Human brain regions involved in visual categorization

Further clarification of cognitive processes of prospective memory in schizophrenia by comparing eye-tracking and ecologically-valid measurements

The aim of this study is to compare ecologically-valid measure (the Cambridge Prospective Memory Test, CAMPROMPT) and laboratory measure (eye-tracking paradigm) in assessing prospective memory (PM) in individuals with schizophrenia spectrum disorders (SSDs). In addition, eye-tracking indices are used to examine the relationship between PM and other cognitive domains in SSDs patients. Initially, the study sample was formed by 32 SSDs patients and 32 healthy control subjects (HCs) who were matched in sociodemographic profile and the performance on CAMPROMPT. An eye-tracking paradigm was employed to examine the differences in PM accuracy and key cognitive processes (e.g., cue monitoring) between the two groups. Additional 31 patients were then recruited to investigate the relationship between PM cue monitoring, other cognitive functions, and the severity of clinical symptoms within the SSDs group. The monitoring of PM cue was reflected in total fixation time and total fixation counts for distractor words. Cognitive functions were assessed using the Chinese version of the MATRICS Consensus Cognitive Battery (MCCB). The Positive and Negative Syndrome Scale (PANSS) was applied to assess psychopathology. SSDs patients exhibited fewer total fixation counts for distractor words and lower PM accuracy compared to HCs, even though they were priori matched on CAMPROMPT. Correlation analysis within the SSDs group (63 cases) indicated a negative correlation between PM accuracy and PANSS total score, and a positive correlation with working memory and attention/vigilance. Regression analysis within the SSDs group revealed that higher visual learning and lower PANSS total scores independently predicted more total fixation counts on distractor words. Impairment in cue monitoring is a critical factor in the PM deficits in SSDs. The eye-tracking laboratory paradigm has advantages over the ecologically-valid measurement in identifying the failure of cue detection, making it a more sensitive tool for PM deficits in patients with SSDs.

The prefrontal cortex: from monkey to man

The prefrontal cortex is so important to human beings that, if deprived of it, our behaviour is reduced to action-reactions and automatisms, with no ability to make deliberate decisions. Why does the prefrontal cortex hold such importance in humans? In answer, this review draws on the proximity between humans and other primates, which enables us, through comparative anatomical-functional analysis, to understand the cognitive functions we have in common and specify those that distinguish humans from their closest cousins. First, a focus on the lateral region of the prefrontal cortex illustrates the existence of a continuum between rhesus monkeys (the most studied primates in neuroscience) and humans for most of the major cognitive functions in which this region of the brain plays a central role. This continuum involves the presence of elementary mental operations in the rhesus monkey (e.g. working memory or response inhibition) that are constitutive of 'macro-functions' such as planning, problem-solving and even language production. Second, the human prefrontal cortex has developed dramatically compared to that of other primates. This increase seems to concern the most anterior part (the frontopolar cortex). In humans, the development of the most anterior prefrontal cortex is associated with three major and interrelated cognitive changes: (i) a greater working memory capacity, allowing for greater integration of past experiences and prospective futures; (ii) a greater capacity to link discontinuous or distant data, whether temporal or semantic; and (iii) a greater capacity for abstraction, allowing humans to classify knowledge in different ways, to engage in analogical reasoning or to acquire abstract values that give rise to our beliefs and morals. Together, these new skills enable us, among other things, to develop highly sophisticated social interactions based on language, enabling us to conceive beliefs and moral judgements and to conceptualize, create and extend our vision of our environment beyond what we can physically grasp. Finally, a model of the transition of prefrontal functions between humans and non-human primates concludes this review.

Theta activity in semantic priming under visual crowding as revealed by magnetoencephalography

Crowding refers to impaired object recognition of peripheral visual targets caused by nearby flankers. It has been shown that the response to a word was faster when it was preceded by a semantically related than unrelated crowded prime, demonstrating that semantic priming survives crowding. This study examines neural correlates of semantic priming under visual crowding using magnetoencephalography with four conditions: prime (isolated, crowded) x prime-target relationship (related, unrelated). Participants judged whether the target was a word or a nonword. We found significant differences in θ activity at the left inferior frontal gyrus (IFG) for both isolated and crowded primes when comparing the unrelated and related conditions, although the activation was delayed with the crowded prime compared to the isolated prime. The locations within the IFG were also different: theta-band activation was at BA 45 in the isolated condition and at BA 47 in the crowded condition. Phase-locking-value analysis revealed that bilateral IFG was more synchronized with unrelated prime-target pairs than related pairs regardless of whether the primes were isolated or crowded, indicating the recruitment of the right hemisphere when the prime-target semantic relationship was remote. Finally, the distinct waveform patterns found in the isolated and crowded conditions from both the source localization and PLV analysis suggest different neural mechanisms for processing semantic information with isolated primes versus crowded primes.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning

To accurately categorize items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to attentional tuning as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

Motor memories of object dynamics are categorically organized

The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors.

Exposure to linguistic labels during childhood modulates the neural architecture of race categorical perception

Perceptually categorizing a face to its racial belonging may have important consequences on interacting with people. However, race categorical perception (CP) has been scarcely investigated nor its developmental pathway. In this study, we tested the neurolinguistics rewiring hypothesis, stating that language acquisition modulates the brain processing of social perceptual categories. Accordingly, we investigated the electrophysiological correlates of race CP in a group of adults and children between 3 and 5 years of age. For both groups we found a greater modulation of the N400 connected with the processing of between category boundaries (i.e., faces belonging to different race groups) than within-category boundaries (i.e., different faces belonging to the same race group). This effect was the same in both adults and children, as shown by the comparable between-group amplitude of the differential wave (DW) elicited by the between-category faces. Remarkably, this effect was positively correlated with racial-labels acquisition, but not with age, in children. Finally, brain source analysis revealed the activation of a more modularized cortical network in adults than in children, with unique activation of the left superior temporal gyrus (STG) and the inferior frontal gyrus (IFG), which are areas connected to language processing. These are the first results accounting for an effect of language in rewiring brain connectedness when processing racial categories.

Different Levels of Category Abstraction by Different Dynamics in Different Prefrontal Areas

Categories can be grouped by shared sensory attributes (i.e., cats) or a more abstract rule (i.e., animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in the ventral PFC processed more low-level abstractions, whereas top-down dynamics (beta power and beta spike-LFP coherence) in the dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories.

Anterior Temporal Lobe Morphometry Predicts Categorization Ability

Categorization is the mental operation by which the brain classifies objects and events. It is classically assessed using semantic and non-semantic matching or sorting tasks. These tasks show a high variability in performance across healthy controls and the cerebral bases supporting this variability remain unknown. In this study we performed a voxel-based morphometry study to explore the relationships between semantic and shape categorization tasks and brain morphometric differences in 50 controls. We found significant correlation between categorization performance and the volume of the gray matter in the right anterior middle and inferior temporal gyri. Semantic categorization tasks were associated with more rostral temporal regions than shape categorization tasks. A significant relationship was also shown between white matter volume in the right temporal lobe and performance in the semantic tasks. Tractography revealed that this white matter region involved several projection and association fibers, including the arcuate fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, and inferior longitudinal fasciculus. These results suggest that categorization abilities are supported by the anterior portion of the right temporal lobe and its interaction with other areas.

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Reduced Frontoparietal Activity in Schizophrenia Is Linked to a Specific Deficit in Goal Maintenance: A Multisite Functional Imaging Study

Patients with schizophrenia (SZ) previously demonstrated specific deficits in an executive function known as goal maintenance, associated with reduced middle frontal gyrus (MFG) activity. This study aimed to validate a new tool-the Dot Pattern Expectancy (DPX) task-developed to facilitate multisite imaging studies of goal maintenance deficits in SZ or other disorders. Additionally, it sought to arrive at recommendations for scan length for future studies using the DPX. Forty-seven SZ and 56 healthy controls (HC) performed the DPX in 3-Tesla functional magnetic resonance imaging (fMRI) scanners at 5 sites. Group differences in DPX-related activity were examined with whole brain voxelwise analyses. SZs showed the hypothesized specific performance deficits with as little as 1 block of data. Reduced activity in SZ compared with HC was observed in bilateral frontal pole/MFG, as well as left posterior parietal lobe. Efficiency analyses found significant group differences in activity using 18 minutes of scan data but not 12 minutes. Several behavioral and imaging findings from the goal maintenance literature were robustly replicated despite the use of different scanners at different sites. We did not replicate a previous correlation with disorganization symptoms among patients. Results were consistent with an executive/attention network dysfunction in the higher levels of a cascading executive system responsible for goal maintenance. Finally, efficiency analyses found that 18 minutes of scanning during the DPX task is sufficient to detect group differences with a similar sample size.

The role of parietal cortex in the formation of color and motion based concepts

Imaging evidence shows that separate subdivisions of parietal cortex, in and around the intraparietal sulcus (IPS), are engaged when stimuli are grouped according to color and to motion (Zeki and Stutters, 2013). Since grouping is an essential step in the formation of concepts, we wanted to learn whether parietal cortex is also engaged in the formation of concepts according to these two attributes. Using functional magnetic resonance imaging (fMRI), and choosing the recognition of concept-based color or motion stimuli as our paradigm, we found that there was strong concept-related activity in and around the IPS, a region whose homolog in the macaque monkey is known to receive direct but segregated anatomical inputs from V4 and V5. Parietal activity related to color concepts was juxtaposed but did not overlap with activity related to motion concepts, thus emphasizing the continuation of the segregation of color and motion into the conceptual system. Concurrent retinotopic mapping experiments showed that within the parietal cortex, concept-related activity increases within later stage IPS areas.

Invariance detection in the brain: revealed in a stepwise category induction task

A critical sub-process of category learning is detecting the invariance between categorical members. To examine brain activation associated with invariance detection at different steps of category learning, a stepwise category induction task was used in the present study. Within each trial, three stimuli were displayed sequentially, and participants were asked to learn the target category corresponding to the invariance among stimuli. Results revealed that invariance detection activated the fronto-parietal network. However, the frontal and parietal cortices functioned differently throughout the different steps of invariance detection. The left middle frontal gyrus (BA 9) was highly activated in both steps of invariance detection, but the posterior parietal regions, especially the right superior parietal lobule (BA 7), were more active in the final step of invariance detection, reflecting increased attention to the completion of category learning and the preparation for a subsequent response. Furthermore, a psychophysiological interaction analysis (PPI) revealed increased connectivity between the left middle frontal gyrus and the bilateral parietal cortex during the final step of invariance detection. Overall, the present findings imply the necessary role of the fronto-parietal network in variance detection.

Increases in functional connectivity between prefrontal cortex and striatum during category learning

Functional connectivity between the prefrontal cortex (PFC) and striatum (STR) is thought critical for cognition and has been linked to conditions like autism and schizophrenia. We recorded from multiple electrodes in PFC and STR while monkeys acquired new categories. Category learning was accompanied by an increase in beta band synchronization of LFPs between, but not within, the PFC and STR. After learning, different pairs of PFC-STR electrodes showed stronger synchrony for one or the other category, suggesting category-specific functional circuits. This category-specific synchrony was also seen between PFC spikes and STR LFPs, but not the reverse, reflecting the direct monosynaptic connections from the PFC to STR. However, causal connectivity analyses suggested that the polysynaptic connections from STR to the PFC exerted a stronger overall influence. This supports models positing that the basal ganglia "train" the PFC. Category learning may depend on the formation of functional circuits between the PFC and STR.

Categorical encoding of color in the brain

The areas of the brain that encode color categorically have not yet been reliably identified. Here, we used functional MRI adaptation to identify neuronal populations that represent color categories irrespective of metric differences in color. Two colors were successively presented within a block of trials. The two colors were either from the same or different categories (e.g., "blue 1 and blue 2" or "blue 1 and green 1"), and the size of the hue difference was varied. Participants performed a target detection task unrelated to the difference in color. In the middle frontal gyrus of both hemispheres and to a lesser extent, the cerebellum, blood-oxygen level-dependent response was greater for colors from different categories relative to colors from the same category. Importantly, activation in these regions was not modulated by the size of the hue difference, suggesting that neurons in these regions represent color categorically, regardless of metric color difference. Representational similarity analyses, which investigated the similarity of the pattern of activity across local groups of voxels, identified other regions of the brain (including the visual cortex), which responded to metric but not categorical color differences. Therefore, categorical and metric hue differences appear to be coded in qualitatively different ways and in different brain regions. These findings have implications for the long-standing debate on the origin and nature of color categories, and also further our understanding of how color is processed by the brain.

How embodied is perceptual decision making? Evidence for separate processing of perceptual and motor decisions

The extent to which different cognitive processes are "embodied" is widely debated. Previous studies have implicated sensorimotor regions such as lateral intraparietal (LIP) area in perceptual decision making. This has led to the view that perceptual decisions are embodied in the same sensorimotor networks that guide body movements. We use event-related fMRI and effective connectivity analysis to investigate whether the human sensorimotor system implements perceptual decisions. We show that when eye and hand motor preparation is disentangled from perceptual decisions, sensorimotor areas are not involved in accumulating sensory evidence toward a perceptual decision. Instead, inferior frontal cortex increases its effective connectivity with sensory regions representing the evidence, is modulated by the amount of evidence, and shows greater task-positive BOLD responses during the perceptual decision stage. Once eye movement planning can begin, however, an intraparietal sulcus (IPS) area, putative LIP, participates in motor decisions. Moreover, sensory evidence levels modulate decision and motor preparation stages differently in different IPS regions, suggesting functional heterogeneity of the IPS. This suggests that different systems implement perceptual versus motor decisions, using different neural signatures.

Quantifying the internal structure of categories using a neural typicality measure

How categories are represented continues to be hotly debated across neuroscience and psychology. One topic that is central to cognitive research on category representation but underexplored in neurobiological research concerns the internal structure of categories. Internal structure refers to how the natural variability between-category members is coded so that we are able to determine which members are more typical or better examples of their category. Psychological categorization models offer tools for predicting internal structure and suggest that perceptions of typicality arise from similarities between the representations of category members in a psychological space. Inspired by these models, we develop a neural typicality measure that allows us to measure which category members elicit patterns of activation that are similar to other members of their category and are thus more central in a neural space. Using an artificial categorization task, we test how psychological and physical typicality contribute to neural typicality, and find that neural typicality in occipital and temporal regions is significantly correlated with subjects' perceptions of typicality. The results reveal a convergence between psychological and neural category representations and suggest that our neural typicality measure is a useful tool for connecting psychological and neural measures of internal category structure.

Effects of category learning on neural sensitivity to non-native phonetic categories

Categorical perception, an increased sensitivity to between- compared with within-category contrasts, is a stable property of native speech perception that emerges as language matures. Although recent research suggests that categorical responses to speech sounds can be found in left prefrontal as well as temporo-parietal areas, it is unclear how the neural system develops heightened sensitivity to between-category contrasts. In the current study, two groups of adult participants were trained to categorize speech sounds taken from a dental/retroflex/velar continuum according to two different boundary locations. Behavioral results suggest that for successful learners, categorization training led to increased discrimination accuracy for between-category contrasts with no concomitant increase for within-category contrasts. Neural responses to the learned category schemes were measured using a short-interval habituation design during fMRI scanning. Whereas both inferior frontal and temporal regions showed sensitivity to phonetic contrasts sampled from the continuum, only the bilateral middle frontal gyri exhibited a pattern consistent with encoding of the learned category scheme. Taken together, these results support a view in which top-down information about category membership may reshape perceptual sensitivities via attention or executive mechanisms in the frontal lobes.

Category learning increases discriminability of relevant object dimensions in visual cortex

Learning to categorize objects can transform how they are perceived, causing relevant perceptual dimensions predictive of object category to become enhanced. For example, an expert mycologist might become attuned to species-specific patterns of spacing between mushroom gills but learn to ignore cap textures attributable to varying environmental conditions. These selective changes in perception can persist beyond the act of categorizing objects and influence our ability to discriminate between them. Using functional magnetic resonance imaging adaptation, we demonstrate that such category-specific perceptual enhancements are associated with changes in the neural discriminability of object representations in visual cortex. Regions within the anterior fusiform gyrus became more sensitive to small variations in shape that were relevant during prior category learning. In addition, extrastriate occipital areas showed heightened sensitivity to small variations in shape that spanned the category boundary. Visual representations in cortex, just like our perception, are sensitive to an object's history of categorization.

Similar or different? The role of the ventrolateral prefrontal cortex in similarity detection

Patients with frontal lobe syndrome can exhibit two types of abnormal behaviour when asked to place a banana and an orange in a single category: some patients categorize them at a concrete level (e.g., “both have peel”), while others continue to look for differences between these objects (e.g., “one is yellow, the other is orange”). These observations raise the question of whether abstraction and similarity detection are distinct processes involved in abstract categorization, and that depend on separate areas of the prefrontal cortex (PFC). We designed an original experimental paradigm for a functional magnetic resonance imaging (fMRI) study involving healthy subjects, confirming the existence of two distinct processes relying on different prefrontal areas, and thus explaining the behavioural dissociation in frontal lesion patients. We showed that: 1) Similarity detection involves the anterior ventrolateral PFC bilaterally with a right-left asymmetry: the right anterior ventrolateral PFC is only engaged in detecting physical similarities; 2) Abstraction per se activates the left dorsolateral PFC.

Neural representations for object perception: structure, category, and adaptive coding

Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses.

Differences between neural activity in prefrontal cortex and striatum during learning of novel abstract categories

Learning to classify diverse experiences into meaningful groups, like categories, is fundamental to normal cognition. To understand its neural basis, we simultaneously recorded from multiple electrodes in lateral prefrontal cortex and dorsal striatum, two interconnected brain structures critical for learning. Each day, monkeys learned to associate novel abstract, dot-based categories with a right versus left saccade. Early on, when they could acquire specific stimulus-response associations, striatum activity was an earlier predictor of the corresponding saccade. However, as the number of exemplars increased and monkeys had to learn to classify them, PFC activity began to predict the saccade associated with each category before the striatum. While monkeys were categorizing novel exemplars at a high rate, PFC activity was a strong predictor of their corresponding saccade early in the trial before the striatal neurons. These results suggest that striatum plays a greater role in stimulus-response association and PFC in abstraction of categories.

Encoding of complexity, shape, and curvature by macaque infero-temporal neurons

We recorded responses of macaque infero-temporal (IT) neurons to a stimulus set of Fourier boundary descriptor shapes wherein complexity, general shape, and curvature were systematically varied. We analyzed the response patterns of the neurons to the different stimuli using multidimensional scaling. The resulting neural shape space differed in important ways from the physical, image-based shape space. We found a particular sensitivity for the presence of curved versus straight contours that existed only for the simple but not for the medium and highly complex shapes. Also, IT neurons could linearly separate the simple and the complex shapes within a low-dimensional neural shape space, but no distinction was found between the medium and high levels of complexity. None of these effects could be derived from physical image metrics, either directly or by comparing the neural data with similarities yielded by two models of low-level visual processing (one using wavelet-based filters and one that models position and size invariant object selectivity through four hierarchically organized neural layers). This study highlights the relevance of complexity to IT neural encoding, both as a neurally independently represented shape property and through its influence on curvature detection.

Category learning in the brain

The ability to group items and events into functional categories is a fundamental characteristic of sophisticated thought. It is subserved by plasticity in many neural systems, including neocortical regions (sensory, prefrontal, parietal, and motor cortex), the medial temporal lobe, the basal ganglia, and midbrain dopaminergic systems. These systems interact during category learning. Corticostriatal loops may mediate recursive, bootstrapping interactions between fast reward-gated plasticity in the basal ganglia and slow reward-shaded plasticity in the cortex. This can provide a balance between acquisition of details of experiences and generalization across them. Interactions between the corticostriatal loops can integrate perceptual, response, and feedback-related aspects of the task and mediate the shift from novice to skilled performance. The basal ganglia and medial temporal lobe interact competitively or cooperatively, depending on the demands of the learning task.

Representation of multiple, independent categories in the primate prefrontal cortex

Neural correlates of visual categories have been previously identified in the prefrontal cortex (PFC). However, whether individual neurons can represent multiple categories is unknown. Varying degrees of generalization versus specialization of neurons in the PFC have been theorized. We recorded from lateral PFC neural activity while monkeys switched between two different and independent categorical distinctions (Cats versus Dogs, Sports Cars versus Sedans). We found that many PFC neurons reflected both categorical distinctions. In fact, these multitasking neurons had the strongest category effects. This stands in contrast to our lab's recent report that monkeys switching between competing categorical distinctions (applied to the same stimulus set) showed independent representations. We suggest that cognitive demands determine whether PFC neurons function as category "multitaskers."

Prefrontal cortex activity during flexible categorization

Items are categorized differently depending on the behavioral context. For instance, a lion can be categorized as an African animal or a type of cat. We recorded lateral prefrontal cortex (PFC) neural activity while monkeys switched between categorizing the same image set along two different category schemes with orthogonal boundaries. We found that each category scheme was largely represented by independent PFC neuronal populations and that activity reflecting a category distinction was weaker, but not absent, when that category was irrelevant. We suggest that the PFC represents competing category representations independently to reduce interference between them.

Assessing the neural basis of uncertainty in perceptual category learning through varying levels of distortion

The formation of new perceptual categories involves learning to extract that information from a wide range of often noisy sensory inputs, which is critical for selecting between a limited number of responses. To identify brain regions involved in visual classification learning under noisy conditions, we developed a task on the basis of the classical dot pattern prototype distortion task [M. I. Posner, Journal of Experimental Psychology, 68, 113-118, 1964]. Twenty-seven healthy young adults were required to assign distorted patterns of dots into one of two categories, each defined by its prototype. Categorization uncertainty was modulated parametrically by means of Shannon's entropy formula and set to the levels of 3, 7, and 8.5 bits/dot within subsets of the stimuli. Feedback was presented after each trial, and two parallel versions of the task were developed to contrast practiced and unpracticed performance within a single session. Using event-related fMRI, areas showing increasing activation with categorization uncertainty and decreasing activation with training were identified. Both networks largely overlapped and included areas involved in visuospatial processing (inferior temporal and posterior parietal areas), areas involved in cognitive processes requiring a high amount of cognitive control (posterior medial wall), and a cortico-striatal-thalamic loop through the body of the caudate nucleus. Activity in the medial prefrontal wall was increased when subjects received negative as compared with positive feedback, providing further evidence for its important role in mediating the error signal. This study characterizes the cortico-striatal network underlying the classification of distorted visual patterns that is directly related to decision uncertainty.

Specialization of the rostral prefrontal cortex for distinct analogy processes

Analogical reasoning is central to learning and abstract thinking. It involves using a more familiar situation (source) to make inferences about a less familiar situation (target). According to the predominant cognitive models, analogical reasoning includes 1) generation of structured mental representations and 2) mapping based on structural similarities between them. This study used functional magnetic resonance imaging to specify the role of rostral prefrontal cortex (PFC) in these distinct processes. An experimental paradigm was designed that enabled differentiation between these processes, by temporal separation of the presentation of the source and the target. Within rostral PFC, a lateral subregion was activated by analogy task both during study of the source (before the source could be compared with a target) and when the target appeared. This may suggest that this subregion supports fundamental analogy processes such as generating structured representations of stimuli but is not specific to one particular processing stage. By contrast, a dorsomedial subregion of rostral PFC showed an interaction between task (analogy vs. control) and period (more activated when the target appeared). We propose that this region is involved in comparison or mapping processes. These results add to the growing evidence for functional differentiation between rostral PFC subregions.

Electrophysiological correlates of high-level perception during spatial navigation

We studied the electrophysiological basis of object recognition by recording scalp electroencephalograms while participants played a virtual-reality taxi driver game. Participants searched for passengers and stores during virtual navigation in simulated towns. We compared oscillatory brain activity in response to store views that were targets or nontargets (during store search) or neutral (during passenger search). Even though store category was solely defined by task context (rather than by sensory cues), frontal electrophysiological activity in the low frequency bands (primarily in the [4-8 Hz] band) reliably distinguished between the target, nontarget, and neutral store views. These results implicate low-frequency oscillatory brain activity in frontal regions as an important variable in the study of the cognitive processes involved in object recognition, categorization, and other forms of high-level perception.

Dissociable prototype learning systems: evidence from brain imaging and behavior

The neural underpinnings of prototype learning are not well understood. A major source of confusion is that two versions of the prototype learning task have been used interchangeably in the literature; one where participants learn to categorize exemplars derived from two prototypes (A/B task), and one where participants learn to categorize exemplars derived from one prototype and noncategorical exemplars (A/non-A). We report results from an fMRI study of A/B and A/non-A prototype learning that allows for a direct contrast of the two learning methods. Accuracy in the two tasks did not correlate within subject despite equivalent average difficulty. The fMRI results revealed neural activation in a network of regions consistent with episodic memory retrieval for the A/B task while greater activation of a nondeclarative learning network was observed for the A/non-A task. The results demonstrate that learning in these two tasks is mediated by different neural systems and that recruitment of each system is dictated by the context of learning rather than the actual category structure.

Effects of category learning on the stimulus selectivity of macaque inferior temporal neurons

Primates can learn to categorize complex shapes, but as yet it is unclear how this categorization learning affects the representation of shape in visual cortex. Previous studies that have examined the effect of categorization learning on shape representation in the macaque inferior temporal (IT) cortex have produced diverse and conflicting results that are difficult to interpret owing to inadequacies in design. The present study overcomes these issues by recording IT responses before and after categorization learning. We used parameterized shapes that varied along two shape dimensions. Monkeys were extensively trained to categorize the shapes along one of the two dimensions. Unlike previous studies, our paradigm counterbalanced the relevant categorization dimension across animals. We found that categorization learning increased selectivity specifically for the category-relevant stimulus dimension (i.e., an expanded representation of the trained dimension), and that the ratio of within-category response similarities to between-category response similarities increased for the relevant dimension (i.e., category tuning). These small effects were only evident when the learned category-related effects were disentangled from the prelearned stimulus selectivity. These results suggest that shape-categorization learning can induce minor category-related changes in the shape tuning of IT neurons in adults, suggesting that learned, category-related changes in neuronal response mainly occur downstream from IT.

Neural substrates for action understanding at different description levels in the human brain

Understanding complex movements and abstract action goals is an important skill for our social interactions. Successful social interactions entail understanding of actions at different levels of action description, ranging from detailed movement trajectories that support learning of complex motor skills through imitation to distinct features of actions that allow us to discriminate between action goals and different action styles. Previous studies have implicated premotor, parietal, and superior temporal areas in action understanding. However, the role of these different cortical areas in action understanding at different levels of action description remains largely unknown. We addressed this question using advanced animation and stimulus generation techniques in combination with sensitive functional magnetic resonance imaging adaptation or repetition suppression methods. We tested the neural sensitivity of fronto-parietal and visual areas to differences in the kinematics and goals of actions using kinematic morphs of arm movements. Our findings provide novel evidence for differential involvement of ventral premotor, parietal, and temporal regions in action understanding. We show that the ventral premotor cortex encodes the physical similarity between movement trajectories and action goals that are important for exact copying of actions and the acquisition of complex motor skills. In contrast, whereas parietal regions and the superior temporal sulcus process the perceptual similarity between movements and may support the perception and imitation of abstract action goals and movement styles. Thus, our findings propose that fronto-parietal and visual areas involved in action understanding mediate a cascade of visual-motor processes at different levels of action description from exact movement copies to abstract action goals achieved with different movement styles.

The representation of perceived shape similarity and its role for category learning in monkeys: a modeling study

Categorization models often assume an intermediate stimulus representation by units implementing "distance functions", that is, units that are activated according to the distance or similarity among stimuli. Here we show that a popular example of these models, ALCOVE, is able to account for the performance of monkeys during category learning when it takes the perceived similarity among stimuli into account. Similar results were obtained with a slightly different model (ITCOVE) that included experimentally measured tuning curves of neurons in inferior temporal (IT) cortex. These results show the intimate link between category learning and perceived similarity as represented in IT cortex.

Flexible coding for categorical decisions in the human brain

Despite the importance of visual categorization for interpreting sensory experiences, little is known about the neural representations that mediate categorical decisions in the human brain. Here, we used psychophysics and pattern classification for the analysis of functional magnetic resonance imaging data to predict the features critical for categorical decisions from brain activity when observers categorized the same stimuli using different rules. Although a large network of cortical and subcortical areas contain information about visual categories, we show that only a subset of these areas shape their selectivity to reflect the behaviorally relevant features rather than simply physical similarity between stimuli. Specifically, temporal and parietal areas show selectivity for the perceived form and motion similarity, respectively. In contrast, frontal areas and the striatum represent the conjunction of spatiotemporal features critical for complex and adaptive categorization tasks and potentially modulate selectivity in temporal and parietal areas. These findings provide novel evidence for flexible neural coding in the human brain that translates sensory experiences to categorical decisions by shaping neural representations across a network of areas with dissociable functional roles in visual categorization.

How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback

This article examines how independent corticostriatal loops linking basal ganglia with cerebral cortex contribute to visual categorization. The first aspect of categorization discussed is the role of the visual corticostriatal loop, which connects the visual cortex and the body/tail of the caudate, in mapping visual stimuli to categories, including evaluating the degree to which this loop may generalize across individual category members. The second aspect of categorization discussed is the selection of appropriate actions or behaviors on the basis of category membership, and the role of the visual corticostriatal loop output and the motor corticostriatal loop, which connects motor planning areas with the putamen, in action selection. The third aspect of categorization discussed is how categories are learned with the aid of feedback linked dopaminergic projections to the basal ganglia. These projections underlie corticostriatal synaptic plasticity across the basal ganglia, and also serve as input to the executive and motivational corticostriatal loops that play a role in strategic use of feedback.

Categorization training results in shape- and category-selective human neural plasticity

Object category learning is a fundamental ability, requiring the combination of "bottom-up" stimulus-driven with "top-down" task-specific information. It therefore may be a fruitful domain for study of the general neural mechanisms underlying cortical plasticity. A simple model predicts that category learning involves the formation of a task-independent shape-selective representation that provides input to circuits learning the categorization task, with the computationally appealing prediction of facilitated learning of additional, novel tasks over the same stimuli. Using fMRI rapid-adaptation techniques, we find that categorization training (on morphed "cars") induced a significant release from adaptation for small shape changes in lateral occipital cortex irrespective of category membership, compatible with the sharpening of a representation coding for physical appearance. In contrast, an area in lateral prefrontal cortex, selectively activated during categorization, showed sensitivity posttraining to explicit changes in category membership. Further supporting the model, categorization training also improved discrimination performance on the trained stimuli.

Close coordination between recognition and action: Really two separate streams?

Somewhat in contrast to their proposal of two separate somatosensory streams, Dijkerman & de Haan (D&dH) propose that tactile recognition involves active manual exploration, and therefore involves parietal cortex. I argue that interactions from perception for action to object recognition can be found also in vision. Furthermore, there is evidence that perception for action and perception for recognition rely on similar processing principles.

Dissociation between Striatal Regions while Learning to Categorize via Feedback and via Observation

Convergent evidence from functional imaging and from neuropsychological studies of basal ganglia disorders indicates that the striatum is involved in learning to categorize visual stimuli with feedback. However, it is unclear which cognitive process or processes involved in categorization is or are responsible for striatal recruitment; different regions of the striatum have been linked to feedback processing and to acquisition of stimulus-category associations. We examined the effect of the presence of feedback during learning on striatal recruitment by comparing feedback learning with observational learning of an information integration task. In the feedback task, participants were shown a stimulus, made a button press response, and then received feedback as to whether they had made the correct response. In the observational task, participants were given the category label before the stimulus appeared and then made a button press indicating the correct category membership. A region-of-interest analysis was used to examine activity in three regions of the striatum: the head of the caudate, body and tail of the caudate, and the putamen. Activity in the left head of the caudate was modulated by the presence of feedback: The magnitude of activation change was greater during feedback learning than during observational learning. In contrast, the bilateral body and tail of the caudate and the putamen were active to a similar degree in both feedback and observational learning. This pattern of results supports a functional dissociation between regions of the striatum, such that the head of the caudate is involved in feedback processing, whereas the body and tail of the caudate and the putamen are involved in learning stimulus-category associations. The hippocampus was active bilaterally during both feedback and observational learning, indicating potential parallel involvement with the striatum in information integration category learning.

Neural bases of categorization of simple speech and nonspeech sounds

Categorization is fundamental to our perception and understanding of the environment. However, little is known about the neural bases underlying the categorization of sounds. Using human functional magnetic resonance imaging (fMRI) we compared the brain responses to a category discrimination task with an auditory discrimination task using identical sets of sounds. Our stimuli differed along two dimensions: a speech-nonspeech dimension and a fast-slow temporal dynamics dimension. All stimuli activated regions in the primary and nonprimary auditory cortices in the temporal cortex and in the parietal and frontal cortices for the two tasks. When comparing the activation patterns for the category discrimination task to those for the auditory discrimination task, the results show that a core group of regions beyond the auditory cortices, including inferior and middle frontal gyri, dorsomedial frontal gyrus, and intraparietal sulcus, were preferentially activated for familiar speech categories and for novel nonspeech categories. These regions have been shown to play a role in working memory tasks by a number of studies. Additionally, the categorization of nonspeech sounds activated left middle frontal gyrus and right parietal cortex to a greater extent than did the categorization of speech sounds. Processing the temporal aspects of the stimuli had a greater impact on the left lateralization of the categorization network than did other factors, particularly in the inferior frontal gyrus, suggesting that there is no inherent left hemisphere advantage in the categorical processing of speech stimuli, or for the categorization task itself.

Recognition memory is modulated by visual similarity

We used event-related fMRI to test whether recognition memory depends on visual similarity between familiar prototypes and novel exemplars. Subjects memorized portraits, landscapes, and abstract compositions by six painters with a unique style, and later performed a memory recognition task. The prototypes were presented with new exemplars that were either visually similar or dissimilar. Behaviorally, novel, dissimilar items were detected faster and more accurately. We found activation in a distributed cortical network that included face- and object-selective regions in the visual cortex, where familiar prototypes evoked stronger responses than new exemplars; attention-related regions in parietal cortex, where responses elicited by new exemplars were reduced with decreased similarity to the prototypes; and the hippocampus and memory-related regions in parietal and prefrontal cortices, where stronger responses were evoked by the dissimilar exemplars. Our findings suggest that recognition memory is mediated by classification of novel exemplars as a match or a mismatch, based on their visual similarity to familiar prototypes.

Event-related fMRI of category learning: Differences in classification and feedback networks

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification choice, visual feedback was presented. The average hemodynamic response was calculated across the eighteen subjects to identify the separate networks associated with both classification and feedback. Random-effects analyses identified the different networks implicated during the classification and feedback phases of each trial. The regions included prefrontal cortex, frontal eye fields, supplementary motor and eye fields, thalamus, caudate, superior and inferior parietal lobules, and areas within visual cortex. The differences between classification and feedback were identified as (i) overall higher volumes and signal intensities during classification as compared to feedback, (ii) involvement of the thalamus and superior parietal regions during the classification phase of each trial, and (iii) differential involvement of the caudate head during feedback. The effects of learning were then evaluated for both classification and feedback. Early in learning, subjects showed increased activation in the hippocampal regions during classification and activation in the heads of the caudate nuclei during the corresponding feedback phases. The findings suggest that early stages of prototype-distortion learning are characterized by networks previously associated with strategies of explicit memory and hypothesis testing. However as learning progresses the networks change. This finding suggests that the cognitive strategies also change during prototype-distortion learning.

Frontal networks for learning and executing arbitrary stimulus-response associations

Flexible rule learning, a behavior with obvious adaptive value, is known to depend on an intact prefrontal cortex (PFC). One simple, yet powerful, form of such learning consists of forming arbitrary stimulus-response (S-R) associations. A variety of evidence from monkey and human studies suggests that the PFC plays an important role in both forming new S-R associations and in using learned rules to select the contextually appropriate response to a particular stimulus cue. Although monkey lesion studies more strongly implicate the ventrolateral PFC (vlPFC) in S-R learning, clinical data and neurophysiology studies have implicated both the vlPFC and the dorsolateral region (dlPFC) in associative rule learning. Previous human imaging studies of S-R learning tasks, however, have not demonstrated involvement of the dlPFC. This may be because of the design of previous imaging studies, which used few stimuli and used explicitly stated one-to-one S-R mapping rules that were usually practiced before scanning. Humans learn these rules very quickly, limiting the ability of imaging techniques to capture activity related to rule acquisition. To address these issues, we performed functional magnetic resonance imaging while subjects learned by trial and error to associate sets of abstract visual stimuli with arbitrary manual responses. Successful learning of this task required discernment of a categorical type of S-R rule in a block design expected to yield sustained rule representation. Our results show that distinct components of the dorsolateral, ventrolateral, and anterior PFC, lateral premotor cortex, supplementary motor area, and the striatum are involved in learning versus executing categorical S-R rules.

Prototype-distortion category learning: a two-phase learning process across a distributed network

This paper reviews a body of work conducted in our laboratory that applies functional magnetic resonance imaging (fMRI) to better understand the biological response and change that occurs during prototype-distortion learning. We review results from two experiments (Little, Klein, Shobat, McClure, & Thulborn, 2004; Little & Thulborn, 2005) that provide support for increasing neuronal efficiency by way of a two-stage model that includes an initial period of recruitment of tissue across a distributed network that is followed by a period of increasing specialization with decreasing volume across the same network. Across the two studies, participants learned to classify patterns of random-dot distortions (Posner & Keele, 1968) into categories. At four points across this learning process subjects underwent examination by fMRI using a category-matching task. A large-scale network, altered across the protocol, was identified to include the frontal eye fields, both inferior and superior parietal lobules, and visual cortex. As behavioral performance increased, the volume of activation within these regions first increased and later in the protocol decreased. Based on our review of this work we propose that: (i) category learning is reflected as specialization of the same network initially implicated to complete the novel task, and (ii) this network encompasses regions not previously reported to be affected by prototype-distortion learning.

The impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease

This study examined the impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease (PD), older controls (OC), and younger controls (YC). Participants were presented with 4-dimensional, binary-valued stimuli and were asked to categorize each into 1 of 2 categories. Category membership was based on the value of a single dimension. Four experimental conditions were administered in which there were zero, 1, 2, or 3 randomly varying irrelevant dimensions. Results indicated that patients with PD were impacted to a greater extent than both the OC and YC participants when the number of randomly varying irrelevant dimensions increased. These results suggest that the degree of working memory and selective attention requirements of a categorization task will impact whether PD patients are impaired in rule-based category learning, and help to clarify recent discrepancies in the literature.

Category learning and multiple memory systems

Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. As different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks.

The roles of the caudate nucleus in human classification learning

The caudate nucleus is commonly active when learning relationships between stimuli and responses or categories. Previous research has not differentiated between the contributions to learning in the caudate and its contributions to executive functions such as feedback processing. We used event-related functional magnetic resonance imaging while participants learned to categorize visual stimuli as predicting "rain" or "sun." In each trial, participants viewed a stimulus, indicated their prediction via a button press, and then received feedback. Conditions were defined on the bases of stimulus-outcome contingency (deterministic, probabilistic, and random) and feedback (negative and positive). A region of interest analysis was used to examine activity in the head of the caudate, body/tail of the caudate, and putamen. Activity associated with successful learning was localized in the body and tail of the caudate and putamen; this activity increased as the stimulus-outcome contingencies were learned. In contrast, activity in the head of the caudate and ventral striatum was associated most strongly with processing feedback and decreased across trials. The left superior frontal gyrus was more active for deterministic than probabilistic stimuli; conversely, extrastriate visual areas were more active for probabilistic than deterministic stimuli. Overall, hippocampal activity was associated with receiving positive feedback but not with correct classification. Successful learning correlated positively with activity in the body and tail of the caudate nucleus and negatively with activity in the hippocampus.