Dataset for paper "Anticipating causes and consequences" - Experiment 1 Late Effect Data with Differences

Data for paper appearing in Journal of Memory and Language 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720ms into display until the end of the display - 10sec after initial presentation)Independent VariableVBias - causal bias of verb (note that consequentiality bias is to the other NP)Sources of random effectsPart - participantsItem - itemsIV is numerical, other variables are factorsOther variables are included in the file.</div

Analysis script for paper "Anticipating causes and consequences" - Example R Analysis for Experiment 1 Very Early Effect - Differences

Data for paper appearing in Journal of Memory and Language 2020Commented R session for analysis of Experiment 1, very early effect data (differences

Example Images for experiment described in paper "Anticipating causes and consequences"

Data for paper appearing in Journal of Memory and Language 2020Example images used in Experiments 1 and

Dataset for paper "Anticipating causes and consequences" - Experiment 2 Late Effect Data with Differences

Data for paper appearing in Journal of Memory and Language, 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720-10000ms from the beginning of the display of the picture)Independent VariablesVbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"Sources of random effectsPart - participantsItem - itemsDV is numerical, other variables are factorsThe file includes other variables</div

Dataset for paper "Anticipating causes and consequences" - Experiment 2 Very Early Data with Differences

Data for paper appearing in Journal of Memory and Language 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1400ms from the beginning of the padding phrase)Independent VariablesVbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"Sources of random effectsPart - participantsItem - itemsDV is numerical, other variables are factorsThe file includes other variables</div

Dataset for paper "Anticipating causes and consequences" - Experiment 2 Early Data with Differences

Data for paper appearing in Journal of Memory and Language 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1100ms from the beginning of the conjunction, "because" or "and so")Independent VariablesVbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"Sources of random effectsPart - participantsItem - itemsDV is numerical, other variables are factorsThe file includes other variables</div

Dataset for paper "Anticipating causes and consequences" - Experiment 1 Early Effect Data with Differences

Dataset for paper appearing in Journal of Memory and Language 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200 - 1200ms from the beginning of the conjunction "and so")Independent VariableVBias - causal bias of verb (note that consequentiality bias is to the other NP)Sources of random effectsPart - participantsItem - itemsIV is numerical, other variables are factorsOther variables are included in the file.</div

Dataset for paper "Anticipating causes and consequences" Experiment 1 Very Early Effect Data with Differences

Dataset for paper appearing in Journal of Memory and Language 2020csv file for import into R as data framefor the analysis:Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200 - 1500ms after beginning of the padding phrase in the first clause)Independent VariableVBias - causal bias of verb (note that consequentiality bias is to the other NP)Sources of random effectsPart - participantsItem - itemsIV is numerical, other variables are factorsOther variables are included in the file.</div

Sentences for experiments described in paper "Anticipating causes and consequences"

Data for paper appearing in Journal of Memory and Language 2020FirstSet - we took the 32 items used in our earlier implicit causality visual world studies and swapped any verbs that were not mental state verbs for verbs of that category. We changed the content of the sentences with the new verbs where necessary. We then constructed consequential ending for each of the sentences. The 32 resulting sentences were used in Experiment 1.We constructed a second set of sentences (SecondSet), with both causal and consequential versions, using the same mental state verbs (because there are only a small number of common mental state verbs with strong biases. Both sets of items, in both versions (Cause and Consequence) were used in Experiment 2.</div

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