Search

Well-practiced or learned behaviors are extremely resilient. For example, it is extremely difficult for a trained typist to forget how to use a keyboard configuration that they are familiar with. While they can be trained on a new keyboard configuration, the original skill quickly comes back when the old keyboard configuration is used again. This resiliency of learned skills is both a blessing and a curse. It makes useful skills durable, but it also makes maladaptive behaviors difficult to extinguish. Crossley et al. (2013) proposed a computational model and behavioral paradigm aimed at unlearning skills using various feedback contingency manipulations during an extinction phase. They showed that partially-valid feedback during extinction removed evidence for fast reacquisition, which they interpreted as evidence for unlearning. In this article, we replicated the Crossley et al. paradigm using fMRI. Univariate analyses showed differences in BOLD signals between the different experiment phases in the frontoparietal attention network. The superior and inferior parietal lobules (SPL and IPL, respectively) showed the largest cluster differences both between experimental phases and between extinction conditions. In contrast, the prefrontal cortex only showed differences in cluster of activities between extinction conditions. Multivariate pattern analysis was also used with seeds in the SPL and IPL. The results showed that these brain areas were critical in detecting changes in experimental phases. Overall, the fMRI results found mixed evidence for the Crossley et al. model and suggest that while unlearning prevents fast reacquisition, the absence of fast reacquisition does not necessarily implies that unlearning occurred. [ABSTRACT FROM AUTHOR]

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

This study explores how 7-month-old infants categorize graphical images varying in basic perceptual features by using a fast periodic visual stimulation (FPVS) task. Most participants were Caucasian and their parents had a higher education, but the family's socioeconomic background was mixed. Experiment 1 (N = 23) tested brain responses to configurational versus scrambled image sequences and found an oddball effect for both conditions, with configurational information leading to stronger neural responses. Experiment 2 explored the role of category learning for later FPVS performance: Group A (control; n = 22) participated in the FPVS categorization task only. Group B (fam; n = 21) was first familiarized with standard exemplars. Group C (fam + contrast; n = 29) was presented an additional exemplar of the contrasting category before the FPVS task. Electroencephalogram analyses revealed a decrease in Nc amplitude (measuring attention) throughout the familiarization phase. Long looking at the out-of-category exemplar in Group C indicated infants' visual interest. Brain responses in the subsequently presented FPVS were strong but did not differ between groups. This highlights the ability of the infant brain to organize perceptual input very rapidly without requiring familiarization but also points to the need for further studies exploring brain processes associated with infants' category learning and categorization at the brain level. Public Significance Statement: Within milliseconds, the brain of 7-month-old infants categorized artificial objects based on low-level perceptual features, but responses were stronger when additional configurational information was provided. A familiarization with the standard category led to neural adaptation and to visual interest in a subsequently presented out-of-category exemplar, but this experience did not affect categorization in a FPVS task, thus pointing to the need of more studies exploring the mechanisms underlying infant categorization. [ABSTRACT FROM AUTHOR]

Mounting evidence suggests that human category learning isachieved by multiple qualitatively distinct biological andpsychological systems. In an information-integration (II)categorization task, optimal performance requires switchingaway from rule and adopting a procedural response strategy.However, many participants perseverate with rules. This articleattempts at understanding the difference between optimal andsuboptimal participants in II categorization. To this end, wecollected data in the Iowa Gambling Task (IGT) and an IIcategorization task. Performance in the IGT was used toestimate each participant’s sensitivity to reward, punishment,and propensity to explore. The results show that optimalparticipants in the II task explored more in the IGT thansuboptimal participants. However, optimal participants in theII task did not show higher sensitivity to punishment or lowersensitivity to reward. We conclude by discussing theimplications of these findings on system-switching andtheoretical work on multiple-systems model of perceptualcategory learning.

Serial dependence refers to the assimilative pull on a judgement response to a current stimulus from that given to the preceding stimulus and has been demonstrated in low- and higher-level perceptual judgements. We tested whether serial dependence in attractiveness judgements is limited by perceptual categorization by measuring serial dependence in within-category attractiveness judgements of faces (male and female; Experiment 1), scenes (indoor and outdoor; Experiment 2), and cross-category attractiveness judgements (faces and scenes; Experiment 3). Serial dependence was evident for within-category judgements, which weakened with the increasing number of intervening trials. Interestingly, this effect on attractiveness ratings was stronger for consecutive trials of same-sex than different-sex faces but was equivalent for consecutive trials of same- or different-scene types. Furthermore, serial dependency was affected by perceptual categorization as the effect weakened when a change in category occurred across stimuli presented in consecutive trials. A serial contrast effect was also evident whereby a stimulus was rated as less attractive when preceded by an attractive stimulus from a different category. Collectively, these findings suggest that the temporal integration of aesthetic appraisal depends on the category context, with each category requiring distinct evaluation processes, and provide insight into the perceptual nature of attractiveness judgements. [ABSTRACT FROM AUTHOR]

Perception of sounds occurs in the context of surrounding sounds. When spectral properties differ between earlier (context) and later (target) sounds, categorization of later sounds becomes biased through spectral contrast effects (SCEs). Past research has shown SCEs to bias categorization of speech and music alike. Recent studies have extended SCEs to naturalistic listening conditions when the inherent spectral composition of (unfiltered) sentences biased speech categorization. Here, we tested whether natural (unfiltered) music would similarly bias categorization of French horn and tenor saxophone targets. Preceding contexts were either solo performances of the French horn or tenor saxophone (unfiltered; 1 second duration in Experiment 1, or 3 seconds duration in Experiment 2) or a string quintet processed to emphasize frequencies in the horn or saxophone (filtered; 1 second duration). Both approaches produced SCEs, producing more "saxophone" responses following horn / horn-like contexts and vice versa. One-second filtered contexts produced SCEs as in previous studies, but 1-second unfiltered contexts did not. Three-second unfiltered contexts biased perception, but to a lesser degree than filtered contexts did. These results extend SCEs in musical instrument categorization to everyday listening conditions. [ABSTRACT FROM AUTHOR]

This article comprehends the design of a Brain-Based Robot (BBR) using hybrid techniques that incorporate both Brain-Based Device (BBD) and computational algorithms. BBDs are biologically inspired machines which have its behavior guided by a simulated nervous system. This nervous system follows detailed neuroanatomy of different brain areas. BBDs tend to have a nervous system with a large number of neurons and synapses. Thus, a huge computational power is required to simulate the nervous system of a BBD. Nevertheless, some of the tasks carried out by the simulated nervous system can be accomplished using computational algorithms which can help reducing the required computational power greatly. In this article, a BBR is built which combines some subsystems from BBD with computer vision algorithms. Computer vision algorithms are applied using OpenCV to extract some features from images, while neuronal-areas are connected together based on a detailed neuroanatomical structure to mimic the human learning process. Nengo python package is used for simulating neuronal areas in the system and monitoring activities of neuronal units. Moreover, the successful integration of the BBD's subsystems with computer vision leads to the perceptual categorization based on invariant object-recognition of various visual cues. To make a fair comparison with BBD, the nervous system of a BBD is built on the same computer used to build the hybrid brain for the proposed BBR. The proposed hybrid brain is then applied to a Nao humanoid robot in V-REP simulation environment to test it. The results obtained through this article prove that the proposed hybrid brain possesses the same intelligence of the BBD and requires much less computational power that it can run on an on-board computer of a robot, which makes it plausible for engineering applications. [ABSTRACT FROM AUTHOR]

After viewing a series of sequentially presented visual stimuli, subjects can readily generate mean representations of various visual features. Although these average representations seem to be formed effortlessly and obligatorily, it is unclear how such averages are actually computed. According to conventional prototype models, the computation entails an equally weighted average taken over all the stimuli. To test this hypothesis, we had subjects estimate the running averages of some feature in a series of sequentially presented stimuli. Part way through the series, we perturbed the distribution from which stimuli were drawn, which allowed us to test alternative models of the computations behind subjects' estimates. In both explanatory and predictive tests, a model in which the most recent items had disproportionate high weight outperformed a model in which all items carried equal weight. Such recency-weighted behavior was shown consistently in multiple experiments in which subjects estimated running averages of length of vertical lines. However, the degree to which recent items were prioritized varied with the type of stimulus, such that when estimating the running averages of a series of numerals, subjects showed less recency prioritization. We conclude that previous evaluations of prototype models have made unrealistic assumptions about the nature of a prototype, and that a reassessment of prototype models of visual memory and perceptual categorization may be in order. [ABSTRACT FROM AUTHOR]

Mounting evidence suggests that category learning is achieved using different psychological and biological systems. While existing multiple-system theories and models of categorization may disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly. However, little empirical data has been collected to test this assumption, and recent available data suggest that system-switching is difficult. The main goal of this article is to identify factors influencing the proportion of participants who successfully learn to switch between procedural and declarative systems on a trial-by-trial basis. Specifically, we tested the effects of preparation time and practice, two factors that have been useful in task-switching, in a system-switching experiment. The results suggest that practice and preparation time can be beneficial to system-switching (as calculated by a higher proportion of switchers and lower switch costs), especially when they are jointly present. However, this improved system-switching comes at the cost of a larger button-switch interference when changing the location of the response buttons. The article concludes with a discussion of the implications of these findings for empirical research on system-switching and theoretical work on multiple-systems of category learning.

The combination of frequency-tagging with electroencephalography (EEG) has recently proved fruitful for understanding the perception of beat and meter in musical rhythm, a common behavior shared by humans of all cultures. EEG frequency-tagging allows the objective measurement of input–output transforms to investigate beat perception, its modulation by exogenous and endogenous factors, development, and neural basis. Recent doubt has been raised about the validity of comparing frequency-domain representations of auditory rhythmic stimuli and corresponding EEG responses, assuming that it implies a one-to-one mapping between the envelope of the rhythmic input and the neural output, and that it neglects the sensitivity of frequency-domain representations to acoustic features making up the rhythms. Here we argue that these elements actually reinforce the strengths of the approach. The obvious fact that acoustic features influence the frequency spectrum of the sound envelope precisely justifies taking into consideration the sounds used to generate a beat percept for interpreting neural responses to auditory rhythms. Most importantly, the many-to-one relationship between rhythmic input and perceived beat actually validates an approach that objectively measures the input–output transforms underlying the perceptual categorization of rhythmic inputs. Hence, provided that a number of potential pitfalls and fallacies are avoided, EEG frequency-tagging to study input–output relationships appears valuable for understanding rhythm perception. [ABSTRACT FROM AUTHOR]

Recording direct neural activity when periodically inserting exemplars of a particular category in a rapid visual stream of other objects offers an objective and efficient way to quantify perceptual categorization and characterize its spatiotemporal dynamics. However, since periodicity entails predictability, perceptual categorization processes identified within this framework may be partly generated or modulated by temporal expectations. Here we present a stringent test of the hypothesis that temporal predictability generates or modulates category-selective neural processes as measured in a rapid periodic visual stimulation stream. In Experiment 1, we compare neurophysiological responses to periodic and nonperiodic (i.e., unpredictable) variable face stimuli in a fast (12 Hz) visual stream of nonface objects. In Experiment 2, we assess potential responses to rare (10%) omissions of periodic face events (i.e., violations of periodicity) in the same fast visual stream. Overall, our observations indicate that category(face)-selective processes elicited in a fast periodic stream of visual objects are immune to temporal predictability. These observations do not support a predictive coding framework interpretation of category-change detection in the human brain and have important implications for understanding automatic human perceptual categorization in a rapidly changing (i.e., dynamic) visual scene. [ABSTRACT FROM AUTHOR]

Distributed neural activity patterns are widely proposed to underlie object identification and categorization in the brain. In the olfactory domain, pattern-based representations of odor objects are encoded in piriform cortex. This region receives both afferent and associative inputs, though their relative contributions to odor perception are poorly understood. Here, we combined a placebo-controlled pharmacological fMRI paradigm with multivariate pattern analyses to test the role of associative connections in sustaining olfactory categorical representations. Administration of baclofen, a GABA(B) agonist known to attenuate piriform associative inputs, interfered with within-category pattern separation in piriform cortex, and the magnitude of this drug-induced change predicted perceptual alterations in fine-odor discrimination performance. Comparatively, baclofen reduced pattern separation between odor categories in orbitofrontal cortex, and impeded within-category generalization in hippocampus. Our findings suggest that odor categorization is a dynamic process concurrently engaging stimulus discrimination and generalization at different stages of olfactory information processing, and highlight the importance of associative networks in maintaining categorical boundaries.

• Model-based reinforcement learning (MB-RL) can be implemented in the prefrontal cortex. • Model-free reinforcement learning (MF-RL) can be implemented in the striatum. • The hyperdirect pathway of the basal ganglia can account for switching between MB-RL and MF-RL. • Switching ability depends on sensitivity to negative feedback and working memory updating. Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system's response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson's disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions. [ABSTRACT FROM AUTHOR]

A difference in fundamental frequency (ΔF0) and a difference in spatial location (ΔSL) are two cues known to provide masking releases when multiple speakers talk at once in a room. We examined situations in which reverberation should have no effect on the mechanisms underlying the releases from energetic masking produced by these two cues. Speech reception thresholds using both unpredictable target sentences and the coordinate response measure followed a similar pattern. Both ΔF0s and ΔSLs provided masking releases in the presence of nonspeech maskers (matched in excitation pattern and temporal envelope to the speech maskers) that, as intended, were robust to reverberation. Larger masking releases were obtained for speech maskers, but critically, they were affected by reverberation. These results suggest that reverberation either limits the amount of informational masking that is present to begin with or affects its release by ΔF0s or ΔSLs. [ABSTRACT FROM AUTHOR]

Although image retrieval for e-commerce field has a huge commercial potential, e-commerce oriented content-based image retrieval is still very raw. Modern online shopping systems have certain limitations. In particular, they use conventional tag-based retrieval and lack making use of visual content. The paper presents a methodology to retrieve images of shopping items based on fuzzy dominant colors. People regard color as an aesthetic issue, especially when it comes to choosing the colors of their clothing, apartment design and other objects around. No doubt, color inuences purchasing behavior - to a certain extent, it is a reection of human's likes and dislikes. The fuzzy color model that we are proposing represents the collection of fuzzy sets, providing the conceptual quantization of crisp HSI space having soft boundaries. The proposed method has two parts: assigning a fuzzy colorimetric profile to the image and processing the user query. We also use underlying mechanisms of attention from a theory of visual attention, like perceptual categorization. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level color visual features and high-level concepts are major issues that we plan to tackle in this research. [ABSTRACT FROM AUTHOR]

To attain native-like competence, second language (L2) learners must establish mappings between familiar speech sounds and new phoneme categories. For example, Spanish learners of English must learn that [d] and [D], which are allophones of the same phoneme in Spanish, can distinguish meaning in English (i.e., /deI/ "day" and /DeI/ "they"). Because adult listeners are less sensitive to allophonic than phonemic contrasts in their native language (L1), novel target language contrasts between L1 allophones may pose special difficulty for L2 learners. We investigate whether advanced Spanish late-learners of English overcome native language mappings to establish new phonological relations between familiar phones. We report behavioral and magnetoencepholographic (MEG) evidence from two experiments that measured the sensitivity and pre-attentive processing of three listener groups (L1 English, L1 Spanish, and advanced Spanish late-learners of English) to differences between three nonword stimulus pairs ([idi]-[iDi], [idi]-[iRi], and [iDi]-[iRi]) which differ in phones that play a different functional role in Spanish and English. Spanish and English listeners demonstrated greater sensitivity (larger d' scores) for nonword pairs distinguished by phonemic than by allophonic contrasts, mirroring previous findings. Spanish late-learners demonstrated sensitivity (large d' scores and MMN responses) to all three contrasts, suggesting that these L2 learners may have established a novel [d]-[D] contrast despite the phonological relatedness of these sounds in the L1. Our results suggest that phonological relatedness influences perceived similarity, as evidenced by the results of the native speaker groups, but may not cause persistent difficulty for advanced L2 learners. Instead, L2 learners are able to use cues that are present in their input to establish new mappings between familiar phones. [ABSTRACT FROM AUTHOR]

The ability to recognize the same image projected to different retinal locations is critical for visual object recognition in natural contexts. According to many theories, the translation invariance for objects extends only to trained retinal locations, so that a familiar object projected to a nontrained location should not be identified. In another approach, invariance is achieved 'online,' such that learning to identify an object in one location immediately affords generalization to other locations. We trained participants to name novel objects at one retinal location using eyetracking technology and then tested their ability to name the same images presented at novel retinal locations. Across three experiments, we found robust generalization. These findings provide a strong constraint for theories of vision. [ABSTRACT FROM AUTHOR]

The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques (such as multivariate pattern analysis) in studying categorical sound representations. With their increased sensitivity to distributed activation changes - even in absence of changes in overall signal level - these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

The ability to categorize stimuli - predator or prey, friend or foe - is an essential feature of the decision-making process. Underlying that ability is the development of an internally generated category boundary to generate decision outcomes. While classic temporal difference reinforcement models assume midbrain dopaminergic neurons underlie the prediction error required to learn boundary location, these neurons also demonstrate a robust response to nonreward incentive stimuli. More recent models suggest that this may reflect a motivational aspect to performing a task which should be accounted for when modeling dopaminergic neuronal behavior. To clarify the role of substantia nigra dopamine neurons in uncertain perceptual decision making, we investigated their behavior using single neuron extracellular recordings in patients with Parkinson's disease undergoing deep brain stimulation. Subjects underwent a simple auditory categorical decision-making task in which they had to classify a tone as either low- or high-pitched relative to an explicit threshold tone and received feedback but no reward. We demonstrate that the activity of human SN dopaminergic neurons is predictive of perceptual categorical decision outcome and is modulated by uncertainty. Neuronal activity was highest during difficult (uncertain) decisions that resulted in correct responses and lowest during easy decisions that resulted in incorrect responses. This pattern of results is more consistent with a 'motivational' role with regards to perceptual categorization and suggests that dopamine neurons are most active when critical information - as represented by uncertainty - is available for learning decision boundaries. [ABSTRACT FROM AUTHOR]

This article explores the visual information used to categorize stimuli drawn from a common stimulus space into verbal and nonverbal categories using 2 experiments. Experiment 1 explores the effect of target duration on verbal and nonverbal categorization using backward masking to interrupt visual processing. With categories equated for difficulty for long and short target durations, intermediate target duration shows an advantage for verbal categorization over nonverbal categorization. Experiment 2 tests whether the results of Experiment 1 can be explained by shorter target duration resulting in a smaller signal-to-noise ratio of the categorization stimulus. To test for this possibility, Experiment 2 used integration masking with the same stimuli, categories, and masks as Experiment 1 with a varying level of mask opacity. As predicted, low mask opacity yielded similar results to long target duration while high mask opacity yielded similar results to short target duration. Importantly, intermediate mask opacity produced an advantage for verbal categorization over nonverbal categorization, similar to intermediate target duration. These results suggest that verbal and nonverbal categorization are affected differently by manipulations affecting the signal-to-noise ratio of the stimulus, consistent with multiple-system theories of categorizations. The results further suggest that verbal categorization may be more digital (and more robust to low signal-to-noise ratio) while the information used in nonverbal categorization may be more analog (and less robust to lower signal-to-noise ratio). This article concludes with a discussion of how these new results affect the use of masking in perceptual categorization and multiple-system theories of perceptual category learning. [ABSTRACT FROM AUTHOR]

Previous studies of perceptual category learning in patients with schizophrenia generally demonstrate impaired perceptual category learning; however, traditional cognitive studies have often failed to address the relationship of different cortical regions to perceptually based category learning and judgments in healthy participants and patients with schizophrenia. In the present study, perceptual category learning was examined in 26 patients with schizophrenia and 25 healthy participants using a dot-pattern category learning task. In the training phase, distortions of a prototypical dot-pattern were presented. In the test phase, participants were shown the prototype, low and high distortions of the prototype, and random dot-patterns. Participants were required to indicate whether the presented dot pattern was a member of the category of dot patterns previously presented during the study phase. Patients with schizophrenia displayed an impaired ability to make judgments regarding marginal members of novel, perceptually-based categories relative to healthy participants. Category judgment also showed opposite patterns of strong, significant correlations with behavioral measures of prefrontal cortex function in patients relative to healthy participants. These results suggest that impaired judgments regarding novel, perceptually based category membership may be due to abnormal prefrontal cortex function in patients with schizophrenia.

The present study investigated whether gender information for human faces was represented by the predictive mechanism indexed by the visual mismatch negativity (vMMN) event – related brain potential (ERP). While participants performed a continuous size-change-detection task, random sequences of cropped faces were presented in the background, in an oddball setting: either various female faces were presented infrequently among various male faces, or vice versa. In Experiment 1 the inter-stimulus- interval (ISI) was 400 ms, while in Experiment 2 the ISI was 2250 ms. The ISI difference had only a small effect on the P1 component, however the subsequent negativity (N1/N170) was larger and more widely distributed at longer ISI, showing different aspects of stimulus processing.As deviant -minus- standard ERP difference, a parieto – occipital negativity (vMMN) emerged in the 200 – 500 ms latency range (~ 350 ms peak latency in both experiments). We argue that regularity of gender on the photographs is automatically registered, and the violation of the gender category is reflected by the vMMN. In conclusion the results can be interpreted as evidence for the automatic activity of a predictive brain mechanism, in case of an ecologically valid category.

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as “evidence” for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition. [ABSTRACT FROM AUTHOR]

Categorical perception (CP) of color manifests as faster or more accurate discrimination of two shades of color that straddle a category boundary (e.g., one blue and one green) than of two shades from within the same category (e.g., two different shades of green), even when the differences between the pairs of colors are equated according to some objective metric. The results of two experiments provide new evidence for a conflict-based account of this effect, in which CP is caused by competition between visual and verbal/categorical codes on within-category trials. According to this view, conflict arises because the verbal code indicates that the two colors are the same, whereas the visual code indicates that they are different. In Experiment 1, two shades from the same color category were discriminated significantly faster when the previous trial also comprised a pair of within-category colors than when the previous trial comprised a pair from two different color categories. Under the former circumstances, the CP effect disappeared. According to the conflict-based model, response conflict between visual and categorical codes during discrimination of within-category pairs produced an adjustment of cognitive control that reduced the weight given to the categorical code relative to the visual code on the subsequent trial. Consequently, responses on within-category trials were facilitated, and CP effects were reduced. The effectiveness of this conflict-based account was evaluated in comparison with an alternative view that CP reflects temporary warping of perceptual space at the boundaries between color categories. [ABSTRACT FROM AUTHOR]

The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations. [ABSTRACT FROM AUTHOR]

Expertise in face recognition is characterized by high proficiency in distinguishing between individual faces. However, faces also enjoy an advantage at the early stage of basic-level detection, as demonstrated by efficient visual search for faces among nonface objects. In the present study, we asked (1) whether the face advantage in detection is a unique signature of face expertise, or whether it generalizes to other objects of expertise, and (2) whether expertise in face detection is intrinsically linked to expertise in face individuation. We compared how groups with varying degrees of object and face expertise (typical adults, developmental prosopagnosics [DP], and car experts) search for objects within and outside their domains of expertise (faces, cars, airplanes, and butterflies) among a variable set of object distractors. Across all three groups, search efficiency (indexed by reaction time slopes) was higher for faces and airplanes than for cars and butterflies. Notably, the search slope for car targets was considerably shallower in the car experts than in nonexperts. Although the mean face slope was slightly steeper among the DPs than in the other two groups, most of the DPs' search slopes were well within the normative range. This pattern of results suggests that expertise in object detection is indeed associated with expertise at the subordinate level, that it is not specific to faces, and that the two types of expertise are distinct facilities. We discuss the potential role of experience in bridging between low-level discriminative features and high-level naturalistic categories. [ABSTRACT FROM AUTHOR]

In the literature of neuropsychological and neuroimaging techniques have been highlighted functional dissociations in brain activity during the processing of stimuli belonging to different semantic categories, such as animals, objects, tools, faces, places. The purpose of the present study was to gather information about the processing of stimuli from different domains, in order to observe the emergence of conceptual knowledge and linguistic categorization linked to a perceptual path. Were recorded Event Related Potentials (ERP), especially N200 and P300 waves; subjects in the sample have performed a task of perceptual recognition of 56 images of animals, objects, and colored backgrounds The animals, presented in different trials were grouped according to the phylogenetic scale in Pisces (F), Reptiles (R), mammals (M). Pictures of animals more "dinstant" from the phylogenetic point of view have activated the cortical processing differently than images of animals "neighbors", regardless of familiarity with the stimulus presented. The right hemisphere seems to be more sensitive to differences related to visual stimuli phylogenetically more "distant" and are processed more slowly. [ABSTRACT FROM AUTHOR]

The present study investigated whether gender information for human faces was represented by the predictive mechanism indexed by the visual mismatch negativity (vMMN) event-related brain potential (ERP). While participants performed a continuous size-change-detection task, random sequences of cropped faces were presented in the background, in an oddball setting: either various female faces were presented infrequently among various male faces, or vice versa. In Experiment 1 the inter-stimulus-interval (ISI) was 400ms, while in Experiment 2 the ISI was 2250ms. The ISI difference had only a small effect on the P1 component, however the subsequent negativity (N1/N170) was larger and more widely distributed at longer ISI, showing different aspects of stimulus processing. As deviant-minus-standard ERP difference, a parieto-occipital negativity (vMMN) emerged in the 200-500ms latency range (~350ms peak latency in both experiments). We argue that regularity of gender on the photographs is automatically registered, and the violation of the gender category is reflected by the vMMN. In conclusion the results can be interpreted as evidence for the automatic activity of a predictive brain mechanism, in case of an ecologically valid category. [ABSTRACT FROM AUTHOR]

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities. [ABSTRACT FROM AUTHOR]