The procedure of Experiment 4 will be the same as in Experiment 1. However, this time participants will all read a vignette in which only one agent (Mark) performs an action. Grammatical aspect of the action will be manipulated. In addition to the questions participants answered in Experiment 1, they will also indicate how many times they think Mark punched John (see [https://osf.io/2xck3/][1]). This question is added to look at influence of grammatical aspect on the temporal dynamics of a described action. Experiment 4 will have a between subjects design. Participants will be randomly assigned to one of two conditions (imperfective vs. perfective aspect). We will record the time people spend reading the vignette. We expect this experiment to take about 5 minutes to complete. Participants will receive $0.5 for their participation. Participants will be recruited trough Amazon Mechanical Turk (http://www.mturk.com). The experiment will be presented online in the Qualtrics survey research suite (http://www.qualtrics.com). We conducted power analyses to determine our sample size for this study (see [https://osf.io/g95ph/][2] and [https://osf.io/8au65/][3]). Based on the smallest effect of grammatical aspect on intentionality we found in our previous studies (Sherrill et al., 2015), we expect to find a medium effect of grammatical aspect on blame judgments (Cohen’s d = .38). This means that we will need at least 87 valid participants per condition to reach a power of .80 (total sample size = 174). Based on the smallest effect we found in our previous studies (Sherrill et al., 2015), we expect to find a large effect of grammatical aspect on temporal dynamics (Cohen’s d = 1.13). This means that we will need at least 11 valid participants per condition to reach a power of .80 (total sample size = 22). Given that we will need more participants to detect an effect of grammatical aspect on blame judgments, we will aim for at least 87 valid participants per condition in this experiment. Therefore, we will recruit 200 participants who will be randomly assigned to one of the two conditions. The data will be analyzed by a frequentist analysis (t-test for independent means). [1]: https://osf.io/2xck3/ [2]: https://osf.io/g95ph/ [3]: https://osf.io/8au65/