How do explicit, implicit, and sociodemographic measures relate to concurrent suicidal ideation? A comparative machine learning approach.

INTRODUCTION: Suicide is a leading cause of death, and decades of research have identified a range of risk factors, including demographics, past self-injury and suicide attempts, and explicit suicide cognitions. More recently, implicit self-harm and suicide cognitions have been proposed as risk factors for the prospective prediction of suicidal behavior. However, most studies have examined these implicit and explicit risk factors in isolation, and little is known about their combined effects and interactions in the prediction of concurrent suicidal ideation. METHODS: In an online community sample of 6855 participants, we used different machine learning techniques to evaluate the utility of measuring implicit self-harm and suicide cognitions to predict concurrent desire to self-harm or die. RESULTS: Desire to self-harm was best predicted using gradient boosting, achieving 83% accuracy. However, the most important predictors were mood, explicit associations, and past suicidal thoughts and behaviors; implicit measures provided little to no gain in predictive accuracy. CONCLUSION: Considering our focus on the concurrent prediction of explicit suicidal ideation, we discuss the need for future studies to assess the utility of implicit suicide cognitions in the prospective prediction of suicidal behavior using machine learning approaches.

Testing the effectiveness of a mobile approach avoidance intervention and measuring approach biases in an ecological momentary assessment context: study protocol for a randomised-controlled trial.

INTRODUCTION: Unhealthy eating behaviour is a major contributor to obesity and related diseases and is associated with a behavioural bias to approach rather than avoid desired foods, as measured with reaction time tasks. Approach-avoidance interventions (AAIs) have been proposed as a way to modify food evaluations and help people to eat in accordance with their dietary goals. Mobile implementations of AAI might be easily accessible, low threshold interventions, but their effectiveness has not been established yet. METHODS AND ANALYSIS: Participants who aim to change their eating behaviour are randomised to intervention or control groups. They complete six sessions of a smartphone-based AAI, in which they push (ie, avoid) or pull (ie, approach) personalised food images. Intervention group participants always avoid foods that they personally want to eat less often and approach foods that they personally want to eat more often. In the control group, images are paired equally often with both response directions. To evaluate contextual and dynamic intervention effects, ecological momentary assessment (EMA) is measured throughout, with questions about food intake, hunger, stress, emotions, eating intentions, food craving and impulsivity twice a day. Additional EMA preintervention and postintervention measures are administered before and after the intervention phase (4 days each) with a 1-day follow-up EMA 4 weeks after the intervention. Multilevel models will examine the temporal covariance between approach bias and self-reported variables as well as short-term and long-term intervention effects on approach bias, food intake and craving. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee of the University of Salzburg. Results will be published in peer-reviewed scientific journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: German Clinical Trials Register DRKS, registration number DRKS00030780.

How pre-processing decisions affect the reliability and validity of the approach-avoidance task: Evidence from simulations and multiverse analyses with six datasets.

Reaction time (RT) data are often pre-processed before analysis by rejecting outliers and errors and aggregating the data. In stimulus-response compatibility paradigms such as the approach-avoidance task (AAT), researchers often decide how to pre-process the data without an empirical basis, leading to the use of methods that may harm data quality. To provide this empirical basis, we investigated how different pre-processing methods affect the reliability and validity of the AAT. Our literature review revealed 108 unique pre-processing pipelines among 163 examined studies. Using empirical datasets, we found that validity and reliability were negatively affected by retaining error trials, by replacing error RTs with the mean RT plus a penalty, and by retaining outliers. In the relevant-feature AAT, bias scores were more reliable and valid if computed with D-scores; medians were less reliable and more unpredictable, while means were also less valid. Simulations revealed bias scores were likely to be less accurate if computed by contrasting a single aggregate of all compatible conditions with that of all incompatible conditions, rather than by contrasting separate averages per condition. We also found that multilevel model random effects were less reliable, valid, and stable, arguing against their use as bias scores. We call upon the field to drop these suboptimal practices to improve the psychometric properties of the AAT. We also call for similar investigations in related RT-based bias measures such as the implicit association task, as their commonly accepted pre-processing practices involve many of the aforementioned discouraged methods. HIGHLIGHTS: • Rejecting RTs deviating more than 2 or 3 SD from the mean gives more reliable and valid results than other outlier rejection methods in empirical data • Removing error trials gives more reliable and valid results than retaining them or replacing them with the block mean and an added penalty • Double-difference scores are more reliable than compatibility scores under most circumstances • More reliable and valid results are obtained both in simulated and real data by using double-difference D-scores, which are obtained by dividing a participant's double mean difference score by the SD of their RTs.

Differential Orbitofrontal Cortex Responses to Chocolate Images While Performing an Approach-Avoidance Task in the MRI Environment.

Background: Chocolate is one of the most frequently craved foods, and it often challenges self-regulation. These cravings may be underpinned by a neural facilitation of approach behavior toward chocolate. This preregistered study investigated the behavioral and neural correlates of such a bias using functional magnetic resonance imaging (fMRI) and reaction times (RTs). Methods: A total of n = 30 frequent chocolate eaters performed a relevant-feature approach-avoidance task (AAT) in the MRI scanner using buttons to enlarge (approach) or to shrink (avoid) pictures of chocolate and inedible control objects. We tested (a) whether implicit RT-based approach biases could be measured in a supine position in the scanner, (b) whether those biases were associated with activity in reward-related brain regions such as the insula, amygdala, striatum, and orbitofrontal cortex (OFC), and (c) whether individual RT-based bias-scores correlated with measures of chocolate craving. Results: Behaviorally, we found a highly reliable approach bias toward chocolate, defined by faster RTs in the compatible conditions (approach chocolate, avoid objects) compared to the incompatible conditions (avoid chocolate, approach objects). Neurally, this compatibility effect involved activity in the left medial OFC, a neural response that was positively correlated with individual approach bias scores. Conclusions: This study shows that the relevant feature AAT can be implemented in an fMRI setting in a supine position using buttons. An approach bias toward chocolate seems related to medial OFC activation that might serve to devalue chocolate when it has to be avoided. Our demonstration of neural and behavioral approach biases for chocolate underscores the need for stimulus-specific cognitive trainings to support healthy consumption and successful self-regulation.

Touchscreen-based approach-avoidance responses to appetitive and threatening stimuli.

BACKGROUND AND OBJECTIVES: Individuals are thought to be biased towards approaching positive stimuli and avoiding negative stimuli. Yet, it is unclear whether this general pattern applies to all stimulus classes or whether biases are more specific. We expected significant approach biases towards two types of positive stimuli, appetitive foods and butterflies; and avoidance biases away from two types of negative stimuli, spoiled foods and spiders. METHODS: A touchscreen-based Approach-Avoidance Task (AAT), using hand gestures toward or away from stimuli assessed biases. Questionnaires and image ratings assessed individual differences in stimulus evaluations. RESULTS: Approach biases for butterflies and appetitive foods were found, the latter being strongest towards individually liked foods. There was no avoidance bias for spoiled foods. An avoidance bias for spiders was found in individuals with elevated spider fear. LIMITATIONS: Incomplete counterbalancing precluded direct comparison between both positive and negative stimuli. CONCLUSIONS: Behavioural biases in the touchscreen AAT generally co-vary with individuals' affective evaluation of the stimuli. Approach biases were elicited by positive stimuli independently of whether they were regularly (foods) or rarely (butterflies) approached in everyday life. This may hint towards a tendency to approach positive stimuli regardless of the specific category, whereas avoidance biases may be more stimulus specific.

Grab it or not? Measuring avoidance of spiders with touchscreen-based hand movements.

BACKGROUND AND OBJECTIVES: Previous studies have shown that humans, particularly those with high levels of spider fear, show automatic avoidance of spiders. However, most tasks used in these studies employ symbolic approach-avoidance movements instead of naturalistic movements. METHODS: The current study employed a touchscreen-based approach-avoidance task in which 86 female participants made hand and arm movements that mimic the real-life touching, grasping, and moving of spiders. Using a large touchscreen, they had to reach for pictures of spiders and neutral objects (leaves), touch them, and drag them either away from themselves or towards themselves. To draw attention to stimulus content, pictures of butterflies were used in additional NoGo trials, they required participants to not move their hand. RESULTS: Avoidance of spiders was observed in both the pre-movement times (time to lift the hand from a starting position) and the grabbing times (time to reach the picture), and this avoidance was larger for more spider-fearful participants. The dragging times (time to swipe the picture), however, revealed a relative spider-approach tendency: Compared to leaves, participants were faster to drag spiders closer than to swipe them away. LIMITATIONS: We discuss potential reasons for this mixed pattern of results, including the low reliability of the dragging times. CONCLUSIONS: We recommend the use of touchscreens for the assessment of approach-avoidance tendencies, and we suggest avenues for further research.

Comparison of different response devices to assess behavioral tendencies towards chocolate in the approach-avoidance task.

Behavioral tendencies in the Approach-Avoidance Task (AAT) have mostly been assessed using a joystick as a response device. In recent years, other hardware devices such as tablets, smartphones, and computer mice have also been used. However, it remains unclear whether different response devices yield similar results and show comparable psychometric properties. The aim of the present study was to assess approach biases towards chocolate with different response devices and to compare their reliability and validity. Forty-five individuals with regular chocolate consumption completed three different AATs (joystick, computer mouse, touchscreen), each comprised of two blocks. In the compatible block of trials, chocolate-related pictures had to be pulled near while object-related pictures had to be pushed away. In the incompatible block of trials, instructions were reversed. Preregistered analyses revealed that participants were faster to pull than to push chocolate-related pictures relative to object-related pictures, indicating an approach bias for chocolate with no significant differences between response devices. Correlations among the three response devices were low to medium. Exploratory analyses revealed that approach biases were moderated by block order such that biases were only present and associated with craving (joystick AAT only) when the incongruent block was completed first. Internal consistencies of the bias score ranged between rSB = 0.67-0.76. Results of the present study point to the existence of an approach bias to chocolate regardless of response device, albeit each task seems to measure a different aspect of it. Order effects point to specific temporal dynamics in the acquisition of stimulus response (e.g., chocolate-pull) mappings that require further study.

Touchscreen-based assessment of food approach biases: Investigating reliability and item-specific preferences.

Strong cravings for unhealthy foods and implicit tendencies to approach them threaten the physical and mental health of vulnerable populations. Yet, implicit measures of food approach tendencies have methodological limitations, as existing approach-avoidance tasks (AAT) are often unreliable and require specialized hardware. We propose a novel method to measure approach biases: on a touchscreen, participants slide their hand either toward a food item (and away from control images) or away from it (and toward control images) in separate blocks. Adequate attention to the stimuli is ensured by the coupling of stimulus category to the required response. We found that this touchscreen-variant of the AAT yielded reliable bias scores when approach and avoidance were defined as movements relative to the stimulus rather than to the body. Compared to control images, we found an approach bias for low-calorie foods but not for high-calorie foods. This bias additionally varied on a food-by-food basis depending on the participant's desire to eat individual food items. Correlations with state and trait cravings were inconclusive. Future research needs to address the order effects that were found, in which participants avoiding foods in the first block showed larger biases than participants approaching food in the first block, likely due to insufficient opportunity to practice the task. Our findings highlight the need for approach bias retraining paradigms to use personalized stimulus sets. The task can enrich the methodological repertoire of research on eating disorders, obesity and cognitive bias modification.

Improving the touchscreen-based food approach-avoidance task: remediated block-order effects and initial findings regarding validity.

Approach biases to foods may explain why food consumption often diverges from deliberate dietary intentions. Yet, the assessment of behavioural biases with the approach-avoidance tasks (AAT) is often unreliable and validity is partially unclear. The present study continues a series of studies that develop a task based on naturalistic approach and avoidance movements on a touchscreen (hand-AAT). In the hand-AAT, participants are instructed to respond based on the food/non-food distinction, thereby ensuring attention to the stimuli. Yet, this implies the use of instruction switches (i.e., 'approach food - avoid objects' to 'avoid food - approach objects'), which introduce order effects. The present study increased the number of instruction switches to potentially minimize order effects, and re-examined reliability. We additionally included the implicit association task (IAT) and several self-reported eating behaviours to investigate the task's validity. Results replicated the presence of reliable approach biases to foods irrespective of instruction order. Evidence for validity, however, was mixed: biases correlated positively with external eating, increase in food craving and aggregated image valence ratings but not with desire to eat ratings of the individual images considered within participants or the IAT. We conclude that the hand-AAT can reliably assess approach biases to foods that are relevant to self-reported eating patterns.

Food approach bias is moderated by the desire to eat specific foods.

In a post-scarcity world, energy intake and excesses therein are mediated by psychological mechanisms, such as implicit inclinations to approach certain foods. We investigated how food deprivation, calorie content and individual food preferences affect this approach bias. Sixty women performed a touchscreen-based approach-avoidance task featuring a wide range of food items, once while satiated and once while food-deprived for 15 h. We found an overall approach bias towards food that was not influenced by food deprivation or calorie density of the food items. Instead, we found that approach bias related to the participants' (lack of) desire to eat specific food items, and to a lesser extent to how much their general desire to eat changed due to food deprivation. Links with food preference were selective to trials in which foods had to be approached, and were absent in trials in which foods had to be avoided, pointing to selectivity to appetitive brain systems and clarifying the nature of the bias. Approach bias was unrelated to overall state or trait food craving. We conclude approach bias for appetitive stimuli may primarily express itself as speeded approach rather than slowed avoidance. Additionally, our results show there is merit in personalizing stimulus selection for approach bias measurement and retraining, as approach bias was concordant with individual food preferences, rather than objective calorie content.