Assessing the convergent validity between the automated emotion recognition software Noldus FaceReader 7 and Facial Action Coding System Scoring

This study validates automated emotion and action unit (AU) coding applying FaceReader 7 to a dataset of standardized facial expressions of six basic emotions (Standardized and Motivated Facial Expressions of Emotion). Percentages of correctly and falsely classified expressions are reported. The validity of coding AUs is provided by correlations between the automated analysis and manual Facial Action Coding System (FACS) scoring for 20 AUs. On average 80% of the emotional facial expressions are correctly classified. The overall validity of coding AUs is moderate with the highest validity indicators for AUs 1, 5, 9, 17 and 27. These results are compared to the performance of FaceReader 6 in previous research, with our results yielding comparable validity coefficients. Practical implications and limitations of the automated method are discussed

Individual Variation in Fathers’ Testosterone Reactivity to Infant Distress Predicts Parenting Behaviors with their 1-Year-Old Infants

Positive father involvement is associated with positive child outcomes. There is great variation in fathers’ involvement and fathering behaviors, and men’s testosterone (T) has been proposed as a potential biological contributor to paternal involvement. Previous studies investigating testosterone changes in response to father-infant interactions or exposure to infant cues are unclear as to whether individual variation in T is predictive of fathering behavior. We show that individual variation in fathers’ T reactivity to their infants during a challenging laboratory paradigm (Strange Situation) uniquely predicted fathers’ positive parenting behaviors during a subsequent father-infant interaction, in addition to other psychosocial determinants of paternal involvement, such as dispositional empathy and marital quality. The findings have implications for understanding fathering behaviors and how fathers can contribute to their children’s socioemotional development

Social closeness increases salivary progesterone in humans

We examined whether interpersonal closeness increases salivary progesterone. One hundred and sixty female college students (80 dyads) were randomly assigned to participate in either a closeness task with a partner versus a neutral task with a partner. Those exposed to the closeness induction had higher levels of progesterone relative to those exposed to the neutral task. Across conditions, progesterone increase one week later predicted the willingness to sacrifice for the partner. These results are discussed in terms of the links between social contact, stress, and health

Exploiting physico-chemical properties in string kernels

<p>Abstract</p> <p>Background</p> <p>String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas.</p> <p>Results</p> <p>We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels.</p> <p>Conclusions</p> <p>In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference.</p> <p>Availability</p> <p>Data sets, code and additional information are available from <url>http://www.fml.tuebingen.mpg.de/raetsch/suppl/aask</url>. Implementations of the developed kernels are available as part of the Shogun toolbox.</p