The role of social cognition in decision making

Successful decision making in a social setting depends on our ability to understand the intentions, emotions and beliefs of others. The mirror system allows us to understand other people's motor actions and action intentions. ‘Empathy’ allows us to understand and share emotions and sensations with others. ‘Theory of mind’ allows us to understand more abstract concepts such as beliefs or wishes in others. In all these cases, evidence has accumulated that we use the specific neural networks engaged in processing mental states in ourselves to understand the same mental states in others. However, the magnitude of the brain activity in these shared networks is modulated by contextual appraisal of the situation or the other person. An important feature of decision making in a social setting concerns the interaction of reason and emotion. We consider four domains where such interactions occur: our sense of fairness, altruistic punishment, trust and framing effects. In these cases, social motivations and emotions compete with each other, while higher-level control processes modulate the interactions of these low-level biases

A catalogue of bright (K <9) M dwarfs

Using the Position and Proper Motion Extended-L (PPMXL) catalogue, we have used optical and near-infrared colour cuts together with a reduced proper motion cut to find bright M dwarfs for future exoplanet transit studies. PPMXL's low proper motion uncertainties allow us to probe down to smaller proper motions than previous similar studies. We have combined unique objects found with this method to that of previous work to produce 8479 K <9 M dwarfs. Low-resolution spectroscopy was obtained of a sample of the objects found using this selection method to gain statistics on their spectral type and physical properties. Results show a spectral-type range of K7-M4V. This catalogue is the most complete collection of K <9 M dwarfs currently available and is made available here.Peer reviewe

Pairwise alignment incorporating dipeptide covariation

Motivation: Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrixes that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations, and by assessing the ability of this algorithm to detect remote homologies. Results: Our analysis indicates that local correlations between substitutions are not strong on the average. Furthermore, incorporating local substitution correlations into pairwise alignment did not lead to a statistically significant improvement in remote homology detection. Therefore, the standard assumption that individual residues within protein sequences evolve independently of neighboring positions appears to be an efficient and appropriate approximation

What we observe is biased by what other people tell us: beliefs about the reliability of gaze behavior modulate attentional orienting to gaze cues

For effective social interactions with other people, information about the physical environment must be integrated with information about the interaction partner. In order to achieve this, processing of social information is guided by two components: a bottom-up mechanism reflexively triggered by stimulus-related information in the social scene and a top-down mechanism activated by task-related context information. In the present study, we investigated whether these components interact during attentional orienting to gaze direction. In particular, we examined whether the spatial specificity of gaze cueing is modulated by expectations about the reliability of gaze behavior. Expectations were either induced by instruction or could be derived from experience with displayed gaze behavior. Spatially specific cueing effects were observed with highly predictive gaze cues, but also when participants merely believed that actually non-predictive cues were highly predictive. Conversely, cueing effects for the whole gazed-at hemifield were observed with non-predictive gaze cues, and spatially specific cueing effects were attenuated when actually predictive gaze cues were believed to be non-predictive. This pattern indicates that (i) information about cue predictivity gained from sampling gaze behavior across social episodes can be incorporated in the attentional orienting to social cues, and that (ii) beliefs about gaze behavior modulate attentional orienting to gaze direction even when they contradict information available from social episodes

Solvable two-dimensional time-dependent non-Hermitian quantum systems with infinite dimensional Hilbert space in the broken PT-regime

We provide exact analytical solutions for a two-dimensional explicitly time-dependent non-Hermitian quantum system. While the time-independent variant of the model studied is in the broken PT-symmetric phase for the entire range of the model parameters, and has therefore a partially complex energy eigenspectrum, its time-dependent version has real energy expectation values at all times. In our solution procedure we compare the two equivalent approaches of directly solving the time-dependent Dyson equation with one employing the Lewis–Riesenfeld method of invariants. We conclude that the latter approach simplifies the solution procedure due to the fact that the invariants of the non-Hermitian and Hermitian system are related to each other in a pseudo-Hermitian fashion, which in turn does not hold for their corresponding time-dependent Hamiltonians. Thus constructing invariants and subsequently using the pseudo-Hermiticity relation between them allows to compute the Dyson map and to solve the Dyson equation indirectly. In this way one can bypass to solve nonlinear differential equations, such as the dissipative Ermakov–Pinney equation emerging in our and many other systems

Big data, method and the ethics of location : a case study of a hookup app for men who have sex with men

With the rise of geo-social media, location is emerging as a particularly sensitive data point for big data and digital media research. To explore this area, we reflect on our ethics for a study in which we analyze data generated via an app that facilitates public sex among men who have sex with men. The ethical sensitivities around location are further heightened in the context of research into such digital sexual cultures. Public sexual cultures involving men who have sex with men operate both in spaces “meant” for public sex (e.g., gay saunas and dark rooms) and spaces “not meant” for public sex (e.g., shopping centers and public toilets). The app in question facilitates this activity. We developed a web scraper that carefully collected selected data from the app and that data were then analyzed to help identify ethical issues. We used a mixture of content analysis using Python scripts, geovisualisation software and manual qualitative coding techniques. Our findings, which are methodological rather than theoretical in nature, center on the ethics associated with generating, processing, presenting, archiving and deleting big data in a context where harassment, imprisonment, physical harm and even death occur. We find a tension in normal standards of ethical conduct where humans are involved in research. We found that location came to the fore as a key - though not the only - actor requiring attention when considering ethics in a big data context

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i