Category-related Information Research Articles

Spatiotemporal neural dynamics of object recognition under uncertainty in humans.

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

Visual segmentation of complex naturalistic structures in an infant eye-tracking search task.

An infant's everyday visual environment is composed of a complex array of entities, some of which are well integrated into their surroundings. Although infants are already sensitive to some categories in their first year of life, it is not clear which visual information supports their detection of meaningful elements within naturalistic scenes. Here we investigated the impact of image characteristics on 8-month-olds' search performance using a gaze contingent eye-tracking search task. Infants had to detect a target patch on a background image. The stimuli consisted of images taken from three categories: vegetation, non-living natural elements (e.g., stones), and manmade artifacts, for which we also assessed target background differences in lower- and higher-level visual properties. Our results showed that larger target-background differences in the statistical properties scaling invariance and entropy, and also stimulus backgrounds including low pictorial depth, predicted better detection performance. Furthermore, category membership only affected search performance if supported by luminance contrast. Data from an adult comparison group also indicated that infants' search performance relied more on lower-order visual properties than adults. Taken together, these results suggest that infants use a combination of property- and category-related information to parse complex visual stimuli.

Localizing category-related information in speech with multi-scale analyses.

Measurements of the physical outputs of speech—vocal tract geometry and acoustic energy—are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.

A classification-driven neuron-grouped SAE for feature representation and its application to fault classification in chemical processes

Recently, deep learning has become a popular tool for fault detection and diagnosis in chemical processes to learn complex nonlinear features. However, the features extracted from most traditional deep networks are only good representation for the raw input data, which may contain irrelevant information with the faults and are not beneficial for the fault classification task. Thus, a novel classification-driven neuron-grouped stacked autoencoder (CG-SAE) is proposed for hierarchical fault-related feature representation in this paper. During the pre-training phase of CG-SAE, the hidden neurons in each CG-AE are divided into several groups, the number of which is equal to that of the data categories. Each group is mainly associated to one type of fault. Moreover, the supervised information is introduced without additional weights by comparing the error between the actual label and the average activation of the corresponding group. In this way, the extracted deep features can contain category-related information and be clustered to different categories. In terms of the classification accuracy and training efficiency, CG-SAE performs better than some advanced supervised methods as verified by the Tennessee Eastman (TE) benchmark and an industrial hydrocracking process.

Flexible categorization in the mouse olfactory bulb

The ability to group sensory stimuli into categories is crucial for efficient interaction with a rich and ever-changing environment. In olfaction, basic features of categorical representation of odors were observed as early as in the olfactory bulb (OB). Categorical representation was described in mitral cells (MCs) as sudden transitions in responses to odors that were morphed along a continuum. However, it remains unclear to what extent such response dynamics actually reflect perceptual categories and decisions therein. Here, we tested the role of learning on category formation in the mouse OB, using invivo two-photon calcium imaging and behavior. We imaged MC responses in naive mice and in awake behaving mice as they learned two tasks with different classification logic. In one task, a one-decision-boundary task, animals learned to classify odor mixtures based on the dominant compound in the mixtures. As expected, categorical representation of odors, which was evident already in naive animals, further increased following learning. In a second task, a multi-decision-boundary task, animals learned to classify odors independent of their chemical similarity. Here, odor discrimination was based on the meaning ascribed to them (either rewarding or not). Following the multi-decision-boundary task, odor representations by MCs reorganized according to the odor value in the new category. This functional reorganization was also reflected as a shift from predominantly excitatory odor responses to predominantly inhibitory odor responses. Our data show that odor representations by MCs are flexible, are shaped by task demands, and carry category-related information.

The architecture of prototype preferences: Typicality, fluency, and valence.

A classic phenomenon known as prototype preference effect (PPE) or beauty-in-averageness effect is that prototypical exemplars of a neutral category are preferred over atypical exemplars. This PPE has been explained in terms of deviance avoidance, hedonic fluency, or preference for certainty and familiarity. However, typicality also facilitates greater activation of category-related information. Thus, prototypes rather than atypical exemplars should be more associated with the valence of the category, either positive or negative. Hence, we hypothesize that the evaluation of a prototype depends on the valence of its category. Results from three experiments crossing a standard PPE paradigm with an evaluative conditioning procedure support our hypothesis. We show that for positive categories, greater typicality increases liking. Critically, for negative categories, greater typicality decreases liking. This pattern of results challenges dominant explanations of prototype evaluation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Visual mismatch negativity elicited by semantic violations in visual words

The remarkable rapidity and effortlessness of speech perception and word reading by skilled listeners or readers suggest implicit or automatic mechanisms underlying language processing. In speech perception, the implicit mechanisms are reflected by the auditory mismatch negativity (MMN) response, suggesting that phonemic, lexical, semantic, and syntactic information are automatically and rapidly processed in the absence of focused attention. In visual word reading, implicit orthographic and lexical processing are reflected by visual mismatch negativity (vMMN), the visual counterpart of auditory MMN. The semantic processing of spoken words is reflected by MMN. This study investigated whether semantic processing is also reflected by vMMN. For this purpose, visual Chinese words belonging to different semantic categories (color, taste, and action) were presented to participants in oddball paradigms. A set of words belonging to the same semantic category was frequently presented as standards; a word belonging to a different semantic category was presented sporadically as deviant. Participants were instructed to perform a visual cross-change detection task and ignore the words. Significant vMMN was elicited in Experiments 1 to 3, in which the deviant word carried a semantic radical that overtly indicated the word’s semantic category information. The vMMNs were most prominent around 260 ms after word onset, were parieto-occipital distributed, and were significantly left-hemisphere lateralized, suggesting rapid semantic processing of the visual words’ category-related information. No significant vMMN was elicited in Experiment 4, in which the deviant word did not carry any semantic radicals. Thus, the semantic radical, which has a high frequency of occurrence because it is carried by many words, may be critical for the elicitation of vMMN.

Long-latency event-related responses to vowels: N1-P2 decomposition by two-step principal component analysis

The N1-P2 complex of the auditory event-related potential (ERP) has been used to examine neural activity associated with speech sound perception. Since it is thought to reflect multiple generator processes, its functional significance is difficult to infer. In the present study, a temporospatial principal component analysis (PCA) was used to decompose the N1-P2 response into latent factors underlying covariance patterns in ERP data recorded during passive listening to pairs of successive vowels. In each trial, one of six sounds drawn from an /i/−/e/ vowel continuum was followed either by an identical sound, a different token of the same vowel category, or a token from the other category. Responses were examined as to how they were modulated by within- and across-category vowel differences and by adaptation (repetition suppression) effects. Five PCA factors were identified as corresponding to three well-known N1 subcomponents and two P2 subcomponents. Results added evidence that the N1 peak reflects both generators that are sensitive to spectral information and generators that are not. For later latency ranges, different patterns of sensitivity to vowel quality were found, including category-related effects. Particularly, a subcomponent identified as the Tb wave showed release from adaptation in response to an /i/ followed by an /e/ sound. A P2 subcomponent varied linearly with spectral shape along the vowel continuum, while the other was stronger the closer the vowel was to the category boundary, suggesting separate processing of continuous and category-related information. Thus, the PCA-based decomposition of the N1-P2 complex was functionally meaningful, revealing distinct underlying processes at work during speech sound perception.

Efficient Information Sampling in Multi-Category RFID Systems

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information sampling</i> . We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.

Spatiotemporal analysis of category and target-related information processing in the brain during object detection

To recognize a target object, the brain implements strategies which involve a combination of externally sensory-driven and internally task-driven mechanisms. While several studies have suggested a role for frontal brain areas in enhancing task-related representations in visual cortices, especially in the lateral-occipital cortex, they have remained silent about the type of information transferred to visual areas. However, the recently developed methods of representational connectivity analysis, allowed us to track the movement of different types of information in the brain. Accordingly, we designed an EEG object detection experiment and inspected the spatiotemporal dynamics of category- and target-related information across the brain. Results showed that the prefrontal area initiated the processing of target-related information. This information was then transferred to posterior brain areas during stimulus presentation probably to facilitate object detection and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral performance more accurately, suggesting the dominant representation of internal compared to external information in brain signals. These results provided new evidence about the role of prefrontal cortices in the processing of task-related information in the brain during object detection.

Distinct neural correlates of social categories and personality traits

Does the processing of social category-related versus trait-related information generate a different pattern of brain activation? In this fMRI study, we compared the processing of behaviors performed by a member of a social category versus an individual, both possessing similar personality traits. Based on previous behavioral studies we predicted that the processing of social category-related information would recruit more activation in brain areas related to mentalizing than individual trait-related information. Participants read sentences describing behaviors performed by a member of a social category (of which the stereotype involves a given trait) or by an individual possessing the same trait. These behavioral sentences varied on both valence (positive versus negative) and consistency (consistent versus inconsistent) with regard to the social category or trait. The results revealed that social category-related behavioral information showed more activation in mentalizing areas (medial prefrontal cortex, anterior temporal lobe, bilateral temporo-parietal junction, posterior cingulate cortex) than trait-related information. This increased activation is interpreted in terms of the impact of autobiographical memories, greater variance among members of social categories than individual traits, a higher construal level (i.e., abstractness), and larger perceived group size. Additionally, inconsistent as opposed to consistent information showed more activation in the right temporo-parietal junction and left lingual gyrus.

Category representation and generalization in the prefrontal cortex

Categorization is a function of the brain that serves to group together items and events in our environments. Here we review the following important issues related to category representation and generalization: namely, where categories are presented in the brain, and how the brain utilizes categorical membership to generate new information. Accumulated experimental evidence shows that the prefrontal cortex (PFC) plays a critical role in category formation and generalization. We propose that prefrontal neurons abstract the commonality beyond individual stimuli, and categorize these based on their common meaning by ignoring their physical properties and learning to represent the boundaries between behaviorally significant categories. We also claim that a subgroup of prefrontal neurons simultaneously receives the category-related information and specific property information (e.g. reward) associated with an exemplar, to form a category-based representation of that property, and propagates it among stimuli of the same category, possibly reflecting a neural basis for category generalization in the PFC. These results suggest that the PFC is involved in representing abstract rules, and generating new information on the basis of previously acquired knowledge.

Disrupting reconsolidation: Pharmacological and behavioral manipulations

We previously demonstrated that disrupting reconsolidation by pharmacological manipulations "deleted" the emotional expression of a fear memory in humans. If we are to target reconsolidation in patients with anxiety disorders, the disruption of reconsolidation should produce content-limited modifications. At the same time, the fear-erasing effects should not be restricted to the feared cue itself considering that fear generalization is a main characteristic of anxiety disorders. In Experiment I and Experiment I(b), we addressed these issues using a within-subject differential startle fear conditioning paradigm and a test of fear generalization. In Experiment II, we tested whether a behavioral approach targeting the reconsolidation through extinction learning was also effective in weakening the original fear memory. A behavioral procedure is evidently preferred over drug manipulations provided that similar effects can be obtained. Here, the extinction procedure subsequent to retrieval did not "erase" the emotional expression of the fear memory as the retrieval techniques (i.e., reminder shocks and reacquisition) unveiled a return of the startle fear response to the fear-relevant stimuli. In contrast, β-adrenergic receptor blockade during reconsolidation selectively deleted the fear-arousing aspects of the memory (i.e., startle fear response) along with its category-related information. The pharmacological manipulation rendered the core memory trace too weak to observe fear generalization after successful reacquisition. Hence, relearning following the disruption of reconsolidation seems to be qualitatively different from initial learning. Our findings demonstrate that disrupting reconsolidation by pharmacological manipulations, although selective, undermines the generalization of fear, a key feature of anxiety disorders.

Window displays and consumer shopping decisions

Window displays are an ubiquitous and prominent but under-researched element of retail strategy. This paper explores how the store and product category information communicated by a store's windows are related to consumers’ shopping decisions, such as store entry and product purchase, and how these relationships vary for consumer segments that differ in terms of their knowledge of the retailer's product(s). Results of a study conducted in the context of clothing retailers demonstrate that the store entry decision is related both directly as well as indirectly (through acquisition of inferred, store-related information) to the acquisition of observed, store-related information from window displays. However, it is product category-related information (e.g. fashion and product-self fit) rather than store-related information (e.g. merchandise and store image) that is more strongly associated with the product purchase decision. Moreover, consumers with medium levels of clothing knowledge are more influenced by windows in their shopping decisions than those with low or high levels.