Category Boundaries Research Articles

Two modes of social impressions and their effects on choice.

Our memories of other people shape how we interact with them. Yet, even when we forget exactly what others said or did, we often remember impressions that capture a general gist of their behavior-whether they were forthright, friendly, or funny. Drawing on fuzzy trace theory, we propose two modes of social impression formation: impressions formed based on ordinal gist ("more competent," "less competent") or categorical gist ("competent," "incompetent"). In turn, we propose that people gravitate toward the simplest representation available and that different modes of memory have distinct consequences for social decisions. Specifically, ordinal impressions lead people to make decisions based on an individual's standing relative to others, whereas categorical impressions lead people to make decisions based on discrete classifications that interpret behavior. In four experiments, participants learned about two groups of individuals who differed in their competence (Studies 1a, 2, and 3) or generosity (Study 1b). When participants encoded impressions as ordinal rankings, they preferred to hire or help a relatively good target from a low-performing group over a relatively bad target from a high-performing group, even though both targets behaved identically and accuracy was incentivized. However, when participants could use categorical boundaries to interpret behavior, this preference was eliminated. In a final experiment, changing the category participants used to encode others' generosity changed their impressions, even when accounting for memory for verbatim details. This work links social impressions to theories of mental representation in memory and judgment, highlighting how distinct representations support divergent patterns of social decision-making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Numeric rating scale for pain should be used in an ordinal but not interval manner. A retrospective analysis of 346,892 patient reports of the quality improvement in postoperative pain treatment registry.

To assess postoperative pain intensity in adults, the numeric rating scale (NRS) is used. This scale has shown acceptable psychometric features, although its scale properties need further examination. We aimed to evaluate scale properties of the NRS using an item response theory (IRT) approach. Data from an international postoperative pain registry (QUIPS) was analyzed retrospectively. Overall, 346,892 adult patients (age groups: 18-20 years: 1.6%, 21-30 years: 6.7%, 31-40 years: 8.3%, 41-50 years: 13.2%, 51-60 years: 17.1%, 61-70 years: 17.3%, 71-80 years: 16.4%, 81-90 years: 3.9%, >90: 0.2%) were included. Among the patients, 55.7% are female and 38% had preoperative pain. Three pain items (movement pain, worst pain, least pain) were analyzed using 4 different IRT models: partial credit model (PCM), generalized partial credit model (GPCM), rating scale model (RSM), and graded response model (GRM). Fit indices were compared to decide the best fitting model (lower fit indices indicate a better model fit). Subgroup analyses were done for sex and age groups. After collapsing the highest and the second highest response category, the GRM outperformed other models (lowest Bayesian information criterion) in all subgroups. Overlapping categories were found in category boundary curves for worst and minimum pain and particularly for higher pain ratings. Response category widths differed depending on pain intensity. For female, male, and age groups, similar results were obtained. Response categories on the NRS are ordered but have different widths. The interval scale properties of the NRS should be questioned. In dealing with missing linearity in pain intensity ratings using the NRS, IRT methods may be helpful.

Sociophonetic Variation in Vowel Categorization of Australian English.

This study involves a perceptual categorization task for Australian English, designed to investigate regional and social variation in category boundaries between close-front vowel contrasts. Data are from four locations in southeast Australia. A total of 81 listeners from two listener groups took part: (a) so-called mainstream Australian English listeners from all four locations, and (b) L1 Aboriginal English listeners from one of the locations. Listeners heard front vowels /ɪ e æ/ arranged in 7-step continua presented at random. Varied phonetic contexts were analyzed, with a focus on coda /l/ because of a well-known prelateral merger of /e æ/ through mid-vowel lowering (e.g., celery-salary) reported to occur in some communities in this part of Australia. The results indicate that regional variation in Australian English is evident in perception. In particular, merging of /el/-/æl/ is shown to occur in the southernmost regions analyzed, but rarely in the northern regions of the geographical area under investigation. Aside from regional variation observed, age was also a factor in how participants responded to the task: older speakers had more merger than younger speakers in many locations, which is a new finding-previously, the merger was thought to be increasing in frequency over time, yet here we see this in only one location. Aboriginal English listeners also responded differently when compared with mainstream Australian English listeners. By analyzing the perception results across a variety of regional locations, with data from two different Australian social groups in the same location, this study adds a new dimension to our understanding of regional and social variations in Australian English.

Stop voicing perception in the societal and heritage language of Spanish-English bilingual preschoolers: The role of age, input quantity and input diversity

This is the first study to examine stop voicing perception in the societal (English) and heritage language (Spanish) of bilingual preschoolers. The study a) compares bilinguals’ English perception patterns to those of monolinguals; b) it examines how child-internal (age) and external variables (input quantity and input diversity) predict English and Spanish perceptual performance; and c) it compares bilinguals’ perception patterns across languages. Perception was assessed through a forced-choice minimal-pair identification task in which children heard synthesized audio stimuli that varied systematically along a /p-b/ and /t-d/ Voice Onset Time (VOT) continuum and were asked to match them with one of two pictures for each contrast. The results of Bayesian mixed-effects logistic regression analyses indicate that the bilinguals’ category boundary for English stops was impacted by their experience with Spanish, with more short-lag VOT tokens being perceived as voiceless consistent with Spanish VOT. Age solely predicted English perceptual skills, whereas input quantity was the only moderator of Spanish perceptual performance. Finally, the bilingual children showed separate stop voicing contrasts in each language, although perceptual performance was already more mature in English by preschool age. Implications for theories of bilingual speech learning and the role of sociolinguistic variables are discussed.

Intermedial Cross-dressing Performance as Cultural Intervention

As a theatrical phenomenon, cross-dressing performance has passed down through many centuries and manifested itself in different parts of the world. In the era of film and television, it was adapted to and has appeared on screen, as a popular means of entertaining the audience, and so plays an important role in influencing public opinions on certain social and cultural issues, such as the politics of gender and interculturalism. In light of theories of gender performativity and cultural agency, the intermediality of cross-dressing performance is approached in this article, mainly based on a careful analysis of Li Yugang’s solo shows on TV, the Chinese film Farewell My Concubine, and the American film M. Butterfly. By having the intermedial intersected with gender and interculturalism, this article argues that intermedial cross-dressing performance, on the one hand, has transformative power over gender politics, as it contributes to the gradual acceptance of differences in gender as well as other social categories. On the other hand, in some cross-dressing performances on screen, the subversive and the reiterative are blended together in cross-dressing performance, which undermines the legitimization of sexual orientations outside of the heterosexual norms. In addition, the entanglement of intermediality and interculturalism in cross-dressing performance in such a film as M. Butterfly contributes to critical reflections on cultural and categorical boundaries, which has profound implications for cross-cultural communications.

Graphfool: Targeted Label Adversarial Attack on Graph Embedding

Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes the research of attack methods on graph embedding. Most attack methods generate perturbations that maximize the deviation of the prediction confidence. They are difficult to accurately misclassify the instances into the target label, and the nonminimized perturbations are more easily detected by defense methods. To address these problems, we propose Graphfool, a novel targeted label adversarial attack on graph embedding. It can generate adversarial graphs to attack graph embedding methods via classification boundary and gradient information in the target graph embedding method. Graphfool first estimates the classification boundaries of different categories. Then, it calculates the minimum perturbation matrix to misclassify the attacked node according to the target classification boundary. Finally, the adjacency matrix is modified according to the maximum absolute value of the perturbation matrix. Extensive experiments demonstrate that Graphfool achieves the state-of-the-art attack performance with minimum perturbations. Besides, the possible defense experiments further prove that the perturbations generated by Graphfool are more imperceptible.

Categorisation of cats: Managing boundary felids in Aotearoa New Zealand and Britain

Abstract Management of domestic and wild animals is an integral part of conservation and is often based on how an animal is categorised. For example, feral cats are often killed, while valued companion cats and native wildcats are protected. Drawing on qualitative research and using the concept of boundary‐work, this paper examines the complex categorisation and management of cats within conservation in Britain and Aotearoa, New Zealand (NZ). We examine how, both in theory and in practice, valued companion and wildcats are distinguished from unprotected feral cats, and in‐between categories of stray and hybrid cats. We demonstrate that stakeholders draw boundaries between cat categories differently. These differences in boundary‐drawing reflect the inherent blurriness of category boundaries, practical challenges and, importantly, differences in values, in particular whether priority is placed on the life of the cat or the cat's potential victim, particularly native or game birds. This can mean that laws outlining protections for specific categories of animals have limited effect if, in practice, those encountering cats draw boundaries differently. This paper also reports on important differences between the two case studies. In NZ, even cat advocates support the humane killing of unambiguously feral cats while this is less true in Britain. Furthermore, due to the nature of the contexts, conservationists in NZ are more inclined to assume that ambiguous cats are feral whereas conservationists in Britain are more inclined to assume that they are wildcats. This paper demonstrates that values not only shape people's perceptions and treatment of animals but also how they draw boundaries between them. This finding may have important implications for understanding other controversies in conservation and animal management. Read the free Plain Language Summary for this article on the Journal blog.

From the Silver Screen to the Streaming Site: Blurring of Category Boundaries in Hollywood

From the Silver Screen to the Streaming Site: Blurring of Category Boundaries in Hollywood

Commensuration and Expanding Category Boundaries: Evidence from Mortgage-Backed Securities

Commensuration and Expanding Category Boundaries: Evidence from Mortgage-Backed Securities

Zero is hero: Round number effects on knowledge-sharing platforms

With the inherent public goods problem embedded in knowledge-sharing platforms, various incentive mechanisms have been implemented, most of which are in the form of gamified elements. Among those motivating elements, reputation points are the most direct feedback about individuals’ contribution effort, which use numerical units indicating progress. Although some research has found that points can incentivize users to contribute, empirical evidence regarding the influential patterns of such numerical units remains limited. Drawing on numerical cognition literature that an individual's evaluation and judgments may be influenced by certain numerical cues, we particularly focus on the round number bias on knowledge-sharing platforms. Several hypotheses regarding users’ behavioral changes when their accumulated points approach round numbers have been proposed, including their contribution level, contribution quality, and writing style. By analyzing data collected from StackOverflow.com, we find that users perceive round numbers as category boundaries or endpoints and crossing such boundaries can motivate aspirational behaviors. Concretely, users significantly increase their post frequency and length, and write answers with more function words and second-person pronouns. Meanwhile, their posts will be more likely to be accepted as the best answers and gain more votes. We also explore the moderating effects of advanced explicit incentives and numbers’ magnitude. Theoretically, our research contributes to a body of literature on knowledge-sharing platform incentive mechanisms to motivate users’ contributions and sheds light on the utilization of numerical cues to guide individuals’ behaviors in user-generated-content (UGC) provision context.

Bilingual Children Shift and Relax Second-Language Phoneme Categorization in Response to Accented L2 and Native L1 Speech Exposure.

Listeners adjust their perception to match that of presented speech through shifting and relaxation of categorical boundaries. This allows for processing of speech variation, but may be detrimental to processing efficiency. Bilingual children are exposed to many types of speech in their linguistic environment, including native and non-native speech. This study examined how first language (L1) Spanish/second language (L2) English bilingual children shifted and relaxed phoneme categorization along the cue of voice onset time (VOT) during English speech processing after three types of language exposure: native English exposure, native Spanish exposure, and Spanish-accented English exposure. After exposure to Spanish-accented English speech, bilingual children shifted categorical boundaries in the direction of native English speech boundaries. After exposure to native Spanish speech, children shifted to a smaller extent in the same direction and relaxed boundaries leading to weaker differentiation between categories. These results suggest that prior exposure can affect processing of a second language in bilingual children, but different mechanisms are used when adapting to different types of speech variation.

The boundary between real and fictional others in the medial prefrontal cortex is blurred in lonelier individuals.

People spend much of their free time engaging with narrative fiction. Research shows that, like real-life friends, fictional characters can sometimes influence individuals' attitudes, behaviors, and self-beliefs. Moreover, for certain individuals, fictional characters can stand in for real-life friends by providing the experience of belonging. Despite these parallels between how people think of real and fictional others, it is unclear whether, and to what degree, their neural representations are similar. Does the brain treat psychologically close fictional others as it does close real-world friends, or are real others somehow privileged in their neural representation? In the present study, fans of the HBO series Game of Thrones performed a trait-evaluation task for the self, 9 real-life friends/acquaintances, and 9 fictional characters from Game of Thrones while undergoing functional magnetic resonance imaging. Using both brain decoding and representational similarity analysis, we found evidence of a categorical boundary between real and fictional others within the medial prefrontal cortex. However, the boundary between these categories was blurred in lonelier individuals. These results suggest that lonelier individuals may turn to fictional characters to meet belongingness needs, and this, in turn, alters the manner in which these categories are encoded within the social brain.

Category Flexibility in Emotion Learning.

Learners flexibly update category boundaries to adjust to the range of experiences they encounter. However, little is known about whether the degree of flexibility is consistent across domains. We examined whether categorization of social input, specifically emotions, is afforded more flexibility as compared to other biological input. To address this question, children (6-12 years; 32 female, 37 male; 7 Hispanic or Latino, 62 not Hispanic or Latino; 8 Black or African American, 14 multiracial, 46 White, 1 selected "other") categorized faces morphed from calm to upset and animals morphed from a horse to a cow across task phases that differed in the distribution of stimuli presented. Learners flexibly adjusted both emotion and animal category boundaries according to distributional information, yet children showed more flexibility when updating their category boundaries for emotions. These results provide support for the idea that children-who must adjust to the vast and varied emotional signals of their social partners-respond to social signals dynamically in order to make predictions about the internal states and future behaviors of others.

Developing a model of guilty plea decision-making: Fuzzy-trace theory, gist, and categorical boundaries.

To date, most research on plea bargaining has used some form of the shadow of the trial (SOT) model to frame defendant decisions. In this research, we proposed and tested a new conceptual model of plea decision-making, based on fuzzy-trace theory (FTT), for the context in which a nondetained, guilty defendant chooses between a guilty plea or trial, where both the plea and potential trial sentence entail incarceration. We predicted that plea decisions would be affected by (a) meaningful, categorical changes in conviction probability (e.g., low to moderate, moderate to high), as opposed to more granular changes within categories and (b) the presence and magnitude of categorical distinctions between plea offer and potential trial sentence rather than fine-grained differences between individual offers. We conducted three vignette-based experiments (Study 1: N = 1,701, Study 2: N = 1,098, Study 3: N = 1,232), using Mechanical Turk participants. In Studies 1 and 2, we manipulated potential trial sentence and conviction probability, asking participants to indicate either the maximum plea sentence they would accept (Study 1) or whether they would plead guilty to a specific offer (Study 2). In Study 3, we manipulated plea discount and potential trial sentence and measured plea acceptance. Maximum acceptable plea sentences were similar within and different between "groupings" of meaningfully similar conviction probabilities (Study 1). Plea rates were similar within and different between groupings that comprised plea offers of similarly meaningful distance from the potential trial sentence (Study 3). The results also provide insight into the plea rates that might be expected under different combinations of the independent variables (Studies 2 and 3). These results support a new conceptual model of plea decision-making that may be better suited to explaining case-level differences in plea outcomes than the SOT model and suggest that future research extending this model to a wider range of contexts would be fruitful. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Startup category membership and boundary expansion in the field of artificial intelligence

PurposeAn important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI).Design/methodology/approachThe first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field.FindingsThe author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field.Research limitations/implicationsThis study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries.Practical implicationsCore AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors.Social implicationsThe social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field.Originality/valueThis paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field.

Face Aging with Feature-Guide Conditional Generative Adversarial Network

Face aging is of great importance for the information forensics and security fields, as well as entertainment-related applications. Although significant progress has been made in this field, the authenticity, age specificity, and identity preservation of generated face images still need further discussion. To better address these issues, a Feature-Guide Conditional Generative Adversarial Network (FG-CGAN) is proposed in this paper, which contains extra feature guide module and age classifier module. To preserve the identity of the input facial image during the generating procedure, in the feature guide module, perceptual loss is introduced to minimize the identity difference between the input and output face image of the generator, and L2 loss is introduced to constrain the size of the generated feature map. To make the generated image fall into the target age group, in the age classifier module, an age-estimated loss is constructed, during which L-Softmax loss is combined to make the sample boundaries of different categories more obvious. Abundant experiments are conducted on the widely used face aging dataset CACD and Morph. The results show that target aging face images generated by FG-CGAN have promising validation confidence for identity preservation. Specifically, the validation confidence levels for age groups 20–30, 30–40, and 40–50 are 95.79%, 95.42%, and 90.77% respectively, which verify the effectiveness of our proposed method.

Colour perception changes with basic colour word comprehension.

Recent work has investigated the origin of infant colour categories, showing pre-linguistic infants categorise colour even in the absence of colour words. These infant categories are similar but not identical to adult categories, giving rise to an important question about how infant colour perception changes with the learning of colour words. Here we present two novel paradigms in which 12- and 19-month-old participants learning English as their first language were assessed on their perception of colour, while data on their colour word comprehension were also collected. Results indicate that participants' perception of colours close to the colour category boundaries dramatically change after colour word learning. The results highlight the shift made from infant colour categories to adult-like linguistically mediated colour categories that accompanies colour wordlearning. RESEARCH HIGHLIGHTS: We aimed to test whether colour perception is linguistically mediated in infants. We used novel eye-tracking and pupillometry paradigms to test infant colour perception either side of learning colour words. Infants' discrimination of colour changes after learning colour words, suggesting a shift due to colour word learning. A shift from pre-linguistic colour representation to linguistically mediated colour representation is discussed.

Perceptual Sensitivity to Tonal Alignment in Nuer.

This paper examines the perceptual threshold in patterns of tonal timing (alignment) of Falling versus Low tones. The results indicate a remarkable sensitivity among the listeners. In a perception experiment with 30 participants, we tested how native speakers of the West Nilotic language Nuer responded to stimuli in which the timing of the F0 fall that distinguishes Low versus Fall following a High target is manipulated. We measured the threshold for the responses to shift tone perception from 25% to 75%. The results show that listeners only needed an average of 19 ms to differentiate between the melodic shapes and as little as 13 ms for one item. Perceptual sensitivity this fine-grained is not expected based on what is known about the Just Noticeable Difference (JND) from previous studies. Results from non-tonal languages report a sensitivity threshold for tonal timing of at least 50 ms at category boundaries. This difference suggests that whether or not subjects speak a tone language may be a determining factor in their JND.

Challenging the fixed-criterion model of perceptual decision-making.

Perceptual decision-making is often conceptualized as the process of comparing an internal decision variable to a categorical boundary or criterion. How the mind sets such a criterion has been studied from at least two perspectives. One idea is that the criterion is a fixed quantity. In work on subjective phenomenology, the notion of a fixed criterion has been proposed to explain a phenomenon called "subjective inflation"-a form of metacognitive mismatch in which observers overestimate the quality of their sensory representation in the periphery or at unattended locations. A contrasting view emerging from studies of perceptual decision-making is that the criterion adjusts to the level sensory uncertainty and is thus sensitive to variations in attention. Here, we mathematically demonstrate that previous empirical findings supporting subjective inflation are consistent with either a fixed or a flexible decision criterion. We further lay out specific task properties that are necessary to make inferences about the flexibility of the criterion: (i) a clear mapping from decision variable space to stimulus feature space and (ii) an incentive for observers to adjust their decision criterion as uncertainty changes. Recent work satisfying these requirements has demonstrated that decision criteria flexibly adjust according to uncertainty. We conclude that the fixed-criterion model of subjective inflation is poorly tenable.

Active learning with effective scoring functions for semi-supervised temporal action localization

Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action instances in untrimmed videos, i.e., start and end time. Existing works usually adopt fully-supervised solutions, however, one of the practical bottlenecks in these solutions is the large amount of labeled training data required. To reduce expensive human label cost, this paper focuses on a rarely investigated yet practical task named semi-supervised TAL and proposes an effective active learning method, named AL-STAL. We leverage four steps for actively selecting video samples with high informativeness and training the localization model, named Train, Query, Annotate, Append. Two scoring functions that consider the uncertainty of localization model are equipped in AL-STAL, thus facilitating the video sample ranking and selection. One takes entropy of predicted label distribution as measure of uncertainty, named Temporal Proposal Entropy (TPE). And the other introduces a new metric based on mutual information between adjacent action proposals, named Temporal Context Inconsistency (TCI). To validate the effectiveness of proposed method, we conduct extensive experiments on three benchmark datasets THUMOS’14, ActivityNet 1.3 and ActivityNet 1.2. Experiment results show that AL-STAL outperforms the existing competitors and achieves satisfying performance compared with fully-supervised learning.