By helping users see alignable differences, SAT-based rendering helps users focus on key variations that are essential to changing the data item's label, making it easier to interpret the effects of changes and their significance.

We argue that these two theories form a symbiotic relationship (Fig. 6). Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

Participants were able to efficiently focus on key differences between the original and counterfactual examples, which facilitated more efficient annotations.

The results from our user study suggest that both the participants and the model benefited from the Variation Theory (VT)-based counterfactuals and Structural Alignment Theory (SAT)-based rendering.

This requires the development of interfaces and visualizations that demystify the generated data, allowing systematic variation and coverage across the concept space.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26].

By incorporating theories such as Structural Alignment Theory and Variation Theory, it aims to support the learning of both the human and the model.

Such studies could determine whether these non-alignable comparisons enhance user performance and elicit deeper insights in human-AI collaborative systems.

Correspondence concerning this article should be addressed to Iza Ray Korsmit (iza.korsmit@mail.mcgill.ca).

The experimental protocol was certified for ethical compliance by the McGill University Research Ethics Board II.

IRK was supported by funding from the Prins Bernhard Cultuurfonds (The Netherlands). This project was also funded by a Canadian Social Sciences and Humanities Research Council Insight Grant (435-2021-0224), a Social Sciences and Humanities Research Council Partnership Grant (895-2018-1023), and a Canada Research Chair (950-231872) to SMc.

Part of this research was presented at the Society for Music Perception and Cognition Conference, Portland, Oregon (2022). The authors would like to thank Bennett K. Smith for programming the experimental interface and assisting with the experiment execution on Prolific, and Philippe Macnab-Seguin for creating the chromatic scales for the second experiment.

Iza Ray Korsmit, Marcel Montrey, Alix Yok Tin Wong-Min, & Stephen McAdams McGill University, Montreal, Canada

Grimaud and Eerola (2022) compared instrument ensembles of strings, woodwinds, and brass in a study where participants either rated the emotions they perceived or manipulated musical parameters to produce a certain emotion. They found that strings were associated with increased anger and fear, woodwinds with decreased anger and fear, and brass with decreased fear, in the cases of both emotion perception and production. For the other emotions (joy, sadness, calmness, power, surprise), however, results were less consistent between perception and production, indicating that the emotion-instrument association may also depend on context of the task.

following the constructionist idea that an individual's personality and background will influence the affect they perceive or feel, we considered several sources of individual differences as moderating factors in the effects of instrument family, pitch height, and affect locus.

This research follows a constructionist approach to musical affect (Cespedes-Guevara & Eerola, 2018). That is, although we are interested in the "bottom-up" influence of certain musical features on musical affect, we believe these cannot be adequately evaluated without considering the "top-down" effects of context and individual differences that are present when affects are constructed. The perception or induction of affect does not merely arise in response to a stimulus but is also formed in relation to the individual and the context.

This indicates that misophonia is not a purely auditory processing disorder but is also influenced by a top-down process of source identification.

an fMRI study found that people with misophonia show increased response in the anterior insular cortex (AIC) in response to misophonic sounds, compared to control participants and other unpleasant or neutral sounds (Kumar et al., 2017).

Both the subjective judgment of aversiveness and the physiological measure of skin conductance response (SCR) increase when people with misophonia are presented with triggers (Edelstein et al., 2013).

The disorder is not yet recognized by the Diagnostic and Statistical Manual − 5th version (DSM-5; American Psychiatric Association, 2013), but there has been an increasing amount of research on the characterization and treatment of misophonia (Vitoratou et al., 2021; see also Brout et al., 2018, for a review).

These results confirm with orchestral excerpts the findings of studies on isolated tones with dyads or triads of instruments in which the presence of impulsive instruments reduces the perception of blend (Lembke et al., 2019; Reuter, 1996; Tardieu & McAdams, 2012).

This provides additional support for its significant contribution to blend in Fischer et al. (2021).

Lembke et al. (2017) demonstrated that combinations of sustained and impulsive instruments blend less well.

structuring by affecting sequential grouping through the segregation of auditory streams played by different instruments and segmental grouping through timbral contrasts (McAdams et al., 2022).

Several other spectral and spectrotemporal descriptors were found to play a role in blend perception in orchestral works by Fischer et al. (2021). These include spectral flatness and spectral crest (different measures of the degree to which the spectrum is denser or has more emergence of spectral components), and spectral variation (the degree of variation of the spectral shape over time).

Fischer et al. (2021) studied the blends of multi-instrument streams in the context of orchestral stream segregation in predominantly Romantic orchestral excerpts. They found that within-family instrument combinations blended better than between-family combinations. They demonstrated the role played by overlap in timbre correlates of spectral flatness (a measure of the tonalness/noisiness or density of the spectrum), spectral skewness (related to the shape of the spectral envelope), and spectral variation (evolution of the spectral envelope over time), as well as cues derived from the scores such as onset synchrony and the consonance of concurrent pitch relations.

McAdams et al. (2022) distinguish two subcategories of these two types of blend: stable and transforming.

Iqa' (plural iqa'at) is used to describe a rhythmic cycle. Iqa'at are made up of two different basic building blocks, the dum and tak, onomatopoeias derived from the sound produced on membranophones such as the darabuka.

H5. Being more culturally bound, musical cues that are learned, such as modal structures, metrical relations, and so on, will exert a greater influence on listeners' perceived valence ratings than on their arousal ratings.

This is a simple PDF file. Fun fun fun.

using SemanticCommit, we recorded all instances of edits, check for conflicts, make change, local, and global resolution actions using telemetry.

A sub-task was considered a failure if the participant was unable to complete it within the time limit.

Finally, we conducted an informal interview about their experience.

After both tasks were completed, participants filled out a final survey to compare the two conditions.

We chose GPT-4o for performance and latency reasons, as it performed optimally against our evals.

We also ran evaluations of model latency and classification performance under varying false positive rates for the following LLMs by OpenAI: GPT-4o, GPT-4o-mini, and o3-mini.

For each task, participants were tasked with integrating three new pieces of information into the memory, one at a time ("sub-tasks").

We ensured each list was 30 items long as our pilot studies suggested this was long enough that manual detection starts to become unwieldy (users need to scroll up and down the document), but short enough that participants could become familiar in a short period.

We adapted two intent specifications from our evals: Mars Game Design Document and Financial Advice AI Agent Memory, as these tasks mapped to the two paradigmatic types covered in Sections 2 and 2.1 (design documents, and AI memory of the user).

We recruited 12 participants (7 female, 5 male) through the mailing lists of two research universities and one multinational technology company.

We chose OpenAI's ChatGPT Canvas as a baseline for five reasons: (i) it is a popular, commercially available tool, hence it is likely familiar to users; (ii) it provides a document editing view, where users can select text and ask GPT to rewrite it, or chat with an AI to make global edits; (iii) it employs a similar class of model (GPT-4o); (iv) it supports similar editing features as SemanticCommit like inline text selection, conflict highlighting, and a diff view, while adding free-form editing; and (v) similar interfaces like Anthropic Artifacts tended to rewrite the specification entirely, and did not offer Canvas's "diff" view to allow for a fair comparison.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.