Category Boundaries Research Articles

Effects of Cognitive Load on the Categorical Perception of Mandarin Tones.

Purpose This study investigated the effect of cognitive load (CL) on the categorical perception (CP) of Mandarin lexical tones to discuss the application of the generalized pulse-skipping hypothesis. This hypothesis assumes that listeners might miss/skip temporal pulses and lose essential speech information due to CL, which consequently affects both the temporal and spectral dimensions of speech perception. Should CL decrease listeners' pitch sensitivity and impair the distinction of tone categories, this study would support the generalized pulse-skipping hypothesis. Method Twenty-four native Mandarin-speaking listeners were recruited to complete a dual-task experiment where they were required to identify or discriminate tone stimuli while concurrently memorizing six Chinese characters or graphic symbols. A no-load condition without a memory recall task was also included as a baseline condition. The position of categorical boundary, identification slope, between- and within-category discrimination, and discrimination peakedness were compared across the three conditions to measure the impact of CL on tone perception. The recall accuracy of Chinese characters and graphic symbols was used to assess the difficulty of memory recall. Results Compared with the no-load condition, both load conditions showed a boundary shift to Tone 3, shallower identification slope, poorer between-category discrimination, and lower discrimination peakedness. Within-category discrimination was negatively affected by CL in the graphic symbol condition only, not in the Chinese character condition. Conclusions CL degraded listeners' sensitivity to subtle fundamental frequency changes and impaired CP of Mandarin lexical tones. This provides support for the generalized pulse-skipping hypothesis. Besides, the involvement of lexical information modulated the effect of CL.

A Commentary on “Eating Disorders and the Rorschach” (Guinzbourg de Braude et al., 2021)

A Commentary on “Eating Disorders and the Rorschach” (Guinzbourg de Braude et al., 2021)

Degrees of Flexibility: Predicting Market Category Change and Stability

We further develop scholarly understandings of market category dynamics by offering a theoretical framework describing the conditions under which any given category is likely to change over time. We argue that a category’s propensity to change is driven by the category’s evaluative flexibility and practice flexibility—the degree to which evaluators will willingly reimagine category boundaries and the degree to which category members will readily deviate from normative practices, respectively. Our theory highlights the interrelationship between category membership, category boundaries, and category meanings, offering an integrated path forward for empirically examining a variety of market category dynamics.

Performing Categories through Material, Spatial and Verbal Practices: the case of “Historical Shops”

Categories make intelligible our world by classifying and labelling objects, actors and events around us. Sometimes categories are created by some promoters re-interpreting already existing entities to re-orientate meaning systems according to strategic goals. This phenomenon is particularly spread in urban and cultural contexts, where policy-makers often found labels to re-qualify, valorise or preserve specific areas of interest. Adopting a qualitative methodology and a multiple nested case study, I analyse the case of creation of the Historical Shops category in two Italian cities with the aim of understanding the conditions and the mechanisms through which a category takes life, is acknowledged and performed on a daily basis, and the ones that, instead, hinder this process. Findings show that the interaction of material, spatial and verbal practices enacted within categorical boundaries co-create an externally perceived experience of the category, which, in turn reinforce the categorical members’ identification with the category, hence sustaining category performance. The initial acts of category creation and actions of category promoters and intermediaries may support or undermine the whole process. The study contributes to categorization studies by providing a situated view on categorization where materiality, place, experience and categorical members’ identification have a fundamental role.

Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization.

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

Mutual Information and Categorical Perception

Categorical perception refers to the enhancement of perceptual sensitivity near category boundaries, generally along dimensions that are informative about category membership. However, it remains unclear exactly which dimensions are treated as informative and why. This article reports a series of experiments in which subjects were asked to learn statistically defined categories in a novel, unfamiliar 2D perceptual space of shapes. Perceptual discrimination was tested before and after category learning of various features in the space, each defined by its position and orientation relative to the maximally informative dimension. The results support a remarkably simple generalization: The magnitude of improvement in perceptual discrimination of each feature is proportional to the mutual information between the feature and the category variable. This finding suggests a rational basis for categorical perception in which the precision of perceptual discrimination is tuned to the statistical structure of the environment.

Neural Hierarchy of Color Categorization: From Prototype Encoding to Boundary Encoding.

A long-standing debate exists on how our brain assigns the fine-grained perceptual representation of color into discrete color categories. Recent functional magnetic resonance imaging (fMRI) studies have identified several regions as the candidate loci of color categorization, including the visual cortex, language-related areas, and non-language-related frontal regions, but the evidence is mixed. Distinct from most studies that emphasized the representational differences between color categories, the current study focused on the variability among members within a category (e.g., category prototypes and boundaries) to reveal category encoding in the brain. We compared and modeled brain activities evoked by color stimuli with varying distances from the category boundary in an active categorization task. The frontal areas, including the inferior and middle frontal gyri, medial superior frontal cortices, and insular cortices, showed larger responses for colors near the category boundary than those far from the boundary. In addition, the visual cortex encodes both within-category variability and cross-category differences. The left V1 in the calcarine showed greater responses to colors at the category center than to those far from the boundary, and the bilateral V4 showed enhanced responses for colors at the category center as well as colors around the boundary. The additional representational similarity analyses (RSA) revealed that the bilateral insulae and V4a carried information about cross-category differences, as cross-category colors exhibited larger dissimilarities in brain patterns than within-category colors. Our study suggested a hierarchically organized network in the human brain during active color categorization, with frontal (both lateral and medial) areas supporting domain-general decisional processes and the visual cortex encoding category structure and differences, likely due to top-down modulation.

Transpinay: Genealogy of a term

This article provides a genealogical analysis of the Philippine category “transpinay,” a compound word combining “trans” and “pinay.” It traces the coining of the term by trans activists in the first decade of the 21st century and examines the ways in which the term gained its currency by drawing out distinctions between gender and sexuality categories. The article investigates what the category includes and what the category excludes, and examines disputes over the term’s categorical boundaries. Overall, the article aims not to determine what the term transpinay is, but rather investigates what the term does and how it came to be.

Emotional Determinants of Categorical Accentuation

The present study aimed to provide preliminary evidence for the role of uncertainty related emotions in categorical accentuation. Participants had to estimate the length of lines varying in length, which depending on the conditions were or were not associated with categorical labels. To explore the emotional determinants of categorical accentuation we included additional label condition, in which participants were induced to feel fear (i.e. the emotion theoretically defined by the appraisal of low certainty). Consistent with the past research the results revealed a classic accentuation effect with participants in label condition showing higher differentiation at category boundaries compared to those in no label condition. More importantly, this effect was strengthened in the condition in which participants were induced with fear suggesting that uncertainty-related emotions could play an important role in the accentuation phenomenon.

RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.

The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed tomography (CT) and X-ray images have been well recognized to be two effective techniques for clinical COVID-19 disease diagnoses. Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment, and treatment. However, considering the similarity between COVID-19 and pneumonia, CXR samples with deep features distributed near category boundaries are easily misclassified by the hyperplanes learned from limited training data. Moreover, most existing approaches for COVID-19 detection focus on the accuracy of prediction and overlook uncertainty estimation, which is particularly important when dealing with noisy datasets. To alleviate these concerns, we propose a novel deep network named RCoNetn}{}^{k}_{s}n for robust COVID-19 detection which employs Deformable Mutual Information Maximization (DeIM), Mixed High-order Moment Feature (MHMF), and Multiexpert Uncertainty-aware Learning (MUL). With DeIM, the mutual information (MI) between input data and the corresponding latent representations can be well estimated and maximized to capture compact and disentangled representational characteristics. Meanwhile, MHMF can fully explore the benefits of using high-order statistics and extract discriminative features of complex distributions in medical imaging. Finally, MUL creates multiple parallel dropout networks for each CXR image to evaluate uncertainty and thus prevent performance degradation caused by the noise in the data. The experimental results show that RCoNetn}{}^{k}_{s}n achieves the state-of-the-art performance on an open-source COVIDx dataset of 15 134 original CXR images across several metrics. Crucially, our method is shown to be more effective than existing methods with the presence of noise in the data.

Exploring the complex origins of energy poverty in The Netherlands with machine learning

Energy poverty is receiving increased attention in developed countries like the Netherlands. Although it only affects a relatively small share of the population, it constitutes a stern challenge that is hard to quantify and monitor, hence difficult to effectively tackle through adequate policy measures. In this paper we introduce a framework to categorize energy poverty risk based on income and energy expenditure. We propose the use of a machine learning classifier to predict energy poverty risk from a broad set of socio-economic parameters: house value, ownership and age, household size, and average population density. While income remains the single most important predictor, we find that the inclusion of these additional socio-economic features is indispensable in order to achieve high prediction reliability. This result forms an indication of the complex nature of the mechanisms underlying energy poverty. Our findings are valid at different geographical scales, i.e. both for single households and for entire neighborhoods. Extensive sensitivity analysis shows that our results are independent of the precise position of risk category boundaries. The outcomes of our study indicate that machine learning could be used as an effective means to monitor energy poverty, and assist the design and implementation of appropriate policy measures.

Validation of the Workplace Integrated Safety and Health (WISH) assessment in a sample of nursing homes using Item Response Theory (IRT) methods

ObjectivesTo validate and test the dimensionality of six constructs from the Workplace Integrated Safety and Health (WISH) assessment, an instrument that assesses the extent to which organisations implement integrated systems...

The techno-social reproduction of taste boundaries on digital platforms: The case of music on YouTube

Recent research argues that digital platforms contribute to the long-lasting erosion of genre boundaries and established cultural classifications in the “heads and habits” of art consumers (DiMaggio 1987). This paper draws on large volumes of YouTube data to illustrate how, on the contrary, cultural boundaries are techno-socially reproduced online, shedding light on the understudied role of algorithmic systems in the classification of cultural goods.Through a ground-up investigation of how both automated recommendations and the taste patterns of 202,509 platform users segment 14,865 music videos, this work provides empirical evidence of the persistent relevance of categorical boundaries in platformized cultural reception. The recommender system used by YouTube largely relates music videos of the same genre, thus reinforcing pre-existing artistic classifications in the digital circulation of culture. The study also documents how most users do not cross categorical borders in their interactions with musical content, and that boundary strength varies across music genres. I discuss how the interplay between the social patterning of taste and the algorithmic filtering of cultural content generates techno-social feedback loops, and conclude by drawing broad implications for the sociological study of culture and consumption.

Songbirds are excellent auditory discriminators, irrespective of age and experience

Human infants but not adults possess the ability to perceive differences between non-native language phoneme categories. The predominant explanation for this age-related decline in discriminative ability is the effect of statistical learning driven by sensory exposure: phoneme categories of the native language take precedence, have a higher frequency of occurrence and may encompass category distinctions in non-native languages. Alternatively, one could explain the decline through a reduction in discriminative abilities attributable to ageing. Thus, to what extent is auditory perception influenced either by experience or by age-related processes? Here, we attempted to answer this question, which cannot easily be disentangled in humans, in songbirds, which share many properties with humans: both learn the statistical distribution of sounds in their environment, both possess neural circuits to process vocalizations of their own species and plasticity in these circuits is subject to critical periods. To study the effects of experience and ageing, we trained zebra finches, Taeniopygia guttata , to discriminate short from long versions of a single zebra finch song syllable type. Birds in four groups distinguished by their age (old versus young) and level of auditory experience (with song experience versus completely isolated from song) could learn to discriminate arbitrarily fine differences between song syllables, although we found a trend that upholds the statistical learning hypothesis: birds with song experience performed better than birds with no experience. Furthermore, birds in all groups were able to generalize their learning to new stimuli of the same type, and they were able to rapidly adapt their learned discrimination boundaries. Finally, we found that songbirds could accurately discriminate randomly selected renditions of a stereotyped adult song syllable, revealing a flexible ability to discriminate conspecific vocalizations. • Sensory experience matters more than age for auditory perception in songbirds. • Songbirds generalize learned categories to novel stimuli. • Learned categories are flexible and category boundaries can be relearned easily. • Songbirds can learn random categories accurately. • Their auditory systems represent species-specific sounds accurately and flexibly.

On Symbiosis of Attribute Prediction and Semantic Segmentation.

Attributes are semantically meaningful characteristics whose applicability widely crosses category boundaries. They are particularly important in describing and recognizing concepts for which no explicit training example is given, e.g., zero-shot learning. Additionally, since attributes are human describable, they can be used for efficient human-computer interaction. In this article, we propose to employ semantic segmentation to improve person-related attribute prediction. The core idea lies in the fact that many attributes describe local properties. In other words, the probability of an attribute to appear in an image is far from being uniform in the spatial domain. We build our attribute prediction model jointly with a deep semantic segmentation network. This harnesses the localization cues learned by the semantic segmentation to guide the attention of the attribute prediction to the regions where different attributes naturally show up. As a result of this approach, in addition to prediction, we are able to localize the attributes despite merely having access to image-level labels (weak supervision) during training. We first propose semantic segmentation-based pooling and gating, respectively denoted as SSP and SSG. In the former, the estimated segmentation masks are used to pool the final activations of the attribute prediction network, from multiple semantically homogeneous regions. This is in contrast to global average pooling which is agnostic with respect to where in the spatial domain activations occur. In SSG, the same idea is applied to the intermediate layers of the network. Specifically, we create multiple copies of the internal activations. In each copy, only values that fall within a certain semantic region are preserved while outside of that, activations are suppressed. This mechanism allows us to prevent pooling operation from blending activations that are associated with semantically different regions. SSP and SSG, while effective, impose heavy memory utilization since each channel of the activations is pooled/gated with all the semantic segmentation masks. To circumvent this, we propose Symbiotic Augmentation (SA), where we learn only one mask per activation channel. SA allows the model to either pick one, or combine (weighted superposition) multiple semantic maps, in order to generate the proper mask for each channel. SA simultaneously applies the same mechanism to the reverse problem by leveraging output logits of attribute prediction to guide the semantic segmentation task. We evaluate our proposed methods for facial attributes on CelebA and LFWA datasets, while benchmarking WIDER Attribute and Berkeley Attributes of People for whole body attributes. Our proposed methods achieve superior results compared to the previous works. Furthermore, we show that in the reverse problem, semantic face parsing significantly improves when its associated task is jointly learned, through our proposed Symbiotic Augmentation (SA), with facial attribute prediction. We confirm that when few training instances are available, indeed image-level facial attribute labels can serve as an effective source of weak supervision to improve semantic face parsing. That reaffirms the need to jointly model these two interconnected tasks.

Falling through the cracks: Modeling the formation of social category boundaries.

Social categorizations divide people into “us” and “them”, often along continuous attributes such as political ideology or skin color. This division results in both positive consequences, such as a sense of community, and negative ones, such as group conflict. Further, individuals in the middle of the spectrum can fall through the cracks of this categorization process and are seen as out-group by individuals on either side of the spectrum, becoming inbetweeners. Here, we propose a quantitative, dynamical-system model that studies the joint influence of cognitive and social processes. We model where two social groups draw the boundaries between “us” and ‘them” on a continuous attribute. Our model predicts that both groups tend to draw a more restrictive boundary than the middle of the spectrum. As a result, each group sees the individuals in the middle of the attribute space as an out-group. We test this prediction using U.S. political survey data on how political independents are perceived by registered party members as well as existing experiments on the perception of racially ambiguous faces, and find support.

Visual statistical learning is modulated by arbitrary and natural categories.

Visual statistical learning (VSL) describes the unintentional extraction of statistical regularities from visual environments across time or space, and is typically studied using novel stimuli (e.g., symbols unfamiliar to participants) and using familiarization procedures that are passive or require only basic vigilance. The natural visual world, however, is rich with a variety of complex visual stimuli, and we experience that world in the presence of goal-driven behavior including overt learning of other kinds. To examine how VSL responds to such contexts, we exposed subjects to statistical contingencies as they learned arbitrary categorical mappings of unfamiliar stimuli (fractals, Experiment 1) or familiar stimuli with preexisting categorical boundaries (faces and scenes, Experiment 2). In a familiarization stage, subjects learned by trial and error the arbitrary mappings between stimuli and one of two responses. Unbeknownst to participants, items were paired such that they always appeared together in the stream. Pairs were equally likely to be of the same or different category. In a pair recognition stage to assess VSL, subjects chose between a target pair and a foil pair. In both experiments, subjects' VSL was shaped by arbitrary categories: same-category pairs were learned better than different-category pairs. Natural categories (Experiment 2) also played a role, with subjects learning same-natural-category pairs at higher rates than different-category pairs, an effect that did not interact with arbitrary mappings. We conclude that learning goals of the observer and preexisting knowledge about the structure of the world play powerful roles in the incidental learning of novel statistical information.

Category structure guides the formation of neural representations.

Perceptual variability is often viewed as having multiple benefits in object learning and categorization. Despite the abundant results demonstrating benefits such as increased transfer of knowledge, the neural mechanisms underlying variability as well as the developmental trajectories of how variability precipitates changes to category boundaries are unknown. By manipulating an individual's exposure to variability of novel, metrically organized categories during an fMRI-adaptation paradigm, we were able to assess the functional differences between similarity and variability in category learning and generalization across two time-points in development: adulthood (n = 14) and late childhood (n = 13). During this study, participants were repeatedly exposed to category members from different distributions. After a period of adaptation, a deviant stimulus that differed from the expected distribution was then presented. This deviant differed in either an invariant dimension (a feature that remained consistent throughout presentation was altered) or a similarity dimension (a feature that changed throughout exposure was changed in a new dimension). Our results can be summarized in three main findings: (1) Variability during exposure recruited the right fusiform gyrus to a greater extent than tight exposure. (2) Deviant items were generalized based on the exemplar distributions during exposure, although children only generalized items if provided variable exposure. (3) Variability influenced release to a greater extent in children than adults. These results are discussed in relation to the variability and category learning literature more broadly.

Distributional learning of speech sound categories is gated by sensitive periods

Perceptual attunement to the native phonetic repertoire occurs over the first year of life: an infant's discrimination of non-native phonetic contrasts declines while their discrimination of native phonetic contrasts improves, with the timing of change consistent with sensitive periods. The statistics of speech sound distributions is one source of input used to collapse non-native phonetic category boundaries, while sharpening native ones. Distributional learning can be a domain-general mechanism, yet given the timing of perceptual attunement, we hypothesized that this learning mechanism may be maturationally delimited in the content domain of phonetic categories. Here, we assessed whether sensitivity to the distribution of speech sounds in the environment declines as the period of perceptual attunement closes. We used electroencephalography (EEG) to investigate whether neuronal responses to native ‘ra’ and ‘la’ phones are modulated differently in older vs young infants by exposure to either a bimodal or unimodal sound distribution spanning the [r] ~ [l] phoneme space. The native contrast, ra-la, is discriminable at all three ages, ensuring that we were testing the distributional learning mechanism, rather than confounding it with a decline in discrimination to a non-native distinction. English monolingual infants (n = 131) at 5-, 9- and 12-months-old were familiarized to either a unimodal or bimodal distribution of /ra/−/la/ speech sounds. Immediately following familiarization, an ERP oddball task was used to assess discrimination. Results showed that brief exposure to a bi- vs uni-modal distribution is sufficient to alter neuronal responses to subsequent /ra/ vs /la/ speech sounds at 5-months and 9-months, but not at 12-months. These results are the first to capture a progressive decline in sensitivity to distributional statistics in the environment. A potential mechanistic explanation based on critical period biology is discussed.

Watered Down: Market Growth, Authenticity, and Evaluation in Craft Beer

Research in organizational theory suggests that category-spanning organizations typically suffer penalties in evaluations, as consumers downgrade producers they see as violating authenticity norms. We challenge this view by linking two heretofore separate insights: first, that categorical boundaries erode as categories become taken for granted and, second, that consumers in a given category tend to become more heterogeneous as their numbers increase. We argue that newer consumers employ diverse evaluative schemata and rely less on established conceptions of authenticity than do veterans, leading to more generous evaluations as the ranks of consumers grow. Using the canonical case of craft beer, we test the effect of audience growth on consumer evaluations, particularly when producers violate categorical authenticity norms. Our analysis of an original dataset of more than 1.2 million unique ratings of craft beers from a popular online forum finds both that overall beer ratings increase and that penalties to authenticity norm violations attenuate as the number of new reviewers participating in the evaluative process rises. These results refine our understanding of shifting demands for categorical purity, conceptions of authenticity, and consumer evaluations as functions of market growth.