Common wisdom has it that Buddhism enhances compassion and self-other integration. We put this assumption to empirical test by comparing practicing Taiwanese Buddhists with well-matched atheists. Buddhists showed more evidence of self-other integration in the social Simon task, which assesses the degree to which people co-represent the actions of a coactor. This suggests that self-other integration and task co-representation vary as a function of religious practice.
When the original approach-avoidance task (AAT; Solarz, 1960) that measures these tendencies was redesigned to run on regular desktop computers, it made the task much more flexible but also sacrificed some important behavioral properties of the original task-most notably its reliance on physical distance change (Chen & Bargh, 1999). Here, we present a new, mobile version of the AAT that runs entirely on smartphones and combines the flexibility of modern tasks with the behavioral properties of the original AAT. In addition, it can easily be deployed in the field and, next to traditional reaction time measurements, includes the novel measurement of response force. In two studies, we demonstrate that the mobile AAT can reliably measure known approach-avoidance tendencies toward happy and angry faces both in the laboratory and in the field.
The approach-avoidance task (AAT) is an implicit task that measures people’s behavioral tendencies to approach or avoid stimuli in the environment. In recent years, it has been used successfully to help explain a variety of health problems (e.g., addictions and phobias). Unfortunately, more recent AAT studies have failed to replicate earlier promising findings. One explanation for these replication failures could be that the AAT does not reliably measure approach-avoidance tendencies. Here, we first review existing literature on the reliability of various versions of the AAT. Next, we examine the AAT’s reliability in a large and diverse sample (N = 1077; 248 of whom completed all sessions). Using a smartphone-based, mobile AAT, we measured participants’ approach-avoidance tendencies eight times over a period of seven months (one measurement per month) in two distinct stimulus sets (happy/sad expressions and disgusting/neutral stimuli). The mobile AAT’s split-half reliability was adequate for face stimuli (r = .85), but low for disgust stimuli (r = .72). Its test–retest reliability based on a single measurement was poor for either stimulus set (all ICC1s < .3). Its test–retest reliability based on the average of all eight measurements was moderately good for face stimuli (ICCk = .73), but low for disgust stimuli (ICCk = .5). Results suggest that single-measurement AATs could be influenced by unexplained temporal fluctuations of approach-avoidance tendencies. These fluctuations could be examined in future studies. Until then, this work suggests that future research using the AAT should rely on multiple rather than single measurements.
IMPORTANCEAlcohol consumption (AC) leads to death and disability worldwide. Ongoing discussions on potential negative effects of the COVID-19 pandemic on AC need to be informed by real-world evidence. OBJECTIVE To examine whether lockdown measures are associated with AC and consumptionrelated temporal and psychological within-person mechanisms.
<b><i>Introduction:</i></b> Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. <b><i>Method:</i></b> In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. <b><i>Results:</i></b> Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that – although more systematic studies are necessary – task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. <b><i>Conclusion:</i></b> Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.
<b><i>Introduction:</i></b> The emergence of Pavlovian-to-instrumental transfer (PIT) research in the human neurobehavioral domain has been met with increased interest over the past two decades. A variety of PIT tasks were developed during this time; while successful in demonstrating transfer phenomena, existing tasks have limitations that should be addressed. Herein, we introduce two PIT paradigms designed to assess outcome-specific and general PIT within the context of addiction. <b><i>Materials and Methods:</i></b> The single-lever PIT task, based on an established paradigm, replaced button presses with joystick motion to better assess avoidance behavior. The full transfer task uses alcohol and nonalcohol rewards associated with Pavlovian cues and instrumental responses, along with other gustatory and monetary rewards. We constructed mixed-effects models with the addition of other statistical analyses as needed to interpret various behavioral measures. <b><i>Results:</i></b> Single-lever PIT: both versions were successful in eliciting a PIT effect (joystick: <i>p</i> < 0.001, η<sub>p</sub><sup>2</sup> = 0.36, button-box: <i>p</i> < 0.001, η<sub>p</sub><sup>2</sup> = 0.30). Full transfer task: it was determined that the alcohol and nonalcoholic reward cues selectively primed their respective reward-associated responses (gustatory version: <i>p</i> < 0.001, <i>r</i> = 0.59, and monetary version: <i>p</i> < 0.001, <i>r</i> = 0.84). The appetitive/aversive cues resulted in a general transfer effect (gustatory: <i>p</i> < 0.001, η<sub>p</sub><sup>2</sup> = 0.09, and monetary: <i>p</i> < 0.001, η<sub>p</sub><sup>2</sup> = 0.17). <b><i>Discussion/Conclusion:</i></b> Single-lever PIT: PIT was observed in both task versions. We posit that the use of a joystick is more advantageous for the analysis of avoidance behavior. It evenly distributes movement between approach and avoid trials, which is relevant to analyzing fMRI data. Full transfer task: While gustatory conditioning has been used in the past to elicit transfer effects, we present the first paradigm that successfully elicits both specific and general transfers in humans with gustatory alcohol rewards.
<b><i>Introduction:</i></b> Positively conditioned Pavlovian cues tend to promote approach and negative cues promote withdrawal in a Pavlovian-to-instrumental transfer (PIT) paradigm, and the strength of this PIT effect was associated with the subsequent relapse risk in alcohol-dependent (AD) patients. When investigating the effect of alcohol-related background cues, instrumental approach behavior was inhibited in subsequent abstainers but not relapsers. An automatic approach bias towards alcohol can be modified using a cognitive bias modification (CBM) intervention, which has previously been shown to reduce the relapse risk in AD patients. Here we examined the effects of such CBM training on PIT effects and explored its effect on the relapse risk in detoxified AD patients. <b><i>Methods:</i></b> <i>N</i> = 81 recently detoxified AD patients performed non-drug-related and drug-related PIT tasks before and after CBM versus placebo training. In addition, an alcohol approach/avoidance task (aAAT) was performed before and after the training to assess the alcohol approach bias. Patients were followed up for 6 months. <b><i>Results:</i></b> A stronger alcohol approach bias as well as a stronger non-drug-related PIT effect predicted relapse status in AD patients. No significant difference regarding relapse status or the number of heavy drinking days was found when comparing the CBM training group versus the placebo group. Moreover, there was no significant modulation effect of CBM training on any PIT effect or the aAAT. <b><i>Conclusion:</i></b> A higher alcohol approach bias in the aAAT and a stronger non-drug-related PIT effect both predicted relapse in AD patients, while treatment outcome was not associated with the drug-related PIT effect. Unlike expected, CBM training did not significantly interact with the non-drug-related or the drug-related PIT effects or the alcohol approach bias.
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