One example is autocorrect, which automatically corrects typing errors while the user is typing. Another example is the use of word predictions, which allow the user to select a word from a set of word suggestions instead of typing out the word in full.

For example, text entry methods such as eye typing are designed to allow nonspeaking users with motor disabilities to enter text using their eye movements only.

Text entry is also a good example of tool use (Chapter 19). A text entry method is a tool that allows the user to communicate with someone or something, typically other people or a service, using asynchronous text messages and longer documents.

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.

In teleological determination, goals or purposes determine interaction in some way.

What happens in interaction is mutually determined by the human and the computer. In other words, what happens in interaction cannot be attributed solely to the human or the computer—the two must be considered together.

"Average movement time can be predicted as linear regression to the index of difficulty."

"The difficulty of selecting a target is proportional to its distance and inversely proportional to its width (index of difficulty)."

"If the user tries to increase speed, accuracy will be compromised, and vice versa: An increase in accuracy reduces speed."

For example, they can talk about information, difficulty, working memory, and so on.

A proposition is a claim about the world.

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

Interaction also occurs in different contexts, including work, leisure, and in-between contexts such as commuting.

Interaction is a dynamic phenomenon that unfolds over time as users and computers influence each other.

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

It has been used to describe individuals, groups, and communities using computers.

Interaction is a core notion in HCI and refers to the mutual influence between people and computers.

Pressing a button takes about a hundred milliseconds; adopting an information system in a large organization can easily take months.

We have used it to discuss various applications, from a user typing on a smartphone to a team of information workers communicating via email.

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

Such points about the origins of data and the processes of their collection are a key factor in civic text visualization. Indeed, a shift to emphasizing paradata can help draw attention to the representativeness of data.

On the other side of this spectrum, at the detail level, articulating nuanced information present in raw text data can enable civic leaders to peruse and sublimate critical insights.

In contrast, we could consider designing explicitly for multiple users. Doing so requires more than designing for different levels of expertise (see the following subsection for more on expertise) or designing for collaborative use, though both those things may be valuable in their own right. Rather, this dimension encourages accounting for the different types of relationalities that users may have with a system [cf. BB17].

Civic text visualizations similarly designed to foreground interpretation could help make clearer who is making these interpretive decisions, thereby highlighting the lack of neutrality and objectivity in data [DK20].

work on visualization evaluation [SP06; IZCC08; LBI*12] has emphasized the importance of close attention to the various contexts in which a visualization will be applied.

It is informative to contrast this analytic emphasis with other evolving discourses in information visualization. The prior work reviewed above illustrates a few alternative orientations, including rhetoric [HD11], feminism [DK16; DK20], ethics [Cor19], and others [DFCC13; VW08].

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

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].

For example, CommunityPulse [JHSM21] uses common, simple visualizations and iconography, such as bar charts and emojis, to provide overviews of people's emotions towards civic agendas and ideas. Similarly, ConsiderIt [KMF*12b] uses bar charts to visualize people's stance towards ballot measures.

For instance, visual analytic systems such as MultiConVis [HC16b] use multiple connected views to enable analysts to filter and explore text data at multiple levels.

Tools such as ConsiderIt [KMF*12b] or Opinion Space [FBRG10] are designed specifically for the public. In contrast, tools such as CommunityPulse [JHSM21] or CommunityClick [JKW*21] are focused more on supporting community leaders and decision makers.

For example, MultiConVis [HC16b] makes prescriptive statements not only as to the sentimental valence of individual conversations but also as to the topics that each conversation is about. Similarly, ConsiderIt [KMF*12b] asks participants to place individual statements as either supporting or opposing a given ballot proposition.

Consider how systems such as MutiConVis [HC16b] and CommunityClick [JKW*21] provide visual representations to help the viewer understand the structure and content of conversations.

tools such as ConsiderIt [KMF*12b] and CommunityPulse [JHSM21] prominently feature specific comments from members of the public (i.e., the data).

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.

Improving the public input process has become an important goal in the field of digital civics [MNC*19; VCL*16; OW15]. To that end, researchers and practitioners have developed a variety of systems for, e.g., sharing public opinions [FBRG10], building consensus [KMF*12a; ZNB15], summarizing public input [19], or identifying people's priorities, reflections, and hidden insights [JHSM21].

Previous work has introduced several online engagement platforms to enable the public to asynchronously provide their comments, ideas, and feedback around civic issues [19; 20b; MJN*18]. These engagement tools have used micro-tasks [MJN*18], visualizations [19], and forum-like discussions [20b] to engage disconnected and disenfranchised populations [MNC*19]. Others have proposed technologies to promote in-person engagement of reticent participants during town halls [JKW*21] and public meetings [LLS] using clicker-like devices.

Despite their central importance in the civic engagement process, members of the general public are not necessarily involved in the analysis process. Hence, they are often left out of the loop when designing civic text visualizations—their requirements, aptitudes, knowledge, etc. are not given central consideration. Integrating participatory approaches in civic text visualization could pave the way not only for more inclusive analysis but also for leveraging the general public's knowledge to gather richer insights.

social dynamics, such as shyness and tendency to avoid confrontation with dominant personalities can also hinder opinion sharing in town halls by favoring privileged individuals who are comfortable or trained to take part in contentious public discussions [27, 127].

town halls inadvertently cater to a small number of privileged individuals, and silent participants often become disengaged despite physically attending the meetings [61]. Due to the lack of inclusivity, the outcome of such meetings often tends to feel unjust and opaque for the general public [39, 54].

designing communitysourcing technologies to include marginalized opinions and amplify participation alone may not be enough to solve inequality of sharing opinions in the civic domain [26, 126]. Despite the success of previous works [25, 53, 90], technology is rarely integrated with existing manual practices and follow-ups of engagements between government officials and community members are seldom propagated to the community.

Marginalization can be broadly defined as the exclusion of a population from mainstream social, economic, cultural, or political life [58], which still stands as a barrier to inclusive participation in the civic domain [48, 94]. Researchers in HCI and CSCW have explored various communitysourcing approaches to include marginalized populations in community activities, proceedings, and designs [48, 53, 81, 93, 132].

To increase broader civic participation, researchers in HCI have proposed both online [4, 5, 7, 81, 93] and face-to-face [21, 80, 91, 125] technological interventions that use the communitysourcing approach.

Prior investigations by Bryan [29] and Gastil [56] showed a steady decline in civic participation in town halls due to the growing disconnect between local government and community members and the decline in social capital [43, 111, 113]. Despite the introduction of online methods to increase public engagement in the last decade [4, 5, 7, 37, 81, 93], government officials continue to prefer face-to-face meetings to engage the community in the decision-making process [32, 52, 94].

To reengage disconnected, reticent, or disenfranchised community members, researchers in HCI and digital civics have offered novel strategies and technological interventions to increase engagement [60, 62, 94, 107, 130].

Bryan [29] and Gastil [56] investigated the state of town halls and demonstrated a steady decline in civic participation due to the growing disconnect between local government and the community.

Traditional community consultation methods, such as town halls, public forums, and workshops are the modus operandi for public engagement [52, 94]. For fair and impartial civic decision-making, the inclusivity of community members' feedback is paramount [60, 94, 126]. However, traditional methods rarely provide opportunities for inclusive public participation [30, 87, 95].

Murphy used such systems to promote democracy and community partnerships [103]. Similarly, Boulianne et al. deployed clicker devices in contentious public discussions about climate change to gauge public opinions [25]. Bergstrom et al. used a single button device where the attendees anonymously voted (agree/disagree) on issues during the meeting. They showed that back-channel voting helped underrepresented users get more involved in the meeting [22].

As evidenced by numerous studies on statistical cognition (Kline, 2004; Beyth-Marom et al, 2008), even trained scientists have a hard time interpreting p-values, which frequently leads to misleading or incorrect conclusions.

few researchers can resist the temptation to conclude that there is no effect, a common fallacy called "accepting the null" which had frequently led to misleading or wrong scientific conclusions (Dienes, 2014, p.1).

Again, p is the probability of seeing results as extreme (or more extreme) as those actually observed if the null hypothesis were true. So p is computed under the assumption that the null hypothesis is true. Yet it is common for researchers, teachers and even textbooks to think of p as the probability of the null hypothesis being true (or equivalently, of the results being due to chance), an error called the "fallacy of the transposed conditional" (Haller and Krauss, 2002; Cohen, 1994, p.999).

Many researchers fail to appreciate that p-values are unreliable and vary widely across replications.

Providing non-misleading interpretations of figures with confidence intervals requires judgment, and no mechanical decision procedure can carry out this job better than a thoughtful investigator.

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".

Estimation seems much more likely to promote clear statistical thinking.

Decades spent educating researchers have had little or no influence on beliefs and practice (Schmidt and Hunter, 1997, pp.20–22).

NHST has been severely criticized for more than 50 years by end users to whom fair statistical communication matters.

This assessment raises two issues. First, it is arbitrary. If 10 of the 15 CIs included the predicted values, would the results also support the theory, or instead refute it? If one instead used 99% CIs, would positive results for 12 of the 15 predictions be enough to support the theory? This arbitrariness arises because CIs offer no principled method for generating an inference regarding the theory.

two out of three necessary conditions for testing theory are missing.

To illustrate this point Oakes posed a series of true/false questions regarding the interpretation of p-vales to seventy experienced researchers and discovered that only two had a sound understanding of the underlying concept of significance [25].

failure to check assumptions about the data required by particular tests, over-testing and using inappropriate tests

abusing statistical tests, making illogical arguments as a result of tests, deriving inappropriate conclusions from nonsignificant results, and confusing the size of p-values with effect sizes.

This approach, fiercely promoted by Fisher in the 1930's [9], has become the gold standard in many disciplines including quantitative evaluations in HCI. However, the approach is rather counter-intuitive; many researchers misinterpret the meaning of the p-value.

We found that using MINE directly gave identical performance when the task was nontrivial, but became very unstable if the target was easy to predict from the context (e.g., when predicting a single step in the future and the target overlaps with the context).

We note that better [49, 27] results have been published on these target datasets, by transfer learning from a different source task.

We also found that not all the information encoded is linearly accessible. When we used a single hidden layer instead the accuracy increases from 64.6 to 72.5, which is closer to the accuracy of the fully supervised model.

For lasertag_three_opponents_small, contrastive loss does not help nor hurt. We suspect that this is due to the task design, which does not require memory and thus yields a purely reactive policy.

Although this is a standard transfer learning benchmark, we found that models that learn better relationships in the childeren books did not necessarily perform better on the target tasks (which are very different: movie reviews etc).

We found that more advanced sentence encoders did not significantly improve the results, which may be due to the simplicity of the transfer tasks (e.g., in MPQA most datapoints consists of one or a few words), and the fact that bag-of-words models usually perform well on many NLP tasks [48].

It is important to note that the window size (maximum context size for the GRU) has a big impact on the performance, and longer segments would give better results. Our model had a maximum of 20480 timesteps to process, which is slightly longer than a second.

Interestingly, CPCs capture both speaker identity and speech contents, as demonstrated by the good accuracies attained with a simple linear classifier, which also gets close to the oracle, fully supervised networks.

Figure 6 shows that for 4 out of the 5 games performance of the agent improves significantly with the contrastive loss after training on 1 billion frames.

CPC 76.9 80.1 91.2 87.7 96.8

CPC 73.6

CPC 48.7

Despite being relatively domain agnostic, CPCs improve upon state-of-the-art by 9% absolute in top-1 accuracy, and 4% absolute in top-5 accuracy.

We also found that not all the information encoded is linearly accessible. When we used a single hidden layer instead the accuracy increases from 64.6 to 72.5, which is closer to the accuracy of the fully supervised model.

Are the following two answers to my question Q semantically equivalent?\n\nQ: ${THE_QUESTION}\nA1: ${GOLD_ANSWER}\nA2: ${PRED_ANSWER}\n\nPlease answer with a single word, either "Yes." or "No.", and explain your reasoning.

Provide your best guess for the following question, and describe how likely it is that your guess is correct as one of the following expressions: ${EXPRESSION_LIST}. Give ONLY the guess and your confidence, no other words or explanation. For example:\n\nGuess: <most likely guess, as short as possible; not a complete sentence, just the guess!>\nConfidence: <description of confidence, without any extra commentary whatsoever; just a short phrase!>\n\nThe question is: ${THE_QUESTION}

Provide your ${k} best guesses and the probability that each is correct (0.0 to 1.0) for the following question. Give ONLY the guesses and probabilities, no other words or explanation. For example:\n\nG1: <first most likely guess, as short as possible; not a complete sentence, just the guess!>\n\nP1: <the probability between 0.0 and 1.0 that G1 is correct, without any extra commentary whatsoever; just the probability!>

Each linguistic likelihood expression is mapped to a probability using responses from a human survey on social media with 123 respondents (Fagen-Ulmschneider, 2023). Ling. 1S-opt. uses a held out set of calibration questions and answers to compute the average accuracy for each likelihood expression, using these 'optimized' values instead.

Finally, our study is limited to short-form question-answering; future work should extend this analysis to longer-form generation settings.

While our work demonstrates a promising new approach to generating calibrated confidences through verbalization, there are limitations that could be addressed in future work. First, our experiments are focused on factual recall-oriented problems, and the extent to which our observations would hold for reasoning-heavy settings is an interesting open question.

the 1-stage and 2-stage verbalized numerical confidence prompts sometimes differ drastically in the calibration of their confidences. How can we reduce sensitivity of a model's calibration to the prompt?

Provide your best guess and the probability that it is correct (0.0 to 1.0) for the following question. Give ONLY the guess and probability, no other words or explanation. For example:\n\nGuess: <most likely guess, as short as possible; not a complete sentence, just the guess!>\n Probability: <the probability between 0.0 and 1.0 that your guess is correct, without any extra commentary whatsoever; just the probability!>\n\nThe question is: ${THE_QUESTION}

Provide your ${k} best guesses and the probability that each is correct (0.0 to 1.0) for the following question. Give ONLY the guesses and probabilities, no other words or explanation.

Provide your best guess for the following question, and describe how likely it is that your guess is correct as one of the following expressions: ${EXPRESSION_LIST}. Give ONLY the guess and your confidence, no other words or explanation.

To fit the temperature that is used to compute ECE-t and BS-t we split our total data into 5 folds. For each fold, we use it once to fit a temperature and evaluate metrics on the remaining folds. We find that fitting the temperature on 20% of the data yields relatively stable temperatures across folds.

To avoid excessive false negatives in our correctness computation as a result of exact-match evaluation, we use either GPT-4 or GPT-3.5 to evaluate whether a response is essentially equivalent to the ground truth answer.

We sample 1000 questions from the validation split of TriviaQA (rc.web.nocontext) and SciQ and all 817 questions from the validation split of TruthfulQA (generation) for our experiments.

Ling. 1S-opt. 0.056 0.051 0.088 0.927 0.028 0.052 0.172 0.828 0.082 0.105 0.212 0.632

Verb. 1S top-4 0.041 0.039 0.081 0.959 0.056 0.059 0.185 0.815 0.198 0.144 0.245 0.619

Ling. 1S-opt. 0.058 0.066 0.135 0.878 0.064 0.068 0.220 0.674 0.125 0.165 0.270 0.492

Verb. 1S top-4 0.054 0.057 0.144 0.896 0.065 0.051 0.209 0.763 0.203 0.189 0.284 0.455

Additionally, the lack of technical details available for many state-of-the-art closed RLHF-LMs may limit our ability to understand what factors enable a model to verbalize well-calibrated confidences and differences in this ability across different models.

With Llama2-70B-Chat, verbalized calibration provides improvement over conditional probabilities across some metrics, but the improvement is much less consistent compared to GPT-* and Claude-*.

The verbal calibration of the open source model Llama-2-70b-chat is generally weaker than that of closed source models but still demonstrates improvement over its conditional probabilities by some metrics, and does so most clearly on TruthfulQA.

Chain-of-thought prompting does not improve verbalized calibration

Among the methods for verbalizing probabilities directly, we observe that generating and evaluating multiple hypotheses improves calibration (see Figure 1), similarly to humans (Lord et al., 1985), and corroborating a similar finding in LMs (Kadavath et al., 2022).

Ling. 1S-opt. 0.060 0.070 0.151 0.874 0.049 0.056 0.214 0.738 0.099 0.130 0.266 0.446

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].

P8 said: "as I get familiar with this system [C3], I feel more skilled" to use the highlighted and grayed phrases.

It is worth noting that P3 and P8 mentioned feeling more comfortable with the more familiar visualization in C1 and C2 during their first impression of the conditions.

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.

Specifically, they underscore the need for co-adaptive systems that can evolve along with users' mental models and definitions of labels.

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.

Most previous research in counterfactual generation has focused on the model side by either generating counterfactuals to improve the model's performance or explaining its behaviors post hoc.

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.

While SAT-based rendering supported human sensemaking in both Gero et al. [29] and Mocha, we also show that the combination of VT and SAT support the model's learning.

This finding is consistent with previous work that supports users' sense-making of text, e.g., by modulating text saliency. Specifically, Gu et al. [32] and Gero et al. [29] both found improved reading efficiency and comprehension with saliency-modulating text renderings.

In decision making, SAT argues that people tend to focus on alignable differences—features that can be directly compared—rather than on differences that cannot be easily aligned.

Structural Alignment Theory (SAT) [27] is a cognitive theory that explains how people make sense of concepts by comparing relational structures between two items.

Specifically, we use Variation Theory of learning [44] which states that for learning to occur, some aspects that define the concept being learned must vary while others are held constant.

According to SAT, humans compare two similar entities by trying to find structural alignments between them, and then comparing corresponding elements, with a special focus on differing aligned elements.

VT posits that human learning occurs when learners experience variation across critical and superficial aspects of a concept—through exposure to contrasting examples that systematically vary along different critical and superficial feature dimensions.

To analyze the annotation efficiency, we first conducted a Kruskal-Wallis rank sum test [39] to determine if there were statistically significant differences in annotation time across the three conditions, because our data violated the homogeneity of variances assumption, making non-parametric methods more appropriate.

Mohamed et al. (2020) put forth the idea of dismantling power assymmetries to resist data colonialism.

Couldry and Mejias (2019) propose 'data colonialism' as a new form of colonialism to make sense of the use of large amounts of data by a small group of corporate and government actors.

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].

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.

the context of a task (like perception, production, or induction) may change the effect of musical features.

Furthermore, as the method of reporting on perceived and induced affect may influence the construction of an affect (e.g., facilitating categorical perception), we also compare two different affect representations.

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.

Cognitive surrenderA paper that came out this year asked: if you’re working with AI a lot, and you’re using it as a machine to answer all of your questions, what happens with System 1 and System 2?

2. Validator Another basic role for AI is validating your understanding. To do this, you ask it to review your notes for errors or gaps, do basic fact checking, or critique your reasoning. Again, you can do this via the chat interface, but I also experimented with passing my notes in Obsidian using the Copilot plugin and in Emacs using gptel. Example: After reading The Epic of Gilgamesh, I wrote a note in Obsidian summarizing its plot. When I asked ChatGPT to critique my summary, it pointed out that I’d given the central character a redemption arc that isn’t present in the text. I’m so accustomed to the standard hero’s journey, that I projected it onto the book — and an LLM helped me correct this ‘hallucination.’ Suggested prompt: Here are my notes on [WORK]. What important ideas did I miss or underemphasize? Don’t rewrite my notes — just flag the gaps.

For the record, my posts aren’t written or conceived with an LLM, although I know an increasing number of people who use one to write a first draft and then edit. I’m not a fan. The whole point of the web — its beauty — is that it’s unrelentingly human and diverse.

Given the high prevalence of such sounds in everyday life, having misophonia can have large negative effects on one's functioning in personal, academic, and work environments.

Although there are many idiosyncrasies in what may trigger a person with misophonia, the most common triggers are created by other humans, such as the sound of someone chewing, clearing their throat, tapping their foot, or typing on a keyboard.

Misophonia is a psychological disorder that is characterized by severe aversive responses to specific environmental sounds (i.e., triggers).

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).

Composers and music researchers had previously analyzed and annotated 65 movements from the Classical, Romantic, and early Modern repertoire in terms of the Taxonomy of Orchestral Grouping Effects (McAdams et al., 2022).

In a study by McAdams and Goodchild (2018), orchestral simulations created with OrchSim were compared perceptually to commercial recordings and were shown to be of high quality.

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).