Latency Distribution Research Articles

A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range.

Light intensities (photons s–1 μm–2) in a natural scene vary over several orders of magnitude from shady woods to direct sunlight. A major challenge facing the visual system is how to map such a large dynamic input range into its limited output range, so that a signal is neither buried in noise in darkness nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single to billions of photons, outperforming man‐made light sensors. This performance requires powerful light adaptation, the neural implementation of which has only become clear recently. A computational fly photoreceptor model, which mimics the real phototransduction processes, has elucidated how light adaptation happens dynamically through stochastic adaptive quantal information sampling. A Drosophila R1–R6 photoreceptor's light sensor, the rhabdomere, has 30,000 microvilli, each of which stochastically samples incoming photons. Each microvillus employs a full G‐protein‐coupled receptor signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (i) the number of photon sampling units in the cell structure (microvilli), (ii) sample size (QB waveform), (iii) latency distribution (time delay between photon arrival and emergence of a QB), and (iv) refractory period distribution (time for a microvillus to recover after a QB). Here, we review how these factors jointly orchestrate light adaptation over a large dynamic range.

Reining in Long Tails in Warehouse-Scale Computers with Quick Voltage Boosting Using Adrenaline

Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service (QoS) of workloads such as Web Search and Memcached. Traditional turbo boost increases a processor’s voltage and frequency during a coarse-grained sliding window, boosting all queries that are processed during that window. However, the inability of such a technique to pinpoint tail queries for boosting limits its tail reduction benefit. In this work, we propose Adrenaline , an approach to leverage finer-granularity (tens of nanoseconds) voltage boosting to effectively rein in the tail latency with query-level precision. Two key insights underlie this work. First, emerging finer granularity voltage/frequency boosting is an enabling mechanism for intelligent allocation of the power budget to precisely boost only the queries that contribute to the tail latency; second, per-query characteristics can be used to design indicators for proactively pinpointing these queries, triggering boosting accordingly. Based on these insights, Adrenaline effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost. By evaluating under various workload configurations, we demonstrate the effectiveness of our methodology. We achieve up to a 2.50 × tail latency improvement for Memcached and up to a 3.03 × for Web Search over coarse-grained dynamic voltage and frequency scaling (DVFS) given a fixed boosting power budget. When optimizing for energy reduction, Adrenaline achieves up to a 1.81 × improvement for Memcached and up to a 1.99 × for Web Search over coarse-grained DVFS. By using the carefully chosen boost thresholds, Adrenaline further improves the tail latency reduction to 4.82 × over coarse-grained DVFS.

Disrupted Saccade Control in Chronic Cerebral Injury: Upper Motor Neuron-Like Disinhibition in the Ocular Motor System.

Saccades rapidly direct the line of sight to targets of interest to make use of the high acuity foveal region of the retina. These fast eye movements are instrumental for scanning visual scenes, foveating targets, and, ultimately, serve to guide manual motor control, including eye–hand coordination. Cerebral injury has long been known to impair ocular motor control. Recently, it has been suggested that alterations in control may be useful as a marker for recovery. We measured eye movement control in a saccade task in subjects with chronic middle cerebral artery stroke with both cortical and substantial basal ganglia involvement and in healthy controls. Saccade latency distributions were bimodal, with an early peak at 60 ms (anticipatory saccades) and a later peak at 250 ms (regular saccades). Although the latencies corresponding to these peaks were the same in the two groups, there were clear differences in the size of the peaks. Classifying saccade latencies relative to the saccade “go signal” into anticipatory (latencies up to 80 ms), “early” (latencies between 80 and 160 ms), and “regular” types (latencies longer than 160 ms), stroke subjects displayed a disproportionate number of anticipatory saccades, whereas control subjects produced the majority of their saccades in the regular range. We suggest that this increase in the number of anticipatory saccade events may result from a disinhibition phenomenon that manifests as an impairment in the endogenous control of ocular motor events (saccades) and interleaved fixations. These preliminary findings may help shed light on the ocular motor deficits of neurodegenerative conditions, results that may be subclinical to an examiner, but clinically significant secondary to their functional implications.

Spatiotemporal Filter for Visual Motion Integration from Pursuit Eye Movements in Humans and Monkeys

Despite the enduring interest in motion integration, a direct measure of the space-time filter that the brain imposes on a visual scene has been elusive. This is perhaps because of the challenge of estimating a 3D function from perceptual reports in psychophysical tasks. We take a different approach. We exploit the close connection between visual motion estimates and smooth pursuit eye movements to measure stimulus-response correlations across space and time, computing the linear space-time filter for global motion direction in humans and monkeys. Although derived from eye movements, we find that the filter predicts perceptual motion estimates quite well. To distinguish visual from motor contributions to the temporal duration of the pursuit motion filter, we recorded single-unit responses in the monkey middle temporal cortical area (MT). We find that pursuit response delays are consistent with the distribution of cortical neuron latencies and that temporal motion integration for pursuit is consistent with a short integration MT subpopulation. Remarkably, the visual system appears to preferentially weight motion signals across a narrow range of foveal eccentricities rather than uniformly over the whole visual field, with a transiently enhanced contribution from locations along the direction of motion. We find that the visual system is most sensitive to motion falling at approximately one-third the radius of the stimulus aperture. Hypothesizing that the visual drive for pursuit is related to the filtered motion energy in a motion stimulus, we compare measured and predicted eye acceleration across several other target forms.SIGNIFICANCE STATEMENT A compact model of the spatial and temporal processing underlying global motion perception has been elusive. We used visually driven smooth eye movements to find the 3D space-time function that best predicts both eye movements and perception of translating dot patterns. We found that the visual system does not appear to use all available motion signals uniformly, but rather weights motion preferentially in a narrow band at approximately one-third the radius of the stimulus. Although not universal, the filter predicts responses to other types of stimuli, demonstrating a remarkable degree of generalization that may lead to a deeper understanding of visual motion processing.

Sneak peek: Raptors search for prey using stochastic head turns

The strategies by which foraging predators decide when to redirect their gaze influence both prey detection rates and the prey's ability to detect and avoid predators. We applied statistical analyses that have been used to study neural decision-making for gaze redirection in primates to 3 species of predatory birds with different sizes, visual systems, habitats, and hunting behaviors: the Northern Goshawk (Accipiter gentilis), Cooper's Hawk (A. cooperii), and Red-tailed Hawk (Buteo jamaicensis). The timing of head saccades was measured during visual searches using field video recordings of foraging raptors, and during a variety of behaviors using a miniature camera mounted on the head of a Northern Goshawk. The resulting statistical distribution of latencies (time between successive head saccades) was compared to predictions from various models proposed to describe visual search strategies. Our results did not support models that assume a constant probability of gaze redirection per unit time, a constant time for “giving up” on the visual search, or an initial setup time before visual search initiation. Instead, our data were fit best by a log-normal distribution, consistent with the raptors stochastically changing their gaze direction on the basis of accumulated environmental information. Specifically, this suggests that saccade initiation arises from a neural computation based on detection of a threshold level of a dynamically updated decision signal that encodes noisy sensory data, similar to the processes inferred from previous studies of visual search strategies in primates. The only significant between-species difference we found was a slower mean gaze-redirection rate for 2 larger species compared to the Cooper's Hawk, even though the latter has hunting behavior and maneuverability similar to that of the Northern Goshawk. Head-saccade latencies measured for a Northern Goshawk during different behaviors showed that the bird changed gaze direction significantly less frequently, on average, while perched than while in motion.

A reconfigurable fabric for accelerating large-scale datacenter services

Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network. We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.

Development and analysis of an operator steering model for teleoperated mobile robots under constant and variable latencies

SUMMARYLatency hinders a mobile robot teleoperator's ability to perform remote tasks. However, this effect is not well modeled. This paper develops a model for teleoperator steering behavior as a PD controller based on projected lateral displacement, which was tuned to reflect user performance determined by a 31-subject user study under constant and variable latency (having mean latencies between 0 and 750 ms). Additionally, we determined that operator performance under variable latency could be mapped to the expected performance of an equivalent constant latency. We then tested additional latency distributions in simulation and demonstrated equivalent steering performance among several different latency distributions.

Vowels and Consonants in the Brain: Evidence from Magnetoencephalographic Studies on the N1m in Normal-Hearing Listeners

Speech sound perception is one of the most fascinating tasks performed by the human brain. It involves a mapping from continuous acoustic waveforms onto the discrete phonological units computed to store words in the mental lexicon. In this article, we review the magnetoencephalographic studies that have explored the timing and morphology of the N1m component to investigate how vowels and consonants are computed and represented within the auditory cortex. The neurons that are involved in the N1m act to construct a sensory memory of the stimulus due to spatially and temporally distributed activation patterns within the auditory cortex. Indeed, localization of auditory fields maps in animals and humans suggested two levels of sound coding, a tonotopy dimension for spectral properties and a tonochrony dimension for temporal properties of sounds. When the stimulus is a complex speech sound, tonotopy and tonochrony data may give important information to assess whether the speech sound parsing and decoding are generated by pure bottom-up reflection of acoustic differences or whether they are additionally affected by top-down processes related to phonological categories. Hints supporting pure bottom-up processing coexist with hints supporting top-down abstract phoneme representation. Actually, N1m data (amplitude, latency, source generators, and hemispheric distribution) are limited and do not help to disentangle the issue. The nature of these limitations is discussed. Moreover, neurophysiological studies on animals and neuroimaging studies on humans have been taken into consideration. We compare also the N1m findings with the investigation of the magnetic mismatch negativity (MMNm) component and with the analogous electrical components, the N1 and the MMN. We conclude that N1 seems more sensitive to capture lateralization and hierarchical processes than N1m, although the data are very preliminary. Finally, we suggest that MEG data should be integrated with EEG data in the light of the neural oscillations framework and we propose some concerns that should be addressed by future investigations if we want to closely line up language research with issues at the core of the functional brain mechanisms.

Saccadic latency and choice in a concurrent random interval reinforcement schedule.

Conventional decision models view reaction time as a consequence of the duration of decision-making process. However, some studies have shown that reaction time distributions may be strongly affected by reinforcement contingencies (Madelain et al., 2007). Here, we probe the possibility to voluntarily control saccadic latencies in a choice paradigm. Three subjects (including the two authors) tracked a visual target stepping horizontally by 10 deg between two fixed locations on a screen. Any trial with saccadic latencies greater than 300 ms or shorter than 80 ms was interrupted. Using the first and last quartiles of individual baseline latency distributions, we first defined two classes, i.e. short and long saccadic latencies, respectively. We then concurrently reinforced each type of latencies in random interval reinforcement schedules: short and long latencies were reinforced with three different sets of probabilities such that the relative ratio of reinforcing short latencies was either 9/1, 1/9 or 1/1. After training (20 800 trials), we observed bimodal latency distributions –with a peak for short and another for long latencies– for two subjects and to a lesser extent for the third subject. To further probe the extent of control over saccadic latencies, we then analyzed the data using the generalized matching law (Baum, 1974) –which states that the relative proportion of choices made to an option matches the relative proportion of reinforcers earned from that option. We found an almost perfect match between the relative proportion of short and long latencies and the relative obtained reinforcement for two subjects (sensitivities were equal to 0.95 and 0.87) and typical undermatching parameter for the third one (sensitivity = 0.58). These results indicate that saccades may be allocated in time following the reinforcement contingencies in force, which support the idea of a voluntary control of saccadic reaction time.

Bimodal latency distribution and distractor effects in Express Saccades in humans.

Bimodal latency distribution and distractor effects in Express Saccades in humans.

Development of a voltage-dependent current noise algorithm for conductance-based stochastic modelling of auditory nerve fibres.

This study presents the development of an alternative noise current term and novel voltage-dependent current noise algorithm for conductance-based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage-dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage, and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies while maintaining its low computational cost and ease of implementation compared to other conductance and Markovian-based stochastic models. The algorithm is applied to a Hodgkin-Huxley-based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.

The random dot tachistogram: a novel task that elucidates the functional architecture of decision.

Reaction times are long and variable, almost certainly because they result from a process that accumulates noisy decision signals over time, rising to a threshold. But the origin of the variability is still disputed: is it because the incoming sensory signals are themselves noisy? Or does it arise within the brain? Here we use a stimulus – the random dot tachistogram – which demands spatial integration of information presented essentially instantaneously; with it, we demonstrate three things. First, that the latency distributions still show the variability characteristic of LATER, implying that there must be two integrators in series. Secondly, that since this variability persists despite removal of all temporal noise from the stimulus, or even trial-to-trial spatial variation, it must come from within the nervous system. Finally, that the average rate of rise of the decision signal depends linearly on how many dots move in a given direction. Taken together, this suggests a rather simple, two-stage model of the overall process. The first, detection, stage performs local temporal integration of stimuli; the local, binary, outcomes are linearly summed and integrated by LATER units in the second stage, that perform the final global decision by a process of racing competition.

Interval timing under a behavioral microscope: Dissociating motivational and timing processes in fixed-interval performance.

The distribution of latencies and interresponse times (IRTs) of rats was compared between two fixed-interval (FI) schedules of food reinforcement (FI 30s and FI 90s), and between two levels of food deprivation. Computational modeling revealed that latencies and IRTs were well described by mixture probability distributions embodying two-state Markov chains. Analysis of these models revealed that only a subset of latencies is sensitive to the periodicity of reinforcement, and prefeeding only reduces the size of this subset. The distribution of IRTs suggests that behavior in FI schedules is organized in bouts that lengthen and ramp up in frequency with proximity to reinforcement. Prefeeding slowed down the lengthening of bouts and increased the time between bouts. When concatenated, latency and IRT models adequately reproduced sigmoidal FI response functions. These findings suggest that behavior in FI schedules fluctuates in and out of schedule control; an account of such fluctuation suggests that timing and motivation are dissociable components of FI performance. These mixture-distribution models also provide novel insights on the motivational, associative, and timing processes expressed in FI performance. These processes may be obscured, however, when performance in timing tasks is analyzed in terms of mean response rates.

Motor areas of the frontal cortex in patients with motor eloquent brain lesions.

OBJECTIVE Because of its huge clinical potential, the importance of premotor areas for motor function itself and plastic reshaping due to tumors or ischemic brain lesions has received increased attention. Thus, in this study the authors used navigated transcranial magnetic stimulation (nTMS) to investigate whether tumorous brain lesions induce a change in motor cortex localization in the human brain. METHODS Between 2010 and 2013, nTMS motor mapping was performed in a prospective cohort of 100 patients with brain tumors in or adjacent to the rolandic cortex. Spatial data analysis was performed by normalization of the individual motor maps and creation of overlays according to tumor location. Analysis of motor evoked potential (MEP) latencies was performed regarding mean overall latencies and potentially polysynaptic latencies, defined as latencies longer than 1 SD above the mean value. Hemispheric dominance, lesion location, and motor-function deficits were also considered. RESULTS Graphical analysis showed that motor areas were not restricted to the precentral gyrus. Instead, they spread widely in the anterior-posterior direction. An analysis of MEP latency showed that mean MEP latencies were shortest in the precentral gyrus and longest in the superior and middle frontal gyri. The percentage of latencies longer than 1 SD differed widely across gyri. The dominant hemisphere showed a greater number of longer latencies than the nondominant hemisphere (p < 0.0001). Moreover, tumor location-dependent changes in distribution of polysynaptic latencies were observed (p = 0.0002). Motor-function deficit did not show any statistically significant effect. CONCLUSIONS The distribution of primary and polysynaptic motor areas changes in patients with brain tumors and highly depends on tumor location. Thus, these data should be considered for resection planning.

A Phenomenological Model of the Electrically Stimulated Auditory Nerve Fiber: Temporal and Biphasic Response Properties.

We present a phenomenological model of electrically stimulated auditory nerve fibers (ANFs). The model reproduces the probabilistic and temporal properties of the ANF response to both monophasic and biphasic stimuli, in isolation. The main contribution of the model lies in its ability to reproduce statistics of the ANF response (mean latency, jitter, and firing probability) under both monophasic and cathodic-anodic biphasic stimulation, without changing the model's parameters. The response statistics of the model depend on stimulus level and duration of the stimulating pulse, reproducing trends observed in the ANF. In the case of biphasic stimulation, the model reproduces the effects of pseudomonophasic pulse shapes and also the dependence on the interphase gap (IPG) of the stimulus pulse, an effect that is quantitatively reproduced. The model is fitted to ANF data using a procedure that uniquely determines each model parameter. It is thus possible to rapidly parameterize a large population of neurons to reproduce a given set of response statistic distributions. Our work extends the stochastic leaky integrate and fire (SLIF) neuron, a well-studied phenomenological model of the electrically stimulated neuron. We extend the SLIF neuron so as to produce a realistic latency distribution by delaying the moment of spiking. During this delay, spiking may be abolished by anodic current. By this means, the probability of the model neuron responding to a stimulus is reduced when a trailing phase of opposite polarity is introduced. By introducing a minimum wait period that must elapse before a spike may be emitted, the model is able to reproduce the differences in the threshold level observed in the ANF for monophasic and biphasic stimuli. Thus, the ANF response to a large variety of pulse shapes are reproduced correctly by this model.

Dyadic Remodeling as a Cause of Impaired E-C Coupling in Early Stages of Myocardial Injury

In failing myocytes, impaired E-C coupling is often associated with loss of t-tubules and appearance of orphaned calcium release units. Impairment of communication between Ca-channels of sarcolemma and of sarcoplasmic reticulum (SR) has been observed in non-failing stages of cardiac hypertrophy. We therefore studied the relationship between Ca-signaling and ultrastructure of dyadic junctions in early stages of myocardial injury. Myocardial injury (IMI) was induced by a single dose of isoproterenol (150 mg/kg, i.p.) in adult male rats. On day 15, isolated myocytes were studied using laser scanning fluorescence confocal microscopy: Ca-spikes were activated by whole-cell patch-clamp and t-tubules were visualized by di-8-ANEPPS. Ultrastructure of dyadic junctions was evaluated from electron-microscopic images. Our results show that remodeling of IMI myocytes transpired as increased proportion of longitudinal elements of tubular system as well as increased proportion of dyads with defective contact between the membranes of sarcolemma and SR; however, there was no actual loss of t-tubules and ultrastructure of t-tubules in dyads of IMI myocytes was similar to control (CTR). In parallel, Ca-spike activation was slower and less synchronized in IMI. In both CTR and IMI, distribution of latencies of Ca-spikes and the time profile of Ca-release flux measured from whole cells consisted of two components. The fraction of the slow component of calcium release in IMI was close to the fraction of dyads with defective contact between the membranes of sarcolemma and SR. We conclude that overload of cardiac myocytes of the working part of injured myocardium leads to damage of tubulo-reticular junctions of dyadic microdomains, which results in impairment of local calcium signaling. Support: APVV-0721-10, VEGA 2/047/14 and VEGA 2/0095/15

A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html.

Performance Analysis of the Physical and Medium Access Control Layer Parameters with Effect of Varying Transmission Power Using IEEE 802.15.4 Standard for Wireless Body Sensor Networks

Wireless Body Area Network (WBAN) consists of miniaturized, tiny, low power body sensor nodes communicating with a BAN (Body Area Node) Coordinator through Radio Frequency (RF) interface link. Recent advancements in the field of Information, Communication and microelectronics have led to the realization of WBAN, which will help in hazardous, long term health monitoring especially for elderly people. Most of the present day's Body Area Network node's MAC/PHY protocols are built using IEEE 802.15.4 and ZigBee standard. This standard will surely make an impact through its improvisation especially in MAC/PHY layers in the days to come. Already researchers have been working on the new WBAN standard. This paper emphasizes the basic structure of IEEE 802.15.5 MAC/PHY layers through experimentation on 7 specific, static body sensor nodes placed at appropriate points on the human body transmitting heterogeneous data using Time division multiple access and Contention access period of CSMA/CA protocol. The body area channels are considered with temporal and fixed path loss values. Physical layer parameters such as latency and fade depth distribution, MAC packets received and breakdown packets of MAC layer are presented. Packet with respect to the unique and useful parameters, packet received from Media Access Control layer of each node under various conditions of the channel and with Carrier Sense Multiple Access/Collision Avoidance comparing with Time Division Multiple Access techniques. Experimental results show that a transmitting-12dBm to -15dBm was found to be suitable for energy efficient WBAN system. For TDMA scheme, the algorithm may be fine-tuned to reduce the number of packet failure which improves the energy efficiency for any kind of channel.

An integrated utility-based model of conflict evaluation and resolution in the Stroop task.

Cognitive control allows humans to direct and coordinate their thoughts and actions in a flexible way, in order to reach internal goals regardless of interference and distraction. The hallmark test used to examine cognitive control is the Stroop task, which elicits both the weakly learned but goal-relevant and the strongly learned but goal-irrelevant response tendencies, and requires people to follow the former while ignoring the latter. After reviewing the existing computational models of cognitive control in the Stroop task, its novel, integrated utility-based model is proposed. The model uses 3 crucial control mechanisms: response utility reinforcement learning, utility-based conflict evaluation using the Festinger formula for assessing the conflict level, and top-down adaptation of response utility in service of conflict resolution. Their complex, dynamic interaction led to replication of 18 experimental effects, being the largest data set explained to date by 1 Stroop model. The simulations cover the basic congruency effects (including the response latency distributions), performance dynamics and adaptation (including EEG indices of conflict), as well as the effects resulting from manipulations applied to stimulation and responding, which are yielded by the extant Stroop literature.

A neural analogue of the worst performance rule: Insights from single-trial event-related potentials

The worst performance rule is the tendency for participants' slowest reaction times to correlate more with psychometric intelligence than their faster reaction times. Reaction times, however, are influenced by the duration of multiple perceptual, attentional, and motor sub-processes, and it is unclear whether the same pattern exists in these sub-processes as well. We used single-trial event-related potentials to identify whether a worst performance rule pattern could be found in stimulus and response-locked P3b latency distributions and scores on a test of non-verbal psychometric intelligence.Fifty participants carried out a set of working memory oddball tasks, while electroencephalographic data were collected, and the British Version of the Intelligence Structure Test, in a separate session. Single-trial P3b latencies were identified in stimulus and response-locked data and a novel quantile bootstrapping method was used to identify which quantiles of the P3b latency distributions correlated most with test scores.In stimulus-locked data, correlations between quantile mean and test scores became more negative with increasing quantile, showing clear evidence of a worst performance rule pattern. In response-locked data, low scorers showed more extreme latencies in both tails of the distribution. However we did not observe a worst performance rule in behavioural data. These data suggest that psychometric intelligence is also associated with response-related processes, which may also contribute to the association between psychometric intelligence and reaction time variability.