174 research outputs found
Ludwig_OpenPracticesDisclosure_rev – Supplemental material for Predicting Exercise With a Personality Facet: Planfulness and Goal Achievement
No full textSupplemental material, Ludwig_OpenPracticesDisclosure_rev for Predicting Exercise With a Personality Facet: Planfulness and Goal Achievement by Rita M. Ludwig, Sanjay Srivastava and Elliot T. Berkman in Psychological Science</p
Planfulness: A Process-Focused Construct of Individual Differences in Goal Achievement
No full textGoal pursuit outcomes are partly caused by the way people think about goals. Specific patterns of thought can increase the likelihood of goal achievement, such as generating heuristics to automate goal-related decision making, orienting present-moment attention to the future to increase the salience of a distal goal, and contrasting the anticipated enjoyment of an achieved goal with the progress required to complete it. However, it is unknown whether there are stable individual differences in the tendency to deploy particular meta-cognitions during goal pursuit. A tool to assess such differences would help to identify and intervene on personal barriers to goal progress. Here, we define a new construct within the conscientiousness domain—planfulness—that captures a person’s proclivity to adopt efficient goal-related cognition in pursuit of their goals. We hypothesize that planfulness consists of three interrelated facets representing distinct mental processes, temporal orientation (TO), cognitive strategies (CS), and mental flexibility (MF), and that planfulness predicts goal achievement on an individual basis. We developed a 30-item Planfulness Scale with three subscales tested and refined across 5 studies and 10 samples (total unique 'N' = 4,318) using iterative exploratory and confirmatory factor analysis on data collected from both student and on-line samples. The Planfulness Scale demonstrated both convergent and discriminant validity when compared to other measurements, and scale scores predicted goal progress in a longitudinal study. We find that planfulness is a useful new construct for self-regulation research, and the 30-item Planfulness Scale to be a valid and reliable measurement of real-world goal achievement
In the trenches of real-world self-control: Neural correlates of breaking the link between craving and smoking
No full textAbstract Successful goal pursuit involves repeatedly engaging self-control against temptations or distractions that arise along the way. Laboratory studies have identified the brain systems recruited during isolated instances of self-control, and ecological studies have linked self-control capacity to goal outcomes. However, no study has identified the neural systems of everyday self-control during long-term goal pursuit. The present study integrated neuroimaging and experiencesampling methods to investigate the brain systems of successful self-control among smokers attempting to quit. A sample of 27 cigarette smokers completed a go/no-go task during functional magnetic resonance imaging before they attempted to quit smoking and then reported everyday self-control using experience sampling eight times daily for 3 weeks while they attempted to quit. Increased activation in right inferior frontal gyrus, pre-supplementary motor area, and basal ganglia regions of interest during response inhibition at baseline was associated with an attenuated association between cravings and subsequent smoking. These findings support the ecological validity of neurocognitive tasks as indices of everyday response inhibition. Keywords self-control; smoking cessation; brain-as-predictor; right inferior frontal gyrus; response inhibition; text messaging Ridding oneself of an unwanted habit or tendency is a war that consists of a series of momentary self-control skirmishes. A longtime smoker may decide to quit, but success in reaching that goal will depend on the individual outcomes of a series of battles with cigarette cravings. Understanding the neural processes involved in these brief repeated struggles, in smoking and in other domains, is essential to understanding how self-control works in the trenches of real-world goal pursuit. The investigation reported here focused on response inhibition as one key factor that influences the ultimate success or failure of goal pursuit, and overcoming addiction in particular. Behavioral studies have examined how response inhibition during lab-based tasks relates to general real-world success at overriding an © The Author(s) 2011 Corresponding Author: Elliot T. Berkman, Department of Psychology, University of Oregon, 1227 University of Oregon, Eugene, OR 97403-1227 [email protected]. Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. Supplemental Material Additional supporting information may be found at http://pss.sagepub.com/content/by/supplemental-data NIH Public Access unwanted habitual behavior in favor of a desired novel one Behavioral performance on simple laboratory response-inhibition tasks (e.g., go/no-go) has been consistently linked to success in reaching a variety of real-world goals that involve self-regulation. For instance, the capacity to engage response inhibition has been linked to success at dieting Neuroscience studies have converged in identifying a consistent network of brain regions that are active during brief, laboratory-based manipulations of response inhibition. A number of functional neuroimaging Thus, on one hand, behavioral measures of response inhibition have been associated with a broad array of real-world outcomes, such as prevention of addiction relapse. On the other hand, the brain systems recruited for inhibiting responses during brief laboratory tasks are being mapped with increasing precision. Juxtaposing the behavioral and neuroscience literatures on response inhibition highlights why the neural processes underlying real-world instances of response inhibition have remained unexplored. There is almost no overlap between these literatures beyond similarity in the tasks used to assess response inhibition. Consequently, it is unknown whether the neural systems involved in laboratory assessments of response inhibition are the same ones recruited in the brief and repeated everyday battles between habit and self-control. For example, it is possible that the neural systems recruited during the stop-signal task are different from those associated with increasing exercise. Linking these disparate levels of analysis (i.e., neural and social/behavioral) and time scales (i.e., seconds/minutes and days/weeks) requires a paradigm for examining response inhibition during real-life situations and also during neuroim-aging tasks in the same sample of individuals. Accordingly, we made this link by measuring the neural mechanisms and everyday implementation of response inhibition within a single study. We recruited a sample of individuals just before they were to engage in the long-term, real-life response-inhibition task of quitting cigarette smoking and used functional MRI (fMRI) to examine their neural activation during a laboratory response-inhibition task. Next, we used experience sampling to track their progress throughout each day for the first 3 weeks of their smoking-cessation attempt Method Participants Thirty-one participants (15 female, 16 male) were recruited from smoking-cessation programs in Los Angeles via in-person announcements at orientation sessions. All participants were heavy smokers (> 10 cigarettes/day, 7 days/week, for at least 1 year and urinary cotinine > 1,000 ng/mL) enrolled in a professionally led cessation program (e.g., Freedom From Smoking). To be included in the study, participants also were required to have a score of 9 or 10 (out of 10) on the Contemplation Ladder, a single-item measure of intentions to quit Of the original 31 participants, all completed the scanning session, but 1 withdrew from participation in the experience-sampling phase, and 3 were excluded for insufficient data; thus, 27 participants were included in the analyses reported here. Participants were compensated 1 for each experience-sampling response returned, for a possible total of 248. All participants provided written informed consent approved by the UCLA Institutional Review Board. Procedure Phone screening-Following recruitment, participants were contacted by telephone to assess their intentions to quit (with the Contemplation Ladder and Readiness to Change Questionnaire) and their targeted quit date (TQD), as well as whether they met any of the Baseline (scanning) session-Participants came to the UCLA Ahmanson-Lovelace Brainmapping Center for a baseline session at least 1 day prior to their quit date We used a go/no-go task to examine the neural activation associated with response inhibition After completing this task, participants were removed from the scanner and brought into a quiet testing room for the duration of the session. Participants completed measures of demographics, smoking history, waking hours, nicotine dependence (Fagerström Test of Nicotine Dependence; Heatherton, Experience sampling-Following the scanning session, and beginning 1 day prior to their quit date, participants received prompts via text message eight times per day for 21 consecutive days. The first text prompt on each day was sent 15 min after morning rise, the last prompt was sent 15 min before bedtime, and the other six were evenly distributed throughout the day. Rise times and bedtimes were adjusted for each participant for weekdays and weekends. The interprompt interval varied across subjects between 1 hr 50 m and 2 hr 25 m. At each prompt, participants responded to three questions: "How many cigarettes have you smoked since the previous signal?" (numerical response), "How much are you craving a cigarette right now?" (0 = not at all, 1 = a little, 2 = somewhat, 3 = a lot, 4 = extremely), and "Overall, how is your mood right now?" (0 = extremely negative, 1 = somewhat negative, NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript End-point session-An end-point session was scheduled within 7 days of the end of the 21-day experience-sampling period. Exhaled CO was reassessed along with nicotine dependence (Fagerström Test of Nicotine Dependence) and smoking urges (Questionnaire on Smoking Urges). Participants were compensated 1 for each text-message response (M = 38). fMRI data acquisition and analysis Brain-imaging data were acquired on a 3-T Siemens Trio scanner at the UCLA AhmansonLovelace Brainmapping Center using standard data-acquisition and preprocessing steps (see the Supplemental Text in the Supplemental Material). The main effect of response inhibition was defined using a linear contrast for each participant (i.e., no-go > go). Contrast images were averaged across runs for each participant and then entered into a random-effects analysis at the group level. We constructed regions of interest (ROIs; Experience-sampling data-analysis strategy Multilevel linear modeling was used to address the nested nature of the experience-sampling data (HLM 6; Scientific Software International, Lincolnwood, IL; Raudenbush, Integration of fMRI and experience-sampling data To assess everyday response inhibition, we estimated the prospective relationship between craving for a cigarette at one time point and smoking at the subsequent time point. The magnitude of the relationship between these measures provided an ecological measure of response inhibition because cravings are among the primary impulses that must be regulated in successful smoking cessation Results Behavioral responses to the go/no-go task Participants completed 108 no-go and 492 go trials across 12 go/no-go blocks. The error rate on no-go trials was 4.6%. Error trials were included in the model but not examined because of insufficient N. The mean response time on go trials was 547.9 ms (SD = 160.5). Experience-sampling response rates Participants responded to 84% of the prompts during the experience-sampling phase of the study (~6.7 responses out of 8 prompts daily). Most responses were sent within 23 min of the signal (SD = 44 min). For a given participant, a day was excluded if it contained fewer than four responses. In total, 90 days were excluded (M = 3.33 per participant). Robustness analyses suggest that the missing data did not affect the results (see Supplemental Text in the Supplemental Material). There were a total of 3,811 Level 1 observations (time points within days), 477 Level 2 observations (days within participants), and 27 Level 3 observations (participants) in our multilevel model. Smoking and craving during experience sampling Participants smoked 20.2 cigarettes per day (SD = 9.4) at baseline and 5.2 cigarettes per day (SD = 5.4) at the end point (mean change = 15.0), t(26) = 7.62, p < .01. Nicotine dependence and urges also decreased significantly There was a positive within-day relationship between craving at one time point and smoking at the next when craving was entered alone into the model (i.e., without neural activations; log-expectation γ = .19, SE = .08), t(476) = 2.14, p < .05. Reductions in the number of cigarettes smoked per day were inversely related to the daily craving-smoking link (see Supplemental Text in the Supplemental Material for analysis of the relationship between the craving-smoking slope and smoking reductions). Predicting everyday response inhibition from neuroimaging data To examine the association between neural activation at baseline and longitudinal outcomes, we extracted activations from the no-go > go contrast for anatomically defined ROIs in the IFG, basal ganglia, and pre-SMA There was an overall positive relationship between craving at time i and smoking at time i + 1. The IFG, basal ganglia, and pre-SMA ROIs each significantly and negatively moderated that slope We also examined the relationship between activity in each ROI and long-term cessation success (i.e., across 4 weeks). Only activity in basal ganglia, and not in the other two ROIs, predicted long-term reductions in smoking as measured by change in exhaled CO (Montreal Neurological Institute coordinates: x = 30, y = 5, z = 4; 86-voxel extent, t = 5.02, falsedetection-rat
Neural Correlates of Attentional Flexibility during Approach and Avoidance Motivation.
No full textDynamic, momentary approach or avoidance motivational states have downstream effects on eventual goal success and overall well being, but there is still uncertainty about how those states affect the proximal neurocognitive processes (e.g., attention) that mediate the longer-term effects. Attentional flexibility, or the ability to switch between different attentional foci, is one such neurocognitive process that influences outcomes in the long run. The present study examined how approach and avoidance motivational states affect the neural processes involved in attentional flexibility using fMRI with the aim of determining whether flexibility operates via different neural mechanisms under these different states. Attentional flexibility was operationalized as subjects' ability to switch between global and local stimulus features. In addition to subjects' motivational state, the task context was manipulated by varying the ratio of global to local trials in a block in light of recent findings about the moderating role of context on motivation-related differences in attentional flexibility. The neural processes involved in attentional flexibility differ under approach versus avoidance states. First, differences in the preparatory activity in key brain regions suggested that subjects' preparedness to switch was influenced by motivational state (anterior insula) and the interaction between motivation and context (superior temporal gyrus, inferior parietal lobule). Additionally, we observed motivation-related differences the anterior cingulate cortex during switching. These results provide initial evidence that motivation-induced behavioral changes may arise via different mechanisms in approach versus avoidance motivational states
A multilab replication of the ego depletion effect
No full textThere is an active debate regarding whether the ego depletion effect is real. A recent preregistered experiment with the Stroop task as the depleting task and the antisaccade task as the outcome task found a medium-level effect size. In the current research, we conducted a preregistered multilab replication of that experiment. Data from 12 labs across the globe (N = 1,775) revealed a small and significant ego depletion effect, d = 0.10. After excluding participants who might have responded randomly during the outcome task, the effect size increased to d = 0.16. By adding an informative, unbiased data point to the literature, our findings contribute to clarifying the existence, size, and generality of ego depletion.sponsorship: Junhua Dang is supported by the Swedish Research Council (2018-06664); Helgi Schioth is supported by the Swedish Research Council; Jacek Buczny was partially supported by SWPS University of Social Sciences and Humanities, Sopot, Poland, Grant BST WSO/2016/A/01, and VU Amsterdam, the Netherlands; Lile Jia was sponsored by Social Psychological and Personality Science Grant R-581-000-165133 from the National University of Singapore; Anna Baumert and Manfred Schmitt were supported by the German Research Foundation (SCHM1092/16-1); Liwei Zhang was supported by Grant 2018YFF0300902 China National Key Research Project; Elliot Berkman was supported by NIH Grants R01 MH107418, R01 CA211224, R21 CA175241, and R01 HD094831. (Swedish Research Council|2018-06664, SWPS University of Social Sciences and Humanities, Sopot, Poland|BST WSO/2016/A/01, VU Amsterdam, the Netherlands, Social Psychological and Personality Science Grant from the National University of Singapore|R-581-000-165133, German Research Foundation|SCHM1092/16-1, China National Key Research Project|2018YFF0300902, NIH|R01 MH107418, NIH|R01 CA211224, NIH|R21 CA175241, NIH|R01 HD094831, National Cancer Institute|R01CA211224, Swedish Research Council|2018-06664)status: Publishe
Using neuroscience to broaden emotion regulation: Theoretical and methodological considerations
No full textBehavioral research on emotion regulation thus far has focused on conscious and deliberative strategies such as reappraisal. Neuroscience investigations into emotion regulation have followed suit. However, neuroimaging tools now open the door to investigate more automatic forms of emotion regulation that take place incidentally and potentially outside of participant awareness that have previously been difficult to examine. The present paper reviews studies on the neuroscience of intentional/deliberate emotion regulation and identifies opportunities for future directions that have not yet been addressed. The authors suggest a broad framework for emotion regulation that includes both deliberative and incidental forms. This framework allows insights from incidental emotion regulation to address open questions about existing work, and vice versa. Several studies relevant to incidental emotion regulation are reviewed with the goal of providing an empirical and methodological groundwork for future research. Finally, several theoretical issues for incidental and intentional emotion regulatio
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