Mathematical formula for self-esteem

A team of researchers has devised a mathematical equation that can explain how our self-esteem is shaped by what other people think of us.


Credit: Geert-Jan Will

The researchers from UCL used the new equation to identify signals in the human brain that explain why self-esteem goes up and down when we learn other people’s judgments of us. They say the findings could help identify people at risk of psychiatric disorders, including eating disorders, anxiety disorders, and depression.

For the study, 40 healthy participants did a social evaluation task while in an MRI scanner. After uploading a profile to an online database, they received feedback, ostensibly given by 184 strangers (actually an algorithm), in the form of a thumbs-up (like) or thumbs-down (dislike). The ‘strangers’ were in different groups so that participants learned to expect positive feedback from some groups of raters, and negative feedback from other groups. After every 2-3 trials, participants reported on their self-esteem at that moment.

Participants expected to be liked by ‘strangers’ in the groups that mostly gave positive feedback, so when they received a thumbs-down from a person in that group, their self-esteem took a hit. These social prediction errors — the difference between expected and received feedback — were key for determining self-esteem.

“We found that self-esteem changes were guided not only by whether other people like you, but were especially dependent on whether you expected to be liked,” Dr Will said.

The research team developed a model of the neural processes at play when appraisals impact self-esteem, finding that social prediction errors and changes in self-esteem resulting from these errors were tied to activity in parts of the brain important for learning and valuation.

The researchers then combined their computational model with clinical questionnaires to explore the neural mechanisms underlying vulnerability to mental health problems. They found that people who had greater fluctuations in self-esteem during the task also had lower self-esteem more generally and reported more symptoms of depression and anxiety. People in this group showed increased prediction error responses in a part of the brain called the insula, which was strongly coupled to activity in the part of the prefrontal cortex that explained changes in self-esteem. The researchers hypothesise that such a pattern of neural activity could be a neurobiological marker that confers increased risk for a range of common mental health problems.

“By combining our mathematical equation for self-esteem with brain scans in people as they found out whether other people liked them, we identified a possible marker for vulnerability to mental health problems. We hope these tools can be used to improve diagnostics, enabling mental health professionals to make more specific diagnoses and targeted treatments,” said Dr Robb Rutledge (Max Planck UCL Centre for Computational Psychiatry & Ageing Research).

Read more (University College London. “Self-esteem mapped in the human brain.” ScienceDaily. ScienceDaily, 24 October 2017.)

Original paper: Will, G.J., Rutledge, R.B., Moutoussis, M. and Dolan, R.J., 2017. Neural and computational processes underlying dynamic changes in self-esteem. Elife6, p.e28098.

Cupido – Love is blind

Video description:

Little Cupido fighting for love in Paris.

Created by Simon Bau, Clémentine Choplain, Marie Ecarlat, Benoît Huguet, Julien Soulage
Music : Damien Deshayes
Recording and Mixing : José Vicente et Yoann Poncet /
Ecole Supérieure des Métiers Artistiques de Montpellier 2012

Luminar’s LiDAR on Toyota Research Institute’s self-driving car


Luminar is a startup that emerged from stealth earlier this year after five years developing its unique LIDAR architecture from the ground up. Now, Luminar is revealing the first of its four current major partners: Toyota Research Institute (TRI), the research and development organization created by the global automaker to focus on robotics, autonomous vehicles and AI breakthroughs.


Credit: Luminar

TRI is using Luminar’s LIDAR in its new Platform 2.1 autonomous test vehicle, having selected the solution because of its unmatched capabilities in terms of being able to see at a distance, and to also perceive objects that traditionally have very low reflectivity for laser light, which makes them a lot harder to pick up via LiDAR. These include common objects on the road including tires, and anything else with a dark, matte surface.

Luminar’s solution uses only a single laser to achieve its measurements, which is remarkable in term of also offering cost and production scale benefits.

Ro: Luminar este un startup care a ieșit din ascunzătoare mai devreme anul acesta, după cinci ani în care au dezvoltat de la zero o arhitectură unică pentru LIDAR. Acum Luminar dezvăluie primul din cei 4 parteneri majori și anume Institutul de Cercetare Toyota (TRI), care a fost creat cu scopul de a se focusa pe robotică, vehicule autonome și pe descoperiri în domeniul inteligenței artificiale.


Credit: Luminar

TRI folosește LIDAR-ul produs de cei de la Luminar pentru noul vehicul autonom pentru testare, ei alegând soluția aceasta pentru capabilitățile senzorului de a vedea la distanță și de a percepe obiecte care în mod tradițional au o reflectivitate scăzută pentru lumina laser, ceea ce le face foarte dificil de detectat cu un senzor laser. Aceste obiecte includ anvelopele sau obiectele mate, închise la culoare.

Soluția celor de la Luminar folosește doar un singur laser pentru a obține măsurătorile, ceea ce este remarcabil, în același timp oferind optimizări ale costului și producției.

Read more here (Darrell Etherington, “Luminar’s game-changing LiDAR makes its way to TRI’s self-driving car”, TechCrunch, 27.09.2017)

Online Cross-Calibration of Camera and LIDAR


Eng: In an autonomous driving system, drift can affect the sensor’s position, introducing errors in the extrinsic calibration. For this reason, we have developed a method which continuously monitors two sensors, camera, and LIDAR with 16 beams, and adjusts the value of their cross-calibration. Our algorithm, starting from correct values of the extrinsic cross-calibration parameters, can detect small sensor drift during vehicle driving, by overlapping the edges from the LIDAR over the edges from the image. The novelty of our method is that in order to obtain edges, we create a range image and filter the data from the 3D point cloud, and we use distance transform on 2D images to find edges. Another improvement we bring is applying motion correction on laser scanner data to remove distortions that appear during vehicle motion. An optimization problem on the 6 calibration parameters is defined, from which we are able to obtain the best value of the cross-calibration, and readjust it automatically. Our system performs successfully in real time, in a wide variety of scenarios, and is not affected by the speed of the car.

Ro: Într-un sistem de conducere autonom, senzorii pot deriva de la poziția lor inițială, ceea ce introduce erori în calibrarea parametrilor extrinseci. De aceea am dezvoltat o metodă care monitorizează continuu cei doi senzori – camera și LIDAR cu 16 straturi, și ajustează valoarea calibrării. Pornind de la un set corect al valorilor parametrilor, algoritmul nostru poate detecta mișcări ale senzorilor în timpul conducerii autovehiculului prin suprapunerea muchiilor din LIDAR peste muchiile din imagini. Noutatea metodei noastre constă în utilizarea unei imagini de adâncime și filtrarea punctelor 3D pentru aflarea muchiilor dintr-un nor de puncte și utilizarea algoritmului distance transform pentru detectarea muchiilor din imagini. O altă îmbunătățire adusă este aplicarea unui corecții a datelor obținute cu senzorul laser în timpul mișcării și înlăturarea deformărilor. O problemă de optimizare a celor 6 parametrii de calibrare este definită, ceea ce permite obținerea celei mai bune valori și reajustarea automatică. Sistemul nostru se execută în timp real, într-o mare varietate de scenarii și nu este afectat de viteza mașinii.