People's reports of unexpected events for everyday scenarios: Over 1000 textual responses, human-labelled for valence/sentiment, controllability and topic category.

With this article, we present a repository containing datasets, analysis code, and some outputs related to a paper in press at Cognition. The data were collected as part of a pre-test, pilot test, and main study all designed in SurveyGizmo and participants recruited via Prolific.co (combined N=303). Datasets consist of raw and annotated data, where participant responses are free-text entries about what unexpected events might occur after a series of events, presented them with based on everyday scenarios. The code consists of all computational additions to the data, and analysis carried out for the results presented in the article. This data is released for the purpose of transparency and to allow for reproducability of the work. This human-labelled data should also be of use to machine learning researchers researching text analytics, natural language processing and sources of common-sense knowledge.

Factors affecting "expectations of the unexpected": The impact of controllability & valence on unexpected outcomes.

Do people have specific "expectations about the unexpected" when they think about the future? Recent work supports a "negativity bias", that people expect future events to be disrupted by unexpected negative outcomes. However, when the current situation is negative, they report more positive unexpected outcomes (e.g., negative experiences lead many to imagine a future of unexpected positive outcomes). The present study (N = 219 Prolific.co participants; with a pre-test of N = 64) explored whether people also show an "uncontrollability bias"; best-laid plans are often disrupted by uncontrollable events. People thought of unexpected outcomes for 8 everyday scenarios, matched on valence and controllability, generating a total of 1752 distinct responses. Participants mainly report negative-uncontrollable unexpected events (34%). However, in contrast to prior work (i) negatively-valenced scenarios elicit more controllable unexpected outcomes, and (ii) uncontrollable scenarios elicit more positive unexpected outcomes. The implications of these results for everyday cognition and decision making are discussed.

Three datasets reporting unexpected events for everyday scenarios: Over 9000 events human-labelled for overall valence/sentiment, topic category, and relationship to the initial goal of the scenario.

The three datasets described in this paper were collected from online experiments distributed via Prolific.co participant system. Together, the three datasets comprise 9720 text responses of unexpected events participants predicted for everyday scenarios such as going shopping or preparing breakfast. Each event was labelled by at least two independent, human raters on their topic or category (relative to their initial scenario), the valence or sentiment of the event, and whether or not the event mentions words related to the goal stated in the initial scenario. We also include summary data from a pre- and post-test conducted in the course of these experiments, as well as the analysis code in the form of Jupyter Notebooks. We provide this data and relevant code for transparency and reproducibility alongside our Cognition paper. The dataset could be useful in training machine learning models on valence/sentiment of everyday unexpected events.

Do we "fear for the worst" or "Hope for the best" in thinking about the unexpected?: Factors affecting the valence of unexpected outcomes reported for everyday scenarios.

Though we often "fear the worst", worrying that unexpectedly bad things will happen, there are times when we "hope for the best", imagining that unexpectedly good things will happen, too. The paper explores how the valence of the current situation influences people's imagining of unexpected future events when participants were instructed to think of "something unexpected". In Experiment 1, participants (N = 127) were asked to report unexpected events to everyday scenarios under different instructional conditions (e.g., asked for "good" or "bad" unexpected events), and manifested a strong negativity bias in response to non-valenced instructions (i.e., being asked to "think of the unexpected" with no valence given). They mainly reported quite "predictable" unexpected outcomes that were negative; however, a post-test (N = 31) showed that the scenarios used were predominantly positive. In Experiment 2 (N = 257), when participants were instructed to think of "something unexpected and bizarre", under the same instructional manipulations as Experiment 1, this negativity bias was replicated. In Experiment 3, using a design in which positive/negative materials were matched (verified by a pre-test, N = 60), it was found that when participants (N = 102) were given negative scenarios, they reported more positive events than they do when they are given positive scenarios. Though responding still retained an overwhelming negative bias, this result provided some evidence for a weaker valence-countering strategy; that is, where a negative scenario can lead to positive unexpected events being mentioned, and a positive scenario leads to negative unexpected events being reported. The implications of these results for people's projections of unexpected futures in their everyday lives is discussed.

Computer-assisted psychological assessment and psychotherapy for collegians.

OBJECTIVE: This article reviews research on computerized and computer-assisted psychological assessment and psychotherapy for college and university students. METHOD: Published reviews of outcome research on the topic are reviewed, along with individual clinical trials and other relevant studies not covered by reviews, as well as reviews of closely-related research. RESULTS: Computer-assisted assessment and psychotherapy have proven effective with collegians across samples, nations, and presenting concerns. CONCLUSIONS: Currently-available digital technologies can address these mental health service delivery challenges: cost, limited human resources, failure of students to seek help, stigmatization of collegians seeking help, premature termination, inadequate process and outcome data to assess and improve treatment effectiveness, and lack of real-time data-based treatment selection.