The emergence of all-or-none retrieval of chunks in verbal serial recall.

People often subdivide a list into smaller pieces, called chunks. Some theories of serial recall assume memories are stored hierarchically, with all-or-none retrieval of chunks, but most mathematical models avoid hierarchical assumptions. Johnson (Journal of Verbal Learning and Verbal Behavior, 8(6), 725-731, 1969) found steep drops in errors following correct recalls (transitional-error probabilities) within putative chunks during multi-trial letter-list learning, and viewed this as evidence for all-or-none retrieval. Here we test whether all-or-none retrieval occurs in lists studied only once. In serial recall of six-word lists (Experiment 1), transitional-error probabilities were inconsistent with all-or-none retrieval, both when participants were instructed to subdivide and when temporal grouping induced subdivision. Curiously, the same analysis of previous temporally grouped nine-letter lists produced compelling evidence for all-or-none retrieval, which may result from recoding rather than the formation of chunks. In Experiment 2, participants were pre-trained on three-word chunks. For nine-word lists constructed from those trained chunks, transitional-error probabilities exhibited more pronounced evidence of all-or-none retrieval. Nearly all effects reversed with post-cued backward recall, suggesting mechanisms that play out over the course of recall rather than encoding of the list. In sum, subdivided lists do not result in hierarchical memories after a single study trial, although they may emerge in lists formed from chunks that are previously learned as such. This suggests a continuous transition from non-hierarchical subdivision of lists to all-or-none retrieval over the course of chunk formation.

Systematic Review of Psychiatric Adverse Effects Induced by Chloroquine and Hydroxychloroquine: Case Reports and Population Studies.

OBJECTIVE: To perform a systematic review on the psychiatric adverse effects of chloroquine (CQ) and hydroxychloroquine (HCQ); to summarize what is known about psychiatric adverse effects of these drugs; to compare clinical trials, populational studies, and case report studies; and to increase awareness of the potential psychiatric adverse effects of these drugs. DATA SOURCES: A literature search of PubMed, Scopus, and Web of Science was performed to identify manuscripts published between December 1962 and June 2022. Search terms included CQ, HCQ, psychiatry, psychosis, depression, anxiety, bipolar disorder, delirium, and psychotic disorders. STUDY SELECTION AND DATA EXTRACTION: Relevant studies included reports of adverse effects after CQ or HCQ ingestion. DATA SYNTHESIS: The current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either CQ or HCQ. However, the populational-level studies presented some limitations regarding the voluntary response in survey data, self-report adverse effects, and placebo group reporting similar symptoms to the case group. Thus, populational-level studies addressing the discussed limitations and the nature and extent of possible psychiatric adverse effects are needed. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: Most of the patients who developed such adverse effects did not report a family history of psychiatric disease. The frequency of psychiatric adverse effects depends on the patient's biological sex, age, and body mass index, but not on the drug dosage. CONCLUSIONS: Based on clinical trials and case reports, the current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either drug.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging.

INTRODUCTION: An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions. METHODS: We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions. RESULTS: BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected). CONCLUSIONS: Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.

Individualized Prospective Prediction of Opioid Use Disorder.

OBJECTIVE: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. METHOD: We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. RESULTS: With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. CONCLUSION: Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

Temporal grouping and direction of serial recall.

When lists are presented with temporal pauses between groups of items, participants' response times reiterate those pauses. Accuracy is also increased, especially at particular serial positions. By comparing forward with backward serial recall, we tested whether the influence of temporal grouping is primarily a function of serial position or output position. Results favored the latter, both when recall direction was known to participants prior to (Experiment 2) or only after (Experiment 2) studying each list. Alongside fits of variants of a temporal distinctiveness-based model, our findings suggest that the influence of temporal grouping is not just a consequence of grouping information stored during the study phase. Rather, it critically depends on participants cueing with within-chunk position during recall, combined with response suppression.

Generality of a congruity effect in judgements of relative order.

The judgement of relative order (JOR) procedure is used to investigate serial-order memory. Measuring response times, the wording of the instructions (whether the earlier or the later item was designated as the target) reversed the direction of search in subspan lists (Chan, Ross, Earle, & Caplan Psychonomic Bulletin & Review, 16(5), 945-951, 2009). If a similar congruity effect applied to above-span lists and, furthermore, with error rate as the measure, this could suggest how to model order memory across scales. Participants performed JORs on lists of nouns (Experiment 1: list lengths = 4, 6, 8, 10) or consonants (Experiment 2: list lengths = 4, 8). In addition to the usual distance, primacy, and recency effects, instructions interacted with serial position of the later probe in both experiments, not only in response time, but also in error rate, suggesting that availability, not just accessibility, is affected by instructions. The congruity effect challenges current memory models. We fitted Hacker's (Journal of Experimental Psychology: Human Learning and Memory, 6(6), 651-675, 1980) self-terminating search model to our data and found that a switch in search direction could explain the congruity effect for short lists, but not longer lists. This suggests that JORs may need to be understood via direct-access models, adapted to produce a congruity effect, or a mix of mechanisms.

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study.

BACKGROUND: Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders. METHODS: We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone. RESULTS: Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors. LIMITATION: While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy. CONCLUSION: Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.

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.

Screening for Adulthood ADHD and Comorbidities in a Tertiary Mental Health Center Using EarlyDetect: A Machine Learning-Based Pilot Study.

Screening for adult Attention-Deficit/Hyperactivity Disorder (ADHD) and differentiating ADHD from comorbid mental health disorders remains to be clinically challenging. A screening tool for ADHD and comorbid mental health disorders is essential, as most adult ADHD is comorbid with several mental health disorders. The current pilot study enrolled 955 consecutive patients attending a tertiary mental health center in Canada and who completed EarlyDetect assessment, with 45.2% of patients diagnosed with ADHD. The best ADHD classification model using composite scoring achieved a balanced accuracy of 0.788, showing a 2.1% increase compared to standalone ADHD screening, detecting four more patients with ADHD per 100 patients. The classification model including ADHD with comorbidity was also successful (balanced accuracy = 0.712). The results suggest the novel screening method can improve ADHD detection accuracy and inform the risk of ADHD with comorbidity, and may further inform specific comorbidity including MDD and BD.

Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort.

BACKGROUND: Early identification of the middle-aged and elderly people with high risk of developing depression disorder in the future and the full characterization of the associated risk factors are crucial for early interventions to prevent depression among the aging population. METHODS: Canadian Longitudinal Study on Aging (CLSA) has collected comprehensive information, including psychological scales and other non-psychological measures, i.e., socioeconomic, environmental, health, lifestyle, cognitive function, personality, about its participants (30,097 subjects aged from 45 to 85) at baseline phase in 2012-2015. We applied machine learning models for the prediction of these participants' risk of depression onset approximately three years later using information collected at baseline phase. RESULTS: Individual-level risk for future depression onset among CLSA participants can be accurately predicted, with an area under receiver operating characteristic curve (AUC) 0.791 ± 0.016, using all baseline information. We also found the 10-item Center for Epidemiological Studies Depression Scale coupled with age and sex information could achieve similar performance (AUC 0.764 ± 0.016). Furthermore, we identified existing subthreshold depression symptoms, emotional instability, low levels of life satisfaction, perceived health, and social support, and nutrition risk as the most important predictors for depression onset independent from psychological scales. LIMITATIONS: Depression was based on self-reported doctor diagnosis and depression screening tool. CONCLUSIONS: The identified risk factors will further improve our understanding of the depression onset among middle-aged and elderly population and the early identification of high-risk subjects is the first step for successful early interventions.

Risk factors for developmental vulnerability: Insight from population-level surveillance using the Early Development Instrument.

Objectives: Population-level studies may elucidate the most promising intervention targets to prevent negative outcomes of developmental vulnerability in children. This study aims to bridge the current literature gap on identifying population-level developmental vulnerability risk factors using combined social and biological/health information. Methods: This study assessed developmental vulnerability among kindergarten children using the 2016 Early Development Instrument (EDI) and identified risk factors of developmental vulnerability using EDI data cross-linked to a population-wide administrative health dataset. A total number of 23,494 children aged 5-6 were included (48% female). Prenatal, neonatal, and early childhood risk factors for developmental vulnerability were investigated, highlighting the most important ones contributing to early development. Results: The main risk factors for developmental vulnerability were children with a history of mental health diagnosis (risk ratio = 1.46), biological sex-male (risk ratio = 1.51), and poor socioeconomic status (risk ratio = 1.58). Conclusion: Our study encompasses both social and health information in a populational-level representative sample of Alberta, Canada. The results confirm evidence established in other geographic regions and jurisdictions and demonstrate the association between perinatal risk factors and developmental vulnerability. Based on these results, we argue that the health system should adopt a multilevel prevention and intervention strategy, targeting individual, family, and community together.

Response trajectories during escitalopram treatment of patients with major depressive disorder.

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report.

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.

Practical pathway for the management of depression in the workplace: a Canadian perspective.

Major depressive disorder (MDD) and other mental health issues pose a substantial burden on the workforce. Approximately half a million Canadians will not be at work in any week because of a mental health disorder, and more than twice that number will work at a reduced level of productivity (presenteeism). Although it is important to determine whether work plays a role in a mental health condition, at initial presentation, patients should be diagnosed and treated per appropriate clinical guidelines. However, it is also important for patient care to determine the various causes or triggers including work-related factors. Clearly identifying the stressors associated with the mental health disorder can help clinicians to assess functional limitations, develop an appropriate care plan, and interact more effectively with worker's compensation and disability programs, as well as employers. There is currently no widely accepted tool to definitively identify MDD as work-related, but the presence of certain patient and work characteristics may help. This paper seeks to review the evidence specific to depression in the workplace, and provide practical tips to help clinicians to identify and treat work-related MDD, as well as navigate disability issues.

Judgments of alphabetical order and mechanisms of congruity effects.

The congruity effect is a highly replicated feature of comparative judgments, and has been recently found in memory judgments of relative temporal order. Specifically, asking "Which came earlier?" versus "Which came later?" facilitates response times and sometimes error rates on judgments toward the beginning or end of the list, respectively. This suggests memory judgments of relative temporal order may be part of a broader class of comparative judgments. If so, the same congruity effect should also be found with the English alphabet, despite the alphabet being a longer, semantic-memory list, with forward directional encoding. A large-sample study (N = 340) produced a clear congruity effect in response time and even error rate (when controlled for response time). The large number of serial positions afforded by the alphabet enabled us to test a repertoire of mathematical models instantiating four distinct mechanisms of the congruity effect, against the empirical serial-position effects. The best-performing model assumed a response bias toward a discrete set of letters conceived of as "early" versus "late," respectively, an account that had previously been ruled out for typical comparative-judgment paradigms. In contrast, models implementing congruity effect mechanisms supported for conventional comparative judgment paradigms (based on reference-point theory or positional discriminability) produced quantitatively poorer fits, with more curvilinear serial-position effects that deviated from the data. The congruity effect thus extends to long, highly directional semantic-memory lists. However, qualitatively different serial-position effects across models suggest that, despite the superficial similarity, there are probably several quite different mechanisms that produce congruity effects, which may, in turn, depend on specific task characteristics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individualized identification of sexual dysfunction of psychiatric patients with machine-learning.

Sexual dysfunction (SD) is prevalent in patients with mental health disorders and can significantly impair their quality of life. Early recognition of SD in a clinical setting may help patients and clinicians to optimize treatment options of SD and/or other primary diagnoses taking SD risk into account and may facilitate treatment compliance. SD identification is often overlooked in clinical practice; we seek to explore whether patients with a high risk of SD can be identified at the individual level by assessing known risk factors via a machine learning (ML) model. We assessed 135 subjects referred to a tertiary mental health clinic in a Western Canadian city using health records data, including age, sex, physician's diagnoses, drug treatment, and the Arizona Sexual Experiences Scale (ASEX). A ML model was fitted to the data, with SD status derived from the ASEX as target outcomes and all other variables as predicting variables. Our ML model was able to identify individual SD cases-achieving a balanced accuracy of 0.736, with a sensitivity of 0.750 and a specificity of 0.721-and identified major depressive disorder and female sex as risk factors, and attention deficit hyperactivity disorder as a potential protective factor. This study highlights the utility of SD screening in a psychiatric clinical setting, demonstrating a proof-of-concept ML approach for SD screening in psychiatric patients, which has marked potential to improve their quality of life.

Depression screening using a non-verbal self-association task: A machine-learning based pilot study.

BACKGROUND: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self- and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers. Thus non-verbal validated brain-behaviour based self-emotion-related assessment data reflect physiological differences and may support individual level screening of depression. METHODS: In this pilot study (n = 84) we collected two longitudinal sessions of behavioural assessment data in a laboratory setting. Depression was assessed using Beck Depression Inventory II (BDI-II), to explore optimal screening methods with machine-learning, and to establish the validity of adapting a novel behavioural assessment focusing on self and emotions for depression screening. RESULTS: The best machine-learning model achieved high performance in depression screening, 10-Fold cross-validation (CV) Area Under the receiver operating characteristic Curve (AUC) of 0.90 and balanced accuracy of 0.81, using a Gradient Boosting algorithm. Prospective prediction using a model trained with session 1 data to predict session 2 depression status achieved a 10-Fold CV AUC of 0.77 and balanced accuracy of 0.66. We also identified interpretable behavioural signatures for depression patients based on the best model. CONCLUSION: The study supports the utility of using behavioural data as a viable and cost-effective solution for depression screening, with a potential wide range of applications in clinical settings.

Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach.

BACKGROUND: Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD), especially among adolescents and young adults. Machine learning models with the capability of suicide risk identification at an individual level could improve suicide prevention among high-risk patient population. METHODS: A cross-sectional assessment was conducted on a sample of 66 adolescents/young adults diagnosed with MDD. The structural T1-weighted MRI scan of each subject was processed using the FreeSurfer software. The classification model was conducted using the Support Vector Machine - Recursive Feature Elimination (SVM-RFE) algorithm to distinguish suicide attempters and patients with suicidal ideation but without attempts. RESULTS: The SVM model was able to correctly identify suicide attempters and patients with suicidal ideation but without attempts with a cross-validated prediction balanced accuracy of 78.59%, the sensitivity was 73.17% and the specificity was 84.0%. The positive predictive value of suicide attempt was 88.24%, and the negative predictive value was 65.63%. Right lateral orbitofrontal thickness, left caudal anterior cingulate thickness, left fusiform thickness, left temporal pole volume, right rostral anterior cingulate volume, left lateral orbitofrontal thickness, left posterior cingulate thickness, right pars orbitalis thickness, right posterior cingulate thickness, and left medial orbitofrontal thickness were the 10 top-ranked classifiers for suicide attempt. CONCLUSIONS: The findings indicated that structural MRI data can be useful for the classification of suicide risk. The algorithm developed in current study may lead to identify suicide attempt risk among MDD patients.

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.

Effects of winning cues and relative payout on choice between simulated slot machines.

BACKGROUND AND AIMS: Cues associated with winning may encourage gambling. We assessed the effects on risky choice of slot machine of: (1) neutral sounds paired with winning, (2) casino-related cues (such as the sound of coins dropping and pictures of dollar signs) and (3) relative payouts. DESIGN: Experimental studies in which participants repeatedly chose between safer and riskier simulated slot machines. Safer slot machines paid the same amount regardless of which symbols lined up. Risky machines paid different amounts depending on which symbols lined up. Effects of initially neutral sounds paired with the best payout were assessed between-groups (experiment 1a) and within-participants (experiment 1b). In experiment 2, pairing of casino-related audiovisual cues with payout was assessed within participants, and cue timing was assessed between groups. SETTING: A university research laboratory in Edmonton, Canada. PARTICIPANTS: Undergraduate students (n = 630 across three experiments). MEASUREMENTS: Preference for riskier over safer machines, preference between machines that differed in cues, payout recall and frequency estimates for payouts. Risky choice was calculated as the proportion of choices of the risky machine when presented with a fixed machine of the same expected value. FINDINGS: In experiment 1a, risky choice was slightly increased by pairing a sound with the best payout compared with pairing the sound with a lower payout (P = 0.04, d = 0.28) but not compared with no sound [P = 0.36, d = 0.13, Bayes factors (BF)10  = 0.22]. In experiment 1b, people did not prefer a machine with a best-payout sound over one with a lower-payout sound (P = 0.67, d = 0.03, BF10  = 0.11). Relative payout affected choice: risky choices were higher for high- than low-payout decisions (P < 0.001, d = 0.53). In experiment 2, people preferred machines with casino-related cues paired with winning (P < 0.001, r2  = 0.11) and cue timing (at choice or concurrently with the win) had no effect (P = 0.95, r2  = 0.0, BF10  = 0.05). Casino-related cues also enhanced payout memory (P = 0.013 and 0.006). Cue effects were not specific to risk: people also preferred fixed-payout machines with casino-related cues (P < 0.001, r2  = 0.16). CONCLUSIONS: In a gambling simulation, student participants chose more risky slot machines when payouts were relatively higher and when casino-related cues were associated with payouts. Pairing a neutral sound with the best payout did not consistently affect slot machine choice, and the effect of casino cues did not depend on their timing. Casino-related cues enhanced payout memory.