Dialogue is about the organization of communication as a series of turns between communication partners. The core elements of dialogue are communication turns, the communication context, and turn interpretation. Dialogue interaction includes speech-based and graphical interactions. Dialogue can be understood as computation, goal-directed action, communication, or embodied action.

Kirsh argued that we are not just passively reacting to computer-generated options. If we look at interaction at a higher level, beyond a single action, we see that users are also actively influencing their environments. Users are 'architects' of their environments, as Kirsh put it.

Kirsh points out that Norman's model makes an unrealistic assumption: The user is assumed to know the environment and its options and is merely picking an option. In practice, we do not always know what the options mean or even what options are available. Kirsh argued that users need to actively explore interfaces to become aware of the available functions and how they work.

Mixed-initiative interaction is the idea of organizing interaction in dialogue where both the computer and the human can take initiative. Unlike in the case of an FSM, the computing system can take action without a command from the user; the initiative is mixed.

Code-switching refers to a switch in language to match the capabilities of the communication partner.

Communication repair refers to the 'work of restoring shared understanding' when conversational partners misunderstand each other.

Robustness refers to the communication partners' ability to achieve shared understanding even in light of misunderstandings and other unanticipated troubles.

Human–machine interaction, according to Suchman, is similar to but different from human–human dialogue. It is similar in the sense that people pursue a shared understanding: They actively work to make themselves understood. It is different in the sense that the communication abilities of computers are limited, which requires humans to adapt.

FSMs are an effective way to describe how dialogue is structured; however, they are limited to memory-free dialogue.

An FSM is a model of discrete computation applicable to dialogues.

A mode refers to the variation in the interpretation of a user's input according to an internal state. In a modeless dialogue, all inputs are possible in all states and their interpretation is always the same.

Dialogue can be described using models of computation from computer science. Such models include finite state machines (FSMs), pushdown automata, and Petri nets.

Affordance, which we discussed in Chapter 3, refers to how well users can interpret what actions are possible with a widget. Visibility is a handy related concept in design that underlies direct manipulation interfaces.

Mapping and feedback are crucial concepts for understanding a computer's turn.

Gulf of evaluation: This gulf refers to knowing how a perceived change in the computer has moved it closer to the intended goal state.

Gulf of execution: This gulf is about knowing what to do to bring about a desired state change in the computer.

Norman offered two central concepts to help us understand these cognitive efforts: the gulf of execution and the gulf of evaluation. These two concepts describe inferential breakpoints for users seeking to express their intentions and interpret feedback from the system, respectively.

A significant early theory of dialogue interaction is the seven-stage model of Norman [600]. It considers interaction as goal-directed, turn-based dialogue.

both the computer and the user may have initiative. For example, a pop-up window can be presented to confirm a risky selection. When there is a misunderstanding about the context of the dialogue, errors may happen, and the partners must recover from them.

both the computer and the human participate in establishing a shared context. The computer does not simply receive a message; it also communicates the effects of that message. Therefore, the design of feedback, affordances, and cues is central to dialogue-based interaction.

in dialogue, acts of communication are conditional: The meaning of a turn depends on the communication context.

the concepts of dialogue are applicable across modalities.

The key idea in the dialogue view of interaction is the organization of communication as a series of turns. Dialogue evolves through communication turns between two or more partners. In one turn, an appropriate communication act is made by one partner based on the communication context.

The theory of task–technology fit (TTF) can illuminate what users consider useful and how this affects their decision to adopt a particular technology. TTF refers to the ability of technology to support a task [197]. The capabilities of the technology should match the demands of the task and the skills of the individual; in this case, the fit is perfect.

Users actively repurpose tools to make them more personally usable and relevant. Design should support such repurposing. For example, Renom et al. [696] conducted a study on text editing using a novel user interface. They found that exploration and technical reasoning facilitate creative tool use.

One prominent definition of accessibility is given by ISO 9241-171, which defines it as 'the usability of a product, service, environment or facility by people with the widest range of capabilities.'

The second dimension, social acceptability, concerns whether interactions map well to the social norms and roles in the settings where they occur.

Acceptability has two main dimensions [591]. The first dimension, practical acceptability, includes costs, the reliability of the interactive system, and its compatibility with other systems. The perceptions of utility and usability may also influence the judgment of practical acceptability.

usability is multidimensional. This means that in most settings, a valid characterization of usability will need to employ several dimensions and measures.

usability is measurable, that is, it is possible to quantify usability based on users' behaviors or opinions.

usability is relational; it arises as an interplay between people, tasks (problems), and interactive systems (tools)

ISO 9241-11 definition... defines usability as the 'extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use.'

One shorthand way of expressing this is that utility is 'whether the functionality of a system in principle can do what is needed' [591, p. 25]. In practice, whether people can do anything concerns—among other things—usability.

The utility of an interactive system concerns its match with the tasks of users. If the match is good, the tool has high utility; if the tasks that users want to do are not supported by the tool, the tool has low utility.

Usability concerns how easily computer-based tools may be operated by users trying to accomplish a task. Usability differs from utility. Usability concerns whether users can use the product in a way that makes it possible to realize its utility; utility is about whether the goal is important to the user. Ideally, the user can use the tool without unnecessary effort so that the use is direct, transparent, and unnoticeable.

Usability is one of the best predictors of users' willingness to adopt software. For example, the User Burden Scale is a questionnaire for measuring the felt burden in software use [806]. It consists of six subscales: difficulty of use, physical burden, time and social burden, mental and emotional burden, privacy burden, and financial burden.

However, self-attention alone is permutation-invariant, i.e., if we reorder the rows of X, then the mechanism has no built-in sense of which token came first. Since word order matters, we must inject positional information. We often add a position vector pt to the token embedding: h(0)_t = e(xt) + pt One classical choice for the positional encoding is called the sinusoidal positional encoding. pt[2k] = sin(t / 10000^{2k/d}), pt[2k+1] = cos(t / 10000^{2k/d}) The sinusoidal features give each position a distinct geometric signature across many frequencies. Nearby positions have related encodings while distant positions remain distinguishable. This lets the network reason about relative offsets.

For example, in control-theoretical analysis, we see the interaction as dynamically changing states.

For example, in interaction-as-rationality, courses of action emerge as a joint function of the user's goals and capabilities and the properties of the environment.

For example, statistical models, such as Fitts' law, describe a relationship that is considered a statistical determination.

For example, interaction-as-tool-use focuses on this idea (Chapter 19). Tools change people and their activities; in turn, this changes the tools.

An interactive AI system can possess an objective function it pursues when acting, for example, when correcting a character it flags as a typo.

In the case of particle physics, the propositions that make up a theory may concern the nature and behavior of particles.

HCI theories contain statements that link humans and technology and possibly some outcomes (e.g., poor usability, high user experience).

A fundamental goal of interaction is to provide users with tools that allow them to achieve goals that they would otherwise not have been able to achieve.

One goal of interaction is communicating intentions, for example, issuing commands to a computer, selecting graphical elements in image processing software, or entering text.

Accessibility is an extension of usability to ensure that as many people as possible find a tool easy to operate, regardless of individual capabilities.

Usability refers to the ease with which a tool is operated.

Utility captures how well a tool supports users in achieving their goals.

A model is a formally expressed set of propositions that follows some axiomatic system, such as algebra, logic, or a programming language.

The explanatory power of a theory refers to the empirical accuracy and coverage of the explanations offered by the theory.

A latent factor is something that affects observations about interaction without being directly observable.

Finally, in quantitative determination, interaction is described as a continuous unfolding of states.

In structural determination, the end results of the interaction are jointly determined by multiple causes that make up the whole.

In statistical determination, there is a stochastic relationship between the two entities.

In mechanical determination, an antecedent determines a consequent.

Propositions characterize entities and link them to other entities, some of which are conceptual.

Phenomena in interaction are emergent, that is, they are not attributable to the user or to the computer alone.

What happens in interaction cannot be attributed solely to the human or the computer—the two must be considered together.

For example, a photocopier can automatically sort and collate copies.

work in the digital humanities often explicitly emphasizes the interpretation both of texts themselves and of computational analysis thereof [Ram03; Joc13; Und14; BSM*20].

Some tools provide both computational and visualization features. For instance, CommunityPulse provides a scaffolding for multifaceted public input analysis using visualizations [JHSM21], and MultiConVis enables multilevel exploration and analysis of threaded conversations [HC16b].

Researchers in HCI and digital civics have begun to explore methods to improve the analysis capabilities of visual analytics tools [JHSM21; MJS20b]. Although the broader community of visualization researchers acknowledges the importance of designing for varied levels of expertise [Mun14; GTS10; SNHS13], existing work on text analytics in general, as well as civic text visualizations in particular, focuses research efforts towards designing for analysts. Less effort has been put on designing and developing text visualization for non-experts—people who are not trained in or have had limited exposure to visualization and analytics.

NHST as it is carried out today consists of this incoherent mix of Fisher and Neyman–Pearson methods (Gigerenzer, 2004).

p-values give a seductive illusion of certainty and truth (Cumming, 2012, Chap. 1). The sacred α = .05 criterion amplifies this illusion, since results end up being either "significant" or "non-significant".

the solution is not to reform p-values or to replace them with some other statistical summary or threshold, but rather to move toward a greater acceptance of uncertainty and embracing of variation.

What, then, can and should be done? I agree with the ASA statement's final paragraph, which emphasizes the importance of design, understanding, and context—and I would also add measurement to that list.

the psychology research community has been strongly questioning the value of NHST in psychology for some years now [6] and calling for a more meaningful reporting of statistical inference based on effect sizes, confidence intervals and Bayesian reasoning [9].

Similarly, if the significance level is set at 0.05, then this is the probability of the data occurring by chance when there is no experimental effect, namely one in twenty times. The more tests that are done on a particular dataset, the more likely it is that some chance variation will be extreme enough to seem like significance.

Violation of the assumptions of any statistical test can produce p values that bear little relation to the actual probabilities of outcomes and hence comparison to the significance level of 0.05 is meaningless.

for an analysis to be sound, it is necessary that in the tests performed the probabilities of outcomes are accurately reflected in the p values produced by the tests. If this is not the case, then the NHST argument form is severely weakened.

NHST is the most commonly encountered form of statistical inference and is what is usually associated with producing a null hypothesis, then testing it to give some statistic such as a t value, and then turning the statistic into a p value.

properly reported non-significant results can help future researchers to provide estimates of effect sizes and associated confidence intervals.

calling for a more meaningful reporting of statistical inference based on effect sizes, confidence intervals and Bayesian reasoning [9].

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives.

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

Mocha addresses two seemingly contradictory objectives: (1) generating labeled data that diversifies the training dataset to aid the model's learning, and (2) maintaining structural consistency across the batches of data presented to users to support their cognitive processes.

The results of our study indicate that participants spent significantly less time annotating batches of counterfactuals when they were rendered according to SAT compared to other conditions i.e., supporting the participants' selective focus on the varying phrases, rather than phrases that stay consistent.

From a cognitive perspective, the theme color aligns with the human's (theorized) structural mapping engine [27] by making relational discrepancies between the original and counterfactual examples more explicit.

Estes and Hasson [17] highlights the significant role of bringing salience to 'non-alignable' differences.

The last two prior works also combine Variation Theory (VT) and SAT together, as we did (i.e., a corollary of SAT referred to as Analogical Transfer/Learning Theory).

Estes and Hasson [17] highlights the significant role of bringing salience to "non-alignable" differences.

Estes and Hasson [17] argue that while alignable differences can be more straightforward and easier for comparison, non-alignable differences can also provide key information that might otherwise remain overlooked.

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

This symbiotic relationship stems from the fact that Structural Alignment Theory (SAT) enhances the salience of differences, while the way we used Variation Theory (VT) to generate contradicting examples across the boundaries of labels ensures that these differences are conceptually informative.

It states that understanding and sensemaking involve mapping the relationships between elements, especially in complex and ambiguous tasks.

Structural Alignment Theory states that humans naturally look for structural mapping between representations of objects to help them understand, compare, and infer relationships between said objects.

According to Variation Theory, learners better understand concepts by observing variations along critical features (dimensions of variation) that define that concept and, separately, observing variations along superficial features that do not define that concept—all while other features, when possible, are held constant.

Mocha exemplified the application of human cognition and concept learning theories in the interactive machine learning pipeline to support the negotiation of conceptual boundaries for bi-directional human-AI alignment.

This pattern of selective attention suggests that the visual cues provided by Mocha effectively guided participants to focus on more relevant information within the context of unchanged text when making their labeling decisions.

Overall, the incorporation of counterfactuals has generally improved the models' F1 scores, driven largely by the improvements in precision. This suggests that counterfactuals have effectively improved performance without necessitating a significant trade-off between precision and recall.

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives. This improvement suggests that the counterfactuals provided essential information that helped refine the models' decision boundaries.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26]. This can not only boosts the model's performance but also enable participants to validate or correct the model during the interactive training process.

Thus, the integration of both theories enables users to efficiently process and compare variations, leading to more informed decisions and a clearer understanding of the model's behavior.

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.

Taken together, these findings almost unanimously show that, on average, AI-supported writing decreases but does not eliminate writer's feelings of ownership, underscoring the need for a larger theory of AI participation in the creative process.

This can be understood through the frame of precarious work [5]; as writers feel that their work is increasingly precarious, the power differential between themselves and the organizations seeking to train LLMs grows larger.

information scholars argue that data should be collected and used in accordance with data subjects' perspectives on the acceptable use of their data [29, 40, 83].

Interviews were video and audio recorded. We transcribed the audio using OpenAI's Whisper automatic speech recognition system and anonymized the transcript before analysis.

Immediately after the focused reading task, we conducted a short interview asking participants to reflect on their experience with both tasks (Appendix J.2), followed by a post-study survey (Appendix J.3).

The study concluded with a 15-minute semi-structured interview. During the interview, participants saw screenshots from the three conditions and were asked which they preferred and disliked, why, what they wished the interface had, what influenced their skimming, and how they normally skimmed texts.

After the interviews, we analyzed the data using the process described in Appendix B

We used these mock-ups as design probes [31] to inspire ideation and elicit creative responses. Specifically, we asked participants to compare and contrast alternative mock-ups and reflect on how they could be used or improved to support their known or emerging synthesis and information-foraging goals.

In the second part of the session, we provided participants with mock-ups of possible reifications of cross-document relationships that might help them synthesize information across abstracts.

In the first part of the session, we asked participants about their strategies for selecting publication venues for their manuscript submissions, how they identify and synthesize information from venues, their approaches to writing manuscripts, and finally, the technology they have used to help with these processes, current technology shortcomings, and ideas for addressing these challenges.

Sessions, which were held on Zoom, lasted 55 minutes on average. Participants were compensated with $15 USD.

The interview sessions were divided into two parts: an open-ended semi-structured interview about their backgrounds and practices, followed by feedback on a range of mock-ups, including novel reified relationships between analogous sentences in different abstracts (Figure 2).

In order to determine (1) the context in which we might offer novel views of scientific abstracts and (2) the intelligibility of various novel prototype designs for reifying cross-abstract relationships, we conducted a formative interview study with 12 active researchers (see Appendix A for participant information).

pre-computing and reifying cross-document analogous relationships make it psychologically possible for users to engage—if they are willing to be guided by it. (Lower NFC users are more likely to fall into this category.)

Participants with lower NFC more frequently preferred and experienced less cognitive load when skimming with all the features enabled.

These results suggest that these three features lose their effectiveness when not used together.

Lower NFC participants were generally guided by emergent visual patterns created by the interactions between features, especially blocks of color spanning multiple sentences created when all three features are turned on.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

We demonstrate how slicing sentences according to roles and visually aligning them can help readers perceive cross-document relationships in a coherent manner.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information. In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Together, the vertical and horizontal juxtapositions are designed to help users identify both high-level commonalities and nuanced variations across structurally similar sentences.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

These observations underscored the need for an improved UI design for authoring custom aspects, especially for those who are less familiar with the research corpus.

Future work could explore more seamless ways of preserving context, such as allowing users to navigate through every sentence of an abstract directly within the Cross-Sentence Relationship pane, fostering a more cohesive understanding of the content.

Supporting a new corpus would require defining new corpus-appropriate predefined aspects and chunk role labels. Other components would likely be able to remain unchanged.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

This ordering prioritizes dominant structural patterns (largest groups first) while exposing fine-grained variations (via length-sorted triplets), mirroring how humans compare sentences, if SMT is an accurate description in this domain of comparative close reading.

Structural mappings between objects are part of the cognitive process of comparison according to the Structure-Mapping Theory [17], and juxtaposition can facilitate humans in recognizing particular possible structural mappings between objects [75].

In SMT terminology, rendering and arranging according to corresponding chunks reify "commonalities in structure," while variation within corresponding chunks are "alignable differences" that users are predicted to notice.

The prior SMT-informed tools in Section 2.3 for both code and natural language corpora suggest that the cognitive process of comparing texts may be no exception to the cognitive processes SMT predicts.

SMT posits that visual alignment helps people perceive relational similarities and differences more clearly, thereby improving their ability to make meaningful comparisons and understand underlying patterns [28, 38, 47].

SMT provides a framework for understanding how humans compare two or more objects by finding common structural alignments between objects.

Structural Mapping Theory (SMT) is a long-standing well-vetted theory from Cognitive Science that describes how humans attend to and try to compare objects by finding mental representations of them that can be structurally mapped to each other (analogies).

This SMT-informed approach, which AbstractExplorer shares, tries to give this mental machinery "a leg up," letting users perhaps skip some steps by accepting reified cross-document relationships identified by the computer.

The human perceptual, comparative mental machinery that SMT describes is part of what enables humans to form more abstract structured mental models from concrete examples, among other critical knowledge tasks.

These examples of text-centric lossless techniques do not abstract away or summarize; they strategically re-organize and re-render the existing text to help enhance readers' own perceptual cognition, informed by Structural Mapping Theory (SMT) [17].

Theory (SMT) to facilitate seeing both the overview and the details at the same time, facilitating abstraction without losing context.

Inspired by GP-TSM [24], AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text. Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

We conducted a qualitative analysis of user study transcripts and survey responses using a Grounded Theory approach [8]. First, the lead researcher collected a list of participants' behaviors, approaches, reflections on their experience, and feedback about the interface. The researcher then systematically coded this data, revisiting the data multiples times and refining the codes to ensure consistency and coherence. Through this process, high-level themes were identified and organized using affinity diagramming. Once the thematic structure was finalized, the researcher gathered supporting evidence for each theme and synthesized the findings, which were reviewed by the research team to ensure agreement on the results.

Activity log data, which revealed how participants actually used the interface, echoed the above findings. According to the log data, participants spent most of their reading time (66.31%) with vertical alignment on the second element in structure pairs, followed by alignment on the first element (29.19%), and left-justified alignment (5.13%). Highlighting usage showed a similar preference: 91.13% of time with all chunks highlighted, 8.25% with partial highlighting, and minimal time (0.63%) without highlights.

In this section, we present findings on how AbstractExplorer supports comparative close reading at scale by integrating quantitative survey responses and log data with qualitative analysis of transcripts and open-ended responses. The qualitative analysis process is described in detail in Appendix H.

Throughout the two tasks, we also collected detailed interaction logs including counts of user-defined aspects created, duration of highlighting usage, and time allocation across the three possible alignment options.

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.