Grid-texture mechanisms in human vision:contrast detection of regular sparse micro-patterns requires specialist templates

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

Dissociation of detection and evaluation of facial expressions in adolescence

Holistic face perception, i.e. the mandatory integration of featural information across the face, hasbeen considered to play a key role when recognizing emotional face expressions (e.g., Tanaka et al.,2002). However, despite their early onset holistic processing skills continue to improvethroughout adolescence (e.g., Schwarzer et al., 2010) and therefore might modulate theevaluation of facial expressions. We tested this hypothesis using an attentional blink (AB)paradigm to compare the impact of happy, fearful and neutral faces in adolescents (10–13 years)and adults on subsequently presented neutral target stimuli (animals, plants and objects) in a rapidserial visual presentation stream. Adolescents and adults were found to be equally reliable whenreporting the emotional expression of the face stimuli. However, the detection of emotional butnot neutral faces imposed a significantly stronger AB effect on the detection of the neutral targetsin adults compared to adolescents. In a control experiment we confirmed that adolescents ratedemotional faces lower in terms of valence and arousal than adults. The results suggest a protracteddevelopment of the ability to evaluate facial expressions that might be attributed to the latematuration of holistic processing skills.

Hope comes in many forms:out-group expressions of hope override low support and promote reconciliation in conflicts

In conflicts, political attitudes are based to some extent on the perception of the outgroup as sharing the goal of peace and supporting steps to achieve it. However, intractable conflicts are characterized by inconsistent and negative interactions, which prevent clear messages of outgroup support. This problem calls for alternative ways to convey support between groups in conflict. One such method is emotional expressions. The current research tested whether, in the absence of outgroup support for peace, observing expressions of outgroup hope induces conciliatory attitudes. Results from two experimental studies, conducted within the Israeli-Palestinian conflict, revealed support for this hypothesis. Expressions of Palestinian hope induced acceptance of a peace agreement through Israeli hope and positive perceptions of the proposal when outgroup support expressions were low. Findings demonstrate the importance of hope as a means of conveying information within processes of conflict resolution, overriding messages of low outgroup support for peace.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.