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The self-prioritization effect (SPE) refers to the effect that performance on cognitive tasks is better when stimuli are related to the self than when they are not. In the last decade, the self -perceptual matching task (SPMT) has emerged as a mainstream paradigm for studying SPE due to its simplicity and elimination of familiarity effects. As a simple button-pressing task, SPMT yields two outcomes: reaction time and accuracy. Other indices can be derived from reaction times and accuracy, including sensitivity d prime under signal-detection theory, the efficiency index through a direct division between reaction times and accuracy, and drift rate (v) and starting point (z) estimated using drift-diffusion models. All these indices have been used to quantify SPE in the literature. However, the reliability of these SPE indices has never been tested. To fill this gap, we will re-analyze existing data from multiple datasets using intraclass correlation coefficient (ICC) and split-half reliability. Our results will provide a benchmark for future studies.

The self is multifaceted. We sometimes bias information about ourselves over others and sometimes we bias information about others such as a friend over strangers, leading to a self-prioritization or friend-prioritization effect. However, it remains unknown how the shifting facet of the self supports such context-dependent social judgement. The self, serving as a navigational reference point, not only reflects an evolutionary pressure to evade threats but also plays a vital role in contemporary social interactions. The bodily self-representation is a cognitive and perceptual process through which individuals develop a sense of body ownership, agency, and awareness of their own bodies at a specialised location (Serino et. al., 2013). Research has shown that there is a self-prioritisation effect in near-space processing (Huang et. al., 2021) and that people have varied preferences for information related to different people in interpersonal spaces with a distorted perception of distance for their belonging needs (Bogdanova et. al., 2021; Knowles, Green & Weidel, 2014; Lenglart et. al., 2022; Perry et. al., 2013). In this study, we will investigate whether manipulating bodily self-consciousness in different spatial contexts (i.e., personal space, social space) can activate distinct aspects of the self, and how accessing specific facets of the self would influence judgement when comparing processing information related to different self-relevance (Self, Friend, and Stranger) within and across spatial dimensions. Three experiments will be conducted in this study. Experiment 1 examines how different facets of the self are activated when information is projected into different spaces (Personal, Social), and how this affects judgement when comparing processing information related to self or others (i.e., Self, Friend, Stranger). Experiment 2 will be a replication of Experiment 1 except that the sizes of personal and social spaces will be defined by participants individually, and Experiment 3 will examine the cultural effect (contact vs. non-contact culture) on the observed patterns in Experiments 1 and 2, given that everyday behaviour is subject to our cultural background. Prior to the experiments, a pilot experiment was carried out to estimate the sample size for the interest interaction between Space (Near-personal vs. Far-social) and three-dimensional objects embedded with different self-relevance (Self, Friend, Stranger) in a distance estimation task utilising virtual reality technology. Participants first conducted a shape-label matching task where three 3-D objects were paired with three people (Self, Friend, Stranger). Following the learning of object-person associations, they performed a distance estimation task. In this task, participants were displayed with multiple objects moving inside an imaginary cube, which was located at different distances (Near-personal Space and Far-social Space) in relation to the participants. They were instructed to use the keyboard to move a red dot to indicate the centre of the imaginary cube in the VR environment. Distance between the estimated and actual centre was recorded as the dependent variable to quantify the performance of spatial perception as a function of Object (Self, Friend, Stranger) and Space (Near-personal vs. Far-social).

Self bias, seen in perceptual matching tasks, has been found to be stable across many settings. However, questions have been raised as to whether this is due to the label present. Therefore, is self bias seen in a perceptual matching task without labels?

Healthy individuals typically show reliable biases favouring the self and positive emotion compared to others and sad expressions. Inducing low mood in healthy people can lead to the reduction of these biases. This study will explore whether changes in self and positive emotional biases are found in individuals with low mood and if the changes predict mood state, well-being (e.g., low levels of life satisfaction and wellbeing, or high anxiety levels), and personality traits.

Strong evidence shows that the self prioritisation effect (faster and more accurate responses to information related to the self) is stable and present across a variety of perceptual matching tasks. However, recent studies have shown that the labels used may be driving this bias. Therefore, we will measure the self prioritisation effect in a perceptual matching task without using labels. Participants will learn pairings of colour and shapes that represent themselves, a friend and stranger and later judge whether pairings shown are the same as those learnt. This will explore whether the self prioritisation effect is found when all familiarity with stimuli is removed.

Healthy individuals typically show reliable biases favouring the self and positive emotion compared to others and sad expressions. Inducing low mood in healthy people can lead to the reduction of these biases. This study will explore whether changes in self and positive emotional biases are found in individuals with low mood and if the changes predict mood state, well-being (e.g., low levels of life satisfaction and wellbeing, or high anxiety levels), and personality traits.

Healthy individuals typically show reliable biases favouring the self and positive emotion compared to others and sad expressions. Inducing low mood in healthy people can lead to the reduction of these biases. This study will explore whether changes in self and positive emotional biases are found in individuals with low mood and if the changes predict mood state, well-being (e.g., low levels of life satisfaction and wellbeing, or high anxiety levels), and personality traits.

Evidence suggests that self-relevant information is processed faster than information not relevant to the self. Similar processing advantages are observed with objects and people close to the self (e.g., friends, family) and even social groups that one belongs to. Importantly, existing studies assessed such ingroup effects using artificial stimuli without accounting for potential effects that social presence of group members may have on information processing. Here we address this issue by comparing responses on a perceptual matching task between ingroup and outgroup members when the stimuli presented are artificial (i.e., color splat) as well as realistic human avatars.

The self prioritisation effect (enhanced performance when information is related to the self) has been widely reported, however, the underlying mechanisms remain uncertain. Some argue that self prioritisation occurs due to self-related information being rewarding and hence reward mechanisms are activated. But, recent studies using redundancy gain experiments have found that reward and self related stimuli lead to different responses. Do stimuli associated with the self and high reward have distinct representations? Does high reward change integrative process in the same way as self-association? This study aims to test this by having self and reward associated with the same response key, and then to assess whether super additivity is generated with the self and high-reward condition.

Self-bias had been proved in many areas, such as human faces, names, self-belongings and so on. Icons and symbols are broadly used in everyday life, people assign them meanings in different ways. In this study, we try to figure out whether icons have the self-bias effect. First of all, to explore the perception difference of icons, we attempt to use the self-association procedure (Sui et al.,2012) to measure the bias effect of icons. Similarly, some common used up-vote icons (ie. thumbs-up sign, upward triangle) may also have the self-bias effect than to the down-vote icons (ie. thumbs-down sign, downward triangle). We plan to apply the self-association procedure to test these two group icons.

The present study aims to test whether and how self-relevance affects cross-modal integration via modulating audio-visual frequency perception. Within an online study, we will assess whether newly-learnt social relevance (by associating a specific event frequency with the self, and another event frequency with a different participant) affects the cross-modal modulation of perceived event frequency. We will use an audio-visual paradigm of perceived event numerosity, also known as the sound-induced flash-illusion (SIFI) in which audition typically exerts a stronger influence over visual information due to its higher temporal reliability (Adams, 2016; Hirst et al., 2019; Shams et al., 2004; Shams et al., 2005). The audio-visual event estimation task will be combined with a matching task, in order to allow better control over the decisional dimension (i.e. whether choices refer to the actual frequency count or the social association). Audio-visual event congruency (i.e. same vs. different amount of events presented across the senses) will be modulated in order to assess whether integration of multisensory information changes with social relevance. The study thereby probes the influence of perceptual processing on the emergence of self-prioritization in information processing, outlining whether and how social relevance is represented in (congruent) audio-visual numerosity information, and whether the integration of conflicting numerosity information across the senses is modulated by social relevance.

Self-related information receives prioritised processing, this is termed self-bias. Self-bias is observed in attention, memory, perception and decision-making. For example, in the shape-label matching tasks, when participants are told to associate shapes with different people, they will be fastest and most accurate at judging whether a presented pairing match one previously learnt when the pairing relates to themselves. The self-bias is usually considered extremely stable; however, mood disorders influence it. Negative mood induction leads to reduced self-biases (Sui et al., 2016). Further research has been conducted in naturally depressed individuals, but no change has been observed in cognitive self-bias. However, extension of the shape-label matching task to include emotional faces has found emotional insensitivity in clinically depressed populations (McIvor et al., 2020). This contrasts the self-negativity bias usually predicted by mood disorder theories (Beck, 1967, 1987). This contradiction may stem from the differences in explicit and implicit expressions of depression. In our recent pilot work using pre-clinically depressed individuals a negative self-bias is found. Currently depression diagnosis and treatment efficacy are largely dependent on explicit expressions measured by self-reporting. The dependency on self-reporting is problematic as it often leads to underreporting and may neglect important changes in cognitive functioning. The aim of this feasibility study is to explore whether implicit cognitive biases (e.g., self emotion-biases measured by the shape-label matching task) can track changes in mood in healthy, pre-clinically depressed and depressed individuals and hence be a potential tool for depression diagnosis, treatment efficacy evaluation and provide models for novel interventions.

This project aims to identify the cognitive and neural correlates of a stable representation of the self, its boundaries and internal organisation. We will assess the role of self-representation in integrating information across perception and decision making, using a self-association paradigm (Sui, He & Humphreys, 2012).

Self-related information receives prioritised processing, this is termed self-bias. Self-bias is observed in attention, memory, perception and decision-making. For example, in the shape-label matching tasks, when participants are told to associate shapes with different people, they will be fastest and most accurate at judging whether a presented pairing match one previously learnt when the pairing relates to themselves (Sui et al., 2012). The self-bias is usually considered extremely stable; however, mood disorders influence it. Negative mood induction leads to reduced self-biases (Sui et al., 2016). Further research has been conducted in naturally depressed individuals, but no change has been observed in self-bias. However, extension of the shape-label matching task to include emotional faces has found emotional insensitivity in depressed populations (McIvor et al., 2020). This contrasts the self-negativity bias usually predicted by mood disorder theories. This contradiction may stem from the differences in explicit and implicit expressions of depression. However, in pilot work using pre-clinically depressed individuals a negative self-bias is found. Traditional interventions such as cognitive behavioural therapy (CBT) have widely been used to reduce the negative biases in depression through conscious thought changes (Ward et al., 2000). CBT leads to a reduction in depression symptom severity and has interestingly been found to also reduce negative attentional biases indicating that attentional biases could be considered as clinical markers of depression and suggests a potential for targets of interventions (Segal & Gemar, 1997; Vazquez et al., 2018). Cognitive bias manipulation (CBM) to induce negative cognitive biases results in increased anxiety and depression. Previous studies using CBM to reduce depression have focused primarily on attention training to reduce focus to threatening stimuli and interpretation training to reduce the negative interpretation of ambiguous events. The results from these studies have been mixed and a meta-analysis finds that CBM has a medium effect on cognitive biases but only a small effect on symptoms (Hallion & Ruscio, 2011). This may be due to CBM targeting generalised negative biases (to attention or events) rather than specific self-negative biases. The aim of this feasibility study is to explore whether training self valenced implicit cognitive biases can increase self-positive biases, and/or reduce self-negative biases and therefore improve mood and reduce depressive symptoms.

Examining the stability of moral self cross different categorization task

Self-related information receives prioritised processing, this is termed self-bias. Self-bias is observed in attention, memory, perception and decision-making. For example, in the shape-label matching tasks, when participants are told to associate shapes with different people, they will be fastest and most accurate at judging whether a presented pairing matches one previously learnt when the pairing relates to themselves. The self-bias is usually considered extremely stable; however, mood disorders influence it. Negative mood induction leads to reduced self-biases. Further research has been conducted in naturally depressed individuals, but no change has been observed in self-bias. However, extension of the shape-label matching task to include emotional faces has found emotional insensitivity in depressed populations. This contrasts the self-negativity bias usually predicted by mood disorder theories. This contradiction may stem from the differences in explicit and implicit expressions of depression. Currently depression diagnosis and treatment efficacy are largely dependent on explicit expressions measured by self-reporting. The dependency on self-reporting is problematic as it often leads to underreporting and may neglect important changes in cognitive functioning. The aim of this feasibility study is to explore whether implicit cognitive biases (e.g., self emotion-biases measured by the shape-label matching task) can track changes in mood and hence be a potential tool for depression diagnosis, treatment efficacy evaluation and provide models for novel interventions.

Individuals prefer stimuli related to themselves and have an advantage in identifying these stimuli, which is known as the self-prioritization effect (SPE). In the past, most cognitive studies on SPE use stimuli that already have learned associations (e.g., self-face, self-name, or words), which usually include cofounding variables such as familiarity and personal preference. To address this issue, Sui and colleagues introduced the self associative learning task (or self-tagging task, shape-matching task) (Sui et al., 2012; Sui et al., 2009), where the stimuli used in the associative learning paradigm are geometric shapes. Typically, researchers found that shapes associated with the self were responded faster and more accurately, comparing to other labels in the matching condition. This effect is termed as “self-prioritization effect”. However, what does the SPE in shape-matching task tell us about human psychological capacity? Also, whether the self is a unique construct in human cognitive processing are still under debates. The current study will combine systematical review of theories and meta-analysis to test the specificity and uniqueness of the effect of self-associative learning on information processing. We hypothesised that under a unified theoretical framework, we can find that self-associative learning has different mechanisms as compared to other associative learning (reward, aversive, or affective learning).

Micro-vibration caused by disturbance sources onboard spacecraft can severely degrade the working environment of high-precision space optical instruments (HPSOIs) in orbit. Some notable micro-vibration suppression methods have been developed in recent decades. Micro-vibration suppression of spacecraft mainly involves three techniques: disturbance source suppression, payload isolation and transfer path suppression. The first two techniques, together forming one of the most practical suppression methods for micro-vibration suppression tasks, are mainly focused on isolation, whereas the third is a novel suppression method developed for the integrated optical satellite (IOS). Two combinations of them have been effectively used in some spacecraft for a number of years. In these three micro-vibration suppression techniques, the inherent structural performance can be adjusted to deal with variations in the space environment. Usually, passive isolation techniques are deployed in aerospace engineering. Active isolators, however, are often proposed to deal with the low-frequency vibration common in spacecraft. Some emerging advanced suppression methods that exploit the benefits of IOS for micro-vibration have also been reported in the literature. This represents an interesting and highly promising approach to solve some challenging problems in the field. This work serves as an effective state-of-the-art review of the micro-vibration suppression methods and/or technologies that have been developed, mainly over the last few years, specifically for or that potentially could be used for micro-vibration suppression onboard spacecraft with HPSOIs.

Supplemental material, sj-docx-1-jop-10.1177_02698811221116928 for Relationship between change in social evaluation learning and mood in early antidepressant treatment: A prospective cohort study in primary care by Catherine Hobbs, Milly Beck, Faye Denham, Laura Pettitt, Julian Faraway, Marcus R Munafò, Jie Sui, David Kessler and Katherine S Button in Journal of Psychopharmacology

Supplemental material, sj-docx-1-jop-10.1177_02698811221116928 for Relationship between change in social evaluation learning and mood in early antidepressant treatment: A prospective cohort study in primary care by Catherine Hobbs, Milly Beck, Faye Denham, Laura Pettitt, Julian Faraway, Marcus R Munafò, Jie Sui, David Kessler and Katherine S Button in Journal of Psychopharmacology

Supplemental material, sj-docx-1-qjp-10.1177_17470218221102887 for The relationship between self, value-based reward, and emotion prioritisation effects by Alla Yankouskaya, Gemma Lovett and Jie Sui in Quarterly Journal of Experimental Psychology

Supplemental material, sj-docx-1-qjp-10.1177_17470218221102887 for The relationship between self, value-based reward, and emotion prioritisation effects by Alla Yankouskaya, Gemma Lovett and Jie Sui in Quarterly Journal of Experimental Psychology

Autonomous driving has attracted great attention from both academics and industries. To realise autonomous driving, Deep Imitation Learning (DIL) is treated as one of the most promising solutions, because it improves autonomous driving systems by automatically learning a complex mapping from human driving data, compared to manually designing the driving policy. However, existing DIL methods cannot generalise well across domains, that is, a network trained on the data of source domain gives rise to poor generalisation on the data of target domain. In the present study, we propose a novel brain-inspired deep imitation method that builds on the evidence from human brain functions, to improve the generalisation ability of deep neural networks so that autonomous driving systems can perform well in various scenarios. Specifically, humans have a strong generalisation ability which is beneficial from the structural and functional asymmetry of the two sides of the brain. Here, we design dual Neural Circuit Policy (NCP) architectures in deep neural networks based on the asymmetry of human neural networks. Experimental results demonstrate that our brain-inspired method outperforms existing methods regarding generalisation when dealing with unseen data. Our source codes and pretrained models are available at https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems}{https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems.

Supplemental material, ChenOpenPracticesDisclosure for Automatic Prioritization of Self-Referential Stimuli in Working Memory by Shouhang Yin, Jie Sui, Yu-Chin Chiu, Antao Chen and Tobias Egner in Psychological Science

Supplemental material, YinSupplementalMaterial for Automatic Prioritization of Self-Referential Stimuli in Working Memory by Shouhang Yin, Jie Sui, Yu-Chin Chiu, Antao Chen and Tobias Egner in Psychological Science

Supplemental material, QJE-STD-17-354.R2-Supplementary_Material for The central locus of self-prioritisation by Markus Janczyk, Glyn W Humphreys and Jie Sui in Quarterly Journal of Experimental Psychology

With increasing occurrence of large-scale and half-closed traffic hubs like subway stations, more and more attention has been paid to emergency evacuation inside them. Based on Social Force Model, add a gas dispersion model into simulation environment. A Combined Social Force Model was built which is suitable to a real situation where there is a sudden toxic gas event. The improved evacuation model optimizes primary Social Force Model by taking into account the cohesion among passengers and the nervousness factor in emergency. The model integrates Gaussian Puff Model and can reflect the process of evacuation accurately. At last a corresponding simulation experiment was conducted and emphasizes the importance of gas positions and detecting emergency in time to efficient evacuation.

cDNA encoding Mn-superoxide dismutase (MnSOD) and 18S rRNA of Phascolosoma esculenta were cloned in this study. The partial MnSOD cDNA obtained consisted of 411 bp, encoded a protein of 137 amino acids and contained a MnSOD signature (DVWEHAYY). Physiological activity responses and expression of MnSOD mRNA in P. Esculenta exposed to benzo(a)pyrene (BaP) for 4, 24, 48 , 96, 168 and 336 h were also studied, and results indicate that P. Esculenta showed a strong ability to resist BaP stress. The expression levels of MnSOD mRNA were highest at 4 h of exposure, while the physiological activity levels of MnSOD were highest at 96 h, but then returned to baseline levels. The different response rates indicated that there was a very intricate regulating mechanism that prevents the damaging effects of the oxidative stress imposed by BaP. This result suggests that the expression of MnSOD mRNA in P. Esculenta in a short time can be used as a sensitive biomarker to alarm an early acute BaP contamination. Key words : Phascolosoma esculenta , benzo(a)pyrene (BaP), Mn-superoxide dismutase (MnSOD), gene expression, physiological activity.