Mind Window

Name: Mind Window
Author: University of Arizona

University of Arizona

DOCUMENTED

Published in academic literature

For:Researchers & Academics

Free

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App Summary

Mind Window is a research-based tool that helps users understand their own thought patterns by prompting them to log their thoughts at random moments throughout the day. The associated research establishes the scientific basis for this approach, demonstrating that the content and dynamics of thought can reveal a "real-time cognitive signature" of rumination (such as trait brooding). By providing personalized feedback on these patterns, Mind Window aims to help users gain insight into how their unique thinking habits may be impacting their well-being.

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Detailed Description

Functionality & Mechanism

Developed at the University of Arizona, Mind Window is a research tool employing experience sampling methodology to longitudinally map thought patterns. The system delivers randomized prompts, guiding the capture of real-time data on the user's stream of consciousness. An integrated interface aggregates these inputs, generating statistical summaries and visualizations of thought content and dynamics. This allows for the exploration of personalized cognitive patterns by day, week, or month, contributing to a large-scale international research database.

Evidence & Research Context

The app's methodology is grounded in research establishing the high ecological validity of the "think aloud" paradigm for quantifying the content and dynamics of resting-state thought.
Associated neuroimaging research identifies distinct, recurring brain states—involving the default mode and executive control networks—that scaffold the emergence and progression of unprompted internal thoughts.
In studies using a related laboratory paradigm, thought patterns characterized as negative, self-focused, and past-oriented were significantly associated with trait brooding, a form of rumination.
Further analysis revealed that the content and dynamics of thought accounted for approximately one-third of the variance in brooding scores, indicating a robust cognitive signature.

Intended Use & Scope

Mind Window is designed as a data collection instrument for researchers and for self-monitoring by adult research participants. Its primary utility is to facilitate large-scale studies on human cognition and to provide individuals with structured insight into their thought patterns. The application is not a diagnostic or therapeutic tool and does not provide clinical advice.

Studies & Publications

2 publications

Peer-reviewed research associated with this app.

Non-Evaluative Reference

Mapping content and dynamics in the stream of consciousness: a latent brain state analysis

Yu et al. (2025) · iScience

Referenced in academic literature; no direct evaluation of the app

Human thoughts often arise unprompted, forming a stream of consciousness. Despite their ubiquity, it remains unknown how the brain supports unprompted thoughts as an integrated experience with rich content and dynamics. Applying latent state modeling to fMRI data collected while individuals spoke aloud their thoughts, we identified recurring brain states linked to moment-to-moment thought content. A brain state with activation in core default mode network (DMN) regions tracked early emergence of internally oriented thought, while a state involving executive control and dorsal attention networks (DANs) tracked externally oriented thought. Another state involving the medial temporal lobe and DAN engaged later during internal thought verbalization, suggesting roles in elaboration. The latter state was also associated with a large-language-model derived surprisal metric, perplexity, signaling novelty and internal event boundaries. Our findings reveal a multistage architecture of unprompted thoughts, where DMN subsystems interact with attention and control networks to scaffold their fluid progression.

Non-Evaluative Reference

The think aloud paradigm reveals differences in the content, dynamics and conceptual scope of resting state thought in trait brooding

Raffaelli et al. (2021) · Scientific Reports

Referenced in academic literature; no direct evaluation of the app

Although central to well-being, functional and dysfunctional thoughts arise and unfold over time in ways that remain poorly understood. To shed light on these mechanisms, we adapted a "think aloud" paradigm to quantify the content and dynamics of individuals' thoughts at rest. Across two studies, external raters hand coded the content of each thought and computed dynamic metrics spanning duration, transition probabilities between affective states, and conceptual similarity over time. Study 1 highlighted the paradigm's high ecological validity and revealed a narrowing of conceptual scope following more negative content. Study 2 replicated Study 1's findings and examined individual difference predictors of trait brooding, a maladaptive form of rumination. Across individuals, increased trait brooding was linked to thoughts rated as more negative, past-oriented and self-focused. Longer negative and shorter positive thoughts were also apparent as brooding increased, as well as a tendency to shift away from positive conceptual states, and a stronger narrowing of conceptual scope following negative thoughts. Importantly, content and dynamics explained independent variance, accounting for a third of the variance in brooding. These results uncover a real-time cognitive signature of rumination and highlight the predictive and ecological validity of the think aloud paradigm applied to resting state cognition.

In the Media

Mind Window: An app to see inside the mind

University of Arizona researchers Eric Andrews and Jessica Andrews-Hanna developed Mind Window to quantify human day-to-day thoughts, using experience sampling that sends six push notifications during users' waking hours. According to Andrews-Hanna, "We are trying to capture people at random moments through their day-to-day life" to gather naturalistic data without lab setting influences. The app is available for both iOS and Android users and aims to build a global dataset to identify thinking patterns associated with mental health and cognitive functioning.

ArizonaRead article

App Unlocks Mysteries of the Mind

University of Arizona researchers Jessica Andrews-Hanna and Eric Andrews developed Mind Window to quantify daily human thought patterns and create the world's largest database on human cognition, using scattered survey questions throughout users' days. "We want to know what differentiates people who find great inspiration, happiness and satisfaction from people who struggle with anxious, hopeless and intrusive thoughts," Andrews-Hanna said. The free app collects anonymized data to help researchers understand cognitive patterns across different ages and cultures.

ArizonaRead article