The role of goals and goal barriers in predicting the outcomes of intentional actions in the contexts of narrative text

ABSTRACT

Predictions about human behaviour can be influenced by the presence and status of goals. The purpose of this study was to assess the impact of an active goal and barriers to that goal on predictions about outcomes experienced by agents. Participants read stories describing characters with goals. The extent that there were barriers to those goals was varied. Participants predicted what happens next in the story, both prior to and after barrier removal. There was support for a goal barrier hypothesis, where the conditions for predicting goal completion involved removing conditions that prevent a goal being achieved (Experiments 1 and 2). At the same time, unachieved goals were more accessible to working memory than completed goals, regardless of a barrier (Experiment 3). These results suggest that participants deliberately decided when it was appropriate to use goal information to predict outcomes of intentional actions conducted by the agents in the stories.

KEYWORDS:

• Prediction
• event cognition
• goals

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 We included all intercepts and random slopes as random effect to maximize the random factor structure (Barr, Levy, Scheepers, & Tily, 2013), but we had to exclude the location by subject random effect because the model would not converge.

2 These data were also analysed with repeated measures ANOVA. The results were consistent with the results of the logistic mixed effects model. Importantly, the effect of location was not significant, F(1, 39) = 1.137, MSE = .104, p = .293. We computed Bayes factors to further explore the null effect of location, and computed BF₀₁ = 3.45. This lends substantial support for the null hypothesis, where these data are 3.45 times more likely under the null than alternative hypothesis. Means were based on variability at the participant level, and therefore do not reflect the error term used by the linear mixed effects model reported in the main body of the paper.

3 An example filler question demanding a “No” response is, Was Betty an orphan? The text was about her buying a present for her mother.

4 Like the analyses for Experiments 1 and 2, we included all intercepts and random slopes as random effect to maximize the random factor structure (Barr et al., 2013), but we had to exclude the location by subject random effect because the model would not converge.

5 These data were also analysed with repeated measures ANOVA. The results were consistent with the results of the logistic mixed effects model. The main effect of location was not significant, F(1, 81) = .002, MSE = 90091, p = .967, partial Eta² = .000. Once again, we computed Bayes factors to further explore the null effect of location, and computed BF₀₁= 8.33, indicating these data are 8.33 times more likely under the null than alternative hypothesis. This lends strong support for the null hypothesis. These means are based on variability at the participant level and do not reflect the error term used by the linear mixed effects model reported in the main body of the paper.