severe antisocial and aggressive behavior. So far the treatments for conduct problems have been focusing on processes involved in children without CU traits; but future interventions can become more individualized and also target the processes involved in children with CU traits such as enhancing empathic responses (Frank et al., 2003). 

Risk Factors 

Several studies have shown that the conduct problems and aggression vary in the context of neighborhoods (Rossa et al., 2005). Exploring this relationship, Rossa et al., (2005) identified three variables that might be influenced by the neighborhood’s quality: child stressful experiences, association with deviant peers, and conflict within parent- child relationship. The purpose of the study is to determine whether these three variables mediate the relation between neighborhood risk and child externalizing behavior for a sample of children from predominantly low-income. Therefore, they conducted a study and measured different variables such as demographic variables, neighborhood risk, criminal events, neighborhood quality, life events, delinquent peers, parent-child conflict, child externalizing behavior and maternal depression. For the child’s externalizing behavior, mothers completed the Child Behavior Checklist (CBCL) and children completed the Youth Self-Report to assess child mental health (Rossa et al., 2005). The study confirmed that children in neighborhoods that mothers perceived as high risk experienced more stressful events than children in lower risk neighborhoods. In addition, children in lower quality neighborhoods reported greater association with deviant peers and more parent-child conflict than children in better quality neighborhoods (Rossa et al., 2005). These results confirm that high risk neighborhoods have the potential to affect children through multiple systems: individual, family and peer. From their study they found evidence that the relationship of neighborhood risk to child externalizing behavior differed by whether the mother was born in the U.S or Mexico. The difference between culture, values and beliefs, lifestyle of families in Mexico and the United States might alter the way neighborhood risk influences children (Rosse et al., 2005). An interesting finding was that the parent-child conflict was not related to child externalizing behavior in Mexican families, but it was significant for families of U.S born mothers. One explanation for this is that the conflict takes place in a safe, strong, and supporting atmosphere when it comes to traditional Mexican homes (Rosse et al., 2005). Future research is needed to better understand this, but an important point is that research should consider cultural background when studying the relationship between neighborhood risk and children’s adjustment. Some limitations of this study: small sample and being cross-sectional study. Longitudinal studies of neighborhood influences on development would provide information about developmental and causal processes that cross-sectional studies cannot (Rosse et al., 2005). 

Interventions 

The significance of antisocial behaviors is heightened by the absence of a clear and effective treatment. The prevalence of childhood mental health in the US is estimated approximately 18% (Tully & Hunt, 2015). Research from the past 30 years has shown that parenting interventions based on social learning and cognitive-behavior theory have been effective, however there is a low participation rate due to the length of interventions (Tully & Hunt 2015). Given the need to try out some other type of intervention that might work, Tully & Hunt (2015) conducted a systematic review to assess the evidence for the efficacy and effectiveness of brief (<8 sessions) individual or group parenting interventions for reducing child externalizing behavior problems. They reviewed 64 articles and only 9 of them met the criteria for brief parenting intervention. This indicates that brief parenting intervention is a topic that requires more research. However, the findings of these articles suggested that brief parenting interventions may be effective in reducing child externalizing behaviors and dysfunctional parenting (Tully &Hunt, 2015).  A key limitation to this study was the inability to conduct a meta-analysis due to heterogeneity of included study. 

Parent management training (PMT) and cognitive-behavioral problem-solving skills training (PSST) are two other treatment approaches. These two approaches are applied separately, however, Kazdin et al., 1987 conducted a study to investigate the effectiveness of using these two approaches combined. The total subjects that participated in the study consisted of 40 children (9 girls and 31 boys) and their parent(s). The children were all in patient of a psychiatric facility admitted for acute disorders including highly aggressive and destructive behavior, suicidal or homicidal ideation, and detreating family conditions. Posttreatment assessment was conducted 1 month after the final treatment and one-year follow up.  The results showed a significant improvement in child behavior such as decreased aggression, behavior problems and increase in social and school performance. Limitations of this study include biased assessments because the results relied upon parent and teacher ratings. The absence of direct observation in the home and at school delimits interpretation of the results ( Kazdin et al., 1987). 

Other treatments such as group therapy, individual therapy, pharmacotherapy are used but there is limited research in the effective use of different techniques (Kazdin, 1987). 

Conclusion and Future Research

Many characteristics of conduct behavior, or antisocial behavior present major challenges identifying an effective treatment. There is limited research of the mechanism or the cause of the antisocial behaviors especially from a neurodevelopmental aspect of it. Heterogeneity of behaviors is what makes this a difficult study. Thus, treatment might focus on one specific conduct problem, leaving another part that might be essential in children’s success (Kazdin, 1987). There is limited evidence to show that the therapeutic techniques, as discussed, effectively alter antisocial behavior in children (Kazdin, 1987).

Future research is needed to create new models and ways to study children’s antisocial behaviors both at an individual level and community level. Workshops and training for both parents and teachers can also be helpful in identifying and better dealing with these problems. These children are the future of our society, therefore more research and more attention to this complicated but very important case will help them and the society to create a better future. 

References

Berger, K. S. (2020). The Developing Person Through the LifeSpan. Worth publishers, Macmillan Learning.

Frick, P. J., Cornell, A. H., Bodin, S. D., Dane, H. E., Barry, C. T., & Loney, B. R. (2003). Callous-unemotional traits and developmental pathways to severe conduct problems. Developmental Psychology, 39(2), 246–260. https://doi.org/10.1037/0012-1649.39.2.246 

Frick, P., & Viding, E. (2009). Antisocial behavior from a developmental psychopathology perspective. Development and Psychopathology, 21(4), 1111-1131. http://doi.org/10.1017/S0954579409990071

Golmaryami, F., Frick, P., Hemphill, S., Kahn, R., Crapanzano, A., & Terranova, A. (2015). The social, behavioral, and emotional correlates of bullying and victimization in a school-based sample. Journal of Abnormal Child Psychology, 44(2), 381–391. https://doi.org/10.1007/s10802-015-9994-x 

Kazdin, A. E. (1987). Treatment of antisocial behavior in children: Current status and future directions. Psychological Bulletin, 102(2), 187–203. https://doi.org/10.1037/0033-2909.102.2.187

Kazdin, A. E., Esveldt-Dawson, K., French, N. H., & Unis, A. S (1987). Effects of parent management training and problem‐solving skills training combined in the treatment of antisocial child behavior. Journal of the American Academy of Child & Adolescent Psychiatry, 26(3), 416–424. https://doi.org/10.1097/00004583-198705000-00024  

Roosa, M. W., Deng, S., Ryu, E., Lockhart Burrell, G., Tein, J.-Y., Jones, S., Lopez, V., & Crowder, S. (2005). Family and child characteristics linking neighborhood context and child externalizing behavior. Journal of Marriage and Family, 67(2), 515–529. https://doi.org/10.1111/j.0022-2445.2005.00132.x 

Tully, L. A., & Hunt, C. (2015). Brief parenting interventions for children at risk of externalizing behavior problems: A systematic review. Journal of Child and Family Studies, 25(3), 705–719. https://doi.org/10.1007/s10826-015-0284-6

van Goozen, S. H. M., Langley, K., & Hobson, C. W. (2022). Childhood antisocial behavior: A neurodevelopmental problem. Annual Review of Psychology, 73(1), 353–377. https://doi.org/10.1146/annurev-psych-052621-045243 

The sleeping and dreaming brain

14 minutes read

Veneta Callpani| Department of Applied Psychology, New York University| Cognitive Neuroscience 

Introduction

We spend one third of our life sleeping and it’s only natural to think about the importance and the function that sleep has in our mental and physical wellbeing. It is obvious that sleep deprivation has many negative effects such as cognitive deficits: attention, working memory, ability to learn, also other symptoms such as lack of energy, compromised immune system, and emotional dysregulation (1). In terms of evolution, sleeping at night might reduce the time spent foraging, reproducing or monitoring the environment for any danger (1). However, it seems that sleep has evolved to have an essential function, alongside with dreaming. Dreams have been of interest since the dawn of time. In the Interpretation of dreams, Freud predicted that: “Deeper research will one day trace the path further and discover an organic basis for the mental event” (2).  While the evolved biological function of sleep has advanced, no equivalent understanding of dreams has emerged (3). Some theories view dreams as epiphenomena, and many of the proposals for their biological function are contradicted by the phenomenology of dreams themselves (3). This review is an attempt to bring together the sleep and dream research in a parallel way and see a big picture of where we are so far in terms of underlying brain mechanisms for sleep and dreams, scientific methods of studying dreams, different theories, and how we can move forward into new areas of investigations into the enigmatic cognitive dimensions of sleep and dreams.

Methods of studying sleep and dreams: sleep stages

Polysomnography (PSG), which is considered “sleep study” (1), Electroencephalogram, fMRI, Electromyogram (EMG) Electrooculogram (EOG), Positron Emission Tomography (PET) (1,4,5) are methods used to study sleep and dreams. In the case of sleep disorders (and sleep disorders breathing) PSG is used in combination with electro-oculogram, chin and leg electromyogram, electrocardiogram, and air flow at the nose and mouth. (1). Before the advanced technology and neuroimaging studies, dreams were studied mostly through dream reporting. However, in order to better understand dreams and dream research, it is important to have some general background of sleep studies and stages. So far, scientists categorize sleep into two major components: Rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep (1). NREM sleep is further divided into: N1, N2, N3. In adults, approximately 5% of the total sleep time is Stage N1; 50% Stage N2; and 20% is Stage N3 sleep. The remaining 25% is REM stage sleep. (1). N1 is considered the stage of transition from wakefulness to sleep, and N3 is considered the “deep sleep” or slow wave sleep, and REM sleep is usually associated with dreaming, but the function of REM sleep is yet uncertain (1). However, there is a difference between NREM and REM phases of sleep in terms of somatic activity. During NREM the blood pressure, heart rate and respiratory rate are decreased while REM sleep is associated with an increase and irregularity of these functions, and a rise in global cerebral glucose metabolism (6). Neuroimaging studies have shown that the visual regions of the brain, the pontine reticular formation, limbic and paralimbic regions have an increased activity during REM sleep (6) and maybe this is one of the reasons scientists believed that dreams occur only during REM sleep. Another reason to believe that, was probably related to the fact that subjects woken from REM sleep reported that they were dreaming 70-95% of the cases, whereas only 5-10%  of the cases after NREM sleep (5). Brain damage or brain lesions are another way to study sleep and dreams. These studies showed that, in fact, REMS can occur without dreaming, and dreaming can occur without REMS (5). Damage in or near the temporo-parieto-occipital junction was associated with a complete loss of dreaming. (5). Also, 80% incidence of nearly complete loss of dreaming was reported in the cases of prefrontal leucotomy. PET functional studies have identified areas that seem responsible for dream characteristics (5). Patients with lesions in the visual association cortex experience loss of visual imagery in dreams and when awake; but is preserved if the lesions are in the primary visual cortex (7).

Another interesting way and method developed to study the brain during dreaming, meditation and non-ordinary brain activities is through dissipative models. This model proposed to study the neuronal dynamics within mathematical frame of many-body physics (8)

Types of dreams 

Knowing the types of dreams helps us categorize them and better understand their possible function and different hypotheses and theories. Prestson Ni (9) summarized 7 types of dreams in his review article: Current/recent events dreams are the most common dreams where events that occurred during the past 24 or 48 hours are reflected in the dream.

Symbolic dreams help us process life events in a metaphorical way, and if properly interpreted can give us insights.

Fantasy/comfort dreams are aspiration, wish-fulfillments, and compensations of the struggles during waking life. It helps us find comfort if we are going through a difficult time.

Creative/ problem solving dreams help us solve problems that we have been thinking a lot during awaking time.

Nightmares can occur during a stressful situation in our life, and might also represent fears, anxieties or unresolved traumas. 

“Supernatural” dreams are very rare but also the most memorable. Examples of these dreams include: premonition dreams in which the dreamer has a vision of the future event which later becomes true; telepathic dreams where the dreamer receives communication about a person or event occurring elsewhere and when the dreamer awakes, details of the dreams are verified to be true; shared dreams happen when different people have the same dream approximately at the same time, and visitation dreams in which a recently deceased family, fiend, partner, or pet appear. These dreams are often vivid, loving and life changing. 

Lucid dreams are very interesting because it allows the dreamer to control their dreams. The dreamer is aware that is dreaming. This type of dream can have many potentials and benefits. A recent study (4) used lucid dreaming to communicate with the dreamer in real time. This study will be discussed further in the dream theories section. 

Dream’s function hypothesis and theories

There are different types of dreams and each of them seems to have a different function and use. However, the question of dream evolution, the mechanism of how dreams are created, and scientific study is still very challenging despite the new technology and different methods used to study sleep and dreams. This might also be related to our limited knowledge, tools and scientific understanding of the complexity of our brain. So far, there are different hypotheses and theories to why the dream has evolved, and what function dreaming might play in our life both psychologically and physically. 

Threat Simulation theory 

The threat simulation theory postulates that dreaming may fulfill a neurobiological function by allowing an offline stimulation of threatening events, and this mechanism would promote adapted behavioral responses in real life situations (10). Continuing in the line of this theory, Stepernich et al. (10) conducted two studies to address two questions: do emotions in dreams (in this case fear) engage the same neural circuits as during wakefulness and is there a link between emotions experienced in dreams and brain responses to emotional stimuli during wakefulness. The aim of the first study was to find the neural correlates of fear in dreams. They woke the dreamers up with an intense alarm in different stages of sleep and got a report of the last thing they dreamed and if fear was involved. They used EEG to study the difference in signals between fear vs non-fear responses during dreaming. REM reports with presence of fear compared to those without fear were associated with decreased delta power in the bilateral insula and midcingulate cortex. The results of the first study indicated that the occurrence of frightening dreams coincided with increased activation of the insular cortex during both REM and NREM sleep, and midcingulate cortex during REM sleep. The aim of the second study was to establish whether neurophysiological responses to fear-eliciting emotions during wakefulness correlated with fear in dreams. Here fMRI was used and during the emotional tasks eye movements and pupil diameter were measured continuously. Taken together, these two studies showed that individuals who reported a high prevalence of fear related emotions in their dreams, had stronger fear inhibition during wakefulness (10).    

Overfitted brain hypothesis

The hypothesis of overfitted brain (OBH) suggests that dreams have evolved to assist generalization (3). This generalization is assisted by stochastic corruptions of normal sensory input, which combats the highly biased nature of inputs during an animal’s daily learning that can lead to overfitting, a ubiquitous problem in artificial neural networks and machine learning in general (3). This hypothesis doesn’t contradict some hypothesis of dreams, it just adds new dimensions. As per OBH hypothesis, by providing departures away from the statistically biased input of an animal’s daily life, dreams can assist and therefore increase performance. (3)

Memory consolidation theory

This theory explains that dreams have evolved for memory consolidation (3). There is still debate about this theory, because a significant line of direct evidence for the consolidation theory comes in the form of “replay” of memories during sleep hypothesis. However, the increased firing in the hippocampus that counts as “replay”, occurs also during wakefulness (3).

Plailly et al. (11) presented an overview of an experiment to further establish the possible link of dreaming and memory consolidation. In this experiment, thirty-two high dream recallers freely explored new visuo-olfactory episodes for 3 consecutive days. The first three sessions were used for encoding, and the retrieval occurred on the fourth session to assess participants memory of the episodes perceived during encoding. All participants had to wear a wrist actimeter to assess sleep parameters, and dream reports were taken. Participants who experienced learning-related and/or experiment related dreams had significantly better visuo-spatial memory of episodes, in comparison with other participants. The results support the hypothesis that the learning phase is loosely incorporated into dreams and that this incorporation is associated with sleep related memory consolidation (11)

Another recent study showed a study method that allowed for a two-way communication between the experimenters and the dreamers (participants). Konkoly et al (4) conducted experiments with 36 individuals in 4 different places: USA, Germany, France and Netherlands. All participants had some prior experience with lucid dreaming, and one of them (French) had narcolepsy and therefore had a lot more experience and abilities with lucid dreaming. Participants were trained to give signals when lucid dreaming (left-right eye movement: LR) and also were trained to answer some questions (math questions or YES and No answers) by either eye movement signals or facial muscle movements. However, they didn’t know which question would be presented in their sleep during the experiment. When conducting the experiment, the information was transmitted to the dreamer in the form of spoken words, flashing lights, or beeping tones. Electroencephalogram (EEG) electrooculogram (EOG) and electromyogram (EMG) were used to measure sleep states and wave patterns, and the dreamers’ signals. Dream reports were obtained after each correct answer. In total, 18.4% of participants answered correctly the questions during sleep. These results document examples of sleep learning. Participants were able to remember pre-sleep instructions on how to respond and apply it in a novel way during sleep. This new method of studying dreams opens up new doors for new experiments and a possible deeper level of understanding of sleep and dreaming. 

Reverse learning hypothesis

This hypothesis was proposed by Notably et al. pointing out that the purpose of dreaming is to remove the “undesirable” connections and help the brain “unlearn” (3). This hypothesis has been ignored largely, however recent computational modeling in spiking neural networks have shown that “reverse learning” in the form of reverse leaning rules can indeed be helpful (3)

Another interesting hypothesis was proposed by Dr. David Mauroce, professor of ocular physiology in the Department of Ophthalmology at Columbia-Presbyterian Medical Center (12). He started to wonder about the function of REM sleep after learning  about a young man who was in an accident and suffered from immobilization of eyes. This leads the cornea to become laced with blood vessels, (normally cornea has no blood vessels) presumably to supply the cornea with oxygen (12). He knew that when eyes are closed during non-REM sleep, oxygen can reach the cornea from the iris only by diffusion across the stagnant aqueous humor. Using a mathematical model, he established that oxygen supplied under those conditions would be insufficient. Therefore, he proposed that humans experience REM sleep to supply much needed oxygen to the cornea of the eye.  

Conclusions

Despite all hypothesis and theories, one thing we know for sure is that sleep is very important for our survival, and we have come to realize that so are dreams. While there is no final answer about the function of either one, it is important to point out that studying only REM sleep to know more about dreams, or studying only the physiological processes, are not enough. Understanding the physiological function of dreams can be insightful and play an important role, however, the psychological component and the meaning of dreams is also important. Humans are creatures of meaning, and as seen in this review, the content and the meaning of dreams can be very revealing and have a huge impact on our mental wellbeing. As everything else, there are pros and cons, and there is always room for misusing scientific knowledge, and maybe this can be an area of future exploration. How do we use the power of dreams; what is the limit, and where should we draw a line? Will there come a time where creating a protocol of lucid dreaming might become necessary as a safety precaution? If dreams (and sleep) can be used as a powerful tool to ‘predict’ the future, to communicate with a deceased person, or communicate with other people via dream sharing; to what extent can we use this without damaging our body? To what extent controlling our dreams won’t turn against us, and to what extent this can interrupt the natural functioning of dreams. 

As seen from this review, dreams can have multiple functions, and dreaming can occur during all stages of sleep. As an analogy; same as neurotransmitters can have a different function when in a different part of the brain, dreams can have different functions (or take on different forms) depending on the stage of sleep. Also, depending on people’s interest, dreams can be used for many different things, and one of them can be as a therapeutic tool. 

Understanding the importance of sleep/ dreams, might also help our society become more aware of the role sleep plays in our life and the cost of sleep deprivation on our body/mind but also in the society as a whole. 

References 

1.    Shrivastava, D., Jung, S., Saadat, M., Sirohi, R., & Crewson, K. (2014). How to interpret the results of a sleep study. Journal of Community Hospital Internal Medicine Perspectives, 4(5), 24983. https://doi.org/10.3402/jchimp.v4.24983 

2.    Nir, Y., & Tononi, G. (2010). Dreaming and the brain: From phenomenology to neurophysiology. Trends in Cognitive Sciences, 14(2), 88–100. https://doi.org/10.1016/j.tics.2009.12.001

3.    Hoel, E. (2021). The overfitted brain: Dreams evolved to assist generalization. Patterns, 2(5), 100244. https://doi.org/10.1016/j.patter.2021.100244

4.    Konkoly, K. R., Appel, K., Chabani, E., Mangiaruga, A., Gott, J., Mallett, R., Caughran, B., Witkowski, S., Whitmore, N. W., Mazurek, C. Y., Berent, J. B., Weber, F. D., Türker, B., Leu-Semenescu, S., Maranci, J.-B., Pipa, G., Arnulf, I., Oudiette, D., Dresler, M., & Paller, K. A. (2021). Real-time dialogue between experimenters and dreamers during rem sleep. Current Biology, 31(7). https://doi.org/10.1016/j.cub.2021.01.026

5.    De Gennaro, L., Marzano, C., Cipolli, C., & Ferrara, M. (2012). How we remember the stuff that dreams are made of: Neurobiological approaches to the brain mechanisms of Dream Recall. Behavioural Brain Research, 226(2), 592–596.  https://doi.org/10.1016/j.bbr.2011.10.017

6.     Bokkon, I. (2005). Dreams and neuroholography: An interdisciplinary. Interpretation of development of homeotherm state in evolution. Sleep and Hypnosis, 7(2), 61-76.

7.    Eiser, A. S. (2005). Physiology and psychology of dreams. Seminars in Neurology, 25(01), 97–105. https://doi.org/10.1055/s-2005-867078

8.    Re, T., & Vitiello, G. (2020). Nonlinear dynamics and chaotic trajectories in brain-mind visual experiences during dreams, meditation, and non-ordinary brain activity states. OBM Neurobiology, 4(2), 1–19. https://doi.org/10.21926/obm.neurobiol.2002061

9.    Ni, Preston. (2020). 7 fascinating types of dreams. Psychology Today. https://www.psychologytoday.com/us/blog/communication-success/202010/7-fascinating-types-dreams

10.Sterpenich, V., Perogamvros, L., Tononi, G., & Schwartz, S. (2019). Fear in dreams and in wakefulness: Evidence for day/night affective homeostasis. Human Brain Mapping, 41(3), 840–850. https://doi.org/10.1002/hbm.24843

11. Plailly, J., Villalba, M., Vallat, R., Nicolas, A., & Ruby, P. (2019). Incorporation of fragmented visuo-olfactory episodic memory into dreams and its association with memory performance. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-51497-y

12.Breacher, M. M. The biology of dreaming: a controversy that won’t go to sleep.  Columbia.edu http://www.columbia.edu/cu/21stC/issue-3.4/breecher.html