Dual time scales for categorical decision making in auditory cortex

Category formation allows us to group perceptual objects into meaningful classes and is fundamental to cognition. Categories can be derived from similarity relationships of object features by using prototypes or multiple exemplars, or from abstract relationships of features and rules . A variety of brain areas have been implicated in categorization processes, but mechanistic insights on the single-cell and local-network level are still rare and limited to the matching of individual objects to categories . For directional categorization of tone steps, as in melody recognition , abstract relationships between sequential events (higher or lower in frequency) have to be formed. To explore the neuronal mechanisms of this categorical identification of step direction, we trained monkeys for more than two years on a contour-discrimination task with multiple tone sequences. In the auditory cortex of these highly trained monkeys, we identified two interrelated types of neuronal firing: Increased phasic responses to tones categorically represented the reward-predicting downward frequency steps and not upward steps; subsequently, slow modulations of tonic firing predicted the behavioral decisions of the monkeys, including errors. Our results on neuronal mechanisms of categorical stimulus identification and of decision making attribute a cognitive role to auditory cortex, in addition to its role in signal processing.     

From perceptual to language-mediated categorization

From at least two months onwards, infants can form perceptual categories. During the first year of life, object knowledge develops from the ability to represent individual object features to representing correlations between attributes and to integrate information from different sources. At the end of the first year, these representations are shaped by labels, opening the way to conceptual knowledge. Here, we review the development of object knowledge and object categorization over the first year of life. We then present an artificial neural network model that models the transition from early perceptual categorization to categories mediated by labels. The model informs a current debate on the role of labels in object categorization by suggesting that although labels do not act as object features they nevertheless affect perceived similarity of perceptually distinct objects sharing the same label. The model presents the first step of an integrated account from early perceptual categorization to language-based concept learning.     

Do Simultaneously Viewed Objects Influence Scene Recognition Individually or as Groups? Two Perceptual Studies

The ability to quickly categorize visual scenes is critical to daily life, allowing us to identify our whereabouts and to navigate from one place to another. Rapid scene categorization relies heavily on the kinds of objects scenes contain; for instance, studies have shown that recognition is less accurate for scenes to which incongruent objects have been added, an effect usually interpreted as evidence of objects'' general capacity to activate semantic networks for scene categories they are statistically associated with. Essentially all real-world scenes contain multiple objects, however, and it is unclear whether scene recognition draws on the scene associations of individual objects or of object groups. To test the hypothesis that scene recognition is steered, at least in part, by associations between object groups and scene categories, we asked observers to categorize briefly-viewed scenes appearing with object pairs that were semantically consistent or inconsistent with the scenes. In line with previous results, scenes were less accurately recognized when viewed with inconsistent versus consistent pairs. To understand whether this reflected individual or group-level object associations, we compared the impact of pairs composed of mutually related versus unrelated objects; i.e., pairs, which, as groups, had clear associations to particular scene categories versus those that did not. Although related and unrelated object pairs equally reduced scene recognition accuracy, unrelated pairs were consistently less capable of drawing erroneous scene judgments towards scene categories associated with their individual objects. This suggests that scene judgments were influenced by the scene associations of object groups, beyond the influence of individual objects. More generally, the fact that unrelated objects were as capable of degrading categorization accuracy as related objects, while less capable of generating specific alternative judgments, indicates that the process by which objects interfere with scene recognition is separate from the one through which they inform it.     

Categorisation of three-dimensional stimuli by humans and baboons: search for prototype effects

A symbolic matching-to-sample procedure was adopted to investigate whether humans (n=2) and baboons (n=2) discriminate more accurately the prototypes of polymorphous categories than less typical exemplars. Subjects were initially trained to discriminate between two categories of stimuli defined by the possession of any two out of three possible binary features. In transfer, prototypes, which contained all the three feature values of their categories, and novel two-out-of-three feature exemplars were presented for discrimination. Humans solved the task in a propositional way, and showed no evidence for a better performance with the prototypes than with other exemplars. By contrast, monkeys classified the prototypes more accurately than the other exemplars. The analysis of training performance showed however, that their discriminations did not involve prototypical representations of the categories, but rather depended upon feature–and exemplar-response associations. It is argued that monkeys' better performance with the prototypes rested on peak shift and/or novelty effects.     

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.     

A hierarchical probabilistic model for rapid object categorization in natural scenes

Humans can categorize objects in complex natural scenes within 100-150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ～6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization.To address this issue, we developed a hierarchical probabilistic model for rapid object categorization in natural scenes. In this model, a natural object category is represented by a coarse hierarchical probability distribution (PD), which includes PDs of object geometry and spatial configuration of object parts. Object parts are encoded by PDs of a set of natural object structures, each of which is a concatenation of local object features. Rapid categorization is performed as statistical inference. Since the model uses a very small number (～100) of structures for even complex object categories such as animals and cars, it requires little training and is robust in the presence of large variations within object categories and in their occurrences in natural scenes. Remarkably, we found that the model categorized animals in natural scenes and cars in street scenes with a near human-level performance. We also found that the model located animals and cars in natural scenes, thus overcoming a flaw in many other models which is to categorize objects in natural context by categorizing contextual features. These results suggest that coarse PDs of object categories based on natural object structures and statistical operations on these PDs may underlie the human ability to rapidly categorize scenes.     

Effects of parametric manipulation of inter-stimulus similarity on 3D object categorization

To explore the nature of the representation space of 3D objects, we studied human performance in forced-choice categorization of objects composed of four geon-like parts emanating from a common center. Two categories were defined by prototypical objects, distinguished by qualitative properties of their parts (bulging vs waist-like limbs). Subjects were trained to discriminate between the two prototypes (shown briefly, from a number of viewpoints, in stereo) in a 1-interval forced-choice task, until they reached a 90% correct-response performance level. After training, in the first experiment, 11 subjects were tested on shapes obtained by varying the prototypical parameters both orthogonally (ORTHO) and in parallel (PARA) to the line connecting the prototypes in the parameter space. For the eight subjects who performed above chance, the error rate increased with the ORTHO parameter-space displacement between the stimulus and the corresponding prototype; the effect of the PARA displacement was weaker. Thus, the parameter-space location of the stimuli mattered more than the qualitative contrasts, which were always present. To find out whether both prototypes or just the nearest one to the test shape influenced the decision, in the second experiment we varied the similarity between the categories. Specifically, in the test stage trials the distance between the two prototypes could assume one of three values (FAR, INTERMEDIATE, and NEAR). For the 13 subjects who performed above chance, the error rate (on physically identical stimuli) in the NEAR condition was higher than in the other two conditions. The results of the two experiments contradict the prediction of theories that postulate exclusive reliance on qualitative contrasts, and support the notion of a representation space in which distances to more than one reference point or prototype are encoded (Edelman, 1998).     

Weber's law and perceptual categories: Another teleological view

As an alternative to optimum-processor models in which sensors attempt to circumvent internal and external noise, a mechanism-independent argument is presented for Weber's law in vision and hearing. In vision, the argument is that categories of objects should be independent of the light intensity on these objects. In hearing, sound categorization should be independent of the distance from the sound source. An analogous desideratum for computer-based image segmentation is also presented.     

Numbering the nationalities: ethnic minorities in Norwegian population censuses 1845-1930

Between 1845 and 1930 the relatively small minority groups in Norway - the Samis and Kvens - were counted in Norwegian censuses on the basis of 'objective' criteria settled by the State's Central Bureau of Statistics. The censuses data supplied a foundation for the repressive policy towards minorities. But in identifying alien nationalities within the Norwegian borders, the census classifications also contributed in shaping the boundaries between the Norwegians and 'the others'. Especially the construction of mixed categories reveals substantial changes in this period. In the last part of the nineteenth century, their categorization was based mostly on language, and the distinctions that were made between Norwegians and minorities were largely cultural. The more pronounced race rhetoric of the interwar period re-shaped the concepts of nationalities and brought about a much greater emphasis on purely genealogical factors.     

A neural model for category learning

We present a general neural model for supervised learning of pattern categories which can resolve pattern classes separated by nonlinear, essentially arbitrary boundaries. The concept of a pattern class develops from storing in memory a limited number of class elements (prototypes). Associated with each prototype is a modifiable scalar weighting factor () which effectively defines the threshold for categorization of an input with the class of the given prototype. Learning involves (1) commitment of prototypes to memory and (2) adjustment of the various factors to eliminate classification errors. In tests, the model ably defined classification boundaries that largely separated complicated pattern regions. We discuss the role which divisive inhibition might play in a possible implementation of the model by a network of neurons.This work was supported in part by the Alfred P. Sloan Foundation and the Ittleson Foundation, Inc.     

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties¹. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties².Many innovative and useful methods currently exist for creating novel objects and object categories^3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.First, shape variations are generally imposed by the experimenter^5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.Second, the existing methods have difficulty capturing the shape complexity of natural objects^11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or ''tuned''). This allows one to formulate the underlying object recognition tasks in quantitative terms.Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called ''digital embryos'' by simulating the biological process of embryogenesis¹⁴. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection^9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics^15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects^9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be ''printed'' as haptic objects using a conventional 3-D prototyper.We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a ''proof of principle'' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.     

Coding of Visual Object Features and Feature Conjunctions in the Human Brain

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object''s features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process - while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200–400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.     

Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces

Journal of Computational Neuroscience - Feed-forward deep neural networks have better performance in object categorization tasks than other models of computer vision. To understand the relationship...     

Perceptual categories for spatial layout.

The central problems of vision are often divided into object identification and localization. Object identification, at least at fine levels of discrimination, may require the application of top-down knowledge to resolve ambiguous image information. Utilizing top-down knowledge, however, may require the initial rapid access of abstract object categories based on low-level image cues. Does object localization require a different set of operating principles than object identification or is category determination also part of the perception of depth and spatial layout? Three-dimensional graphics movies of objects and their cast shadows are used to argue that identifying perceptual categories is important for determining the relative depths of objects. Processes that can identify the causal class (e.g. the kind of material) that generates the image data can provide information to determine the spatial relationships between surfaces. Changes in the blurriness of an edge may be characteristically associated with shadows caused by relative motion between two surfaces. The early identification of abstract events such as moving object/shadow pairs may also be important for depth from shadows. Knowledge of how correlated motion in the image relates to an object and its shadow may provide a reliable cue to access such event categories.     

Generalization and categorization of spectral colors in goldfish I. Experiments with one training wavelength

Goldfish have a tetrachromatic color vision with a high discrimination ability for spectral colors as well as for object colors. We investigate the question whether goldfish organize the high number of discriminable colors in terms of color categories, i.e. in a few larger groups of colors independent of wavelength discrimination. Twenty-four goldfish were trained with food reward, each fish on one out of 13 wavelengths between 371 nm and 630 nm. In transfer tests two different wavelengths were presented, one shorter and one longer than the training wavelength, and the choice behavior was determined. Choice frequencies of ≥50% were assumed to indicate similarity to the training color. The wavelength ranges ≥50% were about 100 nm and twice as large as the just noticeable differences measured in wavelength discrimination tests (Fig. 7). The ranges were surprisingly about the same for all training wavelengths, provided the data were plotted on a wavelength scale weighted according to discrimination ability (Fig. 4). Thus, with the training method chosen goldfish showed a kind of categorization which, however, depends on training wavelength and discrimination ability. Generalization tests in which training wavelength and test wavelengths were shown separately for 2 min each gave the same results as wavelength discrimination tests (Figs. 5 and 6) and are, therefore, not indicative for color categories.     

Ethnicity in discourse: the interactional negotiation of ethnic boundaries in post-apartheid Namibia

To what extent can ethnic boundaries be transcended in interethnic interactions? We are tackling this question in reference to Namibia, a post-apartheid society marked by a legacy of ethnic and racial divisions. Relying on discourse as a source of data, we identify the strategies employed by Namibians in a range of interview data and semi-experimental interethnic interactions for either accentuating or attenuating interethnic boundaries. We identify these strategies at the levels of ethnic categorization, language choice/variation and the management of speaker turns, and place them in the perspective of the participants' perspectives on ethnic Others. Our findings suggest that ethnic categories are salient in our data, although they do not exclude identification with superordinate categories in specific contexts. Our findings also show that patterns of categorization are reflected in language choice and turn management in the interactional context.     

Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity

Within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. Here we first confirmed that humans could categorize “good” exemplars better than “bad” exemplars of six scene categories and then explored whether brain regions previously implicated in natural scene categorization showed a similar sensitivity to how well an image exemplifies a category. In a behavioral experiment participants were more accurate and faster at categorizing good than bad exemplars of natural scenes. In an fMRI experiment participants passively viewed blocks of good or bad exemplars from the same six categories. A multi-voxel pattern classifier trained to discriminate among category blocks showed higher decoding accuracy for good than bad exemplars in the PPA, RSC and V1. This difference in decoding accuracy cannot be explained by differences in overall BOLD signal, as average BOLD activity was either equivalent or higher for bad than good scenes in these areas. These results provide further evidence that V1, RSC and the PPA not only contain information relevant for natural scene categorization, but their activity patterns mirror the fundamentally graded nature of human categories. Analysis of the image statistics of our good and bad exemplars shows that variability in low-level features and image structure is higher among bad than good exemplars. A simulation of our neuroimaging experiment suggests that such a difference in variance could account for the observed differences in decoding accuracy. These results are consistent with both low-level models of scene categorization and models that build categories around a prototype.     

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.     

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.     

Effects of stimulus manipulations on visual categorization in pigeons

Four pigeons were previously trained [Lazareva, O.F., Freiburger, K.L., Wasserman, E.A., 2004. Pigeons concurrently categorize photographs at both basic and superordinate levels. Psychon. Bull. Rev. 11, 1111-1117] to classify color photographs into either their proper basic-level category (cars, chairs, flowers, or people) or a superordinate-level category (nominally natural or artificial). In Experiment 1, the same pigeons were shown either reflected or inverted versions of the training stimuli. Reflection had no effect on pigeons' classification behavior, whereas inversion impaired discrimination of all stimulus categories, except flowers, on the basic-level and superordinate-level tasks. Pixel matching analysis revealed that pattern matching played at most a minor role in the birds' categorization behavior. In Experiment 2, the pigeons were shown test stimuli that were either blurred or quartered and scrambled. Blurring impaired discrimination of cars, but had no effect on discrimination of people and flowers; scrambling impaired discrimination of people and flowers leaving discrimination of cars and chairs unaffected. These results suggest that categorization of flowers and people may be controlled primarily by the overall shape of the object rather than by local features, whereas categorization of cars and chairs may rely primarily on local features rather than the overall shape of the object.