The dynamics of hot matter produced in ultrarelativistic heavy-ion collisions is studied with a cascade simulation. We model the putative quark-gluon plasma with independent globs of high-density matter. The hadronic phase is treated by explicit tracking of pion coordinates. We find that the pions make 0--2 collisions with globs and 1--3 collisions with other pions, under conditions expected for heavy ions at collider energies. The entropy increases by about 20% during the phase transition. The transverse momentum in the final state is almost entirely due to the momentum with which pions are emitted from the globs, except at extremely high densities where the hydrodynamic expansion of the pure plasma phase is significant.
Theories of neural information processing generally assume that sensory input is processed along hierarchical stages that start with analog representations and gradually transition to task-related, abstract representations. While the neural code of such abstract information remains unclear, neurophysiological findings suggest that a scalar code could be used to encode behavioral relevance. Here we test this hypothesis in human fMRI studies, using data from five feature-based attention tasks where participants selected one feature from a compound stimulus containing two features. We found that the majority of voxels in a cortical area showed consistently higher response when subjects attended one feature over the other. We examined this biased coding across brain areas, participants, and stimulus domains, and found robust bias within brain areas, consistent direction of such bias across areas for a given participant, and similar bias for multiple feature types (e.g. color, motion directions and objects). Using a receiver operating characteristics analysis to quantify the magnitude of the bias, we found stronger bias in frontoparietal areas than in visual areas, indicating more abstract representations in high-level areas. We also examined the contribution of this bias to multivariate decoding by removing the mean response from each condition before applying pattern classification. We observed decreased classification accuracies in frontal and parietal areas, but not in visual areas. Our results suggest a gradient coding mechanism along visual hierarchy, where high-dimensional coding in sensory cortex allows fine-grained representation of feature attributes, and low-dimensional (possibly one-dimensional scalar) coding in association cortex facilitates the simple read-out of decision and control variables.
Abstract Selective attention is a core cognitive function for efficient processing of information. Although it is well known that attention can modulate neural responses in many brain areas, the computational principles underlying attentional modulation remain unclear. Contrary to the prevailing view of a high-dimensional, distributed neural representation, here we show a surprisingly simple, biased neural representation for feature-based attention in a large dataset including five human fMRI studies. We found that when participants selected one feature from a compound stimulus, voxels in many cortical areas responded consistently higher to one attended feature over the other. This univariate bias was robust at the level of single brain areas and consistent across brain areas within individual subjects. Importantly, this univariate bias showed a progressively stronger magnitude along the cortical hierarchy. In frontoparietal areas, the bias was strongest and contributed largely to pattern-based decoding, whereas early visual areas lacked such a bias. These findings suggest a gradual transition from a more analog to a more abstract representation of attentional priority along the cortical hierarchy. Biased neural responses in high-level areas likely reflect a low-dimensional neural code that facilitates robust representation and simple read-out of cognitive variables.
Visual attentional selection is influenced by the value of objects. Previous studies have demonstrated that reward-associated items lead to rapid distraction and associated behavioral costs, which are difficult to override with top-down control. However, it has not been determined whether a perceptually competitive environment could render the reward-driven distraction more susceptible to top-down suppression. Here, we trained both genders of human subjects to associate two orientations with high and low magnitudes of reward. After training, we collected fMRI data while the subjects performed a categorical visual search task. The item in the reward-associated orientation served as the distractor, and the relative physical salience between the target and distractor was carefully controlled to modulate the degree of perceptual competition. The behavioral results showed faster searches in the presence of high, relative to low, reward-associated distractors. However, this effect was evident only if the physical salience of the distractor was higher than that of the target, indicating a context-dependent suppression effect of reward salience that relied on high perceptual competition. By analyzing the fMRI data in primary visual cortex, we found that the behavioral pattern of results could be predicted by the suppressed channel responses tuned to the reward-associated orientation in the distractor location, accompanied by increased responses in the midbrain dopaminergic region. Our results suggest that the learned salience of a reward plays a flexible role in solving perceptual competition, enabling the neural system to adaptively modulate the perceptual representation for behavioral optimization. SIGNIFICANCE STATEMENT The predictiveness principle in learning theory suggests that the stimulus with high predictability of reward receives priority in attentional selection. This selection bias leads to difficulties in changing approach behaviors, and thus becomes an important factor related to psychiatric disorders with attentional deficits. Here, we demonstrated that such principle is adaptively implemented in attentional suppression in visual search. We showed that the learned salience induced the suppression of the reward-associated distractor if its competition with the target was strong and could not be readily solved. This behavioral pattern was accompanied by increased midbrain fMRI activity and weakened sensory representation of the reward-associated distractor in V1. Our findings provided direct evidence that our brain flexibly uses learned regularities in attentional control.
Previous studies suggest that human frontoparietal network represents feature-based attentional priority, yet the precise nature of the priority signals remains unclear. Here, we examined whether priority signals vary continuously or discretely as a function of feature similarity. In an fMRI experiment, we presented two superimposed dot fields moving along two linear directions (leftward and rightward) while varying the angular separation between the two directions. Subjects were cued to attend to one of the two dot fields and respond to a possible speed-up in the cued direction. We used multivariate analysis to evaluate how priority representation of the attended direction changes with feature similarity. We found that in early visual areas as well as posterior intraparietal sulcus and inferior frontal junction, the patterns of neural activity became more different as the feature similarity decreased, indicating a continuous representation of the attended feature. In contrast, patterns of neural activity in anterior intraparietal sulcus and frontal eye field remained invariant to changes in feature similarity, indicating a discrete representation of the attended feature. Such distinct neural coding of attentional priority across the frontoparietal network may make complementary contributions to enable flexible attentional control.
Previous studies suggest a functional role of dorsal frontoparietal network in representing feature-based attentional priority, yet how these features are represented remains unclear. In an fMRI experiment, we used a feature cueing paradigm to assess whether attentional priority signals vary continuously or categorically as a function of feature similarity. We presented two superimposed dot fields moving along two linear directions (left-tilted and right-tilted), while varying the angular separation between the two motion directions. Subjects were cued to attend to one of the two dot fields and respond to a possible speed-up in the cued direction. We examined how information contained in the multi-voxel neural patterns changed with the angular separation between the two directions. If attentional priority represents continuous changes of the features, priority signals in the dorsal pathway should become more similar when the angular separation between the attended directions decreases. However, if attentional priority represents attended feature in a categorical manner, then priority signals should remain largely invariant with respect to changes in the angular separation. We trained a classifier to decode the attended direction (left-tilted vs. right-tilted) for each angular separation, and found that the decoding accuracy improved with increasing angular separation in the visual cortex (V1 and V2). In contrast, decoding accuracy remained invariant to the degree of feature similarity (and significantly above chance) in the intraparietal sulcus (IPS) and frontal areas (FEF and IFJ). These results indicate dissociated roles of visual cortex and frontoparietal areas in representing attentional priority, suggesting a flexible transformation of feature-based priority from continuous to categorical representation along the dorsal visual streams. Meeting abstract presented at VSS 2017
Although valuable objects are attractive in nature, people often encounter situations where they would prefer to avoid such distraction while focusing on the task goal. Contrary to the typical effect of attentional capture by a reward-associated item, we provide evidence for a facilitation effect derived from the active suppression of a high reward-associated stimulus when cuing its identity as distractor before the display of search arrays. Selection of the target is shown to be significantly faster when the distractors were in high reward-associated colour than those in low reward-associated or non-rewarded colours. This behavioural reward effect was associated with two neural signatures before the onset of the search display: the increased frontal theta oscillation and the strengthened top-down modulation from frontal to anterior temporal regions. The former suggests an enhanced working memory representation for the reward-associated stimulus and the increased need for cognitive control to override Pavlovian bias, whereas the latter indicates that the boost of inhibitory control is realized through a frontal top-down mechanism. These results suggest a mechanism in which the enhanced working memory representation of a reward-associated feature is integrated with task demands to modify attentional priority during active distractor suppression and benefit behavioural performance.
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