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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-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.

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.

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).