Selfhood triumvirate: From phenomenology to brain activity and back again

Recently, a three-dimensional construct model for complex experiential Selfhood has been proposed (Fingelkurts, Fingelkurts, & Kallio-Tamminen, 2016b,c). According to this model, three specific subnets (or modules) of the brain self-referential network (SRN) are responsible for the manifestation of three aspects/features of the subjective sense of Selfhood. Follow up multiple studies established a tight relation between alterations in the functional integrity of the triad of SRN modules and related to them three aspects/features of the sense of self; however, the causality of this relation is yet to be shown. In this article we approached the question of causality by exploring functional integrity within the three SRN modules that are thought to underlie the three phenomenal components of Selfhood while these components were manipulated mentally by experienced meditators in a controlled and independent manner. Participants were requested, in a block-randomised manner, to mentally induce states representing either increased (up-regulation) or decreased (down-regulation) sense of (a) witnessing agency (“Self”), or (b) body representational-emotional agency (“Me”), or (c) reflective/narrative agency (“I”), while their brain activity was recorded by an electroencephalogram (EEG). This EEG-data was complemented by first-person phenomenological reports and standardised questionnaires which focused on subjective contents of three aspects of Selfhood. The results of the study strengthen the case for a direct causative relationship between three phenomenological aspects of Selfhood and related to them three modules of the brain SRN. Furthermore, the putative integrative model of the dynamic interrelations among three modules of the SRN has been proposed.     

A small look at the ear recognition process using a hybrid approach

The purpose of this document is to offer a combined approach in biometric analysis field, integrating some of the most known techniques using ears to recognize people. This study uses Hausdorff distance as a pre-processing stage adding sturdiness to increase the performance filtering for the subjects to use it in the testing process. Also includes the Image Ray Transform (IRT) and the Haar based classifier for the detection step. Then, the system computes Speeded Up Robust Features (SURF) and Linear Discriminant Analysis (LDA) as an input of two neural networks to recognize a person by the patterns of its ear. To show the applied theory experimental results, the above algorithms have been implemented using Microsoft C#. The investigation results showed robustness improving the ear recognition process.     

Follow Your Heart: Could Psychophysiology Be Associated with Political Discussion Network Homogeneity?

Most Americans are sorted into social networks that are largely politically homogeneous. A large body of political science research has explored the behavioral implications of being embedded in a politically homogeneous or heterogeneous network, but substantially less attention has been given to explaining why some people find themselves in politically homogeneous or heterogeneous social networks. In this article, we explore the psychological and physiological underpinnings of political network homogeneity. We use social network data from an original survey of 129 undergraduates paired with lab experimental evidence that measures individuals' physiological reactivity to an anticipated political discussion. Using our original survey and a separate nationally representative survey, we find suggestive evidence that individuals who are more socially anxious are more likely to share partisanship with their social network ties. Moreover, we find that individuals who experienced a greater increase in heart rate when anticipating a political discussion were more likely to be in homogeneous discussion networks, but we do not find a relationship between electrodermal activity and network homogeneity. Aversion to psychological and physiological discomfort induced by political discussions could contribute to social polarization in the American public.     

社交网站中的自我呈现与青少年网络利他行为:网络社交效能和希望的作用

为考察社交网站中的积极自我呈现、真实自我呈现对青少年网络利他行为的影响以及网络社交效能和希望在其中的中介作用，采用社交网站中的积极自我呈现和真实自我呈现问卷、网络利他行为量表、网络社交效能问卷和希望量表对安徽、福建、河南、湖南、山西、天津、云南等省份八所中学的805名初中二年级学生（男生427名，女生378名；被试平均年龄为13.38岁，SD=0.63）进行问卷调查。研究显示：（1）社交网站中的积极自我呈现和真实自我呈现均能正向预测青少年网络利他行为；（2）网络社交效能在积极自我呈现与青少年的网络利他行为之间起中介作用，也在真实自我呈现与青少年的网络利他行为之间起中介作用；（3）希望在真实自我呈现与青少年的网络利他行为之间起中介作用。     

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Research Highlights

• This study investigates how neural networks address properties of child language.
• We focus on the Agent-First strategy in comprehension of Korean active transitive.
• Results show by-model/condition asymmetries against children's response patterns.
• This implies some limits of neural networks on revealing properties of child language.