Selection rates during testing and trait scores. Mean ± SEM.

<p>Selection rates during testing and trait scores. Mean ± SEM.</p

Probabilistic Selection Task (PST).

<p>A. One training trial in the PST. After fixation, two symbols were presented and participants selected one symbol within 1s. After 1s positive or negative feedback was presented based on the reward probability associated with the selected symbol. RT = response time. B. Reward probabilities associated with each pair and symbol. The symbols associated with each reward probability were randomized between participants.</p

Model-fitted data (Mean ± SEM).

<p>A. To assess approach and avoidance learning in the working memory (WM) system, the approach/avoidance model was fit to participants’ behaviour during the training phase [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166675#pone.0166675.ref012" target="_blank">12</a>]. Model-derived proportion of correct selections during the training phase is displayed by the lines, while actual behaviour is displayed by the circles. B. Approach and avoidance learning rates for the WM system. Approach and avoidance learners did not differ in the learning rates for the WM system. C. To assess approach and avoidance learning in the Habitual learning system, the approach/avoidance model was fit to participants’ behaviour during the testing phase [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166675#pone.0166675.ref012" target="_blank">12</a>]. Model-derived proportion of correct selections during the training phase is displayed by the lines while actual behaviour is displayed by the circles. D. Approach and avoidance learning rates for the Habitual learning system. Approach learners displayed relatively slower learning rates from positive feedback (<i>α</i>_{<i>Approach</i>}) as compared to negative feedback (<i>α</i>_<i>Avoid</i>), while avoidance learners displayed the reverse trend. <i>α</i>_{<i>Approach</i>} = learning rate following positive feedback, <i>α</i>_<i>Avoid</i> = learning rate following negative feedback. • p< 0.10, * p < 0.05, ** p < 0.01, ns. = not significant (p>0.05).</p

Fitted model parameters separately for approach and avoidance learners.

<p>Mean ± SEM.</p

Behavioural data (Mean ± SEM).

<p>A. Performance as a function of training. Performance improved equally for participants in the different groups as training progressed. B. Approach and avoidance performance during testing. Approach learners were relatively better at selecting the A symbol, as compared to rejecting the B symbol, while avoidance learners displayed the reverse trend. Importantly, this interaction simply reflects the assignment of participants to different groups based on their relative performance on selecting the A-rejecting the B symbol. However, approach learners were also better at selecting the A symbol as compared to avoidance learners, while the B symbol was more frequently rejected by avoidance learners. C. Approach and avoidance learners did not differ in BAS nor BIS scores. D. Approach and avoidance learners scored higher on reward sensitivity (SR) and punishment sensitivity (SP), respectively. BAS = behavioural activation system, BIS = behavioural inhibition system, SP = sensitivity to punishment, SR = sensitivity to reward, SPSRQ = Sensitivity to Punishment and Sensitivity to Reward Questionnaire. • p< 0.10, * p < 0.05, ** p < 0.01,*** p < 0.001, ns. = not significant (p>0.05).</p

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.</p