Psychophysical Literature Research Articles

The effect of contrast on pedestrians’ perception of vehicle speed in different road environments

It is well known in the psychophysical literature that low visual contrasts can lead observers to misestimate the speed of moving objects. This effect can have important consequences for traffic safety when navigating under low visibility due to adverse weather conditions (e.g., fog) or visual impairments. So far, road traffic research has primarily focused on the perception of self-motion during driving showing that drivers can both under- and overestimate their own driving speed depending on the spatial distribution of contrast. In two experiments, we used a two-interval forced choice discrimination task to investigate whether pedestrians would be subject to similar biases when estimating the speed of approaching vehicles in a simulated traffic scene. We found that the perceived vehicle speed decreased when the contrast of the view was reduced uniformly but increased when contrast was reduced in a distance-dependent manner, simulating more realistically visibility in fog. The increase of the perceived vehicle speed in simulated fog occurred in bare and visually more complex road environments including either roadside trees or road markings. The origins of such misperceptions, specifically in fog, remain unclear. The temporal integration of motion signals in combination with a lack in speed constancy, and the illusion of acceleration due to the dynamic contrast-change of the vehicle constitute potential explanations that need further investigation.

Cognitive sophistication and deliberation times

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide an individually-measurable correlate of cognitive effort. We test a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games.

Neural model of the visual recognition of social intent

INTRODUCTION: Humans are highly skilled at interpreting intent or social behavior from strongly impoverished stimuli (Heider & Simmel, 1944). It has been hypothesized that this visual function is based on high-level cognitive processes, such as probabilistic reasoning. We demonstrate that several classical observations on animacy and interaction perception can be accounted for by simple and physiologically plausible neural mechanisms, using an appropriately extended hierarchical (deep) model of the visual pathway. METHODS: Building on classical biologically-inspired models for object and action perception (Riesenhuber & Poggio, 1999; Giese & Poggio, 2003), by a front-end that exploits deep learning (VGG-16) for the construction of low and mid-level feature detectors, we propose a learning-based hierarchical neural network model that analyzes shape and motion features from video sequences. The model consists of streams for form and object motion in a retinal frame of reference. We try to account with this model simultaneously for several experimental observations on the perception of animacy and social interaction. RESULTS: Based on input video sequences, the model successfully reproduces results of Tremoulet and Feldman (2000) on the dependence of perceived animacy on motion parameters and the body axis. In addition, the model classifies correctly six categories of social interactions that have been frequently tested in the psychophysical literature (following, fighting, chasing, playing, guarding, and flirting) (e.g. Scholl & McCarthy, 2012; McAleer et al., 2008). In addition, we show that the model can be extended for the processing of simple interactions in real-world movies. CONCLUSION: Since the model accounts simultaneously for a variety of effects related to animacy and interaction perception using physiologically plausible mechanisms, without requiring complex computational inference and optimization processes, it might serve as starting point for the search of neurons that are forming the core circuit of the perceptual processing of animacy and interaction.

Evaluation of Sweetener Synergy in Humans by Isobole Analyses.

The chemical senses and pharmaceuticals fundamentally depend on similar biological processes, but novel molecule discovery has classically been approached from vastly different vantage points. From the perspective of ingredient and flavor companies, there are countless ingredients that act via largely unknown mechanisms, whereas the pharmaceutical industry has numerous mechanisms in search of novel compounds. Mixtures of agonists can result in synergistic (superadditive) responses, which can be quantified via isobole analysis, a well-proven clinical approach in pharmacology. For the food and beverage industries, bulk (caloric) sweeteners like sugars are a key ingredient in sweetened foods and beverages, but consumers also desire products with fewer calories, which has led to the development of sweet enhancers and sweetener blends intended to achieve synergy or superadditivity. Synergistic mixtures are highly attractive targets commercially as they enable lower usage levels and enhanced efficacy. Although the psychophysical literature contains numerous prior reports of sweetener synergy, others have also noted that classical additive models fail to account for nonlinear dose-response functions. To address this shortcoming, here we systematically apply the isobole method from pharmacology to quantify the presence or absence of psychophysical synergy for binary pairs of sweeteners in a series of 15 separate experiments, each with ~100 adult volunteers (total n = 1576). Generally, these data support the hypothesis that structurally similar sweeteners acting as agonists will not synergize, whereas structurally dissimilar sweeteners binding to overlapping or distal sites can act as allosteric agonists or agonist-antagonists, respectively.

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

P170 Neural model for the recognition of agency and social interaction from abstract stimuliMohammad Hovaidi Ardestani1, Martin Giese2, Nitin Saini2 1University Clinic Tübingen, Tübingen, Germany; 2Center for Integrative Neuroscience & University Clinic Tübingen, Dept of Cogniitive Neurology, Germany Correspondence: Martin Giese (martin.giese@uni-tuebingen.de)BMC Neuroscience 2018, 19(Suppl 2):P170Introduction: Humans derive spontaneously judgements about agency and social interactions from strongly impoverished stimuli, as impressively demonstrated by the seminal work by Heider and Simmel (1944). The neural circuits that derive such judgements from image sequences are entirely unknown. It has been hypothesized that this visual function is based on high-level cognitive processes, such as probabilistic reasoning. Taking an alternative approach, we show that such functions can be accomplished by relatively elementary neural networks that can be implemented by simple physiologically plausible neural mechanisms, exploiting an appropriately structure hierarchical (deep) neural model of the visual pathway.Methods: Extending classical biologically-inspired models for object and action perception (Riesenhuber & Poggio 1999; Giese & Poggio 2003) by a front-end that exploits deep learning for the construction of low and mid-level feature detectors, we built a hierarchical neural model that reproduces elementary psychophysical results on animacy and social perception from abstract stimuli. The lower hierarchy levels of the model consist of position-variant neural feature detectors that extract orientation and intermediately complex shape features. The next-higher level is formed by shape-selective neurons that are not completely position-invariant, which extract the 2D positions and orientation of moving agents. A second pathway extracts the 2D motion of the moving agents. Exploiting a gain-field network, we compute the relative positions of the moving agents. The top layers of the model combine the mentioned features into more complex high-level features that represent the speed, smoothness of motion and spatial relationships of the moving agents. The highest level of the model consists of neurons that have learned to classify the agency of the motions, and different categories of social interactions.Results: Based on input video sequences, the model successfully reproduces results of Tremoulet and Feldman (2000) on the dependence of perceived animacy on motion parameters, and its dependence on the alignment of motion and body axis (Hernik et al. 2013). In addition, the model correctly classifies four categories of social interactions that have been frequently tested in the psychophysical literature (following, chasing, fighting, guarding) (e.g. Scholl and McCarthy, 2012; McAleer et al., 2011a).Conclusion: Using simple physiologically plausible neural circuits, the model accounts simultaneously for a variety of effects related to animacy and social interaction perception. This leads to interesting predictions about neurons involved in the visual processing of such stimuli. Acknowledgement This work was supported by: HFSP RGP0036/2016; the European Commission HBP FP7-ICT2013-FET-F/604102 and COGIMON H2020-644727, and the DFG GZ: GI 305/4-1 and KA 1258/15-1.

Cognitive Sophistication and Deliberation Times

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide the missing link, in the form of an individually-measurable correlate of cognitive effort. We present a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games. However, we also show that imputing cognitive depth from choices alone can lead to erroneous conclusions when the features leading to iterative thinking are not salient.

Visual processing in migraine.

Background Migraine is a common neurological condition that often involves differences in visual processing. These sensory processing differences provide important information about the underlying causes of the condition, and for the development of treatments. Review of psychophysical literature Psychophysical experiments have shown consistent impairments in contrast sensitivity, orientation acuity, and the perception of global form and motion. They have also established that the addition of task-irrelevant visual noise has a greater effect, and that surround suppression, masking and adaptation are all stronger in migraine. Theoretical signal processing model We propose utilising an established model of visual processing, based on signal processing theory, to account for the behavioural differences seen in migraine. This has the advantage of precision and clarity, and generating clear, falsifiable predictions. Conclusion Increased effects of noise and differences in excitation and inhibition can account for the differences in migraine visual perception. Consolidating existing research and creating a unified, defined theoretical account is needed to better understand the disorder.

An Image-Based Model for Early Visual Processing

Figure 2: Contrast discrimination. Increment threshold vs. pedestal contrast for gratigs of 8.37 cyc/deg and 1497ms presentation time. Early spatial vision was explored extensively over several decades in many psychophysical detection and discrimination experiments. Thus a large body of such data is available. Goris et al. (2013) integrated this psychophysical literature and proposed a model based on maximumlikelihood decoding of a neurophysiologically inspired population of model neurons. Their neural population model (NPM) is unique in its ability to predict several data sets simultaneously, using a single set of parameters. However, the NPM is only one-dimensional, operating on the activity of abstract spatial frequency channels. Thus it cannot be applied to arbitrary images as a generic front-end to explore the influence of early visual processing on midor high-level vision. Bradley et al. (2014), on the other hand, recently presented an early vision model operating on images. Their model is thus able to make predictions for arbitrary images. However, compared to the NPM, their model lacks in nonlinear processing, which is replaced by an effective masking contrast depending on the detection target. Thus while Bradley et al. fit a range of detection data they do not fit nonlinear aspects of early vision like the dipper function. Here we combine both approaches into a model which includes nonlinear processing and statistically efficient decoding and operates on images explicitly calculating channel activities over the image. The model applies optical degradation and retinal processing to the image before the image is passed to a spatial frequency and orientation decomposition followed by divisive inhibition. We tested the predictions of our model against a broad range of early psychophysical experiments and found it predicts some hallmarks of early visual processing like the contrast sensitivity function (Figure 1), the dipper function for contrast discrimination (Figure 2) and oblique masking data (Figure 3). The measured data we plot against stems from standard 2AFC contrast detection and discrimination experiments run for Wichmann’s DPhil thesis (1999). Early vision was extensively explored empirically and there are plenty of models which explain different parts of the empirical findings. These results provide detailed information about the first processing steps in visual perception thought to happen in the retina, LGN and V1, which stimuli can be differentiated or detected using their output and in what format the output is available to any higher visual processing. However, research on higher visual processing rarely employs the information available from early vision psychophysics. The main reasons may be that the models described in the early spatial vision literature typically do not predict behaviour on arbitrary images, because they often do not process images, but only predict the (putative) responses of the early visual system to Gabor patches or simple grating stimuli. We try to overcome these limitations by explaining many psychophysical observations within a model taking images as input.

An object-identity probability cueing paradigm during grasping observation: the facilitating effect is present only when the observed kinematics is suitable for the cued object.

Electrophysiological and psychophysical data indicate that grasping observation automatically orients attention toward the incoming interactions between the actor’s hand and the object. The aim of the present study was to clarify if this effect facilitates the detection of a graspable object with the observed action as compared to an ungraspable one. We submitted participants to an object-identity probability cueing experiment in which the two possible targets were of the same dimensions but one of them presented sharp tips at one extreme while the other presented flat faces. At the beginning of each trial the most probable target was briefly shown. After a variable interval, at the same position, the same (75%) or a different target (25%) was presented. Participants had to press a key in response to target appearance. Superimposed to the video showing cue and target, an agent performing the reaching and grasping of the target was presented. The kinematics of the action was or was not suitable for grasping the cued target, according to the absence or presence of the sharp tips. Results showed that response was modulated by the probability of target identity but only when the observed kinematics was suitable to grasp the attended target. A further experiment clarified that response modulation was never present when the superimposed video always showed the agent at a rest position. These findings are discussed at the light of neurophysiological and psychophysical literature, considering the relationship between the motor system and the perception of objects and of others’ actions. We conclude that the prediction of the mechanical events that arise from the interactions between the hand and the attended object is at the basis of the capability to select a graspable object in space.

A shared short-term memory system for stimulus duration and stimulus frequency.

Recent research has suggested the existence of a modality-independent memory system that is responsible for storing representations of simple, scalar stimulus attributes, such as the frequency of an auditory pure tone or the duration of a stimulus. In the present study, we modify an existing computational model of short-term memory (STM) for stimulus frequency to allow it to perform STM tasks for both stimulus frequency and stimulus duration, supporting the notion of a common scalar STM system. We further demonstrate the utility of the model by showing that it can reproduce the subjective shortening effect, a classic finding in the psychophysical literature.

Object tracking: absence of long-range spatial interference supports resource theories.

Attentional tracking of a moving target can be impaired by the presence of a second object, particularly if the second object is another target. One potential cause of this impairment is spatial interference. But the impairment may alternatively reflect a need to divide a finite attentional resource among targets. The performance cost of splitting a resource among targets should not be affected by the targets' proximity and should persist even at very large target separations. In contrast, spatial interference should impair performance more when the second object is near than when it is far. Here, we report six experiments that assess the effect of the separation between two targets. Within the crowding zone for target identification found by previous psychophysical literature, tracking performance improved with separation. Beyond the crowding zone, there was no evidence that increases in separation improved two-target performance, suggesting no long-range spatial interference. Unexpectedly, in the one-target condition, greater separation from other distractors reduced performance somewhat. This may reflect a configural tracking process. For the two-target condition, due to the absence of a separation effect beyond the crowding zone, at the largest separations performance at tracking two targets remained much poorer than performance tracking one target. This large additional-target cost is better explained by hemisphere-specific resource theories than by spatial interference.

A computational theory of da Vinci stereopsis.

In binocular vision, occlusion of one object by another gives rise to monocular occlusions—regions visible only in one eye. Although binocular disparities cannot be computed for these regions, monocular occlusions can be precisely localized in depth and can induce the perception of illusory occluding surfaces. The phenomenon of depth perception from monocular occlusions, known as da Vinci stereopsis, is intriguing, but its mechanisms are not well understood. We first propose a theory of the mechanisms underlying da Vinci stereopsis that is based on the psychophysical and computational literature on monocular occlusions. It postulates, among other principles, that monocular areas are detected explicitly, and depth from occlusions is calculated based on constraints imposed by occlusion geometry. Next, we describe a biologically inspired computational model based on this theory that successfully reconstructs depth in a large range of stimuli and produces results similar to those described in the psychophysical literature. These results demonstrate that the proposed neural architecture could underpin da Vinci stereopsis and other stereoscopic percepts.

On the meaning of statements in psychophysics characterizing conditional indeterminacy of percepts

We propose a conceptual framework for the interpretation of statements in the psychophysical literature (especially concerning spatial vision) that represent a combinatorial (rather than probabilistic) way of evaluating the strength of stimulus-percept relationships. First, we introduce elementary set-theoretic guidelines (section 2), and then we explain some exemplary statements from psychophysics following those lines (section 3). After that, we discuss five basic questions which are implied by the assumptions in the conceptual framework and appear crucial for its prospects (section 4). Lastly, we highlight some peculiarities of the framework by a comparison with the probabilistic approach in vision psychophysics (section 5).

Modelling adaptation to directional motion using the Adelson-Bergen energy sensor.

The motion energy sensor has been shown to account for a wide range of physiological and psychophysical results in motion detection and discrimination studies. It has become established as the standard computational model for retinal movement sensing in the human visual system. Adaptation effects have been extensively studied in the psychophysical literature on motion perception, and play a crucial role in theoretical debates, but the current implementation of the energy sensor does not provide directly for modelling adaptation-induced changes in output. We describe an extension of the model to incorporate changes in output due to adaptation. The extended model first computes a space-time representation of the output to a given stimulus, and then a RC gain-control circuit (“leaky integrator”) is applied to the time-dependent output. The output of the extended model shows effects which mirror those observed in psychophysical studies of motion adaptation: a decline in sensor output during stimulation, and changes in the relative of outputs of different sensors following this adaptation.

Identifying the Computational Requirements of an Integrated Top-Down-Bottom-Up Model for Overt Visual Attention within an Active Vision System

Computational visual attention systems have been constructed in order for robots and other devices to detect and locate regions of interest in their visual world. Such systems often attempt to take account of what is known of the human visual system and employ concepts, such as ‘active vision’, to gain various perceived advantages. However, despite the potential for gaining insights from such experiments, the computational requirements for visual attention processing are often not clearly presented from a biological perspective. This was the primary objective of this study, attained through two specific phases of investigation: 1) conceptual modeling of a top-down-bottom-up framework through critical analysis of the psychophysical and neurophysiological literature, 2) implementation and validation of the model into robotic hardware (as a representative of an active vision system). Seven computational requirements were identified: 1) transformation of retinotopic to egocentric mappings, 2) spatial memory for the purposes of medium-term inhibition of return, 3) synchronization of ‘where’ and ‘what’ information from the two visual streams, 4) convergence of top-down and bottom-up information to a centralized point of information processing, 5) a threshold function to elicit saccade action, 6) a function to represent task relevance as a ratio of excitation and inhibition, and 7) derivation of excitation and inhibition values from object-associated feature classes. The model provides further insight into the nature of data representation and transfer between brain regions associated with the vertebrate ‘active’ visual attention system. In particular, the model lends strong support to the functional role of the lateral intraparietal region of the brain as a primary area of information consolidation that directs putative action through the use of a ‘priority map’.

Isolating exogenous and endogenous modes of temporal attention.

The differential allocation of information processing resources over time, here termed "temporal attention," may be achieved by relatively automatic "exogenous" or controlled "endogenous" mechanisms. Over 100 years of research has confounded these theoretically distinct dimensions of temporal attention. The current report seeks to ameliorate this oversight by novel application of 2 experimental methodologies. A scheme imported from the animal learning literature (Rescorla's "truly random control" procedure) was used to eliminate any temporal contingency between signals and targets. An auditory stimulus imported from the psychophysical literature (correlated vs. uncorrelated noise) was used to provide a salient signal that entailed no local or global change in intensity. Purely endogenous temporal attention (generated by a reliable signal-target contingency in the absence of a change in intensity) is characterized by robust improvements in speed and accuracy of responding. Purely exogenous temporal attention (generated by an intensity increase in the absence of contingency) is characterized by a brief period of faster responding. When exogenous temporal attention is elicited in the context of endogenous temporal attention, the decrease in response time that follows an intense signal is accompanied by a decrease in response accuracy.

A neural population model for visual pattern detection.

Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear and independent visual channels whose output is linked to choice behavior in detection tasks via simple read-out mechanisms. This model has proven remarkably successful in accounting for threshold vision. It is fundamentally at odds, however, with current knowledge about the neurophysiological underpinnings of pattern vision. In addition, the principles put forward in the model fail to generalize to suprathreshold vision or perceptual tasks other than detection. We propose an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically inspired model of population activity in primary visual cortex. We demonstrate that this theory explains a broad range of classic detection results. With a single set of parameters, our model can account for several summation, adaptation, and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection.

Range and Absolute Odour Intensity Affect Olfactory Generalisation in the Honeybee

The human olfactory psychophysical literature is rich with anecdotal reports of variation in the perceived quality between weak and strong concentrations of the same odour (Wilson and Stevenson 2006). Psychophysical experiments using animals have also found similar effects of concentration on odour quality. The proboscis extension reflex (PER) is an appetitive Pavlovian conditioning assay that has been used to investigate olfactory stimuli in the honeybee. In this series of experiments I aim to identify the sensitivity of honeybees to changes in odorant concentration across the range of a honeybee’s sensorium (0.01–100 Pa). I compared generalisation to test odours that differed in molecular identity, odour concentration or both and found that large changes in odorant concentration (1,000 fold change) can produce greater shifts in perceptual similarity than a change in the odorant’s molecular structure. Our findings suggest a failure in concentration invariance when identical odours differ greatly in concentration. I also found poorer olfactory sensitivity (between identical odours of differing concentration) and acuity (between novel odours of identical concentration) at low odour concentrations (0.01 Pa).

Please Touch: Object Properties that Invite Touch.

Touch has received increasing interest in marketing, given research indicating that contact with products influences evaluation and the tendency to purchase. However, little is known from the marketing or psychophysical literature about visible attributes of objects that elicit touch for hedonic purposes. In these studies, participants rated the tendency of pictured objects to invite touch, or "touch-ability." Rated touch-ability varied reliably with structural attributes of objects, and the structural influences were distinct from those on other ratings such as attractiveness and apparent expense. Although the trends varied across object sets, touch-ability generally declined as surface textures became markedly rough and shape complexity became extreme. Holding stimulus factors constant, touch-ability also varied with the specific hand movements that were anticipated. Finally, mean touch-ability ratings were correlated across participants with the "Need for Touch" scale, which measures an individual's tendency to touch products. The studies point to touch-ability as a potential factor that might be incorporated into product design.

Bayesian exploration for intelligent identification of textures.

In order to endow robots with human-like abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of prior experience to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate the texture from a set of plausible candidates, a process we call Bayesian exploration. Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures generally required a small number of well-chosen exploratory movements (median = 5) and yielded a 95.4% success rate. The method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.