Imagining the future and remembering the past both involve mental time travel. This commonality could indicate shared mental processes, as held by the Constructive Episodic Simulation Hypothesis (Schacter & Addis, 2008), or else interactive processes that complement one another, a possibility we call the Complementarity Hypothesis. According to the Complementarity Hypothesis, future thoughts are constructed from schemas making them episodically poor, whereas past thoughts are constructed from schemas and direct retrieval of memory traces, making them relatively episodically rich. We tested these hypotheses using machine learning to data mine mental operations in language, much as a geologist can recover physical processes from the geological record. People’s natural, unprompted talk on web blogs was automatically analyzed for past, present, and future references using a temporal orientation classifier. In Study 1, we found that perceptual details were mentioned more often in past than future talk, implying greater use of episodic processing in past than future thinking. In Study 2, a neural network using schemas generated from Latent Dirichlet Allocation better predicted the content of references to the future than the past, implying that constructive processes are more common in future than past thinking. In Study 3, we used the results from the two prior studies to construct an episodic-by-constructive process space. We adapted techniques from fMRI analysis to analyze this space for clusters of activity, as if the frequency of past and future thinking were BOLD responses in cortical space. We found that past and future thinking occupy highly separable regions of processing space, supporting the Complementarity Hypothesis.

Conceptual frameworks such as religion or politics may play a pervasive role in people’s interpretation of experience, but the empirical evidence for such effects is limited. To the extent that conceptual frameworks are real, they should have a pervasive impact on how people talk about the world. Such an influence may be detected in people’s everyday language. In a series of studies, text from the social media platform Reddit was used to train machine learning classifiers to identify people’s association with a particular religion or mental disorder. Impressively, classifiers trained on text focusing on religion and mental disorders could be used to identify people’s association with a particular religion or mental disorder even when the text was not explicitly about these topics, such as when it was about buying a car or playing tennis. Not only could the classifiers predict people’s religion or mental illness in the present, they could also do so prospectively, indicating that people’s everyday language gives away information about the kinds of conceptual frameworks they may hold in the future. An analysis of the features learned by the classifier suggested that they learned features with high face validity for the underlying conceptual framework. Together, the results provide evidence for the existence of conceptual frameworks by virtue of the imprint they leave across a wide range of language contexts.

According to existing accounts of causation, people rely on asingle criterion to identify the cause of an event. Thephenomenon of causal illusions raises problems for suchviews. Causal illusions arise when a particular factor isperceived to be causal despite knowledge indicatingotherwise. According to what we will call the Dual-ProcessHypothesis of Causal Identification, identifying a causeinvolves two cognitive processes: 1) an automatic, intuitiveprocess that identifies possible causes on the basis ofperceptual cues (spatial and temporal) and 2) a slow,reflective process that identifies possible causes on the basisof causal inference, in particular, a consideration of possiblemechanism. Consistent with this hypothesis, we found that inresponse to a causal illusion shown in a naturalistic setting,people’s initial judgments of causation were higher than theirultimate judgments of causation (Experiment 1). Using anonline measure of the time-course of people’s causaljudgments, we found that people initially view animations ofcausal illusions as causal before concluding that they are non-causal (Experiment 2). Finally, we obtained similar resultsusing a deadline procedure, while also finding that the lowerthe cognitive reflectiveness (as measured by the CRT), thestronger people’s impressions of causation were (Experiment3). Implications for different classes of theories of causationare discussed.

Human behavior is plagued by shortsightedness. When faced withtwo options, smaller rewards are often chosen over larger rewards,even when such choices are potentially costly. In threeexperiments, we use big data techniques to examine how suchchoices might be driven by people’s temporal horizons. InExperiment 1, we determine the average distance into the futurepeople talk about in their tweets in order to determine the temporalhorizon of each U.S. state. States with further future horizons hadlower rates of risk taking behavior (smoking, binge drinking) andhigher rates of investment (e.g., education, infrastructure). InExperiment 2, we used an individual’s tweets to establish theirtemporal horizon and found that those with longer temporalhorizons were more willing to wait for larger rewards. InExperiment 3, we were once again able to predict the choicebehaviors of individuals from their tweets, this time showing thatthose with longer future horizons were less likely to take risks. Thefindings help establish a powerful relationship between people’sthoughts about the future and their decisions.

How do representations of the future shape behavior? Prior research has shown that people’s willingness to wait for a future reward decreases with increases in time. At the same time, this research has also shown that such effects can depend on the vividness of the future reward, as well as, on individual differences. The present research offers a potential explanation for these additional effects in demonstrating how representations of the future can depend not only on objective distances in time, but also on how distances in time are construed. In a series of three experiments using a delay discounting paradigm, we show that participants who represent the future as close to the present are more likely to wait for future rewards than those who represent the future as far, even when the objective distances are held constant. Applications are discussed to public policy issues such as global warming, and to episodic future thinking