Effects of colour on preview search: anticipatory and inhibitory biases for colour

Two experiments are reported examining the effects of colour grouping, colour change and target colour foreknowledge under preview search conditions (Watson and Humphreys, 1997). In Experiment 1 we manipulated the colour homogeneity of the old items at initial presentation, and the colour these items subsequently changed into. In all cases participants knew the colour of the target. We found that when the old items changed into the same colour as the new search set, search performance was affected. In Experiment 2 participants did not know the colour of the target. Here we found evidence for a negative colour-based carry-over effect that slowed search for new targets carrying the colour of the old items. This occurred even when the old items changed their original colour and the new target was a singleton. Collectively the results suggest an important role for both colour grouping and colour-based inhibition in the successful rejection of old distractors. The consequence of this, however, is that new stimuli that may carry the critical attribute may take longer to detect. We discuss the results in relation to prior 'feature-blind' accounts of preview effects on visual search.     

The Gaussian derivative model for spatial-temporal vision: I. Cortical model

How do we see the motion of objects as well as their shapes? The Gaussian Derivative (GD) spatial model is extended to time to help answer this question. The GD spatio-temporal model requires only two numbers to describe the complete three-dimensional space-time shapes of individual receptive fields in primate visual cortex. These two numbers are the derivative numbers along the respective spatial and temporal principal axes of a given receptive field. Nine transformation parameters allow for a standard geometric association of these intrinsic axes with the extrinsic environment. The GD spatio-temporal model describes in one framework the following properties of primate simple cell fields: motion properties, number of lobes in space-time, spatial orientation. location, and size. A discrete difference-of-offset-Gaussians (DOOG) model provides a plausible physiological mechanism to form GD-like model fields in both space and time. The GD model hypothesizes that receptive fields at the first stage of processing in the visual cortex approximate 'derivative analyzers' that estimate local spatial and temporal derivatives of the intensity profile in the visual environment. The receptive fields as modeled provide operators that can allow later stages of processing in either a biological or machine vision system to estimate the motion as well as the shapes of objects in the environment.     

A ratio model of perceived speed in the human visual system

The perceived speed of moving images changes over time. Prolonged viewing of a pattern (adaptation) leads to an exponential decrease in its perceived speed. Similarly, responses of neurones tuned to motion reduce exponentially over time. It is tempting to link these phenomena. However, under certain conditions, perceived speed increases after adaptation and the time course of these perceptual effects varies widely. We propose a model that comprises two temporally tuned mechanisms whose sensitivities reduce exponentially over time. Perceived speed is taken as the ratio of these filters' outputs. The model captures increases and decreases in perceived speed following adaptation and describes our data well with just four free parameters. Whilst the model captures perceptual time courses that vary widely, parameter estimates for the time constants of the underlying filters are in good agreement with estimates of the time course of adaptation of direction selective neurones in the mammalian visual system.     

A data-informed mean-field approach to mapping of cortical parameter landscapes

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.     

The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input

Recently, we presented a study of adult neurogenesis in a simplified hippocampal memory model. The network was required to encode and decode memory patterns despite changing input statistics. We showed that additive neurogenesis was a more effective adaptation strategy compared to neuronal turnover and conventional synaptic plasticity as it allowed the network to respond to changes in the input statistics while preserving representations of earlier environments. Here we extend our model to include realistic, spatially driven input firing patterns in the form of grid cells in the entorhinal cortex. We compare network performance across a sequence of spatial environments using three distinct adaptation strategies: conventional synaptic plasticity, where the network is of fixed size but the connectivity is plastic; neuronal turnover, where the network is of fixed size but units in the network may die and be replaced; and additive neurogenesis, where the network starts out with fewer initial units but grows over time. We confirm that additive neurogenesis is a superior adaptation strategy when using realistic, spatially structured input patterns. We then show that a more biologically plausible neurogenesis rule that incorporates cell death and enhanced plasticity of new granule cells has an overall performance significantly better than any one of the three individual strategies operating alone. This adaptation rule can be tailored to maximise performance of the network when operating as either a short- or long-term memory store. We also examine the time course of adult neurogenesis over the lifetime of an animal raised under different hypothetical rearing conditions. These growth profiles have several distinct features that form a theoretical prediction that could be tested experimentally. Finally, we show that place cells can emerge and refine in a realistic manner in our model as a direct result of the sparsification performed by the dentate gyrus layer.     

A saliency-based bottom-up visual attention model for dynamic scenes analysis

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.     

A generative spiking neural-network model of goal-directed behaviour and one-step planning

In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models have been proposed to perform planning as probabilistic inference but this approach encounters a crucial problem: explaining how such inference might be implemented in brain spiking networks. Recently, the literature has proposed some models that face this problem through recurrent spiking neural networks able to internally simulate state trajectories, the core function at the basis of planning. However, the proposed models have relevant limitations that make them biologically implausible, namely their world model is trained ‘off-line’ before solving the target tasks, and they are trained with supervised learning procedures that are biologically and ecologically not plausible. Here we propose two novel hypotheses on how brain might overcome these problems, and operationalise them in a novel architecture pivoting on a spiking recurrent neural network. The first hypothesis allows the architecture to learn the world model in parallel with its use for planning: to this purpose, a new arbitration mechanism decides when to explore, for learning the world model, or when to exploit it, for planning, based on the entropy of the world model itself. The second hypothesis allows the architecture to use an unsupervised learning process to learn the world model by observing the effects of actions. The architecture is validated by reproducing and accounting for the learning profiles and reaction times of human participants learning to solve a visuomotor learning task that is new for them. Overall, the architecture represents the first instance of a model bridging probabilistic planning and spiking-processes that has a degree of autonomy analogous to the one of real organisms.     

Generating oscillatory bursts from a network of regular spiking neurons without inhibition

Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the layer 10 (L10) neurons in the optic tectum and respond with oscillatory bursts to visual stimulation. Our in vitro experiments show that both neuron types respond with regular spiking to somatic current injection and that the feedforward and feedback synaptic connections are excitatory, but of different strength and time course. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation. A scan through the model parameter volume reveals that Ipc oscillatory burst generation can be caused by strong and brief feedforward synaptic conductance changes. The mechanism is sensitive to the parameter values of spike-rate adaptation. In conclusion, we show that a network of regular-spiking neurons with feedforward excitation and spike-rate adaptation can generate oscillatory bursting in response to a constant input.     

A biologically plausible model of time-scale invariant interval timing

The temporal durations between events often exert a strong influence over behavior. The details of this influence have been extensively characterized in behavioral experiments in different animal species. A remarkable feature of the data collected in these experiments is that they are often time-scale invariant. This means that response measurements obtained under intervals of different durations coincide when plotted as functions of relative time. Here we describe a biologically plausible model of an interval timing device and show that it is consistent with time-scale invariant behavior over a substantial range of interval durations. The model consists of a set of bistable units that switch from one state to the other at random times. We first use an abstract formulation of the model to derive exact expressions for some key quantities and to demonstrate time-scale invariance for any range of interval durations. We then show how the model could be implemented in the nervous system through a generic and biologically plausible mechanism. In particular, we show that any system that can display noise-driven transitions from one stable state to another can be used to implement the timing device. Our work demonstrates that a biologically plausible model can qualitatively account for a large body of data and thus provides a link between the biology and behavior of interval timing.     

Eye Movements,Visual Search and Scene Memory,in an Immersive Virtual Environment

Visual memory has been demonstrated to play a role in both visual search and attentional prioritization in natural scenes. However, it has been studied predominantly in experimental paradigms using multiple two-dimensional images. Natural experience, however, entails prolonged immersion in a limited number of three-dimensional environments. The goal of the present experiment was to recreate circumstances comparable to natural visual experience in order to evaluate the role of scene memory in guiding eye movements in a natural environment. Subjects performed a continuous visual-search task within an immersive virtual-reality environment over three days. We found that, similar to two-dimensional contexts, viewers rapidly learn the location of objects in the environment over time, and use spatial memory to guide search. Incidental fixations did not provide obvious benefit to subsequent search, suggesting that semantic contextual cues may often be just as efficient, or that many incidentally fixated items are not held in memory in the absence of a specific task. On the third day of the experience in the environment, previous search items changed in color. These items were fixated upon with increased probability relative to control objects, suggesting that memory-guided prioritization (or Surprise) may be a robust mechanisms for attracting gaze to novel features of natural environments, in addition to task factors and simple spatial saliency.     

Relationship between uniform connectedness and proximity in perceptual grouping

Palmer and Rock proposed that uniform connectedness (UC) occurs prior to classical Gestalt factors to define the primitive units for visual perception. Han, Humphreys and Chen, however, found that grouping by proximity can take place as quickly as that based on UC in a letter discrimination task. The present study employed a letter detection task to examine the relationship between UC and proximity grouping in 3 experiments. We showed that reaction times to targets defined by proximity or UC were equally fast when one or two global objects were presented in the visual field. However, as the number of global objects was increased, responses were faster to targets defined by UC than to targets defined by proximity. In addition, the advantage of UC over proximity was not affected by the space between global objects. The results suggest that UC was more effective than proximity in forming perceptual units under multiple object conditions. Possible reasons for this finding are discussed.     

Relationship between uniform connectedness and proximity in perceptual grouping

1 Introduction The visual world is composed of complex visual scenes that are projected, as two-dimen- sional images, onto the retina. Chunking of visual information is critical for object recognition, because it produces primitive perceptual units for subsequent analyses[1]. Integration of discrete local elements into a global configuration is one of the functions of perceptual grouping (e.g., combining local rectangles into a global letter as shown in fig. 1(b)). When multiple global object…     

Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper Pavan et al.(Journal of Vision 10:1–17, 2013) presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model’s output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a second-order RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multi-stage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales.     

The predation costs of symmetrical cryptic coloration

In psychological studies of visual perception, symmetry is accepted as a potent cue in visual search for cryptic objects, yet its importance for non-human animals has been assumed rather than tested. Furthermore, while the salience of bilateral symmetry has been established in laboratory-based search tasks using human subjects, its role in more natural settings, closer to those for which such perceptual mechanisms evolved, has not, to our knowledge, been investigated previously. That said, the salience of symmetry in visual search has a plausible adaptive rationale, because biologically important objects, such as prey, predators or conspecifics, usually have a plane of symmetry that is not present in their surroundings. We tested the conspicuousness to avian predators of cryptic artificial, moth-like targets, with or without bilateral symmetry in background-matching coloration, against oak trees in the field. In two independent experiments, symmetrical targets were predated at a higher rate than otherwise identical asymmetrical targets. There was a small, but significant, fitness cost to symmetry in camouflage patterns. Given that birds are the most commonly invoked predators shaping the evolution of defensive coloration in insects, this raises the question of why bilateral asymmetry is not more common in cryptic insects.     

Selective visual processing across competition episodes: a theory of task-driven visual attention and working memory

The goal of this review is to introduce a theory of task-driven visual attention and working memory (TRAM). Based on a specific biased competition model, the ‘theory of visual attention’ (TVA) and its neural interpretation (NTVA), TRAM introduces the following assumption. First, selective visual processing over time is structured in competition episodes. Within an episode, that is, during its first two phases, a limited number of proto-objects are competitively encoded—modulated by the current task—in activation-based visual working memory (VWM). In processing phase 3, relevant VWM objects are transferred via a short-term consolidation into passive VWM. Second, each time attentional priorities change (e.g. after an eye movement), a new competition episode is initiated. Third, if a phase 3 VWM process (e.g. short-term consolidation) is not finished, whereas a new episode is called, a protective maintenance process allows its completion. After a VWM object change, its protective maintenance process is followed by an encapsulation of the VWM object causing attentional resource costs in trailing competition episodes. Viewed from this perspective, a new explanation of key findings of the attentional blink will be offered. Finally, a new suggestion will be made as to how VWM items might interact with visual search processes.     

Landmarks help guide attention during visual search

Using a novel visual search paradigm McCarley et al. (2003) concluded that the oculomotor system keeps a history of 3-4 previously attended objects. However, their displays were visually sparse, denying participants structural information which might be used during normal search. This might have underestimated memory capacity. To examine this possibility, we included landmarks in the same search paradigm. Previously examined items were re-examined less frequently when landmarks were present compared to when they were absent. Results indicate that objects in the environment that share no features with search items are used as external support to aid memory in guiding visual search.     

Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model

In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism adaptation, as observed in other experiments.     

Computational modeling and exploration of contour integration for visual saliency

We propose a computational model of contour integration for visual saliency. The model uses biologically plausible devices to simulate how the representations of elements aligned collinearly along a contour in an image are enhanced. Our model adds such devices as a dopamine-like fast plasticity, local GABAergic inhibition and multi-scale processing of images. The fast plasticity addresses the problem of how neurons in visual cortex seem to be able to influence neurons they are not directly connected to, for instance, as observed in contour closure effect. Local GABAergic inhibition is used to control gain in the system without using global mechanisms which may be non-plausible given the limited reach of axonal arbors in visual cortex. The model is then used to explore not only its validity in real and artificial images, but to discover some of the mechanisms involved in processing of complex visual features such as junctions and end-stops as well as contours. We present evidence for the validity of our model in several phases, starting with local enhancement of only a few collinear elements. We then test our model on more complex contour integration images with a large number of Gabor elements. Sections of the model are also extracted and used to discover how the model might relate contour integration neurons to neurons that process end-stops and junctions. Finally, we present results from real world images. Results from the model suggest that it is a good current approximation of contour integration in human vision. As well, it suggests that contour integration mechanisms may be strongly related to mechanisms for detecting end-stops and junction points. Additionally, a contour integration mechanism may be involved in finding features for objects such as faces. This suggests that visual cortex may be more information efficient and that neural regions may have multiple roles.     

A generalized sense of number

Much evidence has accumulated to suggest that many animals, including young human infants, possess an abstract sense of approximate quantity, a number sense. Most research has concentrated on apparent numerosity of spatial arrays of dots or other objects, but a truly abstract sense of number should be capable of encoding the numerosity of any set of discrete elements, however displayed and in whatever sensory modality. Here, we use the psychophysical technique of adaptation to study the sense of number for serially presented items. We show that numerosity of both auditory and visual sequences is greatly affected by prior adaptation to slow or rapid sequences of events. The adaptation to visual stimuli was spatially selective (in external, not retinal coordinates), pointing to a sensory rather than cognitive process. However, adaptation generalized across modalities, from auditory to visual and vice versa. Adaptation also generalized across formats: adapting to sequential streams of flashes affected the perceived numerosity of spatial arrays. All these results point to a perceptual system that transcends vision and audition to encode an abstract sense of number in space and in time.     

A model of motion adaptation and motion after-effects based upon principal component regression

A computational model to help explain effects of adaptation to moving signals is compared with established energy (linear regression) models of motion detection. The proposed model assumes that processed image signals are subject to error in both dimensions of space and time. This assumption constrains models of motion perception to be based upon principal component regression rather than linear regression. It is shown that response suppression of model complex cell neurons that input into the model may account for (1) increases in perceived speed after adaptation to static patterns and testing with slowly moving patterns, (2) significant increases in perceived speed after adaptation to patterns moving at a medium speed and testing at high speed, and (3) decreases in perceived speed in the opponent direction to a quickly moving adapting signal. Neither of predictions (2) or (3) are general features of established accounts of motion detection by visual processes based upon linear regression. Comparisons of the proposed model's speed transfer function with existing psychophysical data suggests that the visual system processes motion signals with the tacit assumption that image measurements are subject to error in both space and time. Received: 24 January 2000 / Accepted in revised form: 8 May 2000