Human Psychophysics Research Articles

Orthogonal neural representations support perceptual judgements of natural stimuli.

In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.

Unifying network model links recency and central tendency biases in working memory

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Unifying network model links recency and central tendency biases in working memory.

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Age-Related Deficits in Binaural Hearing: Contribution of Peripheral and Central Effects.

Pure-tone audiograms often poorly predict elderly humans' ability to communicate in everyday complex acoustic scenes. Binaural processing is crucial for discriminating sound sources in such complex acoustic scenes. The compromised perception of communication signals presented above hearing threshold has been linked to both peripheral and central age-related changes in the auditory system. Investigating young and old Mongolian gerbils of both sexes, an established model for human hearing, we demonstrate age-related supra-threshold deficits in binaural hearing using behavioral, electrophysiological, anatomical, and imaging methods. Binaural processing ability was measured as the binaural masking level difference (BMLD), an established measure in human psychophysics. We tested gerbils behaviorally with "virtual headphones," recorded single-unit responses in the auditory midbrain and evaluated gross midbrain and cortical responses using positron emission tomography (PET) imaging. Furthermore, we obtained additional measures of auditory function based on auditory brainstem responses, auditory-nerve synapse counts, and evidence for central inhibitory processing revealed by PET. BMLD deteriorates already in middle-aged animals having normal audiometric thresholds and is even worse in old animals with hearing loss. The magnitude of auditory brainstem response measures related to auditory-nerve function and binaural processing in the auditory brainstem also deteriorate. Furthermore, central GABAergic inhibition is affected by age. Because the number of synapses in the apical turn of the inner ear was not reduced in middle-aged animals, we conclude that peripheral synaptopathy contributes little to binaural processing deficits. Exploratory analyses suggest increased hearing thresholds, altered binaural processing in the brainstem and changed central GABAergic inhibition as potential contributors.

A back-translational study of descending interactions with the induction of hyperalgesia by high-frequency electrical stimulation in rats and humans.

In humans and animals, high-frequency electrocutaneous stimulation (HFS) induces an "early long-term potentiation-like" sensitisation, where synaptic plasticity is underpinned by an ill-defined interaction between peripheral input and central modulatory processes. The relative contributions of these processes to the initial pain or nociceptive response likely differ from those that underpin development of the heightened response. To investigate the impact of HFS-induced hyperalgesia on pain and nociception in perception and neural terms, respectively, and to explore the impact of descending inhibitory pathway activation on the development of HFS-induced hyperalgesia, we performed parallel studies utilising identical stimuli to apply HFS concurrent to (1) a conditioned pain modulation paradigm during psychophysical testing in healthy humans or (2) a diffuse noxious inhibitory controls paradigm during in vivo electrophysiological recording of spinal neurones in healthy anaesthetised rats. High-frequency electrocutaneous stimulation alone induced enhanced perceptual responses to pinprick stimuli in cutaneous areas secondary to the area of electrical stimulation in humans and increased the excitability of spinal neurones which exhibited stimulus intensity-dependent coded responses to pinprick stimulation in a manner that tracked with human psychophysics, supporting their translational validity. Application of a distant noxious conditioning stimulus during HFS did not alter perceived primary or secondary hyperalgesia in humans or the development of primary or secondary neuronal hyperexcitability in rats compared with HFS alone, suggesting that, upon HFS-response initiation in a healthy nervous system, excitatory signalling escapes inhibitory control. Therefore, in this model, dampening facilitatory mechanisms rather than augmenting top-down inhibitions could prevent pain development.

Metacognitive Psychophysics in Humans, Animals, and AI: A Research Agenda for Mapping Introspective Systems

Kammerer and Frankish (this issue) propose an exciting new research programme on the computational form of introspective systems. Pursuing this goal requires measures that can isolate introspective capacity from response biases and first-order processes. I suggest that metacognitive psychophysics is well placed to meet this challenge, allowing the mapping of introspective architectures in humans, animals, and artificial systems.

Contrast sensitivity function in deep networks

The contrast sensitivity function (CSF) is a fundamental signature of the visual system that has been measured extensively in several species. It is defined by the visibility threshold for sinusoidal gratings at all spatial frequencies. Here, we investigated the CSF in deep neural networks using the same 2AFC contrast detection paradigm as in human psychophysics. We examined 240 networks pretrained on several tasks. To obtain their corresponding CSFs, we trained a linear classifier on top of the extracted features from frozen pretrained networks. The linear classifier is exclusively trained on a contrast discrimination task with natural images. It has to find which of the two input images has higher contrast. The network’s CSF is measured by detecting which one of two images contains a sinusoidal grating of varying orientation and spatial frequency. Our results demonstrate characteristics of the human CSF are manifested in deep networks both in the luminance channel (a band-limited inverted U-shaped function) and in the chromatic channels (two low-pass functions of similar properties). The exact shape of the networks’ CSF appears to be task-dependent. The human CSF is better captured by networks trained on low-level visual tasks such as image-denoising or autoencoding. However, human-like CSF also emerges in mid- and high-level tasks such as edge detection and object recognition. Our analysis shows that human-like CSF appears in all architectures but at different depths of processing, some at early layers, while others in intermediate and final layers. Overall, these results suggest that (i) deep networks model the human CSF faithfully, making them suitable candidates for applications of image quality and compression, (ii) efficient/purposeful processing of the natural world drives the CSF shape, and (iii) visual representation from all levels of visual hierarchy contribute to the tuning curve of the CSF, in turn implying a function which we intuitively think of as modulated by low-level visual features may arise as a consequence of pooling from a larger set of neurons at all levels of the visual system.

Ultrafast Image Categorization in Biology and Neural Models.

Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which these artificial networks are typically trained and evaluated tend to be highly specialized and do not generalize well, e.g., accuracy drops after image rotation. In this respect, biological visual systems are more flexible and efficient than artificial systems for more general tasks, such as recognizing an animal. To further the comparison between biological and artificial neural networks, we re-trained the standard VGG 16 CNN on two independent tasks that are ecologically relevant to humans: detecting the presence of an animal or an artifact. We show that re-training the network achieves a human-like level of performance, comparable to that reported in psychophysical tasks. In addition, we show that the categorization is better when the outputs of the models are combined. Indeed, animals (e.g., lions) tend to be less present in photographs that contain artifacts (e.g., buildings). Furthermore, these re-trained models were able to reproduce some unexpected behavioral observations from human psychophysics, such as robustness to rotation (e.g., an upside-down or tilted image) or to a grayscale transformation. Finally, we quantified the number of CNN layers required to achieve such performance and showed that good accuracy for ultrafast image categorization can be achieved with only a few layers, challenging the belief that image recognition requires deep sequential analysis of visual objects. We hope to extend this framework to biomimetic deep neural architectures designed for ecological tasks, but also to guide future model-based psychophysical experiments that would deepen our understanding of biological vision.

A molecular psychophysical approach to identify cues for tone-in-noise detection in simultaneous masking

Experiments involving the detection of a tone presented in noise form the basis of many models of psychophysics that are used to estimate frequency resolution and cochlear compression for listeners with normal and impaired hearing. Despite the wide-spread application of tone-in-noise experiments, the cues used by listeners to detect the tone are a matter of debate. Here we adopt a molecular psychophysics approach – decision variable correlation (DVC) – to evaluate the extent to which trial-by-trial responses are consistent with candidate decision variables, including stimulus energy or the temporal envelope. We measured detection thresholds in normal-hearing adults for a 1000-Hz tone presented in one-third octave noise centered on the tone frequency. The tone and noise were gated simultaneously and thresholds were measured for durations of 10, 20, 50, and 100ms. We limited the reliability of energy-based cues by incorporating a roving-level paradigm. Similarly, the use of short tones (e.g., 10 ms) limited the reliability of envelope-based cues. Our results reveal that listeners adopted a strategy that emphasized energy-based cues for short-duration tones and envelope-based cues for long-duration tones. This finding suggests that models of human psychophysics may benefit from incorporating a decision device that adjusts cue weights based on stimulus duration.

Model-Based Approach Shows ON Pathway Afferents Elicit a Transient Decrease of V1 Responses.

Neurons in the primary visual cortex (V1) receive excitation and inhibition from distinct parallel pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli, consistent with a preponderance of darker regions in natural images, as well as human psychophysics. However, it has been unclear whether this "dark-dominance" is because of more excitation from the OFF pathway or more inhibition from the ON pathway. To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neurons' responses. Analyzing a sample of 71 neurons (in anesthetized, paralyzed cats of either sex) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations. We show that this asymmetry is predominantly because of slower inhibition to dark stimuli rather than to stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons, we find dark-dominance to solely occur in the early latencies of neurons' responses. Neurons that are strongly dark-dominated also tend to be less orientation-selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.SIGNIFICANCE STATEMENT Neurons in the early visual cortex respond on average more strongly to dark than to light stimuli, but the mechanisms behind this bias have been unclear. Here we address this issue by combining single-unit electrophysiology with a novel machine learning model to analyze neurons' responses to natural image stimuli in primary visual cortex. Using these techniques, we find slower inhibition to light than to dark stimuli to be the leading mechanism behind stronger dark responses. This slower inhibition to light might help explain other empirical findings, such as why orientation selectivity is weaker at earlier response latencies. These results demonstrate how imbalances in excitation versus inhibition can give rise to response asymmetries in cortical neuron responses.

Optimized Electrical Stimulation of C-Nociceptors in Humans Based on the Chronaxie of Porcine C-Fibers

Classically, to electrically excite C-nociceptors, rectangular pulses are used with a duration close to the estimated chronaxie of C-fibres (about 2 ms). Recent results using slow depolarizing stimuli suggest longer chronaxies. We therefore set out to optimize C-fiber stimulation based on recordings of single C-nociceptors in-vivo and C-fiber compound-action-potentials (C-CAP) ex-vivo using half-sine shaped stimuli of durations between 1 and 250ms. Single fiber (n = 45) recording in pigs revealed high chronaxie values for C-touch fibers (15.8 ms), polymodal- (14.2 ms) and silent-nociceptors (16.8 ms). Activation thresholds decreased 2 to 3-fold in all fibre classes when increasing the duration of half-sine pulses from 1 to 25 ms (P < .05). C-CAPs strength-duration curves of the pig saphenous nerve (n = 7) showed the highest sensitivity for half-sine durations between 10 and 25 ms. Half-maximum currents for C-CAPS were reduced 3-fold compared to rectangular pulses (P < .01) whereas the opposite was found for A-fiber compound action potentials. Psychophysics in humans (n = 23) revealed that half-sine stimulus durations >10 ms reduced detection thresholds, pain thresholds, and stimulus current amplitudes required to generate a pain rating of 3 on an 11-point Numeric Rating Scale (NRS) as compared to 1 ms rectangular pulses (P < 0.05). Increasing the duration from 1 to 25 ms led to a 4-fold amplitude reduction for pain-thresholds and stimuli caused an axon-reflex flare. Excitability of single polymodal nociceptors in animals paralleled human psychophysics and we conclude optimized half-sine pulses facilitate C-nociceptor activation. PerspectiveElectrical stimulation with longer lasting half-sine wave pulses preferentially activates C-nociceptors and changes in the strength duration curve may identify nociceptor hyperexcitability in patients with neuropathic pain.

Eye Tracking in Dogs: Achievements and Challenges.

In this article, we review eye-tracking studies with dogs (Canis familiaris) with a threefold goal; we highlight the achievements in the field of canine perception and cognition using eye tracking, then discuss the challenges that arise in the application of a technology that has been developed in human psychophysics, and finally propose new avenues in dog eye-tracking research. For the first goal, we present studies that investigated dogs' perception of humans, mainly faces, but also hands, gaze, emotions, communicative signals, goal-directed movements, and social interactions, as well as the perception of animations representing possible and impossible physical processes and animacy cues. We then discuss the present challenges of eye tracking with dogs, like doubtful picture-object equivalence, extensive training, small sample sizes, difficult calibration, and artificial stimuli and settings. We suggest possible improvements and solutions for these problems in order to achieve better stimulus and data quality. Finally, we propose the use of dynamic stimuli, pupillometry, arrival time analyses, mobile eye tracking, and combinations with behavioral and neuroimaging methods to further advance canine research and open up new scientific fields in this highly dynamic branch of comparative cognition.

Binocular viewing geometry shapes neural processing of slanted planes: Results from theoretical V1 modeling and human psychophysics

An important component of real-world scene perception is the estimation of 3D surface orientation (slant and tilt). However, neither the neural circuits that compute 3D orientation nor the supporting computations have been fully uncovered. To help address that gap, we apply a projective geometry framework to the study of both the neural processing and perception of 3D orientation, building on recent work that suggests the mapping of the 3D environment onto the two retinae shapes both the tuning of neurons in area MT to motion-in-depth and corresponding patterns of perceptual errors in human psychophysics (Bonnen et al., 2020). The projection of slanted planes results in interocular disparities in retinal orientation, and interestingly, binocular V1 neurons can have different monocular orientation preferences (Bridge & Cumming, 2001), which could make them sensitive to orientation disparities. Therefore, we have constructed an encoding model that predicts responses of binocular V1 neurons to the orientation disparities produced by a stimulus plane with varying slant, given each theoretical neuron’s monocular orientation preferences and ocular dominance level. The resulting slant tuning curves are noncanonical in shape, do not tile slant evenly, and are strongly affected by viewing distance. Notably, at greater viewing distances, extreme slants (beyond approximately +/- 70 degrees) are encoded by steep portions of the curves, but moderate and low slants are encoded by relatively flat portions. As expected based on those observations, our model population decoder performs best at extreme slants, and performance degrades as slant approaches zero and as viewing distance increases. We additionally observed these patterns in human perceptual performance on a slant discrimination task. These results support the theory that the neural processing of 3D scene components is shaped by projective geometry and represent a step toward advancing understanding of the computations supporting 3D orientation perception.

Primate monocular vision is intrinsically unstable: a side-effect of binocular homeostasis

Short-term deprivation of one eye of 1-2 hours in human adults results in reciprocal changes in the interocular balance that last 30-90 minutes, assumed to be the consequence of a change in the contrast gain within the binocular circuitry. What is not known is how these changes emerge and with what dynamic. One possibility is that they are initiated once the patch is removed. The alternative is that they result from a dynamic build-up from the moment the patch is applied. In this study, we measure the monocular dynamics of the non-deprived eye activation during the deprivation of the other eye in humans, using psychophysics, and in awake non-human primates, using functional ultrasound imaging (fUS) of the primary visual cortex (V1). In humans, we show that the contrast threshold increases overtime during the deprivation of the other eye. This observation indicates that the patching effect slowly builds up over the deprivation period at a binocular site. In the awake macaque, we show that during monocular deprivation the cerebral blood volume (CBV) increases in the non-deprived eye ocular-dominance bands, but not the deprived eye bands with similar dynamics as observed in human psychophysics. We show that human behavioral results can be predicted by a simple interocular normalization model that uses non-human primate CBV dynamics as a descriptor of the normalization pool activity. In conclusion, our results highlight a hitherto unknown feature of primate vision: monocular vision per se is intrinsically unstable.

FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality.

Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads. Recent advances in neural rendering showed promise of unlocking new possibilities in 3D computer graphics via image-based representations of virtual or physical environments. Specifically, the neural radiance fields (NeRF) demonstrated that photo-realistic quality and continuous view changes of 3D scenes can be achieved without loss of view-dependent effects. While NeRF can significantly benefit rendering for VR applications, it faces unique challenges posed by high field-of-view, high resolution, and stereoscopic/egocentric viewing, typically causing low quality and high latency of the rendered images. In VR, this not only harms the interaction experience but may also cause sickness. To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. We then jointly optimize the latency/performance and visual quality while mutually bridging human perception and neural scene synthesis to achieve perceptually high-quality immersive interaction. We conducted both objective analysis and subjective studies to evaluate the effectiveness of our approach. We find that our method significantly reduces latency (up to 99% time reduction compared with NeRF) without loss of high-fidelity rendering (perceptually identical to full-resolution ground truth). The presented approach may serve as the first step toward future VR/AR systems that capture, teleport, and visualize remote environments in real-time.

A Recurrent Neural Network Model Accounts for Both Timing and Working Memory Components of an Interval Discrimination Task

Abstract Interval discrimination is of fundamental importance to many forms of sensory processing, including speech and music. Standard interval discrimination tasks require comparing two intervals separated in time, and thus include both working memory (WM) and timing components. Models of interval discrimination invoke separate circuits for the timing and WM components. Here we examine if, in principle, the same recurrent neural network can implement both. Using human psychophysics, we first explored the role of the WM component by varying the interstimulus delay. Consistent with previous studies, discrimination was significantly worse for a 250 ms delay, compared to 750 and 1500 ms delays, suggesting that the first interval is stably stored in WM for longer delays. We next successfully trained a recurrent neural network (RNN) on the task, demonstrating that the same network can implement both the timing and WM components. Many units in the RNN were tuned to specific intervals during the sensory epoch, and others encoded the first interval during the delay period. Overall, the encoding strategy was consistent with the notion of mixed selectivity. Units generally encoded more interval information during the sensory epoch than in the delay period, reflecting categorical encoding of short versus long in WM, rather than encoding of the specific interval. Our results demonstrate that, in contrast to standard models of interval discrimination that invoke a separate memory module, the same network can, in principle, solve the timing, WM, and comparison components of an interval discrimination task.

Can You Hear Me Now? Sensitive Comparisons of Human and Machine Perception.

The rise of machine-learning systems that process sensory input has brought with it a rise in comparisons between human and machine perception. But such comparisons face a challenge: Whereas machine perception of some stimulus can often be probed through direct and explicit measures, much of human perceptual knowledge is latent, incomplete, or unavailable for explicit report. Here, we explore how this asymmetry can cause such comparisons to misestimate the overlap in human and machine perception. As a case study, we consider human perception of adversarial speech - synthetic audio commands that are recognized as valid messages by automated speech-recognition systems but that human listeners reportedly hear as meaningless noise. In five experiments, we adapt task designs from the human psychophysics literature to show that even when subjects cannot freely transcribe such speech commands (the previous benchmark for human understanding), they can sometimes demonstrate other forms of understanding, including discriminating adversarial speech from closely matched nonspeech (Experiments 1 and 2), finishing common phrases begun in adversarial speech (Experiments 3 and 4), and solving simple math problems posed in adversarial speech (Experiment 5) - even for stimuli previously described as unintelligible to human listeners. We recommend the adoption of such "sensitive tests" when comparing human and machine perception, and we discuss the broader consequences of such approaches for assessing the overlap between systems.

Suppression without inhibition: how retinal computation contributes to saccadic suppression

Visual perception remains stable across saccadic eye movements, despite the concurrent strongly disruptive visual flow. This stability is partially associated with a reduction in visual sensitivity, known as saccadic suppression, which already starts in the retina with reduced ganglion cell sensitivity. However, the retinal circuit mechanisms giving rise to such suppression remain unknown. Here, we describe these mechanisms using electrophysiology in mouse, pig, and macaque retina, 2-photon calcium imaging, computational modeling, and human psychophysics. We find that sequential stimuli, like those that naturally occur during saccades, trigger three independent suppressive mechanisms in the retina. The main mechanism is triggered by contrast-reversing sequential stimuli and originates within the receptive field center of ganglion cells. It does not involve inhibition or other known suppressive mechanisms like saturation or adaptation. Instead, it relies on temporal filtering of the inherently slow response of cone photoreceptors coupled with downstream nonlinearities. Two further mechanisms of suppression are present predominantly in ON ganglion cells and originate in the receptive field surround, highlighting another disparity between ON and OFF ganglion cells. The mechanisms uncovered here likely play a role in shaping the retinal output following eye movements and other natural viewing conditions where sequential stimulation is ubiquitous.

Sodium-calcium exchanger-3 regulates pain “wind-up”: From human psychophysics to spinal mechanisms

SummaryRepeated application of noxious stimuli leads to a progressively increased pain perception; this temporal summation is enhanced in and predictive of clinical pain disorders. Its electrophysiological correlate is “wind-up,” in which dorsal horn spinal neurons increase their response to repeated nociceptor stimulation. To understand the genetic basis of temporal summation, we undertook a GWAS of wind-up in healthy human volunteers and found significant association with SLC8A3 encoding sodium-calcium exchanger type 3 (NCX3). NCX3 was expressed in mouse dorsal horn neurons, and mice lacking NCX3 showed normal, acute pain but hypersensitivity to the second phase of the formalin test and chronic constriction injury. Dorsal horn neurons lacking NCX3 showed increased intracellular calcium following repetitive stimulation, slowed calcium clearance, and increased wind-up. Moreover, virally mediated enhanced spinal expression of NCX3 reduced central sensitization. Our study highlights Ca2+ efflux as a pathway underlying temporal summation and persistent pain, which may be amenable to therapeutic targeting.

The Ecological View of Selective Attention.

Accumulating evidence is supporting the hypothesis that our selective attention is a manifestation of mechanisms that evolved early in evolution and are shared by many organisms from different taxa. This surge of new data calls for the re-examination of our notions about attention, which have been dominated mostly by human psychology. Here, we present an hypothesis that challenges, based on evolutionary grounds, a common view of attention as a means to manage limited brain resources. We begin by arguing that evolutionary considerations do not favor the basic proposition of the limited brain resources view of attention, namely, that the capacity of the sensory organs to provide information exceeds the capacity of the brain to process this information. Moreover, physiological studies in animals and humans show that mechanisms of selective attention are highly demanding of brain resources, making it paradoxical to see attention as a means to release brain resources. Next, we build on the above arguments to address the question why attention evolved in evolution. We hypothesize that, to a certain extent, limiting sensory processing is adaptive irrespective of brain capacity. We call this hypothesis the ecological view of attention (EVA) because it is centered on interactions of an animal with its environment rather than on internal brain resources. In its essence is the notion that inherently noisy and degraded sensory inputs serve the animal’s adaptive, dynamic interactions with its environment. Attention primarily functions to resolve behavioral conflicts and false distractions. Hence, we evolved to focus on a particular target at the expense of others, not because of internal limitations, but to ensure that behavior is properly oriented and committed to its goals. Here, we expand on this notion and review evidence supporting it. We show how common results in human psychophysics and physiology can be reconciled with an EVA and discuss possible implications of the notion for interpreting current results and guiding future research.