Effects of Microstimulation in the Anterior Intraparietal Area during Three-Dimensional Shape Categorization

The anterior intraparietal area (AIP) of rhesus monkeys is part of the dorsal visual stream and contains neurons whose visual response properties are commensurate with a role in three-dimensional (3D) shape perception. Neuronal responses in AIP signal the depth structure of disparity-defined 3D shapes, reflect the choices of monkeys while they categorize 3D shapes, and mirror the behavioral variability across different stimulus conditions during 3D-shape categorization. However, direct evidence for a role of AIP in 3D-shape perception has been lacking. We trained rhesus monkeys to categorize disparity-defined 3D shapes and examined AIP''s contribution to 3D-shape categorization by microstimulating in clusters of 3D-shape selective AIP neurons during task performance. We find that microstimulation effects on choices (monkey M1) and reaction times (monkey M1 and M2) depend on the 3D-shape preference of the stimulated site. Moreover, electrical stimulation of the same cells, during either the 3D-shape-categorization task or a saccade task, could affect behavior differently. Interestingly, in one monkey we observed a strong correlation between the strength of choice-related AIP activity (choice probabilities) and the influence of microstimulation on 3D-shape-categorization behavior (choices and reaction time). These findings propose AIP as part of the network responsible for 3D-shape perception. The results also show that the anterior intraparietal cortex contains cells with different tuning properties, i.e. 3D-shape- or saccade-related, that can be dynamically read out depending on the requirements of the task at hand.     

Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition.     

A complete categorization of multiscale models of infectious disease systems

Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.     

Categorization of Hospital Emergency Services—Developing Valid Criteria

In a pilot project sponsored by the California State Emergency Medical Services Authority, validated, verifiable criteria were developed for the vertical categorization of hospital emergency services in 11 different groupings of medical and surgical emergencies. We describe the development of an assessment process and categorization criteria to identify the most appropriate receiving facility for interfacility transfer and, in selected instances, field triage of patients with different levels of severity of illness or injury. We propose that this facility assessment project be used in the critical care planning process for the eventual vertical categorization of hospital emergency services in California and as a template for similar projects in other states.     

Population heterogeneity and color stimulus heterogeneity in agent-based color categorization

Investigating the interactions between universal and culturally specific influences on color categorization across individuals and cultures has proven to be a challenge for human color categorization and naming research. The present article simulates the evolution of color lexicons to evaluate the role of two realistic constraints found in the human phenomenon: (i) heterogeneous observer populations and (ii) heterogeneous color stimuli. Such constraints, idealized and implemented as agent categorization and communication games, produce interesting and unexpected consequences for stable categorization solutions evolved and shared by agent populations. We find that the presence of a small fraction of color deficient agents in a population, or the presence of a “region of increased salience” in the color stimulus space, break rotational symmetry in population categorization solutions, and confine color category boundaries to a subset of available locations. Further, these heterogeneities, each in a different, predictable, way, might lead to a change of category number and size. In addition, the concurrent presence of both types of heterogeneity gives rise to novel constrained solutions which optimize the success rate of categorization and communication games. Implications of these agent-based results for psychological theories of color categorization and the evolution of color naming systems in human societies are discussed.     

Ultra Rapid Object Categorization: Effects of Level,Animacy and Context

It is widely agreed that in object categorization bottom-up and top-down influences interact. How top-down processes affect categorization has been primarily investigated in isolation, with only one higher level process at a time being manipulated. Here, we investigate the combination of different top-down influences (by varying the level of category, the animacy and the background of the object) and their effect on rapid object categorization. Subjects participated in a two-alternative forced choice rapid categorization task, while we measured accuracy and reaction times. Subjects had to categorize objects on the superordinate, basic or subordinate level. Objects belonged to the category animal or vehicle and each object was presented on a gray, congruent (upright) or incongruent (inverted) background. The results show that each top-down manipulation impacts object categorization and that they interact strongly. The best categorization was achieved on the superordinate level, providing no advantage for basic level in rapid categorization. Categorization between vehicles was faster than between animals on the basic level and vice versa on the subordinate level. Objects in homogenous gray background (context) yielded better overall performance than objects embedded in complex scenes, an effect most prominent on the subordinate level. An inverted background had no negative effect on object categorization compared to upright scenes. These results show how different top-down manipulations, such as category level, category type and background information, are related. We discuss the implications of top-down interactions on the interpretation of categorization results.     

Automatic assignment of biomedical categories: toward a generic approach

MOTIVATION: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. METHODS: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. RESULTS AND CONCLUSION: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.     

A neural representation of categorization uncertainty in the human brain

The ability to classify visual objects into discrete categories ("friend" versus "foe"; "edible" versus "poisonous") is essential for survival and is a fundamental cognitive function. The cortical substrates that mediate this function, however, have not been identified in humans. To identify brain regions involved in stimulus categorization, we developed a task in which subjects classified stimuli according to a variable categorical boundary. Psychophysical functions were used to define a decision variable, categorization uncertainty, which was systematically manipulated. Using event-related functional MRI, we discovered that activity in a fronto-striatal-thalamic network, consisting of the medial frontal gyrus, anterior insula, ventral striatum, and dorsomedial thalamus, was modulated by categorization uncertainty. We found this network to be distinct from the frontoparietal attention network, consisting of the frontal and parietal eye fields, where activity was not correlated with categorization uncertainty.     

Effects of long-term visual experience on responses of distinct classes of single units in inferior temporal cortex

As a precursor to the selection of a stimulus for gaze and attention, a midbrain network categorizes stimuli into "strongest" and "others." The categorization tracks flexibly, in real time, the absolute strength of the strongest stimulus. In this study, we take a first-principles approach to computations that are essential for such categorization. We demonstrate that classical feedforward lateral inhibition cannot produce flexible categorization. However, circuits in which the strength of lateral inhibition varies with the relative strength of competing stimuli categorize successfully. One particular implementation--reciprocal inhibition of feedforward lateral inhibition--is structurally the simplest, and it outperforms others in flexibly categorizing rapidly and reliably. Strong predictions of this anatomically supported circuit model are validated by neural responses measured in the owl midbrain. The results demonstrate the extraordinary power of a remarkably simple, neurally grounded circuit motif in producing flexible categorization, a computation fundamental to attention, perception, and decision making.     

Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization

Observers can rapidly perform a variety of visual tasks such as categorizing a scene as open, as outdoor, or as a beach. Although we know that different tasks are typically associated with systematic differences in behavioral responses, to date, little is known about the underlying mechanisms. Here, we implemented a single integrated paradigm that links perceptual processes with categorization processes. Using a large image database of natural scenes, we trained machine-learning classifiers to derive quantitative measures of task-specific perceptual discriminability based on the distance between individual images and different categorization boundaries. We showed that the resulting discriminability measure accurately predicts variations in behavioral responses across categorization tasks and stimulus sets. We further used the model to design an experiment, which challenged previous interpretations of the so-called “superordinate advantage.” Overall, our study suggests that observed differences in behavioral responses across rapid categorization tasks reflect natural variations in perceptual discriminability.     

Categorization of humans in biomedical research: genes,race and disease

A debate has arisen regarding the validity of racial/ethnic categories for biomedical and genetic research. Some claim 'no biological basis for race' while others advocate a 'race-neutral' approach, using genetic clustering rather than self-identified ethnicity for human genetic categorization. We provide an epidemiologic perspective on the issue of human categorization in biomedical and genetic research that strongly supports the continued use of self-identified race and ethnicity.     

A data-driven approach for constructing mutation categories for mutational signature analysis

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.     

Task and Spatial Frequency Modulations of Object Processing: An EEG Study

Visual object processing may follow a coarse-to-fine sequence imposed by fast processing of low spatial frequencies (LSF) and slow processing of high spatial frequencies (HSF). Objects can be categorized at varying levels of specificity: the superordinate (e.g. animal), the basic (e.g. dog), or the subordinate (e.g. Border Collie). We tested whether superordinate and more specific categorization depend on different spatial frequency ranges, and whether any such dependencies might be revealed by or influence signals recorded using EEG. We used event-related potentials (ERPs) and time-frequency (TF) analysis to examine the time course of object processing while participants performed either a grammatical gender-classification task (which generally forces basic-level categorization) or a living/non-living judgement (superordinate categorization) on everyday, real-life objects. Objects were filtered to contain only HSF or LSF. We found a greater positivity and greater negativity for HSF than for LSF pictures in the P1 and N1 respectively, but no effects of task on either component. A later, fronto-central negativity (N350) was more negative in the gender-classification task than the superordinate categorization task, which may indicate that this component relates to semantic or syntactic processing. We found no significant effects of task or spatial frequency on evoked or total gamma band responses. Our results demonstrate early differences in processing of HSF and LSF content that were not modulated by categorization task, with later responses reflecting such higher-level cognitive factors.     

“Active” and “passive” ecological restoration strategies in meta‐analysis

One of the means of creating a more robust methodology for ecological restoration involves reducing the gap between ecological theory and restoration practices. A common strategy to do so is using meta‐analysis to understand key drivers of restoration outcomes. “Active” and “passive” is a dichotomy often used to separate restoration strategies in such meta‐analyses. We investigate previously raised concerns about selection bias and subjective categorization of strategies. We promote a paired experimental design in future empirical research and propose the use of three categories of restoration strategy in lieu of “passive” and “active” to alleviate inconsistency in definitions and categorization.     

研究: 异族面孔分类中情绪的调节作用

人们对异族面孔的分类绩效比本族面孔更好,这称作异族分类优势.但面孔异族分类优势是否也和面孔异族效应一样受到情绪的调节作用,尚未可知.本文通过两个实验考察情绪对于面孔种族分类任务的影响.实验一要求40名大学生被试对不同表情面孔(中性,负性,正性)完成种族判断任务,结果发现,正性和负性情绪使年轻人对异族面孔分类变得更慢,削弱了面孔异族分类优势,而且正性情绪对异族分类效应影响更大.实验二以不同情绪加工特点的老年人为被试,发现老年人对负性异族面孔的分类绩效明显更差,负性情绪对异族分类优势的影响更大.这些结果为异族分类优势理论中,异族面孔分类和个体身份加工过程相互制约的潜在假设提供了进一步的支持.     

Perception of Everyday Sounds: A Developmental Study of a Free Sorting Task

ObjectivesThe analysis of categorization of everyday sounds is a crucial aspect of the perception of our surrounding world. However, it constitutes a poorly explored domain in developmental studies. The aim of our study was to understand the nature and the logic of the construction of auditory cognitive categories for natural sounds during development. We have developed an original approach based on a free sorting task (FST). Indeed, categorization is fundamental for structuring the world and cognitive skills related to, without having any need of the use of language. Our project explored the ability of children to structure their acoustic world, and to investigate how such structuration matures during normal development. We hypothesized that age affects the listening strategy and the category decision, as well as the number and the content of individual categories.DesignEighty-two French children (6–9 years), 20 teenagers (12–13 years), and 24 young adults participated in the study. Perception and categorization of everyday sounds was assessed based on a FST composed of 18 different sounds belonging to three a priori categories: non-linguistic human vocalizations, environmental sounds, and musical instruments.ResultsChildren listened to the sounds more times than older participants, built significantly more classes than adults, and used a different strategy of classification. We can thus conclude that there is an age effect on how the participants accomplished the task. Analysis of the auditory categorization performed by 6-year-old children showed that this age constitutes a pivotal stage, in agreement with the progressive change from a non-logical reasoning based mainly on perceptive representations to the logical reasoning used by older children. In conclusion, our results suggest that the processing of auditory object categorization develops through different stages, while the intrinsic basis of the classification of sounds is already present in childhood.     

Linking Perception,Cognition, and Action: Psychophysical Observations and Neural Network Modelling

It has been argued that perception, decision making, and movement planning are in reality tightly interwoven brain processes. However, how they are implemented in neural circuits is still a matter of debate. We tested human subjects in a temporal categorization task in which intervals had to be categorized as short or long. Subjects communicated their decision by moving a cursor into one of two possible targets, which appeared separated by different angles from trial to trial. Even though there was a 1 second-long delay between interval presentation and decision communication, categorization difficulty affected subjects’ performance, reaction (RT) and movement time (MT). In addition, reaction and movement times were also influenced by the distance between the targets. This implies that not only perceptual, but also movement-related considerations were incorporated into the decision process. Therefore, we searched for a model that could use categorization difficulty and target separation to describe subjects’ performance, RT, and MT. We developed a network consisting of two mutually inhibiting neural populations, each tuned to one of the possible categories and composed of an accumulation and a memory node. This network sequentially acquired interval information, maintained it in working memory and was then attracted to one of two possible states, corresponding to a categorical decision. It faithfully replicated subjects’ RT and MT as a function of categorization difficulty and target distance; it also replicated performance as a function of categorization difficulty. Furthermore, this model was used to make new predictions about the effect of untested durations, target distances and delay durations. To our knowledge, this is the first biologically plausible model that has been proposed to account for decision making and communication by integrating both sensory and motor planning information.     

Inferotemporal cortex subserves three-dimensional structure categorization

We perceive real-world objects as three-dimensional (3D), yet it is unknown which brain area underlies our ability to perceive objects in this way. The macaque inferotemporal (IT) cortex contains neurons that respond selectively to 3D structures defined by binocular disparity. To examine the causal role of IT in the categorization of 3D structures, we electrically stimulated clusters of IT neurons with a similar 3D-structure preference while monkeys performed a 3D-structure categorization task. Microstimulation of 3D-structure-selective IT clusters caused monkeys to choose the preferred structure of the 3D-structure-selective neurons considerably more often. Microstimulation in IT also accelerated the monkeys' choice for the preferred structure, while delaying choices corresponding to the nonpreferred structure of a given site. These findings reveal that 3D-structure-selective neurons in IT contribute to the categorization of 3D objects.     

Colour preferences and colour vision in poultry chicks

The dramatic colours of biological communication signals raise questions about how animals perceive suprathreshold colour differences, and there are long-standing questions about colour preferences and colour categorization by non-human species. This study investigates preferences of foraging poultry chicks (Gallus gallus) as they peck at coloured objects. Work on colour recognition often deals with responses to monochromatic lights and how animals divide the spectrum. We used complementary colours, where the intermediate is grey, and related the chicks' choices to three models of the factors that may affect the attractiveness. Two models assume that attractiveness is determined by a metric based on the colour discrimination threshold either (i) by chromatic contrast against the background or (ii) relative to an internal standard. An alternative third model is that categorization is important. We tested newly hatched and 9-day-old chicks with four pairs of (avian) complementary colours, which were orange, blue, red and green for humans. Chromatic contrast was more relevant to newly hatched chicks than to 9-day-old birds, but in neither case could contrast alone account for preferences; especially for orange over blue. For older chicks, there is evidence for categorization of complementary colours, with a boundary at grey.     

Characterizing the Impact of Category Uncertainty on Human Auditory Categorization Behavior

Categorization is an important cognitive process. However, the correct categorization of a stimulus is often challenging because categories can have overlapping boundaries. Whereas perceptual categorization has been extensively studied in vision, the analogous phenomenon in audition has yet to be systematically explored. Here, we test whether and how human subjects learn to use category distributions and prior probabilities, as well as whether subjects employ an optimal decision strategy when making auditory-category decisions. We asked subjects to classify the frequency of a tone burst into one of two overlapping, uniform categories according to the perceived tone frequency. We systematically varied the prior probability of presenting a tone burst with a frequency originating from one versus the other category. Most subjects learned these changes in prior probabilities early in testing and used this information to influence categorization. We also measured each subject''s frequency-discrimination thresholds (i.e., their sensory uncertainty levels). We tested each subject''s average behavior against variations of a Bayesian model that either led to optimal or sub-optimal decision behavior (i.e. probability matching). In both predicting and fitting each subject''s average behavior, we found that probability matching provided a better account of human decision behavior. The model fits confirmed that subjects were able to learn category prior probabilities and approximate forms of the category distributions. Finally, we systematically explored the potential ways that additional noise sources could influence categorization behavior. We found that an optimal decision strategy can produce probability-matching behavior if it utilized non-stationary category distributions and prior probabilities formed over a short stimulus history. Our work extends previous findings into the auditory domain and reformulates the issue of categorization in a manner that can help to interpret the results of previous research within a generative framework.