Does Time Really Slow Down during a Frightening Event?

Observers commonly report that time seems to have moved in slow motion during a life-threatening event. It is unknown whether this is a function of increased time resolution during the event, or instead an illusion of remembering an emotionally salient event. Using a hand-held device to measure speed of visual perception, participants experienced free fall for 31 m before landing safely in a net. We found no evidence of increased temporal resolution, in apparent conflict with the fact that participants retrospectively estimated their own fall to last 36% longer than others' falls. The duration dilation during a frightening event, and the lack of concomitant increase in temporal resolution, indicate that subjective time is not a single entity that speeds or slows, but instead is composed of separable subcomponents. Our findings suggest that time-slowing is a function of recollection, not perception: 1a richer encoding of memory may cause a salient event to appear, retrospectively, as though it lasted longer

The Parietal Reach Region Selectively Anti-Synchronizes with Dorsal Premotor Cortex during Planning

Recent reports have indicated that oscillations shared across distant cortical regions can enhance their connectivity, but do coherent oscillations ever diminish connectivity? We investigated oscillatory activity in two distinct reach-related regions in the awake behaving monkey (Macaca mulatta): the parietal reach region (PRR) and the dorsal premotor cortex (PMd). PRR and PMd were found to oscillate at similar frequencies (beta, 15–30 Hz) during periods of fixation and movement planning. At first glance, the stronger oscillator of the two, PRR, would seem to drive the weaker, PMd. However, a more fine-grained measure, the partial spike-field coherence, revealed a different relationship. Relative to global beta-band activity in the brain, action potentials in PRR anti-synchronize with PMd oscillations. These data suggest that, rather than driving PMd during planning, PRR neurons fire in such a way that they are less likely to communicate information to PMd

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Interaction of Planning Regions in Cortex

To what extent do parietal and frontal areas involved in action planning interact as a monkey plans a movement? This report seeks an answer using the timing relationships between action potentials, local field potentials (LFPs) and behavioral events as a monkey plans reaches and eye movements to remembered targets. Both parietal reach region (PRR) and dorsal premotor cortex (PMd) show similar profiles of activity characteristic of action planning. In some cases, both premotor and intraparietal areas show decision-making activity far earlier than previously anticipated, even before the onset of the trial. However, despite their similarities in action planning, PMd responds tens of milliseconds sooner to targets and movement instructions. These results suggest that PMd precedes PRR, apparently contrary to a common heuristic about the chain of processing from sensation to action. On the other hand, during periods of steady state, as the monkey anticipates information or plans a movement, the apparent directionality of fronto-parietal interaction may reverse. Coherent phase-locking between action potentials and local field potentials (LFPs), which has been implicated in directional influence between brain regions, is highly significant from PRR to PMd, but not vice-versa. Spikes in PRR cohere with LFPs in PMd between 15–25 Hz, whereas spikes in PMd do not cohere with LFPs in PRR at any frequency. This uni-directional spike-LFP coherence varies over the course of the trial, achieving a peak in magnitude and frequency, on average, during the planning period. The phase-locking component of the coherence shows weak but significant variation according to the particular action being planned. The cross-cortical coherence also varies significantly with cortical anatomy. Coherence is stronger between spikes in PRR and LFPs in its anatomical target PMd than between PRR and other recording areas within and beyond the arcuate sulcus (associated with saccades, and not known to be connected with PRR). The asymmetry of spike - LFP coherence, its task–dependence, and variation over cortical territory add to a growing body of knowledge implicating the intraparietal sulcus as the center of a network of beta-band activity characteristic of action planning. This highly specific beta-band oscillation links frontal and parietal planning regions at the single cell level. Overall, these results suggest an interplay between premotor and parietal regions, with influence shifting back and forth according to the phase of behavior

Early Planning Activity in Frontal and Parietal Cortex in a Simplified Task

Cortical planning activity has traditionally been probed with visual targets. However, external sensory signals might obscure early correlates of internally generated plans. We devised a non-spatial decision-making task, in which the monkey is encouraged to randomly decide whether to reach or saccade, in the absence of sensory stimuli. Neurons in frontal and parietal planning areas (in and around the arcuate and intraparietal sulci) showed responses predictive of the monkey's upcoming movement at early stages during the planning process. Neurons predicted the animal's future movements several seconds beforehand, sometimes before the trial even began. These data cast new light on the role of the cerebral cortex in the action planning process, when the animal is free to decide on his own actions in the absence of extraneous sensory cues

A neural model for temporal order judgments and their active recalibration: a common mechanism for space and time?

When observers experience a constant delay between their motor actions and sensory feedback, their perception of the temporal order between actions and sensations adapt (Stetson et al., 2006). We present here a novel neural model that can explain temporal order judgments (TOJs) and their recalibration. Our model employs three ubiquitous features of neural systems: (1) information pooling, (2) opponent processing, and (3) synaptic scaling. Specifically, the model proposes that different populations of neurons encode different delays between motor-sensory events, the outputs of these populations feed into rivaling neural populations (encoding "before" and "after"), and the activity difference between these populations determines the perceptual judgment. As a consequence of synaptic scaling of input weights, motor acts which are consistently followed by delayed sensory feedback will cause the network to recalibrate its point of subjective simultaneity. The structure of our model raises the possibility that recalibration of TOJs is a temporal analog to the motion aftereffect (MAE). In other words, identical neural mechanisms may be used to make perceptual determinations about both space and time. Our model captures behavioral recalibration results for different numbers of adapting trials and different adapting delays. In line with predictions of the model, we additionally demonstrate that temporal recalibration can last through time, in analogy to storage of the MAE

A neural model for temporal order judgments and their active recalibration: a common mechanism for space and time?

When observers experience a constant delay between their motor actions and sensory feedback, their perception of the temporal order between actions and sensations adapt (Stetson et al., 2006). We present here a novel neural model that can explain temporal order judgments (TOJs) and their recalibration. Our model employs three ubiquitous features of neural systems: (1) information pooling, (2) opponent processing, and (3) synaptic scaling. Specifically, the model proposes that different populations of neurons encode different delays between motor-sensory events, the outputs of these populations feed into rivaling neural populations (encoding "before" and "after"), and the activity difference between these populations determines the perceptual judgment. As a consequence of synaptic scaling of input weights, motor acts which are consistently followed by delayed sensory feedback will cause the network to recalibrate its point of subjective simultaneity. The structure of our model raises the possibility that recalibration of TOJs is a temporal analog to the motion aftereffect (MAE). In other words, identical neural mechanisms may be used to make perceptual determinations about both space and time. Our model captures behavioral recalibration results for different numbers of adapting trials and different adapting delays. In line with predictions of the model, we additionally demonstrate that temporal recalibration can last through time, in analogy to storage of the MAE