Noisy Stimuli Research Articles

The attention-weighted sample-size model of visual short-term memory: Attention capture predicts resource allocation and memory load

We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of ∑i(di′)2, the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items in the display captures attention and receives a disproportionate share of resources. The choice probabilities and response time distributions from the task were well described by a diffusion decision model in which the drift rates embodied the assumptions of the attention-weighted sample-size model.

Two Distinct Stimulus Frequencies Delivered Simultaneously at Low Intensity Generate Robust Locomotor Patterns

Explore the primary characteristics of afferent noisy stimuli, which optimally activate locomotor patterns at low intensity. Intracellular and extracellular electrophysiological traces were derived from single motoneurons and from ventral roots, respectively. From these recordings, we obtained noisy stimulating protocols, delivered to a dorsal root (DR) of an isolated neonatal rat spinal cord, while recording fictive locomotion (FL) from ventral roots. We decreased complexity of efficient noisy stimulating protocols down to single cell spikes. Then, we identified four main components within the power spectrum of these signals and used them to construct a basic multifrequency protocol of rectangular impulses, able to induce FL. Further disassembling generated the minimum stimulation paradigm that activated FL, which consisted of a pair of 35 and 172 Hz frequency pulse trains, strongly effective at low intensity when delivered either jointly to one lumbosacral DR or as single simultaneous trains to two distinct DRs. This simplified pulse schedule always activated a locomotor rhythm, even when delivered for a very short time (500 ms). One prerequisite for the two-frequency protocol to activate FL at low intensity when applied to sacrocaudal afferents was the ability to induce ascending volleys of greater amplitude. Multifrequency protocols can support future studies in defining the most effective characteristics for electrical stimulation to reactivate stepping following motor injury.

Cell–cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis

Collective cell responses to exogenous cues depend on cell-cell interactions. In principle, these can result in enhanced sensitivity to weak and noisy stimuli. However, this has not yet been shown experimentally, and little is known about how multicellular signal processing modulates single-cell sensitivity to extracellular signaling inputs, including those guiding complex changes in the tissue form and function. Here we explored whether cell-cell communication can enhance the ability of cell ensembles to sense and respond to weak gradients of chemotactic cues. Using a combination of experiments with mammary epithelial cells and mathematical modeling, we find that multicellular sensing enables detection of and response to shallow epidermal growth factor (EGF) gradients that are undetectable by single cells. However, the advantage of this type of gradient sensing is limited by the noisiness of the signaling relay, necessary to integrate spatially distributed ligand concentration information. We calculate the fundamental sensory limits imposed by this communication noise and combine them with the experimental data to estimate the effective size of multicellular sensory groups involved in gradient sensing. Functional experiments strongly implicated intercellular communication through gap junctions and calcium release from intracellular stores as mediators of collective gradient sensing. The resulting integrative analysis provides a framework for understanding the advantages and limitations of sensory information processing by relays of chemically coupled cells.

Vigilance Enhancement using Challenge Integration in a Naturalistic Surveillance Task

Vigilance Enhancement using Challenge Integration in a Naturalistic Surveillance Task

The Visual Priming of Motion-Defined 3D Objects.

The perception of a stimulus can be influenced by previous perceptual experience, a phenomenon known as perceptual priming. However, there has been limited investigation on perceptual priming of shape perception of three-dimensional object structures defined by moving dots. Here we examined the perceptual priming of a 3D object shape defined purely by motion-in-depth cues (i.e., Shape-From-Motion, SFM) using a classic prime-target paradigm. The results from the first two experiments revealed a significant increase in accuracy when a “cloudy” SFM stimulus (whose object structure was difficult to recognize due to the presence of strong noise) was preceded by an unambiguous SFM that clearly defined the same transparent 3D shape. In contrast, results from Experiment 3 revealed no change in accuracy when a “cloudy” SFM stimulus was preceded by a static shape or a semantic word that defined the same object shape. Instead, there was a significant decrease in accuracy when preceded by a static shape or a semantic word that defined a different object shape. These results suggested that the perception of a noisy SFM stimulus can be facilitated by a preceding unambiguous SFM stimulus—but not a static image or a semantic stimulus—that defined the same shape. The potential neural and computational mechanisms underlying the difference in priming are discussed.

Amygdala and orbitofrontal engagement in breach and resolution of expectancy: A case study.

Constant prediction is a process of future-directed information processing (Rohrmeier & Koelsch, 2012). It is a major factor of the interaction with the complexity of the human environment and contributes to updating of the so-called cognitive world model (Brown & Brune, 2012; Bubic, von Cramon, Jacobsen, Schroger, & Schubotz, 2009; Bubic, von Cramon, & Schubotz, 2010; Friston, 2012). The top-down prediction of what is likely to come next is advantageous, for example, for the preparation of fast adequate responses to incoming stimuli (Friston, 2009). In audition it enables the identification of incomplete, noisy, or ambiguous stimuli, the reduction of the orienting response (Huron, 2001; Summerfield & Egner, 2009), and the preparation of more accurate responses (Schultz, Dayan, & Montague, 1997). Predictions are essential for the organism, often resulting in punishment as a result of failures, and rewards for success. Reward and punishment is reflected in negative and positive affective reactions (Grossberg, 2009; Pearce & Wiggins, 2012).Several music studies have examined emotions associated with expectancy (e.g., Koelsch, Kilches, Steinbeis, & Schelinski, 2008; Salimpoor, Benovoy, Larcher, Dagher, & Zatorre, 2011; Steinbeis, Koelsch, & Sloboda, 2005). One central principle underlying the evocation of emotions with music relies in part on expectancy and prediction (Huron, 2006; Koelsch, 2014; Meyer, 1956). Using EEG, Flores-Gutierrez et al. (2007) found a lateralized frontal alpha suppression during listening to unpredictable music, perhaps originating in limbic/paralimbic structures. Using fMRI, it was found that breaches of expectancy evoke blood-oxygen level dependent (BOLD) changes in the amygdala (Koelsch, Fritz, & Schlaug, 2008) and the orbitofrontal cortex (OFC; Tillmann, Janata, & Bharucha, 2003; Koelsch, Fritz, Schulze, Alsop, & Schlaug, 2005). The emotional reaction to unexpected musical information was found to be reflected in increased electrodermal activity (Koelsch et al., 2008; Steinbeis et al., 2005). These regions play an important role in various emotional processes, including breaches of expectancy (Nobre, Coull, Frith, & Mesulam, 1999). Notably, the OFC is, in addition to numerous other affective functions, associated with the evaluation of the emotional significance of sensory stimuli (Rolls, 2000).In summary, limbic/paralimbic circuits including the OFC and the amygdala appear to be important for emotional reactions to expectancy violations in music. Psychologically, breaches of expectancy in music create not only surprise (Koelsch et al., 2008), but also modulate tension (Lehne, Rohrmeier, & Koelsch, 2014). In tonal music, a structural breach is usually followed by a transitory phase, which leads to the resolution of the breach; this transitory phase represents a delay of the expected resolution and relaxation (Koelsch, 2014). Note that the studies mentioned above only explored the breach of expectancy, and not the effects of the resolution of the resulting tension.Expectancy in music and its resulting tension is a process that occurs over time, and usually involves several discrete events. Koelsch (2014) proposed that the different factors giving rise to tension and resolution contribute to a tension arc, with the different factors underlying tension including the build-up of expectancy, its breach, anticipation of resolution, and resolution (with maximum tension during breaches of expectancies and the subsequent transitory phase).The cognitive processing of unexpected chords (i.e., cognitive aspects of irregularity detection) is neurophysiologically reflected in an early right anterior negativity (ERAN), which is mainly generated in inferior fronto-lateral (neo-)cortex (Lehne et al., 2014; Sammler et al., 2013). Thus, unexpected chords elicit both cognitive and emotional processes. As mentioned above, the emotion reactions to unexpected chords appear to be reflected, for example, in an increased activation of the amygdala (Koelsch et al. …

Marching along to an Offbeat Drum: Entrainment of Synthetic Gene Oscillators by a Noisy Stimulus.

Modulation of biological oscillations by stimuli lies at the root of many phenomena, including maintenance of circadian rhythms, propagation of neural signals, and somitogenesis. While it is well established that regular periodic modulation can entrain an oscillator, an aperiodic (noisy) modulation can also robustly entrain oscillations. This latter scenario may describe, for instance, the effect of irregular weather patterns on circadian rhythms, or why irregular neural stimuli can still reliably transmit information. A synthetic gene oscillator approach has already proven to be useful in understanding the entrainment of biological oscillators by periodic signaling, mimicking the entrainment of a number of noisy oscillating systems. We similarly seek to use synthetic biology as a platform to understand how aperiodic signals can strongly correlate the behavior of cells. This study should lead to a deeper understanding of how fluctuations in our environment and even within our body may promote substantial synchrony among our cells. Specifically, we investigate experimentally and theoretically the entrainment of a synthetic gene oscillator in E. coli by a noisy stimulus. This phenomenon was experimentally studied and verified by a combination of microfluidics and microscopy using the real synthetic circuit. Stochastic simulation of an associated model further supports that the synthetic gene oscillator can be strongly entrained by aperiodic signals, especially telegraph noise. Finally, widespread applicability of aperiodic entrainment beyond the synthetic gene oscillator is supported by results derived from both a model for a natural oscillator in D. discoideum and a model for predator-prey oscillations.

Normative evidence accumulation in unpredictable environments.

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.

Noise-robust recognition of wide-field motion direction and the underlying neural mechanisms in Drosophila melanogaster.

Appropriate and robust behavioral control in a noisy environment is important for the survival of most organisms. Understanding such robust behavioral control has been an attractive subject in neuroscience research. Here, we investigated the processing of wide-field motion with random dot noise at both the behavioral and neuronal level in Drosophila melanogaster. We measured the head yaw optomotor response (OMR) and the activity of motion-sensitive neurons, horizontal system (HS) cells, with in vivo whole-cell patch clamp recordings at various levels of noise intensity. We found that flies had a robust sensation of motion direction under noisy conditions, while membrane potential changes of HS cells were not correlated with behavioral responses. By applying signal classification theory to the distributions of HS cell responses, however, we found that motion direction under noise can be clearly discriminated by HS cells, and that this discrimination performance was quantitatively similar to that of OMR. Furthermore, we successfully reproduced HS cell activity in response to noisy motion stimuli with a local motion detector model including a spatial filter and threshold function. This study provides evidence for the physiological basis of noise-robust behavior in a tiny insect brain.

Faithful Representation of Tactile Intensity under Different Contexts Emerges from the Distinct Adaptive Properties of the First Somatosensory Relay Stations.

Adaptation allows neurons to respond to a wide range of stimulus intensities. However, it also leads to ambiguity as the representation of the external world depends on the context. We recorded neurons from Wistar rats' brainstem nuclei belonging to two major somatosensory pathways (lemniscal and paralemniscal) and explored the way in which they encode noisy stimuli under different contexts. We found that although their unadapted intensity-response curves are very similar, the adapted curves of the two pathways are distinctively different as they are optimized for encoding different intensity ranges. Lemniscal neurons most faithfully encoded stimuli when the background intensity was high, whereas paralemniscal cells best encoded stimuli under low intensity context. Intracellular recordings indicate that these differences emerge already at the synaptic level. We suggest that the two pathways synergistically improve the ability of this system to encode a wide range of intensities during natural stimulation, potentially reducing the inherent ambiguity of adaptive coding.

Optimal Control and Stochastic Synchronization of Phase Oscillators

Synchronization of oscillations is a phenomenon prevalent in natural, social, and engineering systems, such as neural circuitry in the brain, sleep cycles in biology, semiconductor lasers in physics, and periodic vibrations in mechanical engineering. The ability to control synchronization of oscillating systems then has important research and clinical implications, for example, for the study of brain functions. In this paper, we study optimal control and synchronization of nonlinear oscillators described by the phase model. We consider both deterministic and stochastic cases, in which we derive open-loop controls that create desired synchronization patterns and devise feedback controls that maximize the steady-state synchronization probability of coupled oscillators in the presence of shared and unshared noisy stimuli modeled by the Brownian motion.

Functional analysis of challenging behavior in people with severe intellectual disabilities.

Challenging behaviors exhibited by individuals with developmental disabilities often hinder the acquisition of academic, social, and life skills. Functional analysis has been useful for assessing challenging behavior in various settings. The purpose of this study was to implement an operant methodology for recognizing the functional properties of challenging behavior in people with intellectual disabilities. Four adults diagnosed with profound intellectual disability received assessment under several experimental conditions using a functional analysis methodology: social attention as positive reinforcement, negative reinforcement such as the termination of demands, positive tangible reinforcements, absence of social contingencies, and escape from noisy stimuli. Results showed that different types of reinforcement or avoiding contingencies affected the rate of aggression, self-injury, disruption, stereotypy, or socially offensive behaviors, and functional analysis may potentially be a viable alternative for identifying challenging behaviors.

EEG Analysis for Olfactory Perceptual-Ability Measurement Using a Recurrent Neural Classifier

A recurrent neural network model is designed to classify (pretrained) aromatic stimuli and discriminate noisy stimuli of both similar and different genres, using EEG analysis of the experimental subjects. The design involves determining the weights of the selected recurrent dynamics so that for a given base stimulus, the dynamics converges to one of several optima (local attractors) on the given Lyapunov energy surface. Experiments undertaken reveal that for small noise amplitude below a selected threshold, the dynamics essentially converges to fixed stable attractor. However, with a slight increase in noise amplitude above the selected threshold, the local attractor of the dynamics shifts in the neighborhood of the attractor obtained for the noise-free standard stimuli. The other important issues undertaken in this paper include a novel algorithm for evolutionary feature selection and data-point reduction from multiple experimental EEG trials using principal component analysis. The confusion matrices constructed from experimental results show a marked improvement in classification accuracy in the presence of data point reduction algorithm. Statistical tests undertaken indicate that the proposed recurrent classifier outperforms its competitors with classification accuracy as the comparator. The importance of this paper is illustrated with a tea-taster selection problem, where an olfactory perceptual-ability measure is used to rank the tasters.

Stochastic resonance in visual sensitivity.

It is well known from psychophysical studies that stochastic resonance, in its simplest threshold paradigm, can be used as a tool to measure the detection sensitivity to fine details in noise contaminated stimuli. In the present manuscript, we report simulation studies conducted in the similar threshold paradigm of stochastic resonance. We have estimated the contrast sensitivity in detecting noisy sine-wave stimuli, with varying area and spatial frequency, as a function of noise strength. In all the cases, the measured sensitivity attained a peak at intermediate noise strength, which indicate the occurrence of stochastic resonance. The peak sensitivity exhibited a strong dependence on area and spatial frequency of the stimulus. We show that the peak contrast sensitivity varies with spatial frequency in a nonmonotonic fashion and the qualitative nature of the sensitivity variation is in good agreement with human contrast sensitivity function. We also demonstrate that the peak sensitivity first increases and then saturates with increasing area, and this result is in line with the results of psychophysical experiments. Additionally, we also show that critical area, denoting the saturation of contrast sensitivity, decreases with spatial frequency and the associated maximum contrast sensitivity varies with spatial frequency in a manner that is consistent with the results of psychophysical experiments. In all the studies, the sensitivities were elevated via a nonlinear filtering operation called stochastic resonance. Because of this nonlinear effect, it was not guaranteed that the sensitivities, estimated at each frequency, would be in agreement with the corresponding results of psychophysical experiments; on the contrary, close agreements were observed between our results and the findings of psychophysical investigations. These observations indicate the utility of stochastic resonance in human vision and suggest that this paradigm can be useful in psychophysical studies.

Rapid shape detection signals in area V4.

Vision in foveate animals is an active process that requires rapid and constant decision-making. For example, when a new object appears in the visual field, we can quickly decide to inspect it by directing our eyes to the object's location. We studied the contribution of primate area V4 to these types of rapid foveation decisions. Animals performed a reaction time task that required them to report when any shape appeared within a peripherally-located noisy stimulus by making a saccade to the stimulus location. We found that about half of the randomly sampled V4 neurons not only rapidly and precisely represented the appearance of this shape, but they were also predictive of the animal's saccades. A neuron's ability to predict the animal's saccades was not related to the specificity with which the cell represented a single type of shape but rather to its ability to signal whether any shape was present. This relationship between sensory sensitivity and behavioral predictiveness was not due to global effects such as alertness, as it was equally likely to be observed for cells with increases and decreases in firing rate. Careful analysis of the timescales of reliability in these neurons implies that they reflect both feedforward and feedback shape detecting processes. In approximately 7% of our recorded sample, individual neurons were able to predict both the delay and precision of the animal's shape detection performance. This suggests that a subset of V4 neurons may have been directly and causally contributing to task performance and that area V4 likely plays a critical role in guiding rapid, form-based foveation decisions.

Phase noise reveals early category-specific modulation of the event-related potentials.

Previous studies have found that the amplitude of the early event-related potential (ERP) components evoked by faces, such as N170 and P2, changes systematically as a function of noise added to the stimuli. This change has been linked to an increased perceptual processing demand and to enhanced difficulty in perceptual decision making about faces. However, to date it has not yet been tested whether noise manipulation affects the neural correlates of decisions about face and non-face stimuli similarly. To this end, we measured the ERPs for faces and cars at three different phase noise levels. Subjects performed the same two-alternative age-discrimination task on stimuli chosen from young–old morphing continua that were created from faces as well as cars and were calibrated to lead to similar performances at each noise-level. Adding phase noise to the stimuli reduced performance and enhanced response latency for the two categories to the same extent. Parallel to that, phase noise reduced the amplitude and prolonged the latency of the face-specific N170 component. The amplitude of the P1 showed category-specific noise dependence: it was enhanced over the right hemisphere for cars and over the left hemisphere for faces as a result of adding phase noise to the stimuli, but remained stable across noise levels for cars over the left and for faces over the right hemisphere. Moreover, noise modulation altered the category-selectivity of the N170, while the P2 ERP component, typically associated with task decision difficulty, was larger for the more noisy stimuli regardless of stimulus category. Our results suggest that the category-specificity of noise-induced modulations of ERP responses starts at around 100 ms post-stimulus.

Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.

The effect of speaking rate, vowel context, and speaker intelligibility on the perception of consonant vowel consonants in noise

In this paper, we perform pilot experiments to evaluate the feasibility of a model to predict human recognition of speech sounds in the presence of noise at different speaking rates. CVC stimuli comprising a phonetically balanced set of 13 consonants and 3 vowels (/i/, /a/, /u/) were recorded in a sound proof booth by two talkers at two different speaking rates (fast and slow). Noisy stimuli were generated by adding babble noise at different levels to the quiet recordings. These stimuli were used to conduct perceptual experiments in which listeners were asked to listen and repeat back the CVC phrases presented in babble noise under 3 SNR conditions and both speaking rates. The data were transcribed by two trained linguists. Consonant confusion matrices were generated from these data and were analyzed by noise level, speaker, center vowel, and speaking rate. With the exception of /CuC/ stimuli, speaking rate had the most pronounced effect on perception with slow speech being more intelligible than fast speech in noise. /CaC/ stimuli were, on average, more robust than other stimuli in all conditions and one talker was significantly more intelligible than the other. Adetailed analysis of the results will be presented. [Work supported in part by the NSF.]

Learning Optimizes Decision Templates in the Human Visual Cortex

Translating sensory information into perceptual decisions is a core challenge faced by the brain. This ability is understood to rely on weighting sensory evidence in order to form mental templates of the critical differences between objects. Learning is shown to optimize these templates for efficient task performance, but the neural mechanisms underlying this improvement remain unknown. Here, we identify the mechanisms that the brain uses to implement templates for perceptual decisions through experience. We trained observers to discriminate visual forms that were randomly perturbed by noise. To characterize the internal stimulus template that observers learn when performing this task, we adopted a classification image approach (e.g., [5-7]) for the analysis of both behavioral and fMRI data. By reverse correlating behavioral and multivoxel pattern responses with noisy stimulus trials, we identified the critical image parts that determine the observers' choice. Observers learned to integrate information across locations and weight the discriminative image parts. Training enhanced shape processing in the lateral occipital area, which was shown to reflect size-invariant representations of informative image parts. Our findings demonstrate that learning optimizes mental templates for perceptual decisions by tuning the representation of informative image parts in higher ventral cortex.

Reward Optimization in the Primate Brain: A Probabilistic Model of Decision Making under Uncertainty

A key problem in neuroscience is understanding how the brain makes decisions under uncertainty. Important insights have been gained using tasks such as the random dots motion discrimination task in which the subject makes decisions based on noisy stimuli. A descriptive model known as the drift diffusion model has previously been used to explain psychometric and reaction time data from such tasks but to fully explain the data, one is forced to make ad-hoc assumptions such as a time-dependent collapsing decision boundary. We show that such assumptions are unnecessary when decision making is viewed within the framework of partially observable Markov decision processes (POMDPs). We propose an alternative model for decision making based on POMDPs. We show that the motion discrimination task reduces to the problems of (1) computing beliefs (posterior distributions) over the unknown direction and motion strength from noisy observations in a Bayesian manner, and (2) selecting actions based on these beliefs to maximize the expected sum of future rewards. The resulting optimal policy (belief-to-action mapping) is shown to be equivalent to a collapsing decision threshold that governs the switch from evidence accumulation to a discrimination decision. We show that the model accounts for both accuracy and reaction time as a function of stimulus strength as well as different speed-accuracy conditions in the random dots task.