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Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different settings. Here, we test whether this variability in learning ability relates to individual brain oscillatory states. We use a visual flicker paradigm to entrain individuals at their own brain rhythm (i.e. peak alpha frequency) as measured by resting-state electroencephalography (EEG). We demonstrate that this individual frequency-matched brain entrainment results in faster learning in a visual identification task (i.e. detecting targets embedded in background clutter) compared to entrainment that does not match an individual's alpha frequency. Further, we show that learning is specific to the phase relationship between the entraining flicker and the visual target stimulus. EEG during entrainment showed that individualized alpha entrainment boosts alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that brain entrainment facilitates early visual processing to support improved perceptual decisions. These findings suggest that individualized brain entrainment may boost perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition. Expected final online publication date for the Annual Review of Vision Science, Volume 9 is September 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Acknowledgements: We would like to thank the MR physics and radiographer teams at the Cambridge Wolfson Brain Imaging Centre, and Lilia Kukovska for their support and help with data collection. We would also like to thank Martina Callaghan, Nickolas Weiskopf, and the Wellcome Centre for Human Neuroimaging for providing the MPM sequences, and Guy Williams and Steve Sawiak for their expert assistance in MR-sequence testing. We would like to thank Thora Karadottir, Sonja Hofer, Jasper Poort, and Martina Callaghan for helpful comments and suggestions., Experience and training are known to boost our skills and mold the brain's organization and function. Yet, structural plasticity and functional neurotransmission are typically studied at different scales (large-scale networks, local circuits), limiting our understanding of the adaptive interactions that support learning of complex cognitive skills in the adult brain. Here, we employ multimodal brain imaging to investigate the link between microstructural (myelination) and neurochemical (GABAergic) plasticity for decision-making. We test (in males, due to potential confounding menstrual cycle effects on GABA measurements in females) for changes in MRI-measured myelin, GABA, and functional connectivity before versus after training on a perceptual decision task that involves identifying targets in clutter. We demonstrate that training alters subcortical (pulvinar, hippocampus) myelination and its functional connectivity to visual cortex and relates to decreased visual cortex GABAergic inhibition. Modeling interactions between MRI measures of myelin, GABA, and functional connectivity indicates that pulvinar myelin plasticity interacts-through thalamocortical connectivity-with GABAergic inhibition in visual cortex to support learning. Our findings propose a dynamic interplay of adaptive microstructural and neurochemical plasticity in subcortico-cortical circuits that supports learning for optimized decision-making in the adult human brain., This work was supported by grants to ZK from the Wellcome Trust [grant number 205067/Z/16/Z, 221633/Z/20/Z], the Biotechnology and Biological Sciences Research Council [grant numbers H012508, BB/P021255/1].

Peer reviewed: True, Funder: University of Oxford; Id: http://dx.doi.org/10.13039/501100000769, Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.

The use of computational models to assist in theory building and testing has significantly increased across the sciences and particularly within the cognitive psychology domain. There have been recent investigations examining how cognitive systems such as visual perception vary across individuals from different cultures in a branch of cognitive social psychology. Computational models have been effective in simulating many aspects of cognition, but just how extensive have models been used to inform theory and practice in both cognitive and cultural/social psychology? The review will aim to highlight how computational models have assisted domains of cultural-cognitive psychology.

Cultural differences in visual perceptual learning (VPL) could be attributed to differences in the way that people from individualistic and collectivistic cultures preferentially attend to local objects (analytic) or global contexts (holistic). Indeed, individuals from different cultural backgrounds can adopt distinct processing styles and learn to differentially construct meaning from the environment. Therefore, the present work investigates if cross-cultural differences in VPL can vary as a function of holistic processing. A shape discrimination task was used to investigate whether the individualistic versus collectivistic backgrounds of individuals affected the detection of global shapes embedded in cluttered backgrounds. Seventy-seven participants-including Asian (collectivistic background) and European (individualistic background) students-were trained to discriminate between radial and concentric patterns. Singelis's self-construal scale was also used to assess whether differences in learning could be attributed to independent or interdependent self-construal. Results showed that collectivists had faster learning rates and better accuracy performance than individualists following training-thereby reflecting their tendency to attend holistically when learning to extract global forms. Further, we observed a negative association between independent self-construal-which has previously been linked to analytic processing-with performance. This study provides insight into how socio-cultural backgrounds affect VPL.

The early stages of Alzheimer‘s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (-amyloid, medial temporal atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: a) stratifies patients based on future pathological tau accumulation and b) predicts individualised regional future rate of tau accumulation, c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD., This work was supported by grants to: Z.K. from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), Alan Turing Institute (TU/B/000095), Wellcome Trust (205067/Z/16/Z, 221633/Z/20/Z), Royal Society (INF\R2\202107); Z.K. and W.J.J. from the Global Alliance; W.J.J. US National Institute on Aging (AG034570, AG062542, AG024904). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).

The present project is part of a larger project aimed at examining cognitive flexibility, executive functions, and learning in healthy young adults (Tong et al., 2021) in Singapore. The present pre-registration aims to address specific hypotheses of interest derived from the larger project that might not be described here (https://osf.io/6rc9h) in detail. Cognitive flexibility involves the mental ability to switch or shift amongst different conceptual representations in response to the dynamic environment. It is considered to be a component of executive functioning and is critical to behavioral success (Friedman et al., 2006). Despite being an executive function, prior research showed that cognitive flexibility could be distinguished from other executive functions, such as working memory and inhibitory control that were commonly found to be highly correlated with each other (Friedman & Miyake, 2017; Blackwell et al., 2014; Mittal et al., 2015). One of the primary aims of the larger project (Tong et al., 2021) is to examine the separability of cognitive flexibility from other main executive functions, such as working memory and inhibitory control. In the present project, we examine the potential links between cognitive flexibility and socio-cognitive aspects of interest (to be referred to as socio-cognitive variables), namely: (1) multilingualism, (2) cooperative-competitive social decision-making patterns, (3) tolerance of uncertainty, and (4) perceived social support, which lack direct empirical tests, especially in Singapore’s context, in addition to these socio-cognitive variables’ potential relations with outcome variables of interest (i.e., creativity, literacy, numeracy, and problem solving/critical thinking; refer to Tong et al., 2021 at https://osf.io/6rc9h). Multilingualism involves the ability to use multiple languages, and hence the need to switch between linguistic systems, structures, and contexts. Multilingualism has been shown to positively influence executive functions in tasks measuring cognitive flexibility and selective attention (Christoffels et al, 2015; Seçer, 2016; Stocco & Prat, 2014). However, these relationships are not always replicable. Examining the relationships between multilingualism and cognitive flexibility in Singapore, where multilingualism is ubiquitous, allows the present project to additionally consider proficiency levels, communicative contexts, backgrounds, and socioeconomic statuses as potential moderators that may better elucidate the advantages of multilingualism for cognitive flexibility. Social decision-making is almost inseparable from our daily interactions with those we share our social environments with. This is especially so in our highly globalized and interconnected worlds. Social decision-making differs from individual decision-making as, in addition to one’s own preferences, it involves taking into account the preferences of one's interacting partner(s) and strategic considerations to maximize outcomes. As such, a successful social interaction often requires high cognitive functioning to constantly synthesize and (re-)construct the ever-changing mental models of a partner’s beliefs, expectations, emotions, and desires (Ybarra & Winkielman, 2012). To maximize goal pursuits, it may also be essential to flexibly compute costs and benefits based on the contingent circumstances and inhibit socially unfavorable behaviors. In this project, we will examine how cognitive flexibility and other executive functions (e.g., working memory, inhibitory control) contribute to social decision-making by observing levels of individuals’ cooperativeness and competitiveness and the decision-making patterns that lead to the most favorable outcomes. Tolerance of uncertainty is a cognitive tendency characterized by the willingness to engage in unconventional and novel ways of thinking and doing. To navigate uncertain/unstructured information that is commonly encountered in daily life demands the capacity to adopt multiple perspectives, including those that might diverge from one’s own normative or overlearned experiences. Studies have found that societal factors, such as multicultural experiences and encountering novel perspectives from foreign cultures have been associated with increased capacity for creativity and innovations (manifestations of mental flexibility) (Leung & Chiu, 2010; Leung, Maddux, Galinsky, and Chiu, 2008). Using more robust measures and statistical approaches to characterize cognitive flexibility, the present project will examine whether cognitive flexibility may be associated with increased tolerance of uncertainty, which has yet to be directly tested. Perceptions of secure social support from others, such as family, friends, and significant others, may serve as a proxy for diverse constraints and opportunities that may influence cognitive flexibility. This includes the presence of social/interpersonal resources that promote social risk-taking and openness to uncertainty, influences on one’s orientations towards competitiveness versus prosociality, and levels of interpersonal stimulation. Here, we will examine how perceived social support may serve as an environmental catalyst for cognitive flexibility by exploring the nature of their relationship. Considered together, the present project aims to: • Research Aim #1: Identify the socio-cognitive, cultural, and contextual factors that contribute to cognitive flexibility • Research Aim #2: Examine the potential manifestations of cognitive flexibility in interpersonal contexts and socioemotional skills • Research Aim #3: Examine the relationships among the four aforementioned socio-cognitive variables related to cognitive flexibility An understanding of these helps inform the sources and benefits of cognitive flexibility, illuminates the socio-cognitive variables that may moderate outcomes related to cognitive flexibility, and sheds light on the possible mechanisms for cognitive flexibility training.

The present project is part of a larger project aimed at examining cognitive flexibility, other executive functions, and learning in healthy young adults (Tong et al., 2021; Melani et al., 2022) in Singapore. The present pre-registration primarily focuses on addressing the specific hypotheses regarding the relationship between cognitive flexibility, career development resources, behaviours, and outcomes, which might not be described in the main studies (https://osf.io/6rc9h; https://osf.io/ay9gr) in detail. Living in a dynamic and complex environment necessitates all living organisms to be flexible and adaptive to survive (Powell & Ragozzino, 2017); humans are required to equip themselves with the ability to shift their strategies, update their beliefs and actions in response to changing environmental demands (Diamond, 2006; Garcia-Garcia et al. 2010; Geurts et al., 2009). For instance, the increasing volatility in the 21st-century workforce, particularly during the unprecedented COVID-19 pandemic, has served as a timely reminder of the urgency of cultivating the flexibility to respond to unforeseen changes and uncertainties more adaptively in order to stay competitive in turbulent job markets. The view of a linear career path (whereby workers are only trained to perform and master one job) that dominated the mainstream workplace system since the second industrial revolution happened in the 19th century is becoming more irrelevant in the modern-day labour market; it is almost impossible for one to anticipate having the same career path for their entire life (Baruch, 2004). The career construction model of adaptation (Savickas & Porfeli, 2012) posits that the vocational outcomes of individuals depend on how capable they are of negotiating “a lifetime of job changes without losing their sense of self and social identity” (Guichard, 2005; Savickas, 2012). The model – which was designed to examine the interrelationships between personality, psychological self-regulatory resources, career beliefs/behaviours, and outcomes – proposes that adaptivity (individuals’ characteristic of flexibility, or willingness to adapt to a constantly changing work environment, e.g., cognitive flexibility, proactivity, openness) positively influences career adaptability (the psychosocial resources that manage the predictable work roles and the unpredictable changes arise from career growth and transitions), which, in turn, positively influences the adapting responses (the behaviours and beliefs that individuals employ to manage career development tasks and challenges, e.g., career planning, career exploration behaviours), and adaptation outcomes (e.g., job satisfaction, employability). Many past studies (e.g., Chong & Leong, 2017; Gelatt & Gelatt, 2003; Griffin & Hesketh, 2003; Hamtiaux & Houssemand, 2012) suggested a positive association between cognitive flexibility, career adaptability, and related career behaviours/outcomes. Cognitive flexibility is one of the core executive functions – alongside working memory (which relates to one’s limited mental capacity to retain and process information in our brain), and inhibitory control (which relates to one’s self-and inference control, such as selective attention) – that take charges of attention switching, perspective-changing, and selecting and transferring amongst numerous acquired mental representations to new demands/tasks (Diamond 2013; Miyake et al., 2000). Cognitive flexibility is described as a crucial driver that enables humans to adjust their courses of action through time, and as a result, to respond to challenges and uncertainties arising from the ever-changing environments appropriately and efficiently (Dajani & Uddin, 2015; Powell & Ragozzino, 2017). Prior research showed that cognitive flexibility could be distinguished from working memory and inhibitory control – of which the three executive functions were commonly found to be highly correlated with each other (Friedman & Miyake, 2017; Blackwell et al., 2014; Mittal et al., 2015). One of the primary objectives of the main study (Tong et al., 2021) is to examine the separability of cognitive flexibility from the other two core executive functions. Similarly, this project aims to discern the effects of the core executive functions and assess the unique variance in career adaptability that could be accounted for by cognitive flexibility. Furthermore, most existing career studies used self-reported questionnaires that assess the extent to which people believe that they are flexible while examining the relationship between cognitive flexibility and career adaptability. Yet, these measurements do not necessarily correspond to the actual capacity of cognitive flexibility, i.e., the ability to switch attention from one task to another, which is typically assessed by participants’ task performance as opposed to the self-assessments. Without comprehensive empirical evidence, the assertion that humans’ cognitive ability influences their readiness to be flexible to adapt at work – and ultimately, the actions one takes to achieve their career goals – remains questionable. This project aims to utilise both experimental cognitive tasks and psychometric assessments to: 1) assess the degree to which cognitive flexibility contributes to aspects of career adaptability over and above working memory and inhibitory control. 2) examine the interrelationship between cognitive flexibility, career adaptability, and related career beliefs, behaviours, and outcomes.

Cognitive flexibility involves the mental ability to switch or shift amongst different conceptual representations in response to a dynamic environment. It is considered to be a component of executive functioning and is critical to behavioural success (Friedman et al., 2006). Prior research suggests that cognitive flexibility can be distinguished from other executive functions such as working memory and response inhibition (e.g., Friedman & Miyake, 2006; Robbins et al., 1998), whereas the latter two are usually found to be highly correlated with each other (Friedman & Miyake, 2006; Blackwell et al., 2014; Mittal et al., 2015). However, it is unknown whether cognitive flexibility itself may be further fractionated into sub-components (such as rule learning during exploration, as distinct from executive switching), and the extent to which different cognitive flexibility tasks tap into these sub-components. In the present study, we seek to understand the structure and measurability of the psychological construct of cognitive flexibility through structural equation modelling and computational modelling. We aim to generate robust operational measures of cognitive flexibility for our later studies. In addition, within the educational context, studies have increasingly demonstrated strong links between cognitive flexibility and key outcomes of interest such as creativity, literacy, and numeracy (e.g., Mayes et al., 2009; Yeniad et al., 2013). However, many of these studies do not account for potential confounding effects of general intelligence, working memory, or inhibition. Most studies also do not adopt a latent variable approach to elucidate the specific contribution of cognitive flexibility towards the above-mentioned outcomes of interest. Hence, we further aim to better characterise the relationship between the cognitive flexibility construct and our education-related outcomes of interest, i.e., literacy and math skills, creativity, and socio-emotional skills, in the context of Singaporean adolescent learners. It has also been suggested in the literature that, contrary to working memory and response inhibition, cognitive flexibility can be trained (Buttelmann and Karbach, 2017). However, how one might be able to integrate training into the lives of individuals across the lifespan, and how one might measure the effectiveness of such training is less well known. Structure learning may help to answer these questions. It involves seeking patterns in the stochastic presentation of stimuli, without the need for explicit feedback (Wang et al., 2017; Karlaftis et al., 2018, 2019; Giorgio et al., 2018). Since volatility, stochasticity, and uncertainty are the core features of the structure learning, it entails unbounded self-generation of rules or higher order representations. Flexibility may be an emergent consequence of exploring or updating rules and response strategies generated in these dynamic environments. Accordingly, the current study also aims to investigate the relationship between cognitive flexibility and structure learning, with a view toward future development of structure-learning based flexibility training. Finally, much research has indicated that executive functions can be influenced by socio-cognitive variables such as multilingualism, perceived social support, tolerance to uncertainty, and social decision making (e.g., Bialystok et al. 2005). This study will therefore include indices of socio-cognitive variables that may moderate the relationships between cognitive flexibility and other outcomes of interest.

Cognitive flexibility involves the mental ability to switch or shift amongst different conceptual representations in response to a dynamic environment. It is considered to be a component of executive functioning and is critical to behavioural success (Friedman et al., 2006). Prior research suggests that cognitive flexibility can be distinguished from other executive functions such as working memory and response inhibition (e.g., Friedman & Miyake, 2006; Robbins et al., 1998), whereas the latter two are usually found to be highly correlated with each other (Friedman & Miyake, 2006; Blackwell et al., 2014; Mittal et al., 2015). However, it is unknown whether cognitive flexibility itself may be further fractionated into sub-components (such as rule learning during exploration, as distinct from executive switching), and the extent to which different cognitive flexibility tasks tap into these sub-components. In the present study, we seek to understand the structure and measurability of the psychological construct of cognitive flexibility through structural equation modelling and computational modelling. We aim to generate robust operational measures of cognitive flexibility for our later studies. In addition, within the educational context, studies have increasingly demonstrated strong links between cognitive flexibility and key outcomes of interest such as creativity, literacy, numeracy, and critical thinking (e.g., Mayes et al., 2009; Yeniad et al., 2013). However, many of these studies do not account for potential confounding effects of general intelligence, working memory, or inhibition. Most studies also do not adopt a latent variable approach to elucidate the specific contribution of cognitive flexibility towards the above-mentioned outcomes of interest. Hence, we further aim to better characterise the relationship between the cognitive flexibility construct and our outcomes of interest in the context of Singaporean young adult learners. It has also been suggested in the literature that, contrary to working memory and response inhibition, cognitive flexibility can be trained (Buttelmann and Karbach, 2017). However, how one might be able to integrate training into the lives of individuals across the lifespan, and how one might measure the effectiveness of such training is less well known. Structure learning may help to answer these questions. It involves seeking patterns in the stochastic presentation of stimuli, without the need for explicit feedback (Wang et al., 2017; Karlaftis et al., 2018, 2019; Giorgio et al., 2018). Since volatility, stochasticity, and uncertainty are the core features of the structure learning, it entails unbounded self-generation of rules or higher order representations. Flexibility may be an emergent consequence of exploring or updating rules and response strategies generated in these dynamic environments. Accordingly, the current study also aims to investigate the relationship between cognitive flexibility and structure learning, with a view toward future development of structure-learning based flexibility training. Finally, much research has indicated that executive functions can be influenced by socio-cognitive variables such as multilingualism, perceived social support, tolerance to uncertainty, and social decision making (e.g., Bialystok et al., 2005). This study will therefore include indices of socio-cognitive variables that may moderate the relationships between cognitive flexibility and other outcomes of interest.

The present pre-registration seeks to further elaborate on certain hypotheses and analyses related to tolerance of uncertainty (TOU) and perceived social support (PSS) that have been described, but without details, in the Tong et al. (2021; https://osf.io/6rc9h) and Melani et al. (2022; https://osf.io/ay9gr) pre-registrations. Both these projects include an investigation of how the relationship between structure learning and cognitive flexibility is independently moderated by TOU and PSS and includes self-report questionnaires and tasks to measure these variables (see the variable list furnished in the present pre-registration for the details of the questionnaires and tasks used to measure the TOU and PSS variables). Tolerance of uncertainty (TOU) is a cognitive tendency characterized by the willingness to engage in unconventional and novel ways of thinking and doing. The measures we use to assess tolerance of uncertainty include decision-making tasks such as Ambiguity Aversion and questionnaires such as the Need for Closure Scale and Multicultural Experiences Questionnaire (see attached variable list for the full list of measures). Ambiguity aversion is measured with the multiple price list paradigm (Holt & Laury, 2002) in which a participant makes nine binary choices between an ambiguous option and a risky option and choosing more ambiguous options indicates a higher tolerance of uncertainty. Need for closure is defined as a “desire for a firm answer to a question, any firm answer as compared to confusion and/or ambiguity” (Kruglanski, 2004, p. 6) and has been acknowledged by researchers to have conceptual overlap with tolerance of uncertainty (Rosen et al., 2014). Individuals who have had multicultural experiences are also likely to be more tolerant of uncertainty due to the unfamiliarity of foreign cultures and it was found that multicultural experiences reduce need for closure (Tadmor et al., 2018), suggesting that a latent construct underlies these measures. Hence, we aim to extract latent constructs from these measures. The questionnaires/tasks used to measure TOU are listed in the variable list furnished in the present pre-registration. Perceived social support (PSS) is measured by questionnaires including the Provisions for Social Relationships and Adaptations to Stress (PSRAS) scale and the Multidimensional Scale of Perceived Social Support (MSPSS; see attached variable list for the full list of measures). The PSRAS was designed to assess the degree of social support that an individual has (Cutrona & Russell, 1988) and the MSPSS was designed to assess perceptions of social support adequacy (Zimet et al., 1988), suggesting that a latent construct of perceived social support may underlie these measures, which we aim to extract. Therefore, the present pre-registration spells out our aims involving the analysis of the (i) TOU and (ii) PSS variables. Specifically, for TOU and PSS independently: (1) we will generate a score for each associated questionnaire/task (or each of its subscales), either by (a) following established methods as described in the literature (for instance, calculating the score of a questionnaire/task based on the recommendation of a published paper) or by (b) running new analyses (such as factor analysis) if the psychometric properties of some questionnaires/tasks are not good enough for our dataset; (2) we will examine the underlying structure of TOU and PSS variables, which includes (a) whether there is a (set of) latent construct(s)/factor(s) that can sufficiently account for the total variance of all the TOU/PSS variables - thus a (set of) composite score(s) can be derived, and (b) whether there is a hierarchical model of TOU/PSS (Hubbard et al., 2016), in which a general latent construct/factor underlies several specific latent constructs/factors, and each specific latent construct underlies the TOU/PSS variables. Subsequently, depending on the analyses described in aim (2), we will accordingly perform the tests described in Tong et al. (2021) and Melani et al. (2022). If aim (2) is not supported (i.e., there is no evidence for an underlying structure that can reduce the dataset), we will test (3) the association between cognitive flexibility and each of the TOU/PSS variables and related questionnaires (Melani et al., 2022) and (4) how the association between cognitive flexibility and structure learning will change at different levels of each of the TOU/PSS variables and related questionnaires (Tong et al., 2021). If aim (2) is supported and we can indeed extract a (set of) latent construct(s)/factor(s), we will additionally test (5) the association between cognitive flexibility and the latent TOU and PSS construct(s)/factor(s) and (6) how the association between structure learning and cognitive flexibility will change at different levels of the TOU and PSS construct(s)/factor(s). For reasons of completeness and baseline comparisons, we will naturally still conduct the analyses described in (3) and (4) and compare their predictive power with (5) and (6). In summary, the present pre-registration plans to generate specific scores for TOU and PSS, examine possible latent construct(s) for these variables, evaluate their associations with cognitive flexibility, and assess their interactions with the association between cognitive flexibility and structure learning. These analyses will help to identify potential factors that may influence the effectiveness of cognitive flexibility training.

The present pre-registration seeks to further elaborate on certain hypotheses and analyses related to social decision-making that have been described, but without details, in the Tong et al. (2021; https://osf.io/6rc9h) and Melani et al. (2022; https://osf.io/ay9gr) pre-registrations. The Tong et al. (2021) pre-registration focuses on cognitive flexibility and one of its specific aims is to investigate how the relationship between cognitive flexibility and structure learning varies at different levels of social decision-making. As a complement to Tong et al. (2021), the Melani et al. (2022) pre-registration plans to test the association between cognitive flexibility and social decision-making. However, these pre-registrations do not specify how the social decision-making latent construct(s) and the associated composite score(s) will be derived from the social decision-making questionnaires and tasks (The scores of these questionnaires and tasks are referred to as “social decision-making variables” hereafter; see the variable list furnished in the present pre-registration for more details). Therefore, the present pre-registration spells out our aims involving the analysis of the social decision-making variables. Specifically, (1) we will generate a score for each social decision-making questionnaire (or each of its subscales)/task, either by (a) following established methods as described in the literature (for instance, calculating the score of a questionnaire based on the recommendation of a published paper) or by (b) running new analyses (such as factor analysis) if the psychometric properties of some questionnaires/tasks are not good enough for our dataset; (2) we will examine the underlying structure of the social decision-making variables, which includes (a) whether there is/are a (set of) latent construct(s)/factor(s) that can sufficiently account for the total variance of all the social decision-making variables—thus a (set of) composite score(s) can be derived, and (b) whether there is a hierarchical model of social decision-making (Hubbard et al., 2016), in which a general latent construct/factor underlies several specific latent constructs/factors, and each specific latent construct underlies several social decision-making variables. Subsequently, depending on the analyses described in aim (2), we will accordingly perform the tests described in Tong et al.(2021) and Melani et al.(2022). If aim (2) is not supported (i.e., there is no evidence for an underlying structure that can reduce the dataset), we will test (3) the association between cognitive flexibility and each of the social decision-making variables (Melani et al., 2022) and (4) how the association between cognitive flexibility and structure learning will change at different levels of each of the social decision-making variables (Tong et al., 2021). If aim (2) is supported and we can indeed extract a (set of) latent construct(s)/factor(s), we will additionally test (5) the association between cognitive flexibility and the latent social decision-making construct(s)/factor(s) and (6) how the association between cognitive flexibility and structure learning will change at different levels of the social decision-making construct(s)/factor(s). For reasons of completeness and baseline comparisons, we will naturally still conduct the analyses described in (3) and (4) and compare their predictive power with (5) and (6). In summary, the present pre-registration plans to generate specific scores for each social decision-making variable, examine possible latent construct(s) for these variables, evaluate their associations with cognitive flexibility, and assess their interactions with the association between cognitive flexibility and structure learning. These analyses will better our understanding of the components of social decision-making, unveil the connection between social decision-making and cognitive flexibility, and help to identify potential factors that may influence the effectiveness of cognitive flexibility training.