Discrimination training alters object representations in human extrastriate cortex

A 7T fMRI investigation of hand and tool areas in the lateral and ventral occipitotemporal cortex

Previous studies demonstrated the existence of hand and tool areas in lateral and ventral occipitotemporal cortex (OTC), as well as an overlap between them. We reinvestigated this organization using 7T fMRI, benefiting from a higher signal-to-noise ratio than 3T. This enabled us to include a wider array of categories to achieve a more holistic perspective, and to omit certain spatial preprocessing steps. Despite these improvements, univariate analysis confirmed the existence of hand-tool overlap across OTC, which is striking given the omission of the spatial preprocessing steps that can influence overlap. There was significantly more overlap between hands and tools, compared to other overlap types in the left hemisphere of OTC. The overlap was also larger in the left lateral OTC as compared to the right lateral OTC. We found in all hand areas a differentiation between tools and other types of objects, although they still responded more to bodies than to tools. Regarding the tool areas, we observed a differentiation between hands and other categories such as faces and non-tool objects. Left hemisphere tool areas also differentiated between hands and bodies. When excluding the overlapping voxels from the hand and tool areas, they still showed a significant response to tools or hands (compared to objects or faces) respectively. Multi-voxel pattern analysis indicated that neural representations in the hand areas showed greater similarity between hands and tools than between hands and other objects. In the tool areas, the neural representations between tools and hands and between tools and other type of objects were all equally similar. To summarize, capitalizing on the benefits of 7T fMRI, we further substantiate the evidence in favor of hand-tool overlap in several regions of occipitotemporal cortex.

Mechanisms underlying category learning in the human ventral occipito-temporal cortex

The human ventral occipito-temporal cortex (VOTC) has evolved into specialized regions that process specific categories, such as words, tools, and animals. The formation of these areas is driven by bottom-up visual and top-down nonvisual experiences. However, the specific mechanisms through which top-down nonvisual experiences modulate category-specific regions in the VOTC are still unknown. To address this question, we conducted a study in which participants were trained for approximately 13 h to associate three sets of novel meaningless figures with different top-down nonvisual features: the wordlike category with word features, the non-wordlike category with nonword features, and the visual familiarity condition with no nonvisual features. Pre- and post-training functional MRI (fMRI) experiments were used to measure brain activity during stimulus presentation. Our results revealed that training induced a categorical preference for the two training categories within the VOTC. Moreover, the locations of two training category-specific regions exhibited a notable overlap. Remarkably, within the overlapping category-specific region, training resulted in a dissociation in activation intensity and pattern between the two training categories. These findings provide important insights into how different nonvisual categorical information is encoded in the human VOTC.

Dynamic categorization rules alter representations in human visual cortex

Everyday perceptual tasks require sensory stimuli to be dynamically encoded and analyzed according to changing behavioral goals. For example, when searching for an apple at the supermarket, one might first find the Granny Smith apples by separating all visible apples into the categories "green" and "non-green". However, suddenly remembering that your family actually likes Fuji apples would necessitate reconfiguring the boundary to separate "red" from "red-yellow" objects. This flexible processing enables identical sensory stimuli to elicit varied behaviors based on the current task context. While this phenomenon is ubiquitous in nature, little is known about the neural mechanisms that underlie such flexible computation. Traditionally, sensory regions have been viewed as mainly devoted to processing inputs, with limited involvement in adapting to varying task contexts. However, from the standpoint of efficient computation, it is plausible that sensory regions integrate inputs with current task goals, facilitating more effective information relay to higher-level cortical areas. Here we test this possibility by asking human participants to visually categorize novel shape stimuli based on different linear and non-linear boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we found that shape representations in visual cortex became more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these context-driven modulations were associated with improved categorization performance. Together, these findings demonstrate that codes in visual cortex are adaptively modulated to optimize object separability based on currently relevant decision boundaries.

Foveal feedback in perceptual processing: Contamination of neural representations and task difficulty effects

Visual object recognition was traditionally believed to rely on a hierarchical feedforward process. However, recent evidence challenges this notion by demonstrating the crucial role of foveal retinotopic cortex and feedback signals from higher-level visual areas in processing peripheral visual information. The nature of the information conveyed through foveal feedback remains a topic of debate. To address this, we conducted a study employing a foveal mask paradigm with varying stimulus-mask onset asynchronies in a peripheral same/different task, where peripheral objects exhibited different degrees of similarity. Our hypothesis posited that simultaneous arrival of feedback and mask information in the foveal cortex would lead to neural contamination, biasing perception. Notably, when the two peripheral objects were identical, we observed a significant increase in the number of "different" responses, peaking at approximately 100 ms. Similar effect was found when the objects were dissimilar, but with an overall later timing (around 150 ms). No significant difference was found when comparing easy (dissimilar objects) and difficult trials (similar objects). The findings challenge the hypothesis that foveation planning alone accounts for the observed effects. Instead, these and previous observations support the notion that the foveal cortex serves as a visual sketchpad for maintaining and manipulating task-relevant information.

Better Together: Integrating Multivariate with Univariate Methods, and MEG with EEG to Study Language Comprehension

We used MEG and EEG to examine the effects of Plausibility (anomalous vs. plausible) and Animacy (animate vs. inanimate) on activity to incoming words during language comprehension. We conducted univariate event-related and multivariate spatial similarity analyses on both datasets. The univariate and multivariate results converged in their time course and sensitivity to Plausibility. However, only the spatial similarity analyses detected effects of Animacy. The MEG and EEG findings largely converged between 300-500ms, but diverged in their univariate and multivariate responses to the anomalies between 600-1000ms. We interpret the full set of results within a predictive coding framework. In addition to the theoretical significance of these findings, we discuss the methodological implications of the convergence and divergence between the univariate and multivariate results, as well as between the MEG and EEG results. We argue that a deeper understanding of language processing can be achieved by integrating different analysis approaches and techniques.

Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms

The question of whether task performance is best achieved by domain-specific, or domain-general processing mechanisms is fundemental for both artificial and biological systems. This question has generated a fierce debate in the study of expert object recognition. Because humans are experts in face recognition, face-like neural and cognitive effects for objects of expertise were considered support for domain-general mechanisms. However, effects of domain, experience and level of categorization, are confounded in human studies, which may lead to erroneous inferences. To overcome these limitations, we trained deep learning algorithms on different domains (objects, faces, birds) and levels of categorization (basic, sub-ordinate, individual), matched for amount of experience. Like humans, the models generated a larger inversion effect for faces than for objects. Importantly, a face-like inversion effect was found for individual-based categorization of non-faces (birds) but only in a network specialized for that domain. Thus, contrary to prevalent assumptions, face-like effects for objects of expertise do not support domain-general mechanisms but may originate from domain-specific mechanisms. More generally, we show how deep learning algorithms can be used to dissociate factors that are inherently confounded in the natural environment of biological organisms to test hypotheses about their isolated contributions to cognition and behaviour.

The Role of Foveal Cortex in Discriminating Peripheral Stimuli: The Sketchpad Hypothesis

Foveal (central) and peripheral vision are strongly interconnected to provide an integrated experience of the world around us. Recently, it has been suggested that there is a feedback mechanism that links foveal and peripheral vision. This peripheral-to-foveal feedback differs from other feedback mechanisms in that during visual processing a novel representation of a stimulus is formed in a different cortical region than that of the feedforward representation. The functional role of foveal feedback is not yet completely understood, but some evidence from neuroimaging studies suggests a link with peripheral shape processing. Behavioural and transcranial magnetic stimulation studies show impairment in peripheral shape discrimination when the foveal retinotopic cortex is disrupted post stimulus presentation. This review aims to link these findings to the visual sketchpad hypothesis. According to this hypothesis, foveal retinotopic cortex stores task-relevant information to aid identification of peripherally presented objects. We discuss how the characteristics of foveal feedback support this hypothesis and rule out other possible explanations. We also discuss the possibility that the foveal feedback may be independent of the sensory modality of the stimulation.

CNNs reveal the computational implausibility of the expertise hypothesis

Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative "expertise" hypothesis holds that putatively face-specific mechanisms are actually domain-general, and can be recruited for the perception of other objects of expertise (e.g., cars for car experts). Here, we demonstrate the computational implausibility of this hypothesis: Neural network models optimized for generic object categorization provide a better foundation for expert fine-grained discrimination than do models optimized for face recognition.

Decision-making from temporally accumulated conflicting evidence: The more the merrier

How do humans evaluate temporally accumulated discrete pieces of evidence and arrive at a decision despite the presence of conflicting evidence? In the present study, we showed human participants a sequential presentation of objects drawn from two novel object categories and asked them to decide whether a given presentation contained more objects from one or the other category. We found that both a more disparate ratio and greater numerosity of objects improved both reaction time (RT) and accuracy. The effect of numerosity was separate from ratio, where with a fixed object ratio, sequences with more total objects had lower RT and lower error rates than those with fewer total objects. We replicated these results across three experiments. Additionally, even with the total presentation duration equated and with the motor response assignment varied from trial to trial, an effect of numerosity was still found in RT. The same RT benefit was also present when objects were shown simultaneously, rather than sequentially. Together, these results showed that, for comparative numerosity judgment involving sequential displays, there was a benefit of numerosity, such that showing more objects independent of the object ratio and the total presentation time led to faster decision performance.

Neural representational geometry underlies few-shot concept learning

Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing that it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to predictions about behavioral outcomes by delineating several fundamental and measurable geometric properties of neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. This theory reveals, for instance, that high-dimensional manifolds enhance the ability to learn new concepts from few examples. Intriguingly, we observe striking mismatches between the geometry of manifolds in the primate visual pathway and in trained DNNs. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.

Face learning via brief real-world social interactions includes changes in face-selective brain areas and hippocampus

Making new acquaintances requires learning to recognise previously unfamiliar faces. In the current study, we investigated this process by staging real-world social interactions between actors and the participants. Participants completed a face-matching behavioural task in which they matched photographs of the actors (whom they had yet to meet), or faces similar to the actors (henceforth called foils). Participants were then scanned using functional magnetic resonance imaging (fMRI) while viewing photographs of actors and foils. Immediately after exiting the scanner, participants met the actors for the first time and interacted with them for 10 min. On subsequent days, participants completed a second behavioural experiment and then a second fMRI scan. Prior to each session, actors again interacted with the participants for 10 min. Behavioural results showed that social interactions improved performance accuracy when matching actor photographs, but not foil photographs. The fMRI analysis revealed a difference in the neural response to actor photographs and foil photographs across all regions of interest (ROIs) only after social interactions had occurred. Our results demonstrate that short social interactions were sufficient to learn and discriminate previously unfamiliar individuals. Moreover, these learning effects were present in brain areas involved in face processing and memory.

Integrating unsupervised and reinforcement learning in human categorical perception: A computational model

Categorical perception identifies a tuning of human perceptual systems that can occur during the execution of a categorisation task. Despite the fact that experimental studies and computational models suggest that this tuning is influenced by task-independent effects (e.g., based on Hebbian and unsupervised learning, UL) and task-dependent effects (e.g., based on reward signals and reinforcement learning, RL), no model studies the UL/RL interaction during the emergence of categorical perception. Here we have investigated the effects of this interaction, proposing a system-level neuro-inspired computational architecture in which a perceptual component integrates UL and RL processes. The model has been tested with a categorisation task and the results show that a balanced mix of unsupervised and reinforcement learning leads to the emergence of a suitable categorical perception and the best performance in the task. Indeed, an excessive unsupervised learning contribution tends to not identify task-relevant features while an excessive reinforcement learning contribution tends to initially learn slowly and then to reach sub-optimal performance. These results are consistent with the experimental evidence regarding categorical activations of extrastriate cortices in healthy conditions. Finally, the results produced by the two extreme cases of our model can explain the existence of several factors that may lead to sensory alterations in autistic people.

Neural Coding of Visual Objects Rapidly Reconfigures to Reflect Subtrial Shifts in Attentional Focus

Every day, we respond to the dynamic world around us by choosing actions to meet our goals. Flexible neural populations are thought to support this process by adapting to prioritize task-relevant information, driving coding in specialized brain regions toward stimuli and actions that are currently most important. Accordingly, human fMRI shows that activity patterns in frontoparietal cortex contain more information about visual features when they are task-relevant. However, if this preferential coding drives momentary focus, for example, to solve each part of a task in turn, it must reconfigure more quickly than we can observe with fMRI. Here, we used multivariate pattern analysis of magnetoencephalography data to test for rapid reconfiguration of stimulus information when a new feature becomes relevant within a trial. Participants saw two displays on each trial. They attended to the shape of a first target then the color of a second, or vice versa, and reported the attended features at a choice display. We found evidence of preferential coding for the relevant features in both trial phases, even as participants shifted attention mid-trial, commensurate with fast subtrial reconfiguration. However, we only found this pattern of results when the stimulus displays contained multiple objects and not in a simpler task with the same structure. The data suggest that adaptive coding in humans can operate on a fast, subtrial timescale, suitable for supporting periods of momentary focus when complex tasks are broken down into simpler ones, but may not always do so.

Repetition Probability Effects for Chinese Characters and German Words in the Visual Word Form Area

The magnitude of repetition suppression (RS), measured by fMRI, is modulated by the probability of repetitions (P(rep)) for various sensory stimulus categories. It has been suggested that for visually presented simple letters this P(rep) effect depends on the prior practices of the participants with the stimuli. Here we tested further if previous experiences affect the neural mechanisms of RS, leading to the modulatory effects of stimulus P(rep), for more complex lexical stimuli as well. We measured the BOLD signal in the Visual Word Form Area (VWFA) of native Chinese and German participants and estimated the P(rep) effects for Chinese characters and German words. The results showed a significant P(rep) effect for stimuli of the mother tongue in both participant groups. Interestingly, Chinese participants, learning German as a second language, also showed a significant P(rep) modulation of RS for German words while the German participants who had no prior experiences with the Chinese characters showed no such effects. Our findings suggest that P(rep) effects on RS are manifest for visual word processing as well, but only for words of a language with which participants are highly familiar. These results support further the idea that predictive processes, estimated by P(rep) modulations of RS, require prior experiences.

Spatial and Feature-selective Attention Have Distinct, Interacting Effects on Population-level Tuning

Attention can be deployed in different ways: When searching for a taxi in New York City, we can decide where to attend (e.g., to the street) and what to attend to (e.g., yellow cars). Although we use the same word to describe both processes, nonhuman primate data suggest that these produce distinct effects on neural tuning. This has been challenging to assess in humans, but here we used an opportunity afforded by multivariate decoding of MEG data. We found that attending to an object at a particular location and attending to a particular object feature produced effects that interacted multiplicatively. The two types of attention induced distinct patterns of enhancement in occipital cortex, with feature-selective attention producing relatively more enhancement of small feature differences and spatial attention producing relatively larger effects for larger feature differences. An information flow analysis further showed that stimulus representations in occipital cortex were Granger-caused by coding in frontal cortices earlier in time and that the timing of this feedback matched the onset of attention effects. The data suggest that spatial and feature-selective attention rely on distinct neural mechanisms that arise from frontal-occipital information exchange, interacting multiplicatively to selectively enhance task-relevant information.

How Visual Expertise Changes Representational Geometry: A Behavioral and Neural Perspective

Many people develop expertise in specific domains of interest, such as chess, microbiology, radiology, and, the case in point in our study: ornithology. It is poorly understood to what extent such expertise alters brain function. Previous neuroimaging studies of expertise have typically focused upon the category level, for example, selectivity for birds versus nonbird stimuli. We present a multivariate fMRI study focusing upon the representational similarity among objects of expertise at the subordinate level. We compare the neural representational spaces of experts and novices to behavioral judgments. At the behavioral level, ornithologists (n = 20) have more fine-grained and task-dependent representations of item similarity that are more consistent among experts compared to control participants. At the neural level, the neural patterns of item similarity are more distinct and consistent in experts than in novices, which is in line with the behavioral results. In addition, these neural patterns in experts show stronger correlations with behavior compared to novices. These findings were prominent in frontal regions, and some effects were also found in occipitotemporal regions. This study illustrates the potential of an analysis of representational geometry to understand to what extent expertise changes neural information processing.

Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information

Dorsolateral prefrontal cortex (dlPFC) is proposed to drive brain-wide focus by biasing processing in favour of task-relevant information. A longstanding debate concerns whether this is achieved through enhancing processing of relevant information and/or by inhibiting irrelevant information. To address this, we applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding. Participants attended to one feature, whilst ignoring another feature, of a visual object. If dlPFC is necessary for facilitation, disruptive TMS should decrease coding of attended features. Conversely, if dlPFC is crucial for inhibition, TMS should increase coding of ignored features. Here, we show that TMS decreases coding of relevant information across frontoparietal cortex, and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable. This provides causal evidence for a specific role of dlPFC in enhancing task-relevant representations and demonstrates the cognitive-neural insights possible with concurrent TMS-fMRI-MVPA.

Symbolic categorization of novel multisensory stimuli in the human brain

When primates (both human and non-human) learn to categorize simple visual or acoustic stimuli by means of non-verbal matching tasks, two types of changes occur in their brain: early sensory cortices increase the precision with which they encode sensory information, and parietal and lateral prefrontal cortices develop a categorical response to the stimuli. Contrary to non-human animals, however, our species mostly constructs categories using linguistic labels. Moreover, we naturally tend to define categories by means of multiple sensory features of the stimuli. Here we trained adult subjects to parse a novel audiovisual stimulus space into 4 orthogonal categories, by associating each category to a specific symbol. We then used multi-voxel pattern analysis (MVPA) to show that during a cross-format category repetition detection task three neural representational changes were detectable. First, visual and acoustic cortices increased both precision and selectivity to their preferred sensory feature, displaying increased sensory segregation. Second, a frontoparietal network developed a multisensory object-specific response. Third, the right hippocampus and, at least to some extent, the left angular gyrus, developed a shared representational code common to symbols and objects. In particular, the right hippocampus displayed the highest level of abstraction and generalization from a format to the other, and also predicted symbolic categorization performance outside the scanner. Taken together, these results indicate that when humans categorize multisensory objects by means of language the set of changes occurring in the brain only partially overlaps with that described by classical models of non-verbal unisensory categorization in primates.

Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools

The idea that when we use a tool we incorporate it into the neural representation of our body (embodiment) has been a major inspiration for philosophy, science, and engineering. While theoretically appealing, there is little direct evidence for tool embodiment at the neural level. Using functional magnetic resonance imaging (fMRI) in male and female human subjects, we investigated whether expert tool users (London litter pickers: n = 7) represent their expert tool more like a hand (neural embodiment) or less like a hand (neural differentiation), as compared with a group of tool novices (n = 12). During fMRI scans, participants viewed first-person videos depicting grasps performed by either a hand, litter picker, or a non-expert grasping tool. Using representational similarity analysis (RSA), differences in the representational structure of hands and tools were measured within occipitotemporal cortex (OTC). Contrary to the neural embodiment theory, we find that the experts group represent their own tool less like a hand (not more) relative to novices. Using a case-study approach, we further replicated this effect, independently, in five of the seven individual expert litter pickers, as compared with the novices. An exploratory analysis in left parietal cortex, a region implicated in visuomotor representations of hands and tools, also indicated that experts do not visually represent their tool more similar to hands, compared with novices. Together, our findings suggest that extensive tool use leads to an increased neural differentiation between visual representations of hands and tools. This evidence provides an important alternative framework to the prominent tool embodiment theory.SIGNIFICANCE STATEMENT It is commonly thought that tool use leads to the assimilation of the tool into the neural representation of the body, a process referred to as embodiment. Here, we demonstrate that expert tool users (London litter pickers) neurally represent their own tool less like a hand (not more), compared with novices. Our findings advance our current understanding for how experience shapes functional organization in high-order visual cortex. Further, this evidence provides an alternative framework to the prominent tool embodiment theory, suggesting instead that experience with tools leads to more distinct, separable hand and tool representations.

Visual association learning induces global network reorganization

It has been proposed that visual learning is accomplished not only by neural plasticity in the visual cortex, but also by complex interactions between bottom-up and top-down processes that may induce global network reorganization. Here, we applied a multivariate analysis to functional connectivity (FC) patterns across the brain to investigate how visual association learning was achieved through large-scale network reorganization. Participants were trained to associate a set of artificial line-drawing objects with English letters. After three consecutive days of training, participants underwent a functional magnetic resonance imaging scan in which they were presented with the trained stimuli, untrained stimuli, and English words. By calculating pairwise FC between 189 nodes of 10 well-established networks across the brain, we found that the visual association learning induced changes in the global FC pattern when viewing the trained stimuli, rendering it more similar to the FC pattern when viewing English words. Critically, the learning-induced global FC pattern differences were mainly driven by the FC related to the high-level networks involved in attention and cognitive control, suggesting the modification of top-down processes during learning. In sum, our study provides one of the first evidence revealing global network reorganization induced by visual learning and sheds new light on the network mechanisms of top-down influences in learning.

Temporal dissociation of neural activity underlying synesthetic and perceptual colors

Grapheme-color synesthetes experience color when seeing achromatic symbols. We examined whether similar neural mechanisms underlie color perception and synesthetic colors using magnetoencephalography. Classification models trained on neural activity from viewing colored stimuli could distinguish synesthetic color evoked by achromatic symbols after a delay of ∼100 ms. Our results provide an objective neural signature for synesthetic experience and temporal evidence consistent with higher-level processing in synesthesia.

Early Visual Processing and Perception Processes in Object Discrimination Learning

A brief image presentation is sufficient to discriminate and individuate objects of expertise. Although perceptual expertise is acquired through extensive practice that increases the resolution of representations and reduces the latency of image decoding and coarse and fine information extraction, it is not known how the stages of visual processing impact object discrimination learning (ODL). Here, we compared object discrimination with brief (100 ms) and long (1,000 ms) perceptual encoding times to test if the early and late visual processes are required for ODL. Moreover, we evaluated whether encoding time and discrimination practice shape perception and recognition memory processes during ODL. During practice of a sequential matching task with initially unfamiliar complex stimuli, we find greater discrimination with greater encoding times regardless of the extent of practice, suggesting that the fine information extraction during late visual processing is necessary for discrimination. Interestingly, the overall discrimination learning was similar for brief and long stimuli, suggesting that early stages of visual processing are sufficient for ODL. In addition, discrimination practice enhances perceive and know for brief and long stimuli and both processes are associated with performance, suggesting that early stage information extraction is sufficient for modulating the perceptual processes, likely reflecting an increase in the resolution of the representations and an early availability of information. Conversely, practice elicited an increase of familiarity which was not associated with discrimination sensitivity, revealing the acquisition of a general recognition memory. Finally, the recall is likely enhanced by practice and is associated with discrimination sensitivity for long encoding times, suggesting the engagement of recognition memory in a practice independent manner. These findings contribute to unveiling the function of early stages of visual processing in ODL, and provide evidence on the modulation of the perception and recognition memory processes during discrimination practice and its relationship with ODL and perceptual expertise acquisition.

Perceptual expertise with Chinese characters predicts Chinese reading performance among Hong Kong Chinese children with developmental dyslexia

This study explores the theoretical proposal that developmental dyslexia involves a failure to develop perceptual expertise with words despite adequate education. Among a group of Hong Kong Chinese children diagnosed with developmental dyslexia, we investigated the relationship between Chinese word reading and perceptual expertise with Chinese characters. In a perceptual fluency task, the time of visual exposure to Chinese characters was manipulated and limited such that the speed of discrimination of a short sequence of Chinese characters at an accuracy level of 80% was estimated. Pair-wise correlations showed that perceptual fluency for characters predicted speeded and non-speeded word reading performance. Exploratory hierarchical regressions showed that perceptual fluency for characters accounted for 5.3% and 9.6% variance in speeded and non-speeded reading respectively, in addition to age, non-verbal IQ, phonological awareness, morphological awareness, rapid automatized naming (RAN) and perceptual fluency for digits. The findings suggest that perceptual expertise with words plays an important role in Chinese reading performance in developmental dyslexia, and that perceptual training is a potential remediation direction.

Motor Learning Improves the Stability of Large-Scale Brain Connectivity Pattern

Repeated practice is fundamental to the acquisition of skills, which is typically accompanied by increasing reliability of neural representations that manifested as more stable activation patterns for the trained stimuli. However, large-scale neural pattern induced by learning has been rarely studied. Here, we investigated whether global connectivity patterns became more reliable as a result of motor learning using a novel analysis of the multivariate pattern of functional connectivity (MVPC). Human participants were trained with a finger-tapping motor task for five consecutive days and went through Functional magnetic resonance imaging (fMRI) scanning before and after training. We found that motor learning increased the whole-brain MVPC stability of the primary motor cortex (M1) when participants performed the trained sequence, while no similar effects were observed for the untrained sequence. Moreover, the increase of MVPC stability correlated with participants’ improvement in behavioral performance. These findings suggested that the acquisition of motor skills was supported by the increased connectivity pattern stability between the M1 and the rest of the brain. In summary, our study not only suggests global neural pattern stabilization as a neural signature for effective learning but also advocates applying the MVPC analysis to reveal mechanisms of distributed network reorganization supporting various types of learning.

Neuroplasticity-driven timing modulations revealed by ultrafast functional magnetic resonance imaging

Detecting neuroplasticity in global brain circuits in vivo is key for understanding myriad processes such as memory, learning, and recovery from injury. Functional Magnetic Resonance Imaging (fMRI) is instrumental for such in vivo mappings, yet it typically relies on mapping changes in spatial extent of activation or via signal amplitude modulations, whose interpretation can be highly ambiguous. Importantly, a central aspect of neuroplasticity involves modulation of neural activity timing properties. We thus hypothesized that this temporal dimension could serve as a new marker for neuroplasticity. To detect fMRI signals more associated with the underlying neural dynamics, we developed an ultrafast fMRI (ufMRI) approach facilitating high spatiotemporal sensitivity and resolution in distributed neural pathways. When neuroplasticity was induced in the mouse visual pathway via dark rearing, ufMRI indeed mapped temporal modulations in the entire visual pathway. Our findings therefore suggest a new dimension for exploring neuroplasticity in vivo.

Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Does the nature of representation in the category-selective regions in the occipitotemporal cortex reflect visual or conceptual properties? Previous research showed that natural variability in visual features across categories, quantified by image gist statistics, is highly correlated with the different neural responses observed in the occipitotemporal cortex. Using fMRI, we examined whether category selectivity for animals and tools would remain, when image gist statistics were comparable across categories. Critically, we investigated how category, shape, and spatial frequency may contribute to the category selectivity in the animal- and tool-selective regions. Female and male human observers viewed low- or high-passed images of round or elongated animals and tools that shared comparable gist statistics in the main experiment, and animal and tool images of naturally varied gist statistics in a separate localizer. Univariate analysis revealed robust category-selective responses for images with comparable gist statistics across categories. Successful classification for category (animals/tools), shape (round/elongated), and spatial frequency (low/high) was also observed, with highest classification accuracy for category. Representational similarity analyses further revealed that the activation patterns in the animal-selective regions were most correlated with a model that represents only animal information, whereas the activation patterns in the tool-selective regions were most correlated with a model that represents only tool information, suggesting that these regions selectively represent information of only animals or tools. Together, in addition to visual features, the distinction between animal and tool representations in the occipitotemporal cortex is likely shaped by higher-level conceptual influences such as categorization or interpretation of visual inputs.

SIGNIFICANCE STATEMENT Since different categories often vary systematically in both visual and conceptual features, it remains unclear what kinds of information determine category-selective responses in the occipitotemporal cortex. To minimize the influences of low- and mid-level visual features, here we used a diverse image set of animals and tools that shared comparable gist statistics. We manipulated category (animals/tools), shape (round/elongated), and spatial frequency (low/high), and found that the representational content of the animal- and tool-selective regions is primarily determined by their preferred categories only, regardless of shape or spatial frequency. Our results show that category-selective responses in the occipitotemporal cortex are influenced by higher-level processing such as categorization or interpretation of visual inputs, and highlight the specificity in these category-selective regions.

Late disruption of central visual field disrupts peripheral perception of form and color

Evidence from neuroimaging and brain stimulation studies suggest that visual information about objects in the periphery is fed back to foveal retinotopic cortex in a separate representation that is essential for peripheral perception. The characteristics of this phenomenon have important theoretical implications for the role fovea-specific feedback might play in perception. In this work, we employed a recently developed behavioral paradigm to explore whether late disruption to central visual space impaired perception of color. In the first experiment, participants performed a shape discrimination task on colored novel objects in the periphery while fixating centrally. Consistent with the results from previous work, a visual distractor presented at fixation ~100ms after presentation of the peripheral stimuli impaired sensitivity to differences in peripheral shapes more than a visual distractor presented at other stimulus onset asynchronies. In a second experiment, participants performed a color discrimination task on the same colored objects. In a third experiment, we further tested for this foveal distractor effect with stimuli restricted to a low-level feature by using homogenous color patches. These two latter experiments resulted in a similar pattern of behavior: a central distractor presented at the critical stimulus onset asynchrony impaired sensitivity to peripheral color differences, but, importantly, the magnitude of the effect was stronger when peripheral objects contained complex shape information. These results show a behavioral effect consistent with disrupting feedback to the fovea, in line with the foveal feedback suggested by previous neuroimaging studies.

An investigation of far and near transfer in a gamified visual learning paradigm

After training, visual perceptual learning improvements are mostly constrained to the trained stimulus feature and retinal location. The aim of this study is to construct an integrated paradigm where the visual learning happens in a more natural context and in parallel for multiple stimulus types, and to test the generalization of learning-related improvements towards untrained features, locations, and more general cognitive domains. Half the subjects were trained with a gamified perceptual learning paradigm for ten hours, which consisted of an orientation discrimination task and a novel object categorization task embedded in a three-dimensional maze. A second group of subjects, an active control group, played ten hours of Candy Crush Saga. Before and after training, all subjects completed a 'near transfer' orientation discrimination and novel object categorization task, as well as a set of 'far transfer' general cognitive and attentional tasks. During the perceptual learning tasks, two different stimulus features and two retinal location pairs were assessed in each task. For the experimental group, one stimulus feature and retinal location pair was trained, whilst the other one remained untrained. Both features and location pairs were untrained in the control group. Far transfer did occur in some domains across all subjects irrespective of the training regimen (i.e. executive functioning, mental rotation performance, and multitask performance and speed). Near transfer was present in both groups, however only more pronounced for one particular task in the experimental group, namely novel object categorization. To conclude, all but one near transfer task did not generalize more than the control group.

Factors Determining Where Category-Selective Areas Emerge in Visual Cortex

A hallmark of functional localization in the human brain is the presence of areas in visual cortex specialized for representing particular categories such as faces and words. Why do these areas appear where they do during development? Recent findings highlight several general factors to consider when answering this question. Experience-driven category selectivity arises in regions that have: (i) pre-existing selectivity for properties of the stimulus, (ii) are appropriately placed in the computational hierarchy of the visual system, and (iii) exhibit domain-specific patterns of connectivity to nonvisual regions. In other words, cortical location of category selectivity is constrained by what category will be represented, how it will be represented, and why the representation will be used.

Extensive childhood experience with Pokémon suggests eccentricity drives organization of visual cortex

The functional organization of human high-level visual cortex, such as the face- and place-selective regions, is strikingly consistent across individuals. An unanswered question in neuroscience concerns which dimensions of visual information constrain the development and topography of this shared brain organization. To answer this question, we used functional magnetic resonance imaging to scan a unique group of adults who, as children, had extensive visual experience with Pokémon. These animal-like, pixelated characters are dissimilar from other ecological categories, such as faces and places, along critical dimensions (foveal bias, rectilinearity, size, animacy). We show not only that adults who have Pokémon experience demonstrate distinct distributed cortical responses to Pokémon, but also that the experienced retinal eccentricity during childhood can predict the locus of Pokémon responses in adulthood. These data demonstrate that inherent functional representations in the visual cortex-retinal eccentricity-combined with consistent viewing behaviour of particular stimuli during childhood result in a shared functional topography in adulthood.

Adaptation decorrelates shape representations

Perception and neural responses are modulated by sensory history. Visual adaptation, an example of such an effect, has been hypothesized to improve stimulus discrimination by decorrelating responses across a set of neural units. While a central theoretical model, behavioral and neural evidence for this theory is limited and inconclusive. Here, we use a parametric 3D shape-space to test whether adaptation decorrelates shape representations in humans. In a behavioral experiment with 20 subjects, we find that adaptation to a shape class improves discrimination of subsequently presented stimuli with similar features. In a BOLD fMRI experiment with 10 subjects, we observe that adaptation to a shape class decorrelates the multivariate representations of subsequently presented stimuli with similar features in object-selective cortex. These results support the long-standing proposal that adaptation improves perceptual discrimination and decorrelates neural representations, offering insights into potential underlying mechanisms.

Adaptation is thought to improve discrimination by pulling neural representations of similar stimuli farther apart. Here, the authors separately show that adaptation to a 3D shape class leads to better discrimination performance on similar shapes, and activity patterns diverge in object selective cortical areas.

Adaptive coding in the human brain: Distinct object features are encoded by overlapping voxels in frontoparietal cortex

Our ability to flexibly switch between different tasks is a key component of cognitive control. Non-human primate (NHP) studies (e.g., Freedman, Riesenhuber, Poggio, & Miller, 2001) have shown that prefrontal neurons are re-used across tasks, re-configuring their responses to code currently relevant information. In a similar vein, in the human brain, the "multiple demand" (MD) system is suggested to exert control by adjusting its responses, selectively processing information in line with our current goals (Duncan, 2010). However, whether the same or different resources (underlying neural populations) in the human brain are recruited to solve different tasks remains elusive. In the present study, we aimed to bridge the gap between the NHP and human literature by examining human functional imaging data at an intermediate level of resolution: quantifying the extent to which single voxels contributed to multiple neural codes. Participants alternated between two tasks requiring the selection of feature information from two distinct sets of objects. We examined whether neural codes for the relevant stimulus features in the two different tasks depended on the same or different voxels. In line with the electrophysiological literature, MD voxels were more likely to contribute to multiple neural codes than we predicted based on permutation tests. Comparatively, in the visual system the neural codes depended on distinct sets of voxels. Our data emphasise the flexibility of the MD regions to re-configure their responses and adaptively code relevant information across different tasks.

Unsupervised visual discrimination learning of complex stimuli: Accuracy, bias and generalization

Through same-different judgements, we can discriminate an immense variety of stimuli and consequently, they are critical in our everyday interaction with the environment. The quality of the judgements depends on familiarity with stimuli. A way to improve the discrimination is through learning, but to this day, we lack direct evidence of how learning shapes the same-different judgments with complex stimuli. We studied unsupervised visual discrimination learning in 42 participants, as they performed same-different judgments with two types of unfamiliar complex stimuli in the absence of labeling or individuation. Across nine daily training sessions with equiprobable same and different stimuli pairs, participants increased the sensitivity and the criterion by reducing the errors with both same and different pairs. With practice, there was a superior performance for different pairs and a bias for different response. To evaluate the process underlying this bias, we manipulated the proportion of same and different pairs, which resulted in an additional proportion-induced bias, suggesting that the bias observed with equal proportions was a stimulus processing bias. Overall, these results suggest that unsupervised discrimination learning occurs through changes in the stimulus processing that increase the sensory evidence and/or the precision of the working memory. Finally, the acquired discrimination ability was fully transferred to novel exemplars of the practiced stimuli category, in agreement with the acquisition of a category specific perceptual expertise.

How experience modulates semantic memory for food: evidence from elderly adults and centenarians

In order to make sense of the objects we encounter in everyday life we largely rely on previous knowledge stored in our semantic memory. Semantic memory is considered dependent on lifelong experience and cultural knowledge. So far, a few studies have investigated the role of expertise on the organization of semantic memory, whereas life-long experience has largely been overlooked. In this study, we investigated this issue using food concepts. In particular, we administered different semantic tasks using food (natural and transformed) and non-food (living and non-living things) as stimuli to participants belonging to three different age cohorts (56–74, 75–91, 100–108), who were also asked to report on the dietary habits held throughout their life. In addition, we investigated to what extent psycholinguistic variables influence the semantic performance of different age cohorts. Results showed that Centenarians recognized natural food better than transformed food, while the other two groups showed the opposite pattern. According to our analyses, experience is responsible for this effect in Centenarians, as their dietary habits seem to suggest. Moreover, significant correlations between picture naming and age of acquisition, familiarity and frequency were observed. This study indicates that lifelong experience can shape conceptual knowledge of food concepts, and that semantic memory is less resilient to aging than initially thought.

Domain-general and domain-specific neural changes underlying visual expertise

Visual expertise induces changes in neural processing for many different domains of expertise. However, it is unclear how expertise effects for different domains of expertise are related. In the present fMRI study, we combine large-scale univariate and multi-voxel analyses to contrast the expertise-related neural changes associated with two different domains of expertise, bird expertise (ornithology) and mineral expertise (mineralogy). Results indicated distributed expertise-related neural changes, with effects for both domains of expertise in high-level visual cortex and effects for bird expertise even extending to low-level visual regions and the frontal lobe. Importantly, a multivariate generalization analysis showed that effects in high-level visual cortex were specific to the domain of expertise. In contrast, the neural changes in the frontal lobe relating to expertise showed significant generalization, signaling the presence of domain-independent expertise effects. In conclusion, expertise is related to a combination of domain-specific and domain-general changes in neural processing.

Single-exposure visual memory judgments are reflected in inferotemporal cortex

Our visual memory percepts of whether we have encountered specific objects or scenes before are hypothesized to manifest as decrements in neural responses in inferotemporal cortex (IT) with stimulus repetition. To evaluate this proposal, we recorded IT neural responses as two monkeys performed a single-exposure visual memory task designed to measure the rates of forgetting with time. We found that a weighted linear read-out of IT was a better predictor of the monkeys’ forgetting rates and reaction time patterns than a strict instantiation of the repetition suppression hypothesis, expressed as a total spike count scheme. Behavioral predictions could be attributed to visual memory signals that were reflected as repetition suppression and were intermingled with visual selectivity, but only when combined across the most sensitive neurons.

As we go about our daily lives, we store visual memories of the objects and scenes that we encounter. This type of memory, known as visual recognition memory, can be remarkably powerful. Imagine viewing thousands of images for only a few seconds each, for example. Several days later, you will still be able to distinguish most of those images from previously unseen ones. How does the brain do this?

Visual information travels from the eyes to an area of the brain called visual cortex. Neurons in a region of visual cortex called inferotemporal cortex fire in a particular pattern to reflect what is being seen. These neurons also reflect memories of whether those things have been seen before, by firing more when things are new and less when they are viewed again. This decrease in firing, known as repetition suppression, may be the signal in the brain responsible for the sense of remembering.

Meyer and Rust have now tested this idea by training macaque monkeys to report whether images on a screen were new or familiar. The monkeys were very good at remembering the images they had seen more recently, although they tended to forget some of the images with time. Then, the rate at which the monkeys forgot the images was compared with the rate at which repetition suppression disappeared in inferotemporal cortex. The results showed that the total number of firing events in this region was not a great predictor of how long the monkeys remembered images. However, a decrease in the number of firing events for a particular subset of the neurons did predict the remembering and forgetting. Repetition suppression in certain inferotemporal cortex neurons can thus account for visual recognition memory.

Brain disorders and aging can both give rise to memory deficits. Identifying the mechanisms underlying memory may lead to new treatments for memory-related disorders. Visual recognition memory may be a good place to start because of our existing knowledge of how the brain processes visual information. Understanding visual recognition memory could help us understand the mechanisms of memory more broadly.

Preserved Expert Object Recognition in a Case of Visual Hemiagnosia

We examined a stroke patient (HWS) with a unilateral lesion of the right medial ventral visual stream, involving the right fusiform and parahippocampal gyri. In a number of object recognition tests with lateralized presentations of target stimuli, HWS showed significant symptoms of hemiagnosia with contralesional recognition deficits for everyday objects. We further explored the patient's capacities of visual expertise that were acquired before the current perceptual impairment became effective. We confronted him with objects he was an expert for already before stroke onset and compared this performance with the recognition of familiar everyday objects. HWS was able to identify significantly more of the specific (“expert”) than of the everyday objects on the affected contralesional side. This observation of better expert object recognition in visual hemiagnosia allows for several interpretations. The results may be caused by enhanced information processing for expert objects in the ventral system in the affected or the intact hemisphere. Expert knowledge could trigger top–down mechanisms supporting object recognition despite of impaired basic functions of object processing. More importantly, the current work demonstrates that top–down mechanisms of visual expertise influence object recognition at an early stage, probably before visual object information propagates to modules of higher object recognition. Because HWS showed a lesion to the fusiform gyrus and spared capacities of expert object recognition, the current study emphasizes possible contributions of areas outside the ventral stream to visual expertise.

Does Extensive Training at Individuating Novel Objects in Adulthood Lead to Visual Expertise? The Role of Facelikeness

Human adults have a rich visual experience thanks to seeing human faces since birth, which may contribute to the acquisition of perceptual processes that rapidly and automatically individuate faces. According to a generic visual expertise hypothesis, extensive experience with nonface objects may similarly lead to efficient processing of objects at the individual level. However, whether extensive training in adulthood leads to visual expertise remains debated. One key issue is the extent to which the acquisition of visual expertise depends on the resemblance of objects to faces in terms of the spatial configuration of parts. We therefore trained naive human adults to individuate a large set of novel parametric multipart objects. Critically, one group of participants trained with the objects in a "facelike" stimulus orientation, whereas a second group trained with the same objects but with the objects rotated 180° in the picture plane into a "nonfacelike" orientation. We used a fast periodic visual stimulation EEG protocol to objectively quantify participants' ability to discriminate untrained exemplars before and after training. EEG responses associated with the frequency of identity change in a fast stimulation sequence, which reflects rapid and automatic perceptual processes, were observed over lateral occipital sites for both groups before training. There was a significant, albeit small, increase in these responses after training but only for the facelike group and only to facelike stimuli. Our findings indicate that perceived facelikeness plays a role in visual expertise and highlight how the adult perceptual system exploits familiar spatial configurations when learning new object categories.

Individual differences in perceptual abilities in medical imaging: the Vanderbilt Chest Radiograph Test

Radiologists make many important decisions when detecting nodules on chest radiographs. While training can result in high levels of performance of this task, there could be individual differences in relevant perceptual abilities that are present pre-training. A pre-requisite to address this question is a valid and reliable measure of such abilities. The present work introduces a new measure, the Vanderbilt Chest Radiograph Test (VCRT), which aims to quantify individual differences in perceptual abilities for radiograph-related decision-making in novices. We validate the relevance of the test to diagnostic imaging by verifying radiologists’ superior performance on the test compared to novices’. The final VCRT version produces scores with acceptable internal consistency. Then, we investigate how the VCRT can be used in future research by evaluating how the test relates to extant measures of face and object recognition ability. We find that the VCRT shares a small but significant portion of its variance with a measure of novel object recognition, suggesting that some aspect of VCRT performance is driven by a domain-general visual ability.

Neural mechanisms of motion perceptual learning in noise

Practice improves our perceptual ability. However, the neural mechanisms underlying this experience-dependent plasticity in adult brain remain unclear. Here, we studied the long-term neural correlates of motion perceptual learning. Subjects' behavioral performance and BOLD signals were tracked before, immediately after, and 2 weeks after practicing a motion direction discrimination task in noise over six daily sessions. Parallel to the specificity and persistency of the behavioral learning effect, we found that training sharpened the cortical tuning in MT, and enhanced the connectivity strength from MT to the intraparietal sulcus (IPS, a motion decision-making area). In addition, the decoding accuracy for the trained motion direction was improved in IPS 2 weeks after training. The dual changes in the sensory and the high-level cortical areas suggest that learning refines the neural representation of the trained stimulus and facilitates the information transmission in the decision process. Our findings are consistent with the functional specialization in the visual cortex, and provide empirical evidence to the reweighting theory of perceptual learning at a large spatial scale. Hum Brain Mapp 38:6029-6042, 2017. © 2017 Wiley Periodicals, Inc.

The Quest for the FFA and Where It Led

This article tells the story behind our first paper on the fusiform face area (FFA): how we chose the question, developed the methods, and followed the data to find the FFA and subsequently many other functionally specialized cortical regions. The paper's impact had less to do with the particular findings in the paper itself and more to do with the method that it promoted and the picture of the human mind and brain that it led to. The use of a functional localizer to define a candidate region in each subject individually enabled us not just to make pictures of brain activation, but also to ask principled, hypothesis-driven questions about a thing in nature. This method enabled stronger and more extensive tests of the function of each cortical region than had been possible before in humans and, as a result, has produced a large body of evidence that the human cortex contains numerous regions that are specifically engaged in particular mental processes. The growing inventory of cortical regions with distinctive and often very specific functions can be seen as an initial sketch of the basic components of the human mind. This sketch also serves as a roadmap into the vast and exciting new landscape of questions about the computations, structural connections, time course, development, plasticity, and evolution of each of these regions, as well as the hardest question of all: how do these regions work together to produce human intelligence?

The Face-Processing Network Is Resilient to Focal Resection of Human Visual Cortex

Human face perception requires a network of brain regions distributed throughout the occipital and temporal lobes with a right hemisphere advantage. Present theories consider this network as either a processing hierarchy beginning with the inferior occipital gyrus (occipital face area; IOG-faces/OFA) or a multiple-route network with nonhierarchical components. The former predicts that removing IOG-faces/OFA will detrimentally affect downstream stages, whereas the latter does not. We tested this prediction in a human patient (Patient S.P.) requiring removal of the right inferior occipital cortex, including IOG-faces/OFA. We acquired multiple fMRI measurements in Patient S.P. before and after a preplanned surgery and multiple measurements in typical controls, enabling both within-subject/across-session comparisons (Patient S.P. before resection vs Patient S.P. after resection) and between-subject/across-session comparisons (Patient S.P. vs controls). We found that the spatial topology and selectivity of downstream ipsilateral face-selective regions were stable 1 and 8 month(s) after surgery. Additionally, the reliability of distributed patterns of face selectivity in Patient S.P. before versus after resection was not different from across-session reliability in controls. Nevertheless, postoperatively, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1 of the resected hemisphere. Diffusion weighted imaging in Patient S.P. and controls identifies white matter tracts connecting retinotopic areas to downstream face-selective regions, which may contribute to the stable and plastic features of the face network in Patient S.P. after surgery. Together, our results support a multiple-route network of face processing with nonhierarchical components and shed light on stable and plastic features of high-level visual cortex following focal brain damage.

SIGNIFICANCE STATEMENT

Brain networks consist of interconnected functional regions commonly organized in processing hierarchies. Prevailing theories predict that damage to the input of the hierarchy will detrimentally affect later stages. We tested this prediction with multiple brain measurements in a rare human patient requiring surgical removal of the putative input to a network processing faces. Surprisingly, the spatial topology and selectivity of downstream face-selective regions are stable after surgery. Nevertheless, representations of visual space were typical in dorsal face-selective regions but atypical in ventral face-selective regions and V1. White matter connections from outside the face network may support these stable and plastic features. As processing hierarchies are ubiquitous in biological and nonbiological systems, our results have pervasive implications for understanding the construction of resilient networks.

The Effect of Spatial Smoothing on Representational Similarity in a Simple Motor Paradigm

Multi-voxel pattern analyses (MVPA) are often performed on unsmoothed data, which is very different from the general practice of large smoothing extents in standard voxel-based analyses. In this report, we studied the effect of smoothing on MVPA results in a motor paradigm. Subjects pressed four buttons with two different fingers of the two hands in response to auditory commands. Overall, independent of the degree of smoothing, correlational MVPA showed distinctive patterns for the different hands in all studied regions of interest (motor cortex, prefrontal cortex, and auditory cortices). With regard to the effect of smoothing, our findings suggest that results from correlational MVPA show a minor sensitivity to smoothing. Moderate amounts of smoothing (in this case, 1−4 times the voxel size) improved MVPA correlations, from a slight improvement to large improvements depending on the region involved. None of the regions showed signs of a detrimental effect of moderate levels of smoothing. Even higher amounts of smoothing sometimes had a positive effect, most clearly in low-level auditory cortex. We conclude that smoothing seems to have a minor positive effect on MVPA results, thus researchers should be mindful about the choices they make regarding the level of smoothing.

Categorical learning revealed in activity pattern of left fusiform cortex

The brain is organized such that it encodes and maintains category information about thousands of objects. However, how learning shapes these neural representations of object categories is unknown. The present study focuses on faces, examining whether: (1) Enhanced categorical discrimination or (2) Feature analysis enhances face/non-face categorization in the brain. Stimuli ranged from non-faces to faces with two-toned Mooney images used for testing and gray-scale images used for training. The stimulus set was specifically chosen because it has a true categorical boundary between faces and non-faces but the stimuli surrounding that boundary have very similar features, making the boundary harder to learn. Brain responses were measured using functional magnetic resonance imaging while participants categorized the stimuli before and after training. Participants were either trained with a categorization task, or with non-categorical semblance analyzation. Interestingly, when participants were categorically trained, the neural activity pattern in the left fusiform gyrus shifted from a graded representation of the stimuli to a categorical representation. This corresponded with categorical face/non-face discrimination, critically including both an increase in selectivity to faces and a decrease in false alarm response to non-faces. By contrast, while activity pattern in the right fusiform cortex correlated with face/non-face categorization prior to training, it was not affected by learning. Our results reveal the key role of the left fusiform cortex in learning face categorization. Given the known right hemisphere dominance for face-selective responses, our results suggest a rethink of the relationship between the two hemispheres in face/non-face categorization. Hum Brain Mapp 38:3648-3658, 2017. © 2017 Wiley Periodicals, Inc.