SlowGuess/ACL_2020_OCR huggingface dataset:
Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing.
Dongyeop Kang, Hiroaki Hayashi, Alan W Black, and Eduard Hovy. 2019. Linguistic versus latent relations for modeling coherent flow in paragraphs. In EMNLP.
Marlieke T. R. van Kesteren, Dirk J. Ruiter, Guillén Fernández, and Richard N. Henson. 2012. How schema and novelty augment memory formation. Trends in Neurosciences, 35(4).
Walter Kintsch. 1988. The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95(2):163.
Roger Levy. 2008. Expectation-based syntactic comprehension. Cognition, 106(3):1126-1177.
Ken McRae and Michael Jones. 2013. Semantic memory. In Daniel Reisberg, editor, *The Oxford Handbook of Cognitive Psychology*, Psychology Publications.
Farah Nadeem, Huy Nguyen, Yang Liu, and Mari Ostendorf. 2019. Automated essay scoring with Discourse-Aware neural models. In Workshop on Innovative Use of NLP for Educational Applications @ ACL.
Keith Oatley. 1999. Why fiction may be twice as true as fact: Fiction as cognitive and emotional simulation. Review of general psychology, 3(2):101-117.
Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination. In ACL.
James W. Pennebaker, Roger J. Booth, Ryan L. Boyd, and Martha E. Francis. 2015. Linguistic inquiry and word count: LIWC 2015.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. Unpublished.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. Unpublished.
Valerie F. Reyna and Charles J. Brainerd. 1995. Fuzzytrace theory: An interim synthesis. Learning and Individual Differences, 7(1):1-75.
Henry L Roediger III, J Derek Jacoby, and Kathleen B McDermott. 1996. Misinformation effects in recall: Creating false memories through repeated retrieval. Journal of Memory and Language, 35(2):300-318.
Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents. Text Mining: Applications and Theory, 1:1-20.
Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019. ATOMIC: An atlas of machine commonsense for if-then reasoning. In AAAI.
Roger C. Schank and Robert P. Abelson. 1977. Scripts, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures. Lawrence Erlbaum.
H. Andrew Schwartz, Gregory Park, Maarten Sap, Evan Weingarten, Johannes Eichstaedt, Margaret Kern, David Stillwell, Michal Kosinski, Jonah Berger, Martin Seligman, and Lyle Ungar. 2015. Extracting human temporal orientation from Facebook language. In NAACL.
Matthew Sims, Jong Ho Park, and David Bamman. 2019. Literary event detection. In ACL.
Andrea Smorti and Chiara Fioretti. 2016. Why narrating changes memory: a contribution to an integrative model of memory and narrative processes. Integrative Psychological and Behavioral Science, 50(2):296-319.
Youngseo Son, Anneke Buffone, Joe Raso, Allegra Larche, Anthony Janocko, Kevin Zembroski, H Andrew Schwartz, and Lyle Ungar. 2017. Recognizing counterfactual thinking in social media texts. In ACL.
Yla R. Tausczik and James W. Pennebaker. 2010. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24-54.
Yaacov Trope and Nira Liberman. 2010. Construal-level theory of psychological distance. Psychological review, 117(2):440.
Endel Tulving. 1972. Episodic and semantic memory. Organization of Memory, 1:381-403.
Endel Tulving and Daniel L. Schacter. 1990. Priming and human memory systems. Science, 247(4940):301-306.
Gal Zauberman, B Kyu Kim, Selin A Malkoc, and James R Bettman. 2009. Discounting time and time discounting: Subjective time perception and intertemporal preferences. Journal of Marketing Research, 46(4):543-556.
(a) Recalled main events
(b) Imagined main events
Figure 4: We extract phrases from the main themes of recalled (left) and imagined (right) stories, using RAKE (Rose et al., 2010); size of words corresponds to frequency in corpus, and color is only for readability.
A Data Collection
We describe the data collection in further detail, and release our MTurk annotation templates. $^{11}$
A.1 Post-Writing Questionnaire
After each writing stage (recalled, imagined, retold), we ask workers to rate "how impactful, important, or personal" the story was to them (for imagined and recalled stories), "how similar" to their own lives the story felt (imagined only), and "how often [they] think or talk about the events" in the story (recalled only), on a Likert scale from 1-5. Workers also take the four "openness" items from the Mini-IPIP personality questionnaire (Donnellan et al., 2006) as an assessment of overall creativity. Finally, workers optionally report their demographic information (age, gender, race).
A.2 Worker Demographics
Our stories are written by 5,387 unique U.S.-based workers, who were  $47\%$  male and  $52\%$  female ( $<1\%$  non-binary,  $<1\%$  other). Workers were 36 years old on average (s.d. 10 years), and predominantly white ( $73\%$ , with  $10\%$  Black,  $6\%$  Hispanic,  $5\%$  Asian). We find no significant differences in demographics between the authors of imagined and recalled stories,[12] but authors of imagined stories scored slightly higher on measures of creativity and openness to experience (Cohen's  $d = 0.08$ ,  $p = 0.01$ ).
Note that we randomly paired story summaries to workers. We did not attempt to match the demographics of the recalled story to the demographics
of the imagined author. Future work should investigate whether there are linguistic effects of differing demographics between the two authors.[13]
B Episodic vs. Semantic Knowledge
B.1 Realis Events
To detect realis events in our stories, we train a tagger (using BERT-base; Devlin et al., 2019) on the annotated corpus by Sims et al. (2019). This corpus contains 8k realis events annotated by experts in sentences drawn from 100 English books. With development and test  $F_{1}$  scores of  $83.7\%$  and  $75.8\%$ , respectively, our event tagger slightly outperforms the best performing model in Sims et al. (2019), which reached  $73.9\% F_{1}$ . In our analyses, we use our tagger to detect the number of realis event mentions.
B.2 Commonsense Knowledge Matching
We quantify the prevalence of commonsense knowledge in stories, as a proxy for measuring the traces of semantic memory (Tulving and Schacter, 1990). Semantic memory is thought to encode commonsense as well as general semantic knowledge (McRae and Jones, 2013).
We design a commonsense extraction tool that aligns sentences in stories with commonsense tuples, using a heuristic matching algorithm. Given a story, we match possible ATOMIC events to sentences by selecting events that share noun chunks and verb phrases with sentences. For every sentence  $s_i$  that matches an event  $E$  in ATOMIC, we check surrounding sentences for mentions of commonsense inferences (using the same noun and verb phrase matching strategy); specifically, we
<sup>13</sup>Future work could investigate social distance alongside other types of psychological distances (e.g., physical, temporal), using the framework given by Construal Theory (Trope and Liberman, 2010).
11Available at http://aka.ms/hippocorpus.
<sup>12</sup>We run Chi-squared tests for gender ( $\chi^2 = 1.01$ ,  $p = 0.80$ ), for age ( $\chi^2 = 9.99$ ,  $p = 0.26$ ), and for race ( $\chi^2 = 9.99$ ,  $p = 0.35$ ).
Figure 5: Average negative log likelihood (NLL) of sentences conditioned on varying sizes of histories of included sentences for recalled (green) and imagined (purple) stories (with  $95\%$  confidence intervals). For history sizes  $>1$ , differences are significant when controlling for multiple comparisons  $(p < 0.001)$ .
check the  $n_c$  preceding sentences for matches of causes of  $E$ , and the  $n_e$  following sentences for event  $E$ 's effects.
To measure the prevalence of semantic memory in a story, we count the number of sentences that matched ATOMIC knowledge tuples in their surrounding context. We use a context window of size  $n_c = n_e = 2$  to match inferences, and use the spaCy pipeline (Honnibal and Montani, 2017) to extract noun and verb phrases.
C Recollection vs. Imagination
C.1 Linearity with Varying Context Size
Shown in Figure 5, we compare the negative log-likelihood of sentences when conditioned on varying history sizes (using the story summary as context  $\mathcal{E}$ ). As expected, conditioning on longer histories increases the predictability of a sentence. However, this effect is significantly larger for imagined stories, which suggests that imagined stories flow more linearly than recalled stories.
variable	β
w/o rand. eff.	β
w/ rand. eff.
story length	0.319***	0.159**
Δl (linearity)	-0.454***	-0.642***
realis events	0.147***	0.228***
commonsense	-0.144***	-0.157***
Table 3: Results of multivariate linear regression models (with and without participants random effects), regressing onto story type (0: imagined vs. 1: recalled) as the dependent variable. All effects are significant  $(^{**}:p < 0.005,^{***}:p < 0.001)$ .
C.2 Robustness of Findings
To confirm the validity of our measures, we report partial correlations between each of our measures, controlling for story length. We find that our realis measure is negatively correlated with our commonsense measures (Pearson  $r = -0.137$ ,  $p < 0.001$ ), and positively correlated with our linearity measure ( $r = 0.111$ ,  $p < 0.001$ ). Linearity and commonsense were not significantly correlated ( $r = -0.02$ ,  $p = 0.21$ ).
Additionally, we confirm that our findings still hold when controlling for other measures and participant random effects. Notably, we find stronger associations between our measures and story type when controlling for other measures, as shown in Table 3. We see a similar trend when additionally controlling for individual variation in workers.