Summary The difficulty of visual recognition stems from the need to achieve high selectivity while maintaining robustness to object transformations within hundreds of milliseconds. Theories of visual recognition differ in whether the neuronal circuits invoke recurrent feedback connections or not. The timing of neurophysiological responses in visual cortex plays a key role in distinguishing between bottom-up and top-down theories. Here we quantified at millisecond resolution the amount of visual information conveyed by intracranial field potentials from 912 electrodes in 11 human subjects. We could decode object category information from human visual cortex in single trials as early as 100 ms post-stimulus. Decoding performance was robust to depth rotation and scale changes. The results suggest that physiological activity in the temporal lobe can account for key properties of visual recognition. The fast decoding in single trials is compatible with feed-forward theories and provides strong constraints for computational models of human vision.

Response inhibition, or the suppression of prepotent, but contextually inappropriate behaviors, is essential to adaptive, flexible responding. In autism spectrum disorders (ASD), difficulty inhibiting prepotent behaviors may contribute to restricted, repetitive behavior (RRB). Individuals with ASD consistently show deficient response inhibition while performing antisaccades, which require one to inhibit the prepotent response of looking towards a suddenly appearing stimulus (i.e., a prosaccade), and to substitute a gaze in the opposite direction. Here, we used fMRI to identify the neural correlates of this deficit. We focused on two regions that are critical for saccadic inhibition: the frontal eye field (FEF), the key cortical region for generating volitional saccades, and the dorsal anterior cingulate cortex (dACC), which is thought to exert top-down control on FEF. We also compared ASD and control groups on the functional connectivity of the dACC and FEF during saccadic performance. In the context of an increased antisaccade error rate, ASD participants showed decreased functional connectivity of the FEF and dACC and decreased inhibition-related activation (based on the contrast of antisaccades and prosaccades) in both regions. Decreased dACC activation correlated with a higher error rate in both groups, consistent with a role in top-down control. Within the ASD group, increased FEF activation and dACC/FEF functional connectivity were associated with more severe RRB. These findings demonstrate functional abnormalities in a circuit critical for volitional ocular motor control in ASD that may contribute to deficient response inhibition and to RRB. More generally, our findings suggest reduced cognitive control over behavior by the dACC in ASD.Autism spectrum disorders (ASD) are common neurodevelopmental disorders that are characterized by restricted, repetitive behavior (RRB) and marked impairments in socialization and communication. These three symptom clusters are thought to arise from distinct genetic and cognitive mechanisms (Happe et al 2006;London 2007), but these mechanisms are not well-understood. Accumulating evidence suggests that executive function deficits contribute to these core symptoms of ASD (Hill 2004;Lopez et al 2005;South et al 2007). ResponseCorresponding author: Dara S. Manoach, 149 13th St., Room 2608, Charlestown, MA 02129; tel: 617 724 6148; fax: 617 726 4078; dara@nmr.mgh.harvard.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptNeuroimage. Author manuscript; available in PMC 2011 August 1. Published ...

Recognizing errors and adjusting responses are fundamental to adaptive behavior. The error-related negativity (ERN) and errorrelated functional MRI (fMRI) activation of the dorsal anterior cingulate cortex (dACC) index these processes and are thought to reflect the same neural mechanism. In the present study, we evaluated this hypothesis. Although errors elicited robust dACC activation using fMRI, combined electroencephalography and magnetoencephalography data localized the ERN to the posterior cingulate cortex (PCC). ERN amplitude correlated with fMRI activation in both the PCC and dACC, and these two regions showed coordinated activity based on functional connectivity MRI. Finally, increased microstructural integrity of the posterior cingulum bundle, as measured by diffusion tensor imaging, predicted faster error correction. These findings suggest that the PCC generates the ERN and communicates with the dACC to subserve error processing. They challenge current models that view fMRI activation of the dACC as the hemodynamic reflection of the ERN. U nderstanding the nature of brain mechanisms that flexibly modify behavior in response to its outcome is a basic goal of neuroscience. Errors provide critical information for adjusting behavior to optimize outcomes. Neuroimaging studies have identified two highly reliable neural correlates of errors: the error-related negativity (ERN), an event-related potential that peaks ∼100 ms following an error, and functional MRI (fMRI) activation of the dorsal anterior cingulate cortex (dACC) for erroneous compared with correct responses (1). Both electroencephalography (EEG) and magnetoencephalography (MEG) (2) studies of the ERN have reported a source in the dACC (a list of studies is presented in Table S1), which is consistent with models that attribute these error markers to a common underlying mechanism (1, 3, 4). The primary goal of the present study was to evaluate the hypothesis of a common mechanism by determining whether the ERN is generated by the dACC region that shows error-related fMRI activation.The ERN has been extensively characterized. Its amplitude is greater when accuracy is emphasized over speed (5), when errors are corrected (6), and when errors incur greater loss (7). Larger ERNs are associated with lower error rates (3) and greater posterror slowing of responses (8). ERN latency predicts the speed of self-corrections (9). These findings suggest that the ERN is sensitive to the value of outcomes and mediates dynamic performance adjustments. Like the ERN, greater error-related fMRI activation of the dACC is associated with fewer errors (10, 11) and increased posterror slowing (12)(13)(14).Although error-related dACC activation is the putative hemodynamic reflection of the ERN (1, 4), these error markers have largely been studied separately using different samples and paradigms. The few studies that have directly investigated their relationship report correlations of fMRI activation of the ACC with the ERN and/or the error waveform or response-locked electr...

A fundamental challenge in neuroscience is to understand the mechanisms by which multicomponent actions are represented and sequenced for production. We addressed this challenge with a movement-imitation task in which subjects viewed the quasi-random, two-dimensional movements of a disc and then used a stylus to reproduce the remembered trajectory. The stimulus disc moved along straight segments, which differed sufficiently from one another that it was possible to trace individual segments' fate in the resulting movement imitation. A biologically based segmentation algorithm decomposed each imitation into segments whose directions could be compared with those of homologous segments in the model. As the number of linked segments in a stimulus model grew from three to seven, imitation became less accurate, with segments more likely to be deleted, particularly from a model's final stages. When fidelity of imitation was assessed segment by segment, the resulting serial position curves showed a strong primacy effect and a moderate recency effect. Analysis of pairwise transposition errors revealed a striking preponderance of exchanges between adjacent segments that, along with the serial position effects, supports a competitive queuing model of sequencing. In analogy to results with verbal serial recall, repetition of one directed segment in the model reduced imitation quality. Results with longer stimulus models suggest that the segment-by-segment imitation generator may be supplemented in the final stages of imitation by an error-signal driven overlay that produces a late-course, real-time correction. Results are related to neural mechanisms that are known to support sequential motor behavior and working memory.

There is ongoing debate concerning the functions of resting-state brain activity. Prior work demonstrates that memory encoding enhances subsequent resting-state functional connectivity within task-relevant networks and that these changes predict better recognition. Here, we used functional connectivity MRI (fcMRI) to examine whether task-induced changes in resting-state connectivity correlate with performance improvement after sleep. In two separate sessions, resting-state scans were acquired before and after participants performed a motor task. In one session participants trained on the motor sequence task (MST), a well-established probe of sleep-dependent memory consolidation, and were tested the next day, after a night of sleep. In the other session they performed a motor control task (MCT) that minimized learning. In an accompanying behavioral control study, participants trained on the MST and were tested after either a night of sleep or an equivalent interval of daytime wake. Both the fcMRI and the sleep control groups showed significant improvement of MST performance, while the wake control group did not. In the fcMRI group, increased connectivity in bilateral motor cortex following MST training correlated with this next-day improvement. This increased connectivity did not appear to reflect initial learning since it did not correlate with learning during training and was not greater after MST training than MCT performance. Instead, we hypothesize that this increased connectivity processed the new memories for sleep-dependent consolidation. Our findings demonstrate that physiological processes immediately after learning correlate with sleep-dependent performance improvement and suggest that the wakeful resting brain prepares memories of recent experiences for later consolidation during sleep.

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromallyunaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

SUMMARY We can recognize objects in a fraction of a second in spite of the presence of other objects [1–3]. The responses in macaque areas V4 and inferior temporal cortex [4–15] to a neuron’s preferred stimuli are typically suppressed by the addition of a second object within the receptive field (see however [16, 17]). How can this suppression be reconciled with rapid visual recognition in complex scenes? One option is that certain “special categories” are unaffected by other objects [18] but this leaves the problem unsolved for other categories. Another possibility is that serial attentional shifts help ameliorate the problem of distractor objects [19–21]. Yet, psychophysical studies [1–3], scalp recordings [1] and neurophysiological recordings [14, 16, 22–24], suggest that the initial sweep of visual processing contains a significant amount of information. We recorded intracranial field potentials in human visual cortex during presentation of flashes of two-object images. Visual selectivity from temporal cortex during the initial ~200 ms was largely robust to the presence of other objects. We could train linear decoders on the responses to isolated objects and decode information in two-object images. These observations are compatible with parallel, hierarchical and feed-forward theories of rapid visual recognition [25] and may provide a neural substrate to begin to unravel rapid recognition in natural scenes.