it is not enough to consider the distribution of examples given to learn-ers; one must consider the processes learners are applying

In their 2003 paper, Pierce et al. [32] define psychological ownership as "that state where an individual feels as though the target of ownership or a piece of that target is 'theirs'."

In the field of psychology, there have been numerous theoretical propositions and empirical studies attempting to explain the formation of psychological ownership. Several scholars have created frameworks based on decades of psychological research that capture key themes that have emerged time and again such as effectance and control of possessions [10, 25, 44], positive affect [10], and symbolic meaning and personhood [35].

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment [34].

One of the most fundamental materialist theories is Locke's labor theory, which posits that "every man has a property in his own person," and thereby goes on to argue that when one mixes their labor with natural resources, the resulting good becomes their property - evoking the embodiment theory of personhood [22, 34].

Materialist theories stem from notions of property as control over material entities, going as far as to stipulate that physical, material states are the ultimate determinants of reality, taking precedence over thought, consciousness, and abstract entities [27, 38]. On the contrary, idealism posits that something mental is the ultimate foundation of reality, and idealist theories of property and personhood are concerned with symbolic and mental conceptions of ownership [12].

Distributed Cognition theory in HCI views cognition as distributedacross individuals, artefacts, and the physical and social environ-ments rather than residing solely in individual minds [25, 28 ]. Cog-nitive processes emerge from interactions among people, tools, andculturally embedded practices, captured by DC’s three tenets: cog-nition is (1) socially distributed, (2) embodied, and (3) culturallyshaped. This positions DC as a framework for analysing collabo-rative work through the coordination of actors, representationalstructures, and artefacts. DC treats these elements as functionallyintegrated parts of a system organised around accomplishing aspecific task. This perspective enables analysis of how knowledgeand decision-making are distributed across and shaped by the in-teractions within the system [25].

In multitasking, tasks compete for limited sensory, motor, and central (cognitive) capacities

Visual objects that are unique in their visual primitives attract user's attention.

HCI phenomena span eye movements, emotional reactions, aesthetic experiences, social interactions, and organizational structures; they also span behaviors from the millisecond level to changes in the use of interactive systems over decades as well as the individual, group, and societal levels.

Users continuously adapt their social behavior to compensate for the lack of social cues in computer-mediated communication

Users' performance in providing input to a computer is limited by a speed–accuracy trade-off

A mental model captures how people understand something. For instance, people have vastly different beliefs about how calculators work [598]. These beliefs can explain the errors and the issues they face when using calculators.

He isn’t Dario EA levels of evil, like the EA people have a plan for you and it’s never good when someone has a plan for you.

A system that can look up any fact has not been forced to find structure. It has not been forced to generalize.

Theories of rationality have increased our understanding of how users fail to be optimal.

They share a focus on the emergence of interactive behavior; in other words, they predict how users choose to behave in certain given circumstances.

Theories of rationality can be used to inform the design of information environments, addressing considerations such as how to distribute and shape information.

These four theories differ in the factors they include and how the agent's decision-making problem is formulated. As such, the theories differ in how easily they help us find a solution to the user's decision-making problem.

Theories of rationality can make quantitative predictions on user behavior in settings where the user's environment and goals (rewards) are well known.

Theories of rationality do not describe what a user has done but ask what that user could have done. Rational behavior refers to behavior that seeks to maximize the expected utility to the user.

Descriptive theories attempt to capture causes behind behaviour that, from a normative perspective, may appear irrational. This view enables predictions of user behaviour in real-world circumstances.

Descriptive theories attempt to capture causes behind behavior that, from a normative perspective,may appear irrational. This view enables predictions of user behavior in real-world circumstances.

In UTAUT, Venkatesh extended TAM by incorporating two constructs not directly related to a system's perceived properties, but derived from external aspects: social influence and facilitating conditions. Additionally, UTAUT posits four mediating factors that moderate the impact of each key construct on usage intention and behavior, namely gender, age, experience, and voluntariness of use.

While our key focus is to build a theoretical model that explains the process through which older adults accept (or reject) mobile technology, which can provide theoretical guidelines when designing a technology, and which may also be able to generate new investigations and experiments.

We analyzed the second-round interview data using inductive and deductive approaches informed by grounded theory and other qualitative analysis methods [33, 22].

We inductively analyzed the first-round interview data using thematic analysis based on a grounded theory approach [33]. Grounded theory methods build theory iteratively from the data, using rigorous coding practices.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

our key focus is to build a theoretical model that explains the process through which older adults accept (or reject) mobile technology, which can provide theoretical guidelines when designing a technology, and which may also be able to generate new investigations and experiments.

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Employing the grounded theory method [33], we allowed recurring themes and concepts in relation to technology acceptance behaviors to arise from the data itself. Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Extending the original TAM and consolidating the constructs of several other existing models, Venkatesh et al. proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) [37].

AI is here to stay. If used right, chances are it will make us all more productive. That, on the other hand, does not mean it will be a good investment.

AB-MCTS（Adaptive Branching Monte Carlo Tree Search）です。これは、推論のプロセスを「木の探索」として捉え

In a task-switching situation,the user must activate resources for the second task and inhibit resources for the first task. If theuser fails to do so efficiently, performance is reduced, sometimes dramatically.

The first is called thesingle channel theory, which posits that there is limited capacity in the human information pro-cessing system in a time-sharing scenario. When the channel capacity is exceeded, multiple taskstransition from parallel processing to serial processing.

If both tasks demandcontrolled processing, then the strategy in processing is split into two mechanisms: facilitationand inhibition.

The implementation of such a strategy requires attentional resources, which canlead to task interference when the demand exceeds the available capacity.

The third theory is information processing analysis theory. Ifat least one task can be carried out automatically, the other task can be carried out with little orno impact on performance (at an appropriate time–error trade-off point).

The second theoryis the multiple resources model, which states that resource limitation concerns the entire systemrather than a channel (Chapter 5).

This theory is contradicted by empiricalresearch showing that a multi-processor model better explains empirical data.

We introduce a minimal hierarchical partially observed control model with latent dynamics, structured episodic memory, observer-belief state, option-level actions, and delayed verifier signals.

Incidental learning typically occurs as a byproduct of other activities (e.g., problem solving, advice seeking) rather than as a result of explicit or formal educational activities [47]. However, like formal learning, incidental learning can only occur if people engage deeply with information.

This would suggest that this design did not fully reach the constructive level from the ICAP framework [15, 16].

While prior work has highlighted the critical role of explanations in promoting learning [10, 18], our work additionally demonstrated the value of creating the conditions for learners to engage constructively (as defined in the ICAP framework [15, 16]) with the explanations.

We hypothesize that the observed difference in learning gain was due to the degree of cognitive engagement with AI-generated information. When individuals were provided with a solution to their task (in the form of a decision recommendation), they did not need to engage deeply with the explanations and could simply proceed with action. However, when they needed to arrive at their own decisions, they needed to engage with the provided explanations and synthesize the information to arrive at the conclusions.

Rotgans and Schmidt attributes these differences in cognitive engagement to different degrees of autonomy afforded by different learning tasks [59].

While some authors discuss cognitive engagement as a personal trait of a student that does not depend on context [3], others suggest that cognitive engagement depends on the structure of each task [15, 30, 59].

al. propose Interactive-Constructive-Active-Passive (ICAP) framework to describe a continuum of learning behaviors (from passive, to active, to constructive, to interactive) and argue that each subsequent level leads to an increase in cognitive engagement and learning [15, 16].

Research in cognitive psychology suggested that people process information on different levels. Deep processing occurs when individuals engage in more meaningful analysis of information and link it to existing knowledge structures [2]. In learning sciences, depth of processing is often associated with the degree of cognitive engagement, which is described as a "psychological state in which students put in a lot of effort to truly understand a topic and in which students persist studying over a long period of time." [59].

Researchers in learning sciences use the term "cognitive engagement" to describe learners' engagement with the learning process. When people are cognitively engaged with instructional process and materials, they are more likely to benefit from instruction and are more likely to acquire new skills and knowledge.

This step coincides with the recently introduced Controlled Text Reduction task (CTR; Slobodkin et al., 2022), which produces a coherent fused version of the content of marked spans ("highlights") in a source document, as interpreted within the context of the full text.

SUMMHELPER is a modular system consisting of separate components, each performing one subtask, allowing user modifications of that sub-task's output. Such decomposition has been studied before in the context of fully automated summarization, with several works separating the process into salience detection and generation components (Barzilay and McKeown, 2005; Li et al., 2018; Ernst et al., 2022). These works focused on optimizing each component as part of a fully-automatic summarization process in order to improve the overall performance of the model. In contrast, our work uses this modularity to not only improve overall system output, but to also give more control to the user over each step in the summarization process.

Our objective in this paper is to promote such a human-involved approach to summarization, allowing to better tailor the eventual output to real-world user needs, and to synergize the efficiency of the computer with the quality of the human (Hoc, 2000; Pacaux-Lemoine et al., 2017; Flemisch et al., 2019).

LLM annotators and human annotators should not be treated the same, and annotation tools should carefully design their data models and workflows to accommodate both types of annotators.

we go beyond using LLMs to assist annotation for human annotators or to replace human annotators. Rather, MEGAnno+ advocates for a collaboration between humans and LLMs with our dedicated system design and annotation-verification workflows.

Despite these advancements, it is essential to acknowledge that LLMs have limitations, necessitating human intervention in the data annotation process. One challenge is that the performance of LLMs varies extensively across different tasks, datasets, and labels. LLMs often struggle to comprehend subtle nuances or contexts in natural language, making involvement of humans with social and cultural understanding or domain expertise crucial.

Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks. Despite their prowess, LLMs may fall short in understanding of complex, sociocultural, or domain-specific context, potentially leading to incorrect annotations. Therefore, we advocate a collaborative approach where humans and LLMs work together to produce reliable and high-quality labels.

We also identified the factors that are critical to older adults but did not appear in the existing models. Finally, we applied the existing vocabulary to our model to comply with the conventional terms in the field.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Employing the grounded theory method [33], we allowed recurring themes and concepts in relation to technology acceptance behaviors to arise from the data itself.

We inductively analyzed the first-round interview data using thematic analysis based on a grounded theory approach [33]. Grounded theory methods build theory iteratively from the data, using rigorous coding practices. Initial open codes are primarily descriptive. These may be combined into more sophisticated related sets of descriptors, in which each set is referred to as an axial code. Subsequently, axial codes are combined into more theoretically powerful code complexes, called selective codes. Our approach included a process of open coding, axial coding, and selective coding.

Lastly, while our findings are based on only 24 participants, the sample size is commensurate with the Ground Theory approach.

We analyzed the second-round interview data using inductive and deductive approaches informed by grounded theory and other qualitative analysis methods [33, 22].

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over.

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Although many researchers have sought to understand and predict technology acceptance behavior, there has been relatively less effort to build a theoretical model for older adults, with one exception (STAM).

Extending the original TAM and consolidating the constructs of several other existing models, Venkatesh et al. proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) [37].

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

The beauty of the GP-TSM technique lies in its simplicity: at its core, all GP-TSM does is change the visual saliency of words by adjusting their opacity. This preserves the integrity of the original text and minimizes "ergonomic obtrusiveness" [100] while providing readers with a form of "contextual cuing" to arm them with "incidental knowledge about global context", which they can harness to better assign visual attention and memory when reading [40].

Furthermore, according to Stevens's power law, people perceive changes in gray scale not linearly, but rather by a factor of approximately 0.5 [71]. For instance, a threefold increase in opacity might only be perceived as 1.5 times more significant, further complicating the differentiation of levels.

This sequence resonates with efficient content absorption strategies highlighted in speed reading literature, where readers first capture the gist and then delve deeper [1, 63]. The interface, therefore, may inadvertently facilitate this structured, layered reading approach, which might explain the improvement in reading efficiency and comprehension.

We adopt the term "saliency" based on its definition (a "bottom-up, stimulus-driven perceptual quality which makes some items stand out from their neighbors") [42], and its use in augmented reality [85, 88], computer vision [17, 55], and cognitive science [37, 56].

Modulating text saliency is a widely studied aspect of textual information representation. This technique modifies the visual attributes of text to promote words of interest and guide readers' attention, making pertinent information more perceptible and thereby enhancing comprehension and the user experience [12, 42].

compressive summarization aims to select the shortest subsequence of words within a sentence that yields an informative and grammatical sentence [64]. This framework allows for a more concise representation of the original content while retaining the essence of its meaning.

Given the cognitive effort reading requires, readers frequently resort to skimming, which is a rapid, selective, and non-linear form of reading [2]. Eye tracking studies [30, 74] validate that such behavior is extremely common. However, multiple studies have suggested a significant trade-off between reading speed and comprehension [65, 66, 76, 87].

Specifically, automated summarization methods can introduce multiple types of errors: "crimes" of omission, hallucination, and misrepresentation.

Automated text summarization techniques, including but not limited to crowd-powered systems [10], prompting large language models (LLMs) [105], and other AI technologies, can address a subset of these difficulties, i.e., the resulting text may be shorter, with simpler sentence structures and fewer unusual words [62]. However, unless there is information within the original document that is truly redundant, the result is a lossy representation of the original document, regardless of whether the process is abstractive or extractive.

Established theories of human cognition describe how exposure to variation and consistency within prescribed structures can help people more robustly form mental models of a phenomenon, e.g., how an LLM behaves. Specifically, in line with Variation Theory [35], the features we instantiate identify patterns of consistency (Figure 1d, "Exact Matches"), variation (Figure 1c, "Unique Words"), or both (Figures 1a, 1b, "Positional Diction Clustering (PDC)"—a novel algorithm we introduce in this paper). In line with Analogical Learning Theory [13], PDC highlights analogous text across LLM responses, i.e., positionally consistent and similar in diction, such that users can see emergent relationships.

One prior piece of HCI work, ParaLib [51], does explicitly exploit these theories for system feature design, but does this in the domain of code.

There are two hypothesized benefits of this view. One is based on an understanding of human perception: the grid layout should help users compare more LLM responses because the spatial arrangement assists their memory. The other benefit is based on Variation Theory, which posits that discerning the impact of a critical aspect, for example model temperature, is only possible when experiencing variation along that dimension, isolated from variation along other dimensions.

Given that the features implemented in this work are in line with design implications of Variation Theory and Analogical Learning Theory, the results suggest that there may be further utility of these theories for guiding the design of future systems that help users make sense of data and form mental models from examples.

Theories of human concept learning suggest that a key step in forming accurate, robust mental models of a phenomenon is to be able to discern the underlying dimensions of variation (Variation Theory) and any latent structures beneath superficial details (Analogical Learning Theory). By detecting and communicating which sentences are both structurally analogous (by virtue of their position within the response) and semantically related (by virtue of highly overlapping content), users should be able to more easily identify emergent structures, as well as compare and contrast particular compositions of structural elements across responses and syntactic elements that may vary in meaningful ways across analogous sentences within those responses. These theories assert that these subtasks are key ingredients in forming those robust accurate mental models, i.e., learning from the LLM responses in order to better perform their overarching task.

In this work, in line with Variation Theory, the existing and novel features instantiated and described in the next subsection collectively identify patterns of consistency, variation, or both; they are explicitly designed to make emergent dimensions of consistency and variation easier for the user to perceive.

Variation Theory describes how helping people perceive the different dimensions of consistency and variation across examples (here, LLM responses) of the object of learning helps them more quickly and robustly leap to more accurate mental models. Analogical Learning Theory describes how people can form mental models or schema from perceiving structural analogical relationships across superficially varying examples (again, here LLM responses).

Variation Theory [35] and Analogical Learning Theory [13, 14] each propose mechanisms for how people may conceive and update their mental models based on concrete examples, or use their mental model in new situations.

The basic assumption under-lying theories of rationality is that people choose how to act according to their benefit; in otherwords, they are rational.

For example, we may choose a song to play to regulate our emotions or influence co-present others.

Many things we do with computers, such as listening to music or postingmessages, are about control.

Moreover, control theory offers a broad view of interaction; itis not limited to input.

In text entry, the theory provides us with concepts andmetrics that aid our understanding of typing performance.

For example, the information rate ofa text entry method can be quantified using the concept of throughput from information theory.

As another example, text entry often relies on language models, and such modeling is based oninformation theory.

Informationtheory provides a rigorous formalism to understand and quantify interaction via the concept ofpassing messages through a noisy channel.

This con-cept, communication of information, is rooted in information theory (Chapter 17).

To conclude, Bunge’stypology is useful in understanding the landscape of HCI theories.

Intentionality is a litmus test for HCI theories.

All theories of interaction discussed in this part assume intentionality: People have goalsthat have a (mutually) causal role in interaction.

One type of determination is shared by all theories of interaction in HCI: teleological determina-tion.

Here, the statistical model (regression) links the time it takes for a user toelicit a response to the design of the task environment (distance and width of buttons).

Bunge analyzed theories in scientific fields, from biology to physics, anddeveloped a rich typology of mutual determination.

According to Bunge, causal determination is only one of the forms of causal relationships usedin scientific theories.

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

However, such a description does not count as a theory ofinteraction because it does not link the effect back to the cause. It is silent on how the design ofthe button affects its pressing.

In other words, they must be about mutual determinacy.

In HCI, propositions must generally link aspects of people with aspects of technology or design.

The theory starts with a high-level statement: The motorsystem is a limited-capacity channel. From there, it derives propositions about the relationshipbetween performance and interface design through the construct of a target.

Theories of interaction illuminate interactive phenomena beyond intuition and helpsolve design and engineering problems in HCI.

“Attempts at reaching a target are limited by the speed and variance (accuracy) of themovement involved.”

“Human motor system is a limited capacity information channel.”

Even seemingly mundane activities such as enteringtext have rich structures that can be uncovered by these theories.

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

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

In general, a theory consists of a set of propositions, or statements.

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.

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.

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.

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].

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.

An understanding of a tonal schema with its associations to happiness and sadness has been consistently found to influence listeners who have grown up in a culture of Western music.

Its mirmode function estimates the modal strength of the music in terms of ''majorness'' and ''minorness.''

The perception of consonance also plays an important role in the music listening process—combinations of tones that are consonant are perceived as more positively valenced than dissonant ones (Harrison & Pearce, 2020).

Musical structures such as pitch relations are perceptually salient and provide important information for listeners (e.g., Gabrielsson & Lindstrom, 2010; Krumhansl, 1998; Krumhansl & Kessler, 1982).

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.

These semantic conflicts require dedicated support to detect, visualize, and resolve. Semantic conflict resolution interfaces must go beyond visualizing what changes were made, to what changes could be made, where they should be made, and what the effects might be. This resembles feedforward: affordances that help the user foresee the impact of an action [67, 93].

Today with LLMs, we are less limited by this constraint, and solutions to the problem of human-machine communication might be better found in cybernetics theory [9] than static formalism.

This reflects the principle of feedforward [67, 93] in communication theory—"a needed prescription or plan for a feedback, to which the actual feedback may or may not confirm" [79]—where a communicator provides "the context of what one was planning to talk about" [64, p. 179-80] in order to "pre-test the impact of [its output]" on the listener [34, p. 65].

We consider common sequences of chunk roles to be alignable structures that could be used to support users in identifying structural similarities and differences across sentences in different abstracts, in line with Structure-Mapping Theory [17].

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.

According to SMT, this generalization depends on most documents having some shared implicit structure.

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.

AbstractExplorer instantiates new minimally lossy2 SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

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.

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.

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 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).

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

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.

AbstractExplorer specifically leverages the shared structure in scientific abstracts to facilitate abstract corpus skimming and comparative reading at scale.

This systematic rebuildingpermitted the solution of deep number-theoreticproblems, among them the final step in the proofof the Weil Conjectures by Deligne, the proof ofthe Mordell Conjecture by Faltings, and the solu-tion of Fermat’s Last Problem by Wiles.

According to the mathematician Ferdinand von Lindemann, who proved that π isn’t algebraic — you can never pose an ordinary algebra problem where π is the answer — Kronecker once told him his work was worthless, since such “transcendental” numbers didn’t exist.

Dedekind quickly replied that neither could he — but that he’d worked out a proof that the algebraic numbers (the numbers you get as solutions to algebra problems) could be counted.

Peter Naur reminded us some decades ago that a program is more than its source code. Rather a program is a theory that lives in the minds of the developer(s) capturing what the program does, how developer intentions are implemented, and how the program can be changed over time. Usually this theory is not just in the minds of one developer but fragments of this theory are distributed across the minds of many, if not thousands, of other developers.

The Sumerian term for king, “lugal,” literally meant “big man.”

Defining a person’s sex identity using decontextualized “facts” is unscientific and dehumanizing.

While this is a small overview, the science is clear and conclusive: sex is not binary, transgender people are real.

“It is hard to overstate the importance of the ideas of Phil Anderson to the science of SFI and complexity in general," said Santa Fe Institute President David Krakauer. "His 'More is Different' article from Science in 1972 was the most important and rigorous refutation of the foolishness of reductionism for complex systems yet published. Not only did Phil articulate why confusing parts for the whole was a problem, but in the process, he explained why different fields of inquiry – from genetics to economics – needed to exist. This was a supreme act of intellectual modesty and generosity."

Complexity, by contrast, recognizes that once lots of particles come together to produce macroscopic things—such as organisms—knowing everything about particles isn’t enough to understand reality. An early pioneer of this approach was the physicist Philip W. Anderson, who succinctly framed the nascent anti-reductionist perspective with the phrase “More is different.”

Enter spoon theory, developed in 2003 by writer Christine Miserandino.