20 Apr

Real-time fMRI neurofeedback and self-regulation of negative emotional affectivity

A review paper describing progress and challenges of rtfMRI neurofeedback in the regulation of negative emotions. The review critically describes and contrasts a selection of the existing scientific literature on this topic, and discusses the contribution of these sources to your central question.

The paper was written for a writing assignment during my second year bachelor degree.

Abstract

Research showed that negative emotions not only lead to psychological problems and mental disorders such as anxiety and depression, but they can also cause impairment of physical health including tobacco dependence, coronary heart disease and cancer. Real-time functional magnetic resonance imaging (rtfMRI) neurofeedback is a new technique that involves training patients to control the activation in specific brain regions. The method involves individualizing the brain area where the activity needs to be regulated and providing the participant with a real-time feedback about the activation. rtfMRI neurofeedback has been used to train subjects to regulate brain regions involved in negative affectivity. The most important brain regions involved in emotional regulation are the amygdala, the anterior cingulate cortex (ACC), and the insula. The paper describes current discoveries in which participants learned to regulate these brain areas giving satisfactory evidence for emotional regulation with rtfMRI, and proposes new challenges for future research.

Click to read the full paper

09 Apr

SNIF-ACT- A Model of Information Foraging on the World Wide Web

The presentatoin describes a computational model that mimics people interaction with the WWW. This model is important because provides principles for improving usability and helps people to understand the best way to find and use information on the web for significant problems such as health, finance, career, etc.

Authors: Pirolli, P. L. & Fu, W-T.

Year: 2003

Link to the original paper

SNIF-ACT integrates Information Foraging Theory and the ACT-R model. The latter is a computational model developed by Anderson to model human psychology. In order to understand how humans interact with the web, the authors registered all states and events of two users while they interacted with the web. These data were saved in a database and compared via a user-tracing architecture to the data produced by the SNIF-ACT model.


The ACT-R architecture is based on two major components:

Declarative memory includes knowledge represented in chunks that can be either active or not active. Active chunks represent the knowledge required in a particular time. For example, they could represent the current goal of finding an information of the WWW.
Procedural memory includes skills. These skills tell the model how to transform declarative knowledge into behaviour. They are also called conditional production rules. An example of a rule could be “Use-search-engine”: IF the user needs to go on a website and he is in front of a browser, THEN he uses a search engine”. Only one rule at the time can be applied and if there are many matches for one goal, the conflict resolution mechanism decides which rule has the highest utility for the current goal. The utility function is provided by information foraging theory.

The utility function used used by the conflict resolution mechanism is based on information scent. Information scent includes the cues (links, images, and text) that the user process in making judgements. When real-users use the Internet, they mostly focus of the content of the webpages visited. For this reason, information scent is content-based.
The information scent of every page visited activates chunks in declarative knowledge via a spreading activation network. This activation indicates how relevant the page is to the current goal. For example, if the user is looking for a photo of a friend, a webpage with the friend's name would have high information scent, while a page with no content related to the friend would have low information scent. The model predicts that user will choose the rule that leads to the highest information scent.

The authors predicted that novice users would follow links that have high information scent. The second prediction was that users would leave a webpage when the utility of the page diminishes below the utility of moving to a new website.

The model used thes two prediction to match the behaviour of two users performing two different tasks online such as finding some posters to buy or the date of an event. While completing the tasks, user tracing instrumentation was used to register the behaviour of the user interacting with the web. The researhcers recorded eye movement, a file log of the actions on the screen, and a video recording of the user thinking aloud.

After the data was recorded, a user trace comparator controlled the SNIF-ACT simulation model and matched the simulation behaviour with the user data for each step. Each production rule that was selected by SNIF-ACT was compared to the user action. If the two actions matched, the production selected by SNIF-ACT was executed. If they did not match, the production that matched the user action was executed.

The SNIF-ACT model modelled the user behaviour very well. For link-following actions, the model reliably predicted which link the user chose. The distribution of predicted link selection was significantly different from random selection (first graph). For site-leaving actions, the model also correctly matched the user behaviour of leaving a page. The second graph shows the four last pages visited by a user before leaving the webpage. The dotted line represents the mean of the information scent of the page appearing after the user left the website. This was consistent with the prediction: users left the pages when the utility (information scent) of the page was below the utility of moving to a new website.

Pirolli, P. L. & Fu, W-T. (2003)SNIF-ACT: A Model of Information Foraging on the World Wide Web. Ninth International Conference on User Modeling, Johnstown, PA .
09 Nov

Robot Mobile

Robot Mobile is a project I realised for my high school graduation exam year 2009/10.

The robot has 3 wheels (2 wheel-drive and one rotating in the front), and can move on a plane area avoiding obstacles. It has also a switch in the front which helps the robot to intercept a step o any other situation that might make it fall down on its track.

The project was realised by Lorenzo Frangella, Luca Simeone and Matteo Raffaeli.

 

This document includes functional specifications, the initial analysis phase results, the software, the realisation, and shows detailed pictures of the project.

Documentazione robot mobile

/* firmware V1.0 Controllo robot mobile */
/* Frangella Lorenzo - Simeone Luca - Raffaeli Matteo */
/* per maggiori informazioni : frangella@email.it */

#include <p18cxxx.h>
//#include <p18f452.h>
//#include <stdio.h>
//#include <ancomp.h>
//#include <usart.h>
#pragma config OSC = HS//10 Mhz
#pragma config WDT = OFF
#pragma config PWRT = ON
#pragma config LVP = OFF

#pragma config PBADEN = OFF // altrimenti porta B partirebbe come ingresso analogico

//funzioni di controllo
int input_radio(int);
int input_sensori(void);
int muovi(int, int, int);

//funzioni di movimento
void azioneA(void);
void azioneB(void);
void azioneC(void);
void azioneD(void);
void azioneE(void);
void azioneF(void);
void azioneG(void);
void azioneH(void);
void azioneI(void);

#define A 0
#define B 1
#define C 2
#define D 3
#define E 4
#define F 5
#define G 6
#define H 7
#define I 8

//funzioni di ritardo
void ritardo(void);
void ritardobreve(void);
void ritardolungo(void);

//funzione di impostazione delle porte di input e output del pic
void imposta_input_output(void);

//INIZIO VARIABILI GLOBALI
// comportamenti da tenere in ogni cambio di stato
int IN1[] =  {E,E,E,E,E,H,E,E,E};
int IN2[] =  {A,B,A,B,F,F,F,F,F};
int IN3[] =  {A,B,A,B,A,F,I,F,F};
int IN4[] =  {A,B,A,D,D,B,D,B,B};
int IN5[] =  {A,B,C,B,C,A,C,A,A};
int IN6[] =  {A,B,A,B,B,B,B,B,B};
int IN7[] =  {A,B,A,B,A,A,A,A,A};


int stato=E;//variabile per tenere traccia della stato precedente

//int interruttore = 0;
//FINE VARIABILI GLOBALI

//INIZIO funzione MAIN - funzione principale
void main (void){
int sensori=1;//controlla cosa vogliono fare i sensori
/* significato valori possibili:
    1 -> IN1 -> via libera
    2 -> IN2 -> ostacolo da tutti i sensori
    3 -> IN3 -> ostacolo centrale
    4 -> IN4 -> ostacolo a destra
    5 -> IN5 -> ostacolo a sinistra
    6 -> IN6 -> ostacolo avanti e a destra
    7 -> IN7 -> ostacolo avanti e a sinistra
*/

int radio=0;//controlla cosa vuole fare il telecomando
/* significato valori possibili:
    0 -> deufalt -> lascia il controllo ai sensori
    1 -> parti
    2 -> ruota sinistra
    3 -> ruota detra
    4 -> fermo
*/

    imposta_input_output();
    
    //abilitiamo circuito di controllo motori
    PORTCbits.RC1=1;//enable 16
    PORTCbits.RC5=1;//enable 24
    
    
    //avviamo robot avanti
    azioneE();
        //ciclo principale
        while(1) {    
            radio = input_radio(radio);
            //sensori = input_sensori();
            stato = muovi(radio, sensori, stato);
            ritardo();    
            //radio = 0;        
        }
}
//FINE funzione MAIN
        
void ritardo(void){
    unsigned long int i;
    for(i=0; i< 5000; i++);
}

void ritardobreve(void){
    unsigned long int i;
    for(i=0; i< 100; i++);
}

void ritardolungo(void){
    unsigned long int i;
    for(i=0; i< 30000; i++);
}

//funzione di controllo input dal radiocomando
int input_radio(int radio){
/*sensori:
PORTDbits.RB2 -> radio C1 - parti
PORTDbits.RB3 -> radio C2 - sx
PORTDbits.RB4 -> radio C3 - dx
PORTDbits.RB5 -> radio C4 - fermo
*/
    if(PORTBbits.RB2==1){
        ritardobreve();
        if(PORTBbits.RB2==1){
            return 1;//parti
        }
    }
    if(PORTBbits.RB3==1){
        ritardobreve();
        if(PORTBbits.RB3==1){
        return 2;//sx
        }
    }
    if(PORTBbits.RB4==1){
        ritardobreve();
        if(PORTBbits.RB4==1){
        return 3;//dx
        }
    }
    if(PORTBbits.RB5==1){
        ritardobreve();
        if(PORTBbits.RB5==1){
        return 4;//fermo
        }
    }
    return radio;
}

//funzione di controllo input dai sensori
int input_sensori(void){
/*sensori:
PORTDbits.RD7 -> sens sx
PORTDbits.RD6 -> sens c
PORTDbits.RD5 -> sens dx
*/
    if(PORTDbits.RD7==0&&PORTDbits.RD6==0&&PORTDbits.RD5==0){
        return 1;//via libera
    }
    if(PORTDbits.RD7==1&&PORTDbits.RD6==1&&PORTDbits.RD5==1){
        return 2;//ostacolo da tt i sens
    }
    if(PORTDbits.RD7==0&&PORTDbits.RD6==1&&PORTDbits.RD5==0){
        return 3;//ostacolo centrale
    }
    if(PORTDbits.RD7==0&&PORTDbits.RD6==0&&PORTDbits.RD5==1){
        return 4;//ostacolo dx
    }
    if(PORTDbits.RD7==1&&PORTDbits.RD6==0&&PORTDbits.RD5==0){
        return 5;//ostacolo sx
    }
    if(PORTDbits.RD7==0&&PORTDbits.RD6==1&&PORTDbits.RD5==1){
        return 6;//ostacolo avanti dx
    }
    if(PORTDbits.RD7==1&&PORTDbits.RD6==1&&PORTDbits.RD5==0){
        return 7;//ostacolo avanti sx
    }
    return 1;
}

//funzione che decide il prossimo stato del robot
int muovi(int radi, int sens, int stat){

int old_stato = stat; // serve per ricordare lo stato al tempo precedente
/* solamente se da una rilevazione alla successiva lo stato cambia, allora
si prende una nuova decisione di movimento, altrimenti si continua il comportamento
scelto nel giro precedente
*/

// parte 1) determinare il nuovo stato

    if(radi != 0){
        switch(radi){
            case 1:
                stat = E;
                //interruttore = 0;
                break;
            case 2:
                stat = H;
                break;
            case 3:
                stat = I;
                break;
            case 4:
                stat = G;
                //interruttore = 1;
                break;
        }
        
    } else {
        switch(sens){
            case 1:
                stat = IN1[stat];
                break;
            case 2:
                stat = IN2[stat];
                break;
            case 3:
                stat = IN3[stat];
                break;
            case 4:
                stat = IN4[stat];
                break;
            case 5:
                stat = IN5[stat];
                break;
            case 6:
                stat = IN6[stat];
                break;
            case 7:
                stat = IN7[stat];
                break;
        }
    }

    /*if(interruttore){
        stat = G;
    }*/
    
// parte 2, se lo stato Ë cambiato, scegliere una nuova azione
    if (old_stato != stat) {
        //azioneG();
        switch( stat ) {
            case 0:
                azioneA() ;
                break;
            case 1:
                azioneB() ;
                break;
            case 2:
                azioneC() ;
                break;
            case 3:
                azioneD() ;
                break;
            case 4:
                azioneE() ;
                break;
            case 5:
                azioneF() ;
                break;
            case 6:
                azioneG() ;
                break;
            case 7:
                azioneH() ;
                break;
            case 8:
                azioneI() ;
                break;

        }    
    }


    return stat;
}

//A - ruota destra
void azioneA(void){
    //motore dx indietro
    PORTCbits.RC4=1;
    PORTCbits.RC6=0;
    //motore sx avanti
    PORTCbits.RC0=0;
    PORTCbits.RC2=1;
    ritardolungo();
}

//B - ruota sinistra
void azioneB(void){
    //motore dx avanti
    PORTCbits.RC4=0;
    PORTCbits.RC6=1;
    //motore sx indietro
    PORTCbits.RC0=1;
    PORTCbits.RC2=0;
    ritardolungo();
}

//C - vai a destra
void azioneC(void){
    //motore dx fermo
    PORTCbits.RC4=1;
    PORTCbits.RC6=1;
    //motore sx avanti
    PORTCbits.RC0=0;
    PORTCbits.RC2=1;
    ritardolungo();
}

//D - vai a sinistra
void azioneD(void){
    //motore dx avanti
    PORTCbits.RC4=0;
    PORTCbits.RC6=1;
    //motore sx fermo
    PORTCbits.RC0=1;
    PORTCbits.RC2=1;
    ritardolungo();
}

//E - vai avanti
void azioneE(void){
    //motore dx avanti
    PORTCbits.RC4=0;
    PORTCbits.RC6=1;
    //motore sx avanti
    PORTCbits.RC0=0;
    PORTCbits.RC2=1;
    //ritardolungo();
}

//F - vai indietro
void azioneF(void){
    //motore dx indietro
    PORTCbits.RC4=1;
    PORTCbits.RC6=0;
    //motore sx indietro
    PORTCbits.RC0=1;
    PORTCbits.RC2=0;
    ritardolungo();
}

//G - fermo
void azioneG(void){
    //motore dx fermo
    PORTCbits.RC4=1;
    PORTCbits.RC6=1;
    //motore sx fermo
    PORTCbits.RC0=1;
    PORTCbits.RC2=1;
    ritardolungo();
}

//H - vai indietro verso destra
void azioneH(void){
    //motore dx fermo
    PORTCbits.RC4=1;
    PORTCbits.RC6=1;
    //motore sx indietro
    PORTCbits.RC0=1;
    PORTCbits.RC2=0;
    ritardolungo();
}

//I - vai indietro verso sinistra
void azioneI(void){
    //motore dx indietro
    PORTCbits.RC4=1;
    PORTCbits.RC6=0;
    //motore sx fermo
    PORTCbits.RC0=1;
    PORTCbits.RC2=1;
    ritardolungo();
}



//impostazione porte: portA -> out, portB -> in, portC -> out, portD -> in
void imposta_input_output(void){
  PORTA = 0;
  TRISA = 0b00000000;
  PORTB = 0;
  TRISB = 0b11111111;
  PORTC = 0;
  TRISC = 0b00000000;
  PORTD = 0;
  TRISD = 0b11111111;
}
28 Apr

The social animal – Part 1

The social animal is an academic textbook aimed to give the reader an introduction to the subject of social psychology. The author is the American psychologist Elliot Aronson.
A few years ago, while I was walking in Pavia (Italy) with a friend of mine, I told him that since I had attended a job course about interaction with customers, persuasion, and Neuro Linguistic Programmation, I had figured out that I was very interested to social psychology. I didn’t have a clear idea about what psychology was, but I wanted to understand more about the human brain and the behave of people in the society. Since my friend was a psychology student, he advised me the book “The social animal of Elliot Aronson”, telling me I would have liked it very much.
Well, I took more than 2 years to follow my friend’s advice, but I can now say that he was completely right. I enjoyed to read this book, and I decided to sum up the best of it in my blog. I would like to thank my friend Franco for the good advise. I strongly recommend the book to anybody who would like to understand more about people and their behaviors in our society. 🙂

The Social Animal

– People who do crazy things are not necessarily crazy.
This is what Aronson calls ‘his first law’. As you can imagine he doesn’t want to justify thieves and murderers with that statement, but he simply wants to explain that our behave and our decisions can be easily influenced by things and people around us. As a social psychologist, the author researched and studied those triggers, and organised his findings in his book.
Often, what seems to be craziness is only an automatic mechanism in our mind, brought from years of evolution.
Humans are social animals, and this is not new. It’s easy to see how much each of us interact with others everyday. Examples are social networks and smartphones that let us communicate very easily, and today have huge impacts on our lives.

— — —
The first topic explained is conformity. Try to imagine that you are walking in a supermarket and suddenly everybody start running in one direction. You have not idea of what it’s going on but my guess is that you won’t lose too much time trying to understand it, rather you will start running as fast as you can in the same direction. That is exactly what conformity is. Last year I was living in Sydney and everyday I had to walk to the station and across busy streets. As everyone knows the red man on the traffic light means that people have to stop and wait, and although this is an important rule, it is not always followed. Someone might be late and might refuse to wait if there are no cars coming. This would be an exception, but what surprised me was that once the first brave person crossed the street, he or she was followed from many others. They were probably thinking: “If he can do it, why I can’t?…”.

Conformity

This behave to conform to others is very common in our society, and largely explained in the book. Aronson gives three reasons of why we conform to society and people around us.
Our tendency to conform to avoid a punishment or to gain a reward is called compliance. This reason to conform does not change our beliefs or attitudes. For example, if while we are driving we slow down only because we know there is a speed camera, we are conforming to the street rules, but we won’t change our belief. We will conform only to avoid a big fine, and if the speed camera will be removed we might decide to exceed the limit when the road is free and we think it’s not dangerous.
The second reason why we conform to others is identification. It differs from compliance because when we conform for identification we don’t do it to avoid a reward or punishment, but because we like a person or a group and we want to be like them. This reason for conform might change with time our own values and beliefs. However, we may turn back to our own believes if our opinion for this person or group change.
The third, and most permanent reason to conform is internalisation. The motivation we have to internalise a particular belief and make it our own is the desire to be right. When we think a person is trustworthy and with good judgment, we accept the belief he or she advocates and we integrate it into our system of values. Once it is part of us, it’ll become very resistant to change. Conform for identification is not as easy as it seems. Indeed, Aronson shows with a few experiments that it is fundamental “who says what to who?”. Our opinions are influenced only by expert and trustworthy individuals. We will then trust more a doctor than our uncle on medical decisions and so on.

— — —
A second topic I found interesting was the explanation of judgmental heuristics.
Heuristics are something we use everyday in almost every situation of our life. They can be translated in “mental shortcuts”: simple, and often approximate rules or strategy for solving a problem. We make decisions and judgements all the time, and if we carefully consider and analyse every possible outcome of them, we would not do anything else. Our mind provide us with heuristics to make every decision easier for us, without the need to think about every option every time.

Heuristic

For example, if you meet three people from a different country and they are all amusing, you will assume that the country has an amusing culture and that most other people from there will also be fun and pleasant. This is an example of one of three common heuristics Aronson mentions in his book: the representative heuristic.
The representative heuristic facilitate us to make a decision by comparing information to our mental prototypes. In the previous example, the three amusing people will be our prototype for everyone else from that country. If I see two bottles of wine on the shelf and one has a higher price, I leap to the conclusion that the more expensive one is the better wine because I know that high-quality products are usually expensive. If at the supermarket we see cereals with a colorful box we are driven to believe they are full of sugar, while if they have a brown pack with spikes of grain’s picture they must be full of fiber and good proprieties. We often select only one feature to decide if wine or cereal are good or bad, and probably the wrong feature.
The second common heuristic is the availability heuristic, which helps us to make a decision based on how easy it is to bring something to mind. An example of it is the huge influence that media has on people. We are likely to overestimate each event we see in television or on social networks because it comes easily to mind. For example, after seeing several news reports about car thefts, you might believe that vehicle theft is more common than it really is in your country.
The attitude heuristic is the last the author quotes in the book. An attitude is a special type of belief
that includes an emotional component. The attitude heuristic uses the emotional component to assign an object or a person to a favorable or unfavorable class. To better understand this heuristic, another dimension of it is the halo effect, which I described better in my last article.

02 Apr

The Halo Effect

Our brain is an incredible tool, but despite its power and all its amazing features it isn’t perfect and it can make us believe to wrong information or make the wrong decision. In my opinion everyone should have a little knowledge about these errors (biases) and limitations of our mind. Once we know some of the common biases of the human mind, we can begin to think a little better and make smarter decisions. My idea is to write about some of these limitations. In my first article I talked about the Optimistic bias which help us to be optimistic about ourself even if sometimes it can be a double side sword. Today I’ll talk about another very interesting bias which leads people to like or dislike everything about one person or object just after the first impression. The name of this bias is the Halo Effect. The term has been in use in psychology for a century but it has not come in use in everyday language. The psychologist Edward Thorndike was the first who studied the halo effect and gave the phenomenon its name in his 1920 article “A Constant Error in Psychological Ratings”.

If you think about your favourite actress, you will probably realise that you like her voice as well as her films and appearance. On the other hand, if you dislike a politician, you probably disagree with him and hate the way he talks. This tendency is the halo effect. Being able to recognise it can be very useful for many reason: we may realise how important the first impression is when we try to sell something or when we meet a person for the first time. Furthermore, we might avoid to make hasty decisions we would make because of it.
We tend to think that celebrities are smarter, healthier and more creative. What we know of them is a very small part of their personality but our brain assumes that if they are attractive, they are probably also intelligent. Since when we were children, we learned that the good is beautiful, and the bad is ugly. The princess was always kept in prison by the ugly witch and she needed to be saved from the awesome prince. Beautiful people are always advantaged because unconsciously considered better from our society.

Halo Effect

An advantage of the halo effect is that it works as an heuristic, or mental shortcut. We don’t need to analyse every action someone do but we can base our decision to trust someone or not on our overall impression. This might also be a disadvantage though. For instance, people who are physically attractive can better persuade others. They are also considered friendlier and more talented. That’s the reason why sales men are always well dressed and tidy. The first impression results to be valid in all sorts of domains: job opportunities, dating, and even daily life. An enthusiastic behaviour with a remarkable appearance make the difference between a successful meeting and a regrettable one. The first impression will remain the same, bringing advantages (or disadvantages) in the interaction with people over the time. If people like you, they will forgive you for your “wrongs” and remember your “rights”.

As the image above wants to demonstrate, the halo effect may often drive us to the wrong conclusion. Even if we now know how strong is the first impression we cannot remove this bias from our brain. However, if we are aware of our tendency to overestimate a beautiful smile and underestimate a serious person, we can try to learn better about someone or something before to make a decision. A second big advantage is that we may use this bias to improve our interaction with people. The importance of the first sentences in a presentation and a big smile are straightforward. The first 5 minutes might create the idea one person has about you for the next 5 years.

Remember that you never get a second chance to get a first impression.

16 Mar

The optimistic bias

In this my first article about psychology, I will talk about the optimistic bias: the tendency of people to overestimate good events like our career in the future and underestimate bad events. A personal example that demonstrate the optimistic bias is the certainty I had last year to permanently live in Australia for more than 2 years. I did not evaluate if it would have been worth it or not, and I am now not as sure as before. Another example are marriages. 100% of spouses believe that their marriage will last forever, but as you can see in everyday life the divorce rates in the world is very high: from 20% to even more than 50% in some countries like Spain or Portugal.

Are you more or less intelligent of the average person? What about your driving capability or the capacity to get along with people? Most of us believe to be over the average person in these and many other abilities. Well, we cannot all be better than everyone else because this is statistically impossible. 🙂

OptimisticBias

So, is the optimistic bias positive or negative? Most of the people might assume that this bias is not positive and that to have low expectation is better because when things don’t happen we are not going to be disappointed. Furthermore, if something good happen we are happier because it wasn’t expected.
This assumption turn out to be false. Tali Sharot, psychologist and author of the book “The optimistic bias”, gives three reasons why an high expectation is better in her Ted Talk.

First, the interpretation of an event matters. People with high expectation will always feel better because when they succeed they attribute that success to their own characteristics and when they fail they attribute the reason of that failure to other factors. They know that the failure is just an exception and therefore they know the next time they’ll do better. On the other hand, people with low expectation do the opposite: when they fail they know it was because they were not able to do better, and when the succeed it was just because they were lucky and next time they will do bad as usual.

The second reason is that anticipation enhance reality. This means that envisioning future positive events can produce a very positive emotional response. The happiness of doing something we’ve been looking forward to does not increase only in that day but also during the days before. In a study which asked people if they were more willing to pay to kiss your favourite celebrity after 1 hour, or after 3 days, the majority of them chose the second option. The extra hours gave people more time to imagine the event. This is also the reason why people prefer Friday to Sunday. On Friday people can anticipate their weekend, on Sunday instead the only thing they can anticipate is the work week.

The third reason is that optimism is not only related to success but it also leads to success. Experiments have demonstrated that if we expect an amazing future, stress and anxiety are reduced. Furthermore, if we have high expectations we are much more willing to work harder to achieve our goals and succeed. Optimism has a lot of benefits.

Optimism - Like a picture

Of course, too much optimism may be also dangerous. If some smokers think the probability they’ll get lung cancer is 10%, but studies says the average is 5%, they’ll efficiently change their believe to 5 or 6 percent. On the other hand, if smokers think the probability for them is 1 or 2 percent, they will not change their believe even after the statistical results say it’s 5%. This means that we believe to signal like “Smoking kills” but we think that mostly it kills other people. Moreover, the optimistic bias can lead us to unnecessary risks with our health or finance. An example might be people who don’t respect speed limits in highways or people who invest huge amounts of money even if they know the percentage of success is incredibly low.

It is scientifically possible to eliminate the bias with electromagnetic impulses to some area of our brain. The question is “Do we really want to get rid of the optimistic bias?”. We have seen the benefits and harms of this bias. Being aware of them means that we may be able to control it more. It is important to have high expectation to improve our future but if we jump from a cliff without a parachute because we are optimist, that might not be a very good decision. The solution is to balance and follow our dreams being aware of the reality