Normal people have a strikingly low ability to detect changes in a visual scene. This has been taken as evidence that the brain represents only a few objects at a time, namely those currently in the focus of attention. In the present study, subjects were asked to detect changes in the orientation of rectangular figures in a textured display across a 1600 ms gray interval. In the first experiment, change detection improved when the location of a possible change was cued during the interval. The cue remained effective during the entire interval, but after the interval, it was ineffective, suggesting that an initially large representation was overwritten by the post-change display. To control for an effect of light intensity during the interval on the decay of the representation, we compared performance with a gray or a white interval screen in a second experiment. We found no difference between these conditions. In the third experiment, attention was occasionally misdirected during the interval by first cueing the wrong figure, before cueing the correct figure. This did not compromise performance compared to a single cue, indicating that when an item is attentionally selected, the representation of yet unchosen items remains available. In the fourth experiment, the cue was shown to be effective when changes in figure size and orientation were randomly mixed. At the time the cue appeared, subjects could not know whether size or orientation would change, therefore these results suggest that the representation contains features in their 'bound' state. Together, these findings indicate that change blindness involves overwriting of a large capacity representation by the post-change display.

Attention to a stimulus enhances both neuronal responses and gamma frequency synchrony in visual area V4, both of which should increase the impact of attended information on downstream neurons. To determine whether gamma synchrony is common throughout the ventral stream, we recorded from neurons in the superficial and deep layers of V1, V2, and V4 in two rhesus monkeys. We found an unexpected striking difference in gamma synchrony in the superficial vs. deep layers. In all three areas, spike-field coherence in the gamma (40-60 Hz) frequency range was largely confined to the superficial layers, whereas the deep layers showed maximal coherence at low frequencies (6-16 Hz), which included the alpha range. In the superficial layers of V2 and V4, gamma synchrony was enhanced by attention, whereas in the deep layers, alpha synchrony was reduced by attention. Unlike these major differences in synchrony, attentional effects on firing rates and noise correlation did not differ substantially between the superficial and deep layers. The results suggest that synchrony plays very different roles in feedback and feedforward projections. electrophysiology | macaque | oscillation A natomical and physiological studies have characterized the afferent inputs to and efferent inputs from neurons in different layers of visual cortical areas. However, physiological distinctions across layers, such as synchronous interactions, have not been fully identified. We first came across laminar differences in synchrony serendipitously. Gamma-band synchrony, measured either by spike-field or spike-spike interactions across multiple electrodes, is a prominent feature in visual cortex, and several studies have shown that attention enhances gamma-band synchrony in area V4 (1-5). In our first recordings in area V1, we also found prominent gamma-band synchrony, although the effects of attention, if any, were much smaller than what we previously found in V4 (1). However, in our first recordings in area V2 in the lunate sulcus, we were surprised to find hardly any gamma-band synchrony. We initially had no explanation for why V2 should be so different from V1 and V4. Probing at greater electrode depths led to the discovery that V2 cells do show gamma-band synchrony but only at those deeper electrode depths. Because V2 in the lunate sulcus bends under V1, layer 6 cells are closer to V1 on the occipital surface than are layer 1 cells. Thus, our deeper electrode recordings were actually located in the more superficial layers of V2. Because we typically studied the first responsive cells found in any penetration, this must have strongly biased our first recordings in V2 to the deep layers, and these deep layers apparently had little gamma-band synchrony. Conversely, the same tendency to sample the first responsive cells on a penetration would have resulted in a strong bias to record cells in the superficial layers of V1 and V4, from which we recorded directly on the cortical surface. This possibility led us to test whether the deep layers of V1 and V4 were...

The visual processing of behaviorally relevant stimuli is enhanced through top-down attentional feedback. One possibility is that feedback targets early visual areas first and the attentional enhancement builds up at progressively later stages of the visual hierarchy. An alternative possibility is that the feedback targets the higher-order areas first and the attentional effects are communicated "backward" to early visual areas. Here, we compared the magnitude and latency of attentional enhancement of firing rates in V1, V2, and V4 in the same animals performing the same task. We found a reverse order of attentional effects, such that attentional enhancement was larger and earlier in V4 and smaller and later in V1, with intermediate results in V2. These results suggest that attentional mechanisms operate via feedback from higher-order areas to lower-order ones.attention | Macaque | vision | feedback N europhysiologic and brain imaging studies in monkeys and humans have shown that attended stimuli evoke larger responses in visual cortex than unattended distracters (1-6), giving attended stimuli a competitive advantage for representation in the cortex (7). These top-down attentional effects are thought to be mediated in part by feedback from prefrontal and posterior parietal cortex (8-12) acting directly or indirectly on all visual areas in the dorsal and ventral stream, including V1. However, the mechanism of this feedback is unclear. In particular, a first-order question is whether the top-down feedback targets V1 [or even the lateral geniculate nucleus (LGN)] first and then is passed on successively to later areas, or whether it targets higher-order areas first and then is fed back to successively lower areas. Without an understanding of the basic functional anatomy of the attentional feedback, it will be difficult to make progress in unraveling the circuitry for attention.The magnitude and timing of attentional effects on visual responses in all of the different visual structures should, in principle, give insight into the direction of attentional effects along the visual pathways. However, a comparison of the magnitude of attentional effects across visual areas in different studies leads to a confusing picture. On the one hand, imaging studies in humans typically find that attentional effects on evoked responses become larger as one moves from V1 into higher-order areas (13,14). Several neurophysiologic studies in monkeys also often report small (4) or even nonexistent (6, 15, 16) attentional enhancement of firing rates to stimuli in the receptive fields (RFs) of V1 cells (17-19), compared with reliable findings of attentional effects in higher-order areas such as V4 in conventional target detection or discrimination paradigms. On the other hand, other primate studies report substantial attentional effects in V1 in complex tasks such as covertly tracking along curved lines with spatially directed attention (18). It has recently been demonstrated that even with a relatively simple selection task, very large at...

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In the search for the neural correlate of visual awareness, much controversy exists about the role of primary visual cortex. Here, the neurophysiological data from V1 recordings in awake monkeys are examined in light of two general classes of models of visual awareness. In the first model type, visual awareness is seen as being mediated either by a particular set of areas or pathways, or alternatively by a specific set of neurons. In these models, the role of V1 seems rather limited, as the mere activity of V1 cells seems insufficient to mediate awareness. In the second model type, awareness is hypothesized to be mediated by a global mechanism, i.e. a specific kind of activity not linked to a particular area or cell type. Two separate versions of global models are discussed, synchronous oscillations and spike rate modulations. It is shown that V1 synchrony does not reflect perception but rather the horizontal connections between neurons, indicating that V1 synchrony cannot be a direct neural correlate of conscious percepts. However, the rate of spike discharges of V1 neurons is strongly modulated by perceptual context, and these modulations correlate very well with aspects of perceptual organization, visual awareness, and attention. If these modulations serve as a neural correlate of visual awareness, then V1 contributes to that neural correlate. Whether V1 plays a role in the neural correlate of visual awareness thus strongly depends on the way visual awareness is hypothesized to be implemented in the brain.

Molecular genetic tools have had a profound impact on neuroscience, but until recently their application has largely been confined to a few model species, most notably mouse, zebrafish, Drosophila melanogaster and Caenorhabditis elegans. With the development of new genome engineering technologies such as CRISPR, it is becoming increasingly feasible to apply these molecular tools in a wider range of species, including nonhuman primates. This will lead to many opportunities for brain research, but it will also pose challenges. Here we identify some of these opportunities and challenges in light of recent and foreseeable technological advances and offer some suggestions. Our main focus is on the creation of new primate disease models for understanding the pathological mechanisms of brain disorders and for developing new approaches to effective treatment. However, we also emphasize that primate genetic models have great potential to address many fundamental questions about brain function, providing an essential foundation for future progress in disease research.

This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. The network in this paper can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input. The network vastly increases data analysis capacity for researchers interested in studying marmoset vocalizations, and allows data collection in the home cage, in group housed animals. V

In primates, visual stimuli with social and emotional content tend to attract attention. Attention might be captured through rapid, automatic, subcortical processing or guided by slower, more voluntary cortical processing. Here we examined whether irrelevant faces with varied emotional expressions interfere with a covert attention task in macaque monkeys. In the task, the monkeys monitored a target grating in the periphery for a subtle color change while ignoring distracters that included faces appearing elsewhere on the screen. The onset time of distracter faces before the target change, as well as their spatial proximity to the target, was varied from trial to trial. The presence of faces, especially faces with emotional expressions interfered with the task, indicating a competition for attentional resources between the task and the face stimuli. However, this interference was significant only when faces were presented for greater than 200 ms. Emotional faces also affected saccade velocity and reduced pupillary reflex. Our results indicate that the attraction of attention by emotional faces in the monkey takes a considerable amount of processing time, possibly involving cortical-subcortical interactions. Intranasal application of the hormone oxytocin ameliorated the interfering effects of faces. Together these results provide evidence for slow modulation of attention by emotional distracters, which likely involves oxytocinergic brain circuits.attention | faces | oxytocin | social | vision