Adaptive Sampling of Information in Perceptual Decision-Making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.     

Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data

This research investigates the performance of graphical dot arrays designed to make discrimination of relative numerosity as effortless as possible at the same time as making absolute (quantitative) numerosity estimation as effortful as possible. Comparing regular, random, and hybrid (randomized regular) configurations of dots, the results indicate that both random and hybrid configurations reduce absolute numerosity estimation precision, when compared with regular dots arrays. However, discrimination of relative numerosity is significantly more accurate for hybrid dot arrays than for random dot arrays. Similarly, human subjects report significantly lower levels of subjective confidence in judgments when using hybrid dot configurations as compared with regular configurations; and significantly higher levels of subjective confidence as compared with random configurations. These results indicate that data graphics based on the hybrid, randomized-regular configurations of dots are well-suited to applications that require decisions to be based on numerical data in which the absolute quantities are less certain than the relative values. Examples of such applications include decision-making based on the outputs of empirically-based mathematical models, such as health-related policy decisions using data from predictive epidemiological models.     

Perception of shape-from-motion in macaque monkeys and humans

Motion is one of the most efficient cues for shape perception. We conducted behavioral experiments to examine how monkeys perceive shapes defined by motion cues and whether they perceive them as humans do. We trained monkeys to perform a shape discrimination task in which shapes were defined by the motion of random dots. Effects of dot density and dot speed on the shape perception of monkeys were examined. Human subjects were also tested using the same paradigm and the test results were compared with those of monkeys. In both monkeys and humans, correct performance rates declined when density or speed of random dots was reduced. Both of them tended to confuse the same combinations of shapes frequently. These results suggest that monkeys and humans perceive shapes defined by motion cues in a similar manner and probably have common neural mechanisms to perceive them. Electronic Publication     

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.     

Continuous evolution of statistical estimators for optimal decision-making

In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For example, when asked to combine information from multiple sources we often assign greater weight to the more reliable information. It has been proposed that statistical-optimality often observed in human perception and decision-making requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore, subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie optimal decision making.     

Stress,Uncertainty and Decision Confidence

We successfully manipulated decision confidence in a probabilistic prediction task by means of stress as induced by excessive cognitive demands. In particular, our results indicate that decisions (based on high and low, but not intermediate levels of uncertainty) made under stress (confirmed by skin conductance measures) are associated with increased confidence when outcome probabilities are incompletely known (20% residual uncertainty). A different pattern was found when outcome probabilities were completely known (0% residual uncertainty). Here, stress led to decreased decision confidence when decisions were associated with intermediate levels of uncertainty but had no effect in case of high and low levels of uncertainty. In addition we provide evidence for ambiguity—(understood as implicit-risk) assessment being impaired under stress conditions.     

Visual extrapolation under risk: human observers estimate and compensate for exogenous uncertainty

Humans commonly face choices between multiple options with uncertain outcomes. Such situations occur in many contexts, from purely financial decisions (which shares should I buy?) to perceptuo-motor decisions between different actions (where should I aim my shot at goal?). Regardless of context, successful decision-making requires that the uncertainty at the heart of the decision-making problem is taken into account. Here, we ask whether humans can recover an estimate of exogenous uncertainty and then use it to make good decisions. Observers viewed a small dot that moved erratically until it disappeared behind an occluder. We varied the size of the occluder and the unpredictability of the dot''s path. The observer attempted to capture the dot as it emerged from behind the occluded region by setting the location and extent of a ‘catcher’ along the edge of the occluder. The reward for successfully catching the dot was reduced as the size of the catcher increased. We compared human performance with that of an agent maximizing expected gain and found that observers consistently selected catcher size close to this theoretical solution. These results suggest that humans are finely tuned to exogenous uncertainty information and can exploit it to guide action.     

Psychophysical study of image orientation perception

The experiment reported here investigates the perception of orientation of color photographic images. A collection of 1000 images (mix of professional photos and consumer snapshots) was used in this study. Each image was examined by at least five observers and shown at varying resolutions. At each resolution, observers were asked to indicate the image orientation, the level of confidence, and the cues they used to make the decision. The results show that for typical images, accuracy is close to 98% when using all available semantic cues from high-resolution images, and 84% when using only low-level vision features and coarse semantics from thumbnails. The accuracy by human observers suggests an upper bound for the performance of an automatic system. In addition, the use of a large, carefully chosen image set that spans the 'photo space' (in terms of occasions and subject matter) and extensive interaction with the human observers reveals cues used by humans at various image resolutions: sky and people are the most useful and reliable among a number of important semantic cues.     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

Collaborative Brain-Computer Interface for Aiding Decision-Making

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.     

The Value of Artificial Stimuli in Behavioral Research: Making the Case for Egg Rejection Studies in Avian Brood Parasitism

Experimentation is at the heart of classical and modern behavioral ecology research. The manipulation of natural cues allows us to establish causation between aspects of the environment, both internal and external to organisms, and their effects on animals' behaviors. In recognition systems research, including the quest to understand the coevolution of sensory cues and decision rules underlying the rejection of foreign eggs by hosts of avian brood parasites, artificial stimuli have been used extensively, but not without controversy. In response to repeated criticism about the value of artificial stimuli, we describe four potential benefits of using them in egg recognition research, two each at the proximate and ultimate levels of analysis: (1) the standardization of stimuli for developmental studies and (2) the disassociation of correlated traits of egg phenotypes used for sensory discrimination, as well as (3) the estimation of the strength of selection on parasitic egg mimicry and (4) the establishment of the evolved limits of sensory and cognitive plasticity. We also highlight constraints of the artificial stimulus approach and provide a specific test of whether responses to artificial cues can accurately predict responses to natural cues. Artificial stimuli have a general value in ethological research beyond research in brood parasitism and may be especially critical in field studies involving the manipulation of a single parameter, where other, confounding variables are difficult or impossible to control experimentally or statistically.     

Defining Natural Categories in Acoustic Signals: Comparison of Three Methods Applied to ‘Chick-a-dee’ Call Notes

Chicks were trained to discriminate between two identical boxes on the basis of their position. Subsequently, the colour of parts of the positive (reinforced) box was changed and chicks were retrained. Results showed that chicks were more or less impaired during retraining depending on the spatial distribution of the changed stimuli. Chicks behaved as if a figure (a disc or a spot of dots) painted on a box was irrelevant to them, whereas they did respond to changes in the colour of a uniformly coloured box or of scattered dots painted on a box. Similar results were obtained in simultaneous discrimination learning tasks involving addition of cues (e.g. colour plus position). Addition of cues facilitated learning using boxes the same colour all over or with painted scattered dots, but not using boxes with a disc or a spot of dots. Furthermore, addition of shape and position information had different outcomes depending on the use of three-dimensional objects or of painted figures: learning facilitation occurred only using three-dimensional objects. Results are interpreted in terms of an “object hypothesis”, and the validity and usefulness of traditional terms such as cues is questioned.     

Human wavelength discrimination of monochromatic light explained by optimal wavelength decoding of light of unknown intensity

We show that human ability to discriminate the wavelength of monochromatic light can be understood as maximum likelihood decoding of the cone absorptions, with a signal processing efficiency that is independent of the wavelength. This work is built on the framework of ideal observer analysis of visual discrimination used in many previous works. A distinctive aspect of our work is that we highlight a perceptual confound that observers should confuse a change in input light wavelength with a change in input intensity. Hence a simple ideal observer model which assumes that an observer has a full knowledge of input intensity should over-estimate human ability in discriminating wavelengths of two inputs of unequal intensity. This confound also makes it difficult to consistently measure human ability in wavelength discrimination by asking observers to distinguish two input colors while matching their brightness. We argue that the best experimental method for reliable measurement of discrimination thresholds is the one of Pokorny and Smith, in which observers only need to distinguish two inputs, regardless of whether they differ in hue or brightness. We mathematically formulate wavelength discrimination under this wavelength-intensity confound and show a good agreement between our theoretical prediction and the behavioral data. Our analysis explains why the discrimination threshold varies with the input wavelength, and shows how sensitively the threshold depends on the relative densities of the three types of cones in the retina (and in particular predict discriminations in dichromats). Our mathematical formulation and solution can be applied to general problems of sensory discrimination when there is a perceptual confound from other sensory feature dimensions.     

Motor Cost Influences Perceptual Decisions

Perceptual decision making has been widely studied using tasks in which subjects are asked to discriminate a visual stimulus and instructed to report their decision with a movement. In these studies, performance is measured by assessing the accuracy of the participants’ choices as a function of the ambiguity of the visual stimulus. Typically, the reporting movement is considered as a mere means of reporting the decision with no influence on the decision-making process. However, recent studies have shown that even subtle differences of biomechanical costs between movements may influence how we select between them. Here we investigated whether this purely motor cost could also influence decisions in a perceptual discrimination task in detriment of accuracy. In other words, are perceptual decisions only dependent on the visual stimulus and entirely orthogonal to motor costs? Here we show the results of a psychophysical experiment in which human subjects were presented with a random dot motion discrimination task and asked to report the perceived motion direction using movements of different biomechanical cost. We found that the pattern of decisions exhibited a significant bias towards the movement of lower cost, even when this bias reduced performance accuracy. This strongly suggests that motor costs influence decision making in visual discrimination tasks for which its contribution is neither instructed nor beneficial.     

Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain

We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain''s ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.     

Relating Pupil Dilation and Metacognitive Confidence during Auditory Decision-Making

The sources of evidence contributing to metacognitive assessments of confidence in decision-making remain unclear. Previous research has shown that pupil dilation is related to the signaling of uncertainty in a variety of decision tasks. Here we ask whether pupil dilation is also related to metacognitive estimates of confidence. Specifically, we measure the relationship between pupil dilation and confidence during an auditory decision task using a general linear model approach to take into account delays in the pupillary response. We found that pupil dilation responses track the inverse of confidence before but not after a decision is made, even when controlling for stimulus difficulty. In support of an additional post-decisional contribution to the accuracy of confidence judgments, we found that participants with better metacognitive ability – that is, more accurate appraisal of their own decisions – showed a tighter relationship between post-decisional pupil dilation and confidence. Together our findings show that a physiological index of uncertainty, pupil dilation, predicts both confidence and metacognitive accuracy for auditory decisions.     

Analysing neuronal correlates of the comparison of two sequentially presented sensory stimuli

In a typical sequential sensory discrimination task, subjects are required to make a decision based on comparing a sensory stimulus against the memory trace left by a previous stimulus. What is the neuronal substrate for such comparisons and the resulting decisions? This question was studied by recording neuronal responses in a variety of cortical areas of awake monkeys (Macaca mulatta), trained to carry out a vibrotactile sequential discrimination task. We describe methods to analyse responses obtained during the comparison and decision phases of the task, and describe the resulting findings from recordings in secondary somatosensory cortical area (S2). A subset of neurons in S2 become highly correlated with the monkey''s decision in the task.     

Tuned inhibition in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple neural network model incorporating a known property of sensory neurons: tuned inhibition. The key idea of the model is that the level of inhibition that each accumulator unit receives from units with the opposite tuning preference, i.e. its inhibition ‘tuning’, dictates its contribution to perceptual decisions versus confidence judgments, such that units with higher tuned inhibition (computing relative evidence for different perceptual interpretations) determine perceptual discrimination decisions, and units with lower tuned inhibition (computing absolute evidence) determine confidence. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature where confidence and decision accuracy dissociate. By comparing model fits, we further demonstrate that a full complement of behavioral data across several previously published experimental results—including accuracy, reaction time, mean confidence, and metacognitive sensitivity—is best accounted for when confidence is computed from units without, rather than units with, tuned inhibition. Finally, we discuss predictions of our results and model for future neurobiological studies. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by relying on the diversity of neural resources available.     

Lack of species discrimination based on chemical cues by male sailfin mollies, <Emphasis Type="Italic">Poecilia latipinna</Emphasis>

When making mating decisions, individuals may rely on multiple cues from either the same or multiple sensory modalities. Although the use of visual cues in sexual selection is well studied, fewer studies have examined the role of chemical cues in mate choice. In addition, few studies have examined how visual and/or chemical cues affect male mating decisions. Male mate choice is important in systems where males must avoid mating with heterospecific females, as is found in a mating complex of Poecilia. Male sailfin mollies, Poecilia latipinna, are sexually parasitized by gynogenetic Amazon mollies, P. formosa. Little is known about the mechanism by which male sailfin mollies base their mating decisions. Here we tested the hypothesis that male sailfin mollies from an allopatric and a sympatric population with Amazon mollies use multiple cues to distinguish between conspecific and heterospecific females. We found that male sailfin mollies recognized the chemical cues of conspecific females, but we found no support for the hypothesis that chemical cues are by themselves sufficient for species discrimination. Lack of discrimination based on chemical cues alone may be due to the close evolutionary history between P. latipinna and P. formosa. Males from populations sympatric with Amazon mollies did not differentially associate with females of either of the two species when given access to both visual and chemical cues of the females, yet males from the allopatric population did associate more with conspecific females than with heterospecific females in the presence of both chemical and visual cues. The lack of discrimination by males from the sympatric population between conspecific and heterospecific females based on both chemical and visual cues suggests that these males require more complex combinations of cues to distinguish species, possibly due to the close relatedness of these species.