The retrograde effects of negative emotion on memory for conceptually related events: a registered report.

Emotional events are often remembered better than neutral ones; however, emotion can also spill over and affect our memory for neutral experiences that precede an emotional event. Theories suggest that emotion can retroactively enhance memory for preceding neutral events that are considered high-priority while impairing memory for events deemed low-priority. However, the impact of conceptual relationships (i.e., semantic connections) between preceding neutral information and emotional events on memory for the preceding information has received little attention. This study investigated the influence of conceptual relatedness on the retroactive effects of emotion on memory. Participants sequentially encoded pairs of images that were high or low in conceptual relatedness, each comprising a neutral object followed by either a negative or neutral image. Participants returned the next day for a recognition memory assessment. The results indicated an interactive effect of emotion and conceptual relatedness on memory: In a "discovery" sample, memory was poorer for images preceding conceptually unrelated negative (vs. neutral) images, while the opposite pattern was seen for conceptually related images. In a "replication" sample, these effects were partially replicated, with the former impairment effect statistically observed but not the latter augmentation effect. Hence, conceptual relatedness affects how negative emotion influences memory.

Cerebellar Theta Burst Stimulation Impairs Working Memory.

Working memory refers to the process of temporarily storing and manipulating information. The role of the cerebellum in working memory is thought to be achieved through its connections with the prefrontal cortex. Previous studies showed that theta burst stimulation (TBS), a form of repetitive transcranial magnetic stimulation, of the cerebellum changes its functional connectivity with the prefrontal cortex. Specifically, excitatory intermittent TBS (iTBS) increases, whereas inhibitory continuous TBS (cTBS) decreases this functional connectivity. We hypothesized that iTBS on the cerebellum will improve working memory, whereas cTBS will disrupt it. Sixteen healthy participants (10 women) participated in this study. Bilateral cerebellar stimulation was applied with a figure-of-eight coil at 3 cm lateral and 1 cm below the inion. The participants received iTBS, cTBS, and sham iTBS in three separate sessions in random order. Within 30 min after TBS, the participants performed four working memory tasks: letter 1-Back and 2-Back, digit span forward, and digit span backward. Repeated measures analysis of variance revealed a significant effect of the type of stimulation (iTBS/cTBS/Sham) on performance in the digit span backward task (p = 0.02). The planned comparison showed that the cTBS condition had significantly lower scores than the sham condition (p = 0.01). iTBS and cTBS did not affect performance in the 1- and 2-Back and the digit span forward tasks compared to sham stimulation. The findings support the hypothesis that the cerebellum is involved in working memory, and this contribution may be disrupted by cTBS.

Risky effort.

Decision-making involves weighing up the outcome likelihood, potential rewards, and effort needed. Previous research has focused on the trade-offs between risk and reward or between effort and reward. Here we bridge this gap and examine how risk in effort levels influences choice. We focus on how two key properties of choice influence risk preferences for effort: changes in magnitude and probability. Two experiments assessed people's risk attitudes for effort, and an additional experiment provided a control condition using monetary gambles. The extent to which people valued effort was related to their pattern of risk preferences. Unlike with monetary outcomes, however, there was substantial heterogeneity in effort-based risk preferences: People who responded to effort as costly exhibited a "flipped" interaction pattern of risk preferences. The direction of the pattern depended on whether people treated effort as a loss of resources. Most, but not all, people treat effort as a loss and are more willing to take risks to avoid potentially high levels of effort.

Untangling the threads of motivated memory: Independent influences of reward and emotion.

Motivational and emotional influences on memory have been studied extensively; however, despite the link between these constructs, they have been studied in separate lines of research, with very little work examining their effects concurrently. The current study takes a novel approach to manipulate motivational and emotional influences orthogonally, and within the same task, to test their interplay on intentional memory formation. If emotion and reward motivation are tightly linked, they may rely on overlapping cognitive mechanisms, thus we would not expect emotion and reward to interact in memory. Alternatively, they could be distinct constructs and therefore would boost memory when both are included in the same experimental trial, above and beyond additive effects. To test these competing predictions, in Experiment 1, participants (n = 180) completed an old/new recognition memory task with emotional (negative, positive) and neutral words intentionally encoded with high or low reward anticipation cues. In Experiment 2, participants (n = 159) encoded emotional and neutral words with a high or low reward cue, but memory was tested with free recall using study-test blocks. The findings from both experiments converged. There were main effects of emotion and reward in generally hypothesized directions, but no evidence of an interaction between these factors. This is in line with the prediction that emotion and reward motivation are similar constructs. Their combination within a trial does not boost memory above and beyond either of these factors alone perhaps indicating these constructs have similar cognitive mechanisms.

Collaborative imagination synchronizes representations of the future and fosters social connection in the present.

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies (N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition.

Brain age of rhesus macaques over the lifespan.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

"What will you do after?": Lessons from Academia and the World Beyond.

Determining post-PhD career options is a challenge for many Psychology PhD graduates. Here I provide a comprehensive overview of the diverse career trajectories available to graduates, drawing from interviews with 53 PhD graduates conducted as part of the two-volume Academia and the World Beyond book series. From these, I conducted a hierarchical qualitative classification to categorise and characterise potential career paths. The findings reveal a spectrum of opportunities, from traditional academic roles to "academic adjacent" and "skill-transfer" careers. This work underscores the versatility of Psychology doctoral training, providing skills that can support a wide array of career possibilities. The results serve as a guide for current and prospective PhD students-and their mentors-emphasising the variety of professional contexts where doctoral training is beneficial.

Subjective memory measures: Metamemory questionnaires currently in use.

Subjective memory evaluation is important for assessing memory abilities and complaints alongside objective measures. In research and clinical settings, questionnaires are used to examine perceived memory ability, memory complaints, and memory beliefs/knowledge. Although they provide a structured measure of self-reported memory, there is some debate as to whether subjective evaluation accurately reflects memory abilities. Specifically, the disconnect between subjective and objective memory measures remains a long-standing issue within the field. Thus, it is essential to evaluate the benefits and limitations of questionnaires that are currently in use. This review encompasses three categories of metamemory questionnaires: self-efficacy, complaints, and multidimensional questionnaires. Factors influencing self-evaluation of memory including knowledge and beliefs about memory, ability to evaluate memory, recent metamemory experiences, and affect are examined. The relationship between subjective and objective memory measures is explored, and considerations for future development and use of metamemory questionnaires are provided.

The effects of emotion on retrospective duration memory using virtual reality.

Our memories for temporal duration may be colored by the emotions we experience during an event. While emotion generally enhances some aspects of memory, temporal duration has been shown to be particularly susceptible to emotion-induced distortions. However, prior work has faced difficulty when studying this phenomenon, having to make some trade-offs on ecological validity or experimental control. Here, we sought to bridge this gap by studying the effects of emotion on temporal duration memory using virtual reality. In the present study, a final sample of 69 participants experienced a series of negative-emotional and neutral worlds within virtual reality. Following this, participants provided ratings of emotionality (arousal, valence, pleasantness) and retrospective duration estimates (i.e., remembered time). We hypothesized that negative events would be recalled as having a greater duration than neutral events (H1). We additionally hypothesized that negative, but not neutral, events would be recalled as being longer than the true duration (H2). The results supported H1 while failing to provide evidence in support of H2. Together, the results bolster the importance of emotion, especially negative emotion, in shaping how we remember the temporal unfolding of the past.

A phenomenological exploration of the feedback experience of medical students after summative exam failure.

BACKGROUND: Preventing medical students entering cycles of underperformance following assessment is a priority due to the consequences for the student, faculty, and wider society. The benefits from feedback may be inadequately accessed by students in difficulty due to the emotional response evoked by examination failure. This study aims to explore medical students' experiences of receiving feedback after summative assessment failure and investigate the role of emotions on motivation for learning after underperformance, to better support remediation and preparation for future assessments. METHODS: This study used interpretative phenomenological analysis (IPA) to explore the experiences of four medical students who failed summative assessments. Additionally, a content analysis was conducted using Linguistic Inquiry and Word Count (LIWC) to investigate the characteristics and use of language to describe their emotional response. RESULTS: Anger, fear, anxiety, and sadness were emotions frequently experienced after examination failure. These emotions led to feelings of mistrust of the medical school and subsequent distrust in the university's assessment processes, impacting on the desire to engage with feedback. There was dissonance between the students' perceptions of what feedback should provide and what benefit feedback provided after summative assessments. The linguistic inquiry further confirmed an initial (and sometimes long lived) negative affective state after experiencing failure, and a barrier to engagement with remediation when not effectively managed. CONCLUSIONS: A range of emotions, directed at themselves and the medical school are experienced by students following exam failure. These emotions lead to a range of negative feelings and responses that affect how students make sense of and move on from the failure experience. There is a need for educators to better understand and support students to manage, reflect and contextualise their emotional responses, minimise external attribution and to enable focus on remediation and learning.

Rare and extreme outcomes in risky choice.

Many real-world decisions involving rare events also involve extreme outcomes. Despite this confluence, decisions-from-experience research has only examined the impact of rarity and extremity in isolation. With rare events, people typically choose as if they underestimate the probability of a rare outcome happening. Separately, people typically overestimate the probability of an extreme outcome happening. Here, for the first time, we examine the confluence of these two biases in decisions-from-experience. In a between-groups behavioural experiment, we examine people's risk preferences for rare extreme outcomes and for rare non-extreme outcomes. When outcomes are both rare and extreme, people's risk preferences shift away from traditional risk patterns for rare events: they show reduced underweighting for events that are both rare and extreme. We simulate these results using a small-sample model of decision-making that accounts for both the underweighting of rare events and the overweighting of extreme events. These separable influences on risk preferences suggest that to understand real-world risk for rare events we must also consider the extremity of the outcomes.

Learning emotional dialects: A British population study of cross-cultural communication.

The aim of the current research was to explore whether we can improve the recognition of cross-cultural freely-expressed emotional faces in British participants. We tested several methods for improving the recognition of freely-expressed emotional faces, such as different methods for presenting other-culture expressions of emotion from individuals from Chile, New Zealand and Singapore in two experimental stages. In the first experimental stage, in phase one, participants were asked to identify the emotion of cross-cultural freely-expressed faces. In the second phase, different cohorts were presented with interactive side-by-side, back-to-back and dynamic morphing of cross-cultural freely-expressed emotional faces, and control conditions. In the final phase, we repeated phase one using novel stimuli. We found that all non-control conditions led to recognition improvements. Morphing was the most effective condition for improving the recognition of cross-cultural emotional faces. In the second experimental stage, we presented morphing to different cohorts including own-to-other and other-to-own freely-expressed cross-cultural emotional faces and neutral-to-emotional and emotional-to-neutral other-culture freely-expressed emotional faces. All conditions led to recognition improvements and the presentation of freely-expressed own-to-other cultural-emotional faces provided the most effective learning. These findings suggest that training can improve the recognition of cross-cultural freely-expressed emotional expressions.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Using Evidence-Based Learning Strategies to Improve Medical Education.

Medical education research has been adopting principles from psychology to improve student learning. Here is an overview and illustrative examples of six evidence-based learning strategies that have been thoroughly researched and validated in the psychology literature: spacing, interleaving, retrieval practice, elaboration, dual coding, and concrete examples. For each of these, their use within medical education and considerations that may influence efficacy are discussed. Medical education researchers should collaborate more with psychology researchers in transdisciplinary teams to better implement these strategies and more directly benefit from advances made in the psychology literature.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation.

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.