Lawful relation between perceptual bias and discriminability

A Bayesian observer model reveals a prior for natural daylights in hue perception

Incorporating statistical characteristics of stimuli in perceptual processing can be highly beneficial for reliable estimation from noisy sensory measurements but may generate perceptual bias. According to Bayesian inference, perceptual biases arise from the integration of internal priors with noisy sensory inputs. In this study, we used a Bayesian observer model to derive biases and priors in hue perception based on discrimination data for hue ensembles with varying levels of chromatic noise. Our results showed that discrimination thresholds for isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space exhibited a bimodal pattern, with lowest thresholds near a non-cardinal blue-yellow axis that aligns closely with the variation of natural daylights. Perceptual biases showed zero crossings around this axis, indicating repulsion away from yellow and attraction towards blue. These biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our findings suggest that visual processing takes advantage of knowledge of the distribution of colors in natural environments for hue perception.

Reward prediction error neurons implement an efficient code for reward

We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.

(Dis)similarities between non-symbolic and symbolic number representations: Insights from vector space models

Empirical evidence in support of a shared system for non-symbolic and symbolic number processing has been inconclusive. The current study aims to address this question in a novel way, specifically by testing whether the efficient coding principle based on co-occurrence of number symbols in natural language holds for both non-symbolic and symbolic number processing. The efficient coding principle postulates that perception is optimized when stimuli frequently co-occur in a natural environment. We hypothesized that both numerical ratios and co-occurrence frequencies of symbolic numbers would significantly influence participants' performance on a non-symbolic and symbolic number comparison task. To test this hypothesis, we employed latent semantic analysis on a TASA corpus to quantify number co-occurrence in natural language and calculate language similarity estimates. We engaged 73 native English speakers (mean age = 19.36, standard deviation = 1.83) with normal or corrected vision and no learning disorders in a number comparison task involving non-symbolic (dot arrays) and symbolic stimuli (Arabic numerals and English number words). Results showed that numerical ratios significantly predicted participants' performances across all number formats (ps < 0.001). Language similarity estimates derived from everyday language also predicted performance on the non-symbolic task and the symbolic task involving number words (ps < 0.007). Our results highlight the complex nature of numerical processing, pointing to the co-occurrence of number symbols in natural language as an auxiliary factor in understanding the shared characteristics between non-symbolic and symbolic number representations. Given that our study focused on a limited number range (5 to 16) and a specific task type, future studies should explore a wider range of tasks and numbers to further test the role of the efficient coding principle in number processing.

A unified framework for perceived magnitude and discriminability of sensory stimuli

The perception of sensory attributes is often quantified through measurements of sensitivity (the ability to detect small stimulus changes), as well as through direct judgments of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgments of stimulus intensity (e.g., as measured through rating scales) reflect the mean value of an internal representation, and sensitivity reflects a combination of mean value and noise properties, as quantified by the statistical measure of Fisher information. Unique identification of these internal representation properties can be achieved by combining measurements of sensitivity and judgments of intensity. As a central example, we show that Weber's law of perceptual sensitivity can coexist with Stevens' power-law scaling of intensity ratings (for all exponents), when the noise amplitude increases in proportion to the representational mean. We then extend this result beyond the Weber's law range by incorporating a more general and physiology-inspired form of noise and show that the combination of noise properties and sensitivity measurements accurately predicts intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two primary perceptual measurements-thresholds for sensitivity and rating scales for intensity-and provides a neural interpretation for the underlying representation.

Oblique warping: A general distortion of spatial perception

There are many putatively distinct phenomena related to perception in the oblique regions of space. For instance, the classic oblique effect describes a deficit in visual acuity for oriented lines in the obliques, and classic "prototype effects" reflect a bias to misplace objects towards the oblique regions of space. Yet these effects are explained in very different terms: The oblique effect itself is often understood as arising from orientation-selective neurons, whereas prototype effects are described as arising from categorical biases. Here, we explore the possibility that these effects (and others) may stem from a single underlying spatial distortion. We show that there is a general distortion of (angular) space in the oblique regions that influences not only orientation judgments, but also location, extent, and size. We argue that these findings reflect oblique warping, a general distortion of spatial representations in the oblique regions which may be the root cause of many oblique effects.

Conflicts between short- and long-term experiences affect visual perception through modulating sensory or motor response systems: Evidence from Bayesian inference models

The independent effects of short- and long-term experiences on visual perception have been discussed for decades. However, no study has investigated whether and how these experiences simultaneously affect our visual perception. To address this question, we asked participants to estimate their self-motion directions (i.e., headings) simulated from optic flow, in which a long-term experience learned in everyday life (i.e., straight-forward motion being more common than lateral motion) plays an important role. The headings were selected from three distributions that resembled a peak, a hill, and a flat line, creating different short-term experiences. Importantly, the proportions of headings deviating from the straight-forward motion gradually increased in the peak, hill, and flat distributions, leading to a greater conflict between long- and short-term experiences. The results showed that participants biased their heading estimates towards the straight-ahead direction and previously seen headings, which increased with the growing experience conflict. This suggests that both long- and short-term experiences simultaneously affect visual perception. Finally, we developed two Bayesian models (Model 1 vs. Model 2) based on two assumptions that the experience conflict altered the likelihood distribution of sensory representation or the motor response system. The results showed that both models accurately predicted participants' estimation biases. However, Model 1 predicted a higher variance of serial dependence compared to Model 2, while Model 2 predicted a higher variance of the bias towards the straight-ahead direction compared to Model 1. This suggests that the experience conflict can influence visual perception by affecting both sensory and motor response systems. Taken together, the current study systematically revealed the effects of long- and short-term experiences on visual perception and the underlying Bayesian processing mechanisms.

Perceptual similarity judgments do not predict the distribution of errors in working memory

Population coding models provide a quantitative account of visual working memory (VWM) retrieval errors with a plausible link to the response characteristics of sensory neurons. Recent work has provided an important new perspective linking population coding to variables of signal detection, including d-prime, and put forward a new hypothesis: that the distribution of recall errors on, for example, a color wheel, is a consequence of the psychological similarity between points in that stimulus space, such that the exponential-like psychophysical distance scaling function can fulfil the role of population tuning and obviate the need to fit a tuning width parameter to recall data. Using four different visual feature spaces, we measured psychophysical similarity and memory errors in the same participants. Our results revealed strong evidence for a common source of variability affecting similarity judgments and recall estimates but did not support any consistent relationship between psychophysical similarity functions and VWM errors. At the group level, the responsiveness functions obtained from the psychophysical similarity task diverged strongly from those that provided the best fit to working memory errors. At the individual level, we found convincing evidence against an association between observed and best-fitting similarity functions. Finally, our results show that the newly proposed exponential-like responsiveness function has in general no advantage over the canonical von Mises (circular normal) function assumed by previous population coding models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stereoscopic slant contrast revisited

The perceived slant of a stereoscopic surface is altered by the presence of a surrounding surface, a phenomenon termed stereo slant contrast. Previous studies have shown that a slanted surround causes a fronto-parallel surface to appear slanted in the opposite direction, an instance of "bidirectional" contrast. A few studies have examined slant contrast using slanted as opposed to fronto-parallel test surfaces, and these also have shown slant contrast. Here, we use a matching method to examine slant contrast over a wide range of combinations of surround and test slants, one aim being to determine whether stereo slant contrast transfers across opposite directions of test and surround slant. We also examine the effect of the test on the perceived slant of the surround. Test slant contrast was found to be bidirectional in virtually all test-surround combinations and transferred across opposite test and surround slants, with little or no decline in magnitude as the test-surround slant difference approached the limit. There was a weak bidirectional effect of the test slant on the perceived slant of the surround. We consider how our results might be explained by four mechanisms: (a) normalization of stereo slant to vertical; (b) divisive normalization of stereo slant channels in a manner analogous to the tilt illusion; (c) interactions between center and surround disparity-gradient detectors; and (d) uncertainty in slant estimation. We conclude that the third of these (interactions between center and surround disparity-gradient detectors) is the most likely cause of stereo slant contrast.

A unifying theory explains seemingly contradictory biases in perceptual estimation

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations.

Motor "laziness" constrains fixation selection in real-world tasks

Humans coordinate their eye, head, and body movements to gather information from a dynamic environment while maximizing reward and minimizing biomechanical and energetic costs. However, such natural behavior is not possible in traditional experiments employing head/body restraints and artificial, static stimuli. Therefore, it is unclear to what extent mechanisms of fixation selection discovered in lab studies, such as inhibition-of-return (IOR), influence everyday behavior. To address this gap, participants performed nine real-world tasks, including driving, visually searching for an item, and building a Lego set, while wearing a mobile eye tracker (169 recordings; 26.6 h). Surprisingly, in all tasks, participants most often returned to what they just viewed and saccade latencies were shorter preceding return than forward saccades, i.e., consistent with facilitation, rather than inhibition, of return. We hypothesize that conservation of eye and head motor effort ("laziness") contributes. Correspondingly, we observed center biases in fixation position and duration relative to the head's orientation. A model that generates scanpaths by randomly sampling these distributions reproduced all return phenomena we observed, including distinct 3-fixation sequences for forward versus return saccades. After controlling for orbital eccentricity, one task (building a Lego set) showed evidence for IOR. This, along with small discrepancies between model and data, indicates that the brain balances minimization of motor costs with maximization of rewards (e.g., accomplished by IOR and other mechanisms) and that the optimal balance varies according to task demands. Supporting this account, the orbital range of motion used in each task traded off lawfully with fixation duration.

Rationality, preferences, and emotions with biological constraints: it all starts from our senses

Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.

What's in a sample? Epistemic uncertainty and metacognitive awareness in risk taking

In a fundamentally uncertain world, sound information processing is a prerequisite for effective behavior. Given that information processing is subject to inevitable cognitive imprecision, decision makers should adapt to this imprecision and to the resulting epistemic uncertainty when taking risks. We tested this metacognitive ability in two experiments in which participants estimated the expected value of different number distributions from sequential samples and then bet on their own estimation accuracy. Results show that estimates were imprecise, and this imprecision increased with higher distributional standard deviations. Importantly, participants adapted their risk-taking behavior to this imprecision and hence deviated from the predictions of Bayesian models of uncertainty that assume perfect integration of information. To explain these results, we developed a computational model that combines Bayesian updating with a metacognitive awareness of cognitive imprecision in the integration of information. Modeling results were robust to the inclusion of an empirical measure of participants' perceived variability. In sum, we show that cognitive imprecision is crucial to understanding risk taking in decisions from experience. The results further demonstrate the importance of metacognitive awareness as a cognitive building block for adaptive behavior under (partial) uncertainty.

Attention influences the effects of the previous form orientation on the current motion direction estimation

Recent studies have found that the estimates of motion directions are biased toward the previous form orientations, showing serial dependence, and the serial dependence does not involve cognitive abilities. In the current study, we conducted two experiments to investigate whether and how attention-a cognitive ability-affected the serial dependence. The results showed that serial dependence was present in the current study, reproducing the previous findings. Importantly, when the attentional load reduced the reliability (i.e., estimation accuracy and precision) of previous form orientations (Experiment 1), the serial dependence decreased, meaning that the biases of motion direction estimates toward previous form orientations were reduced; in contrast, when the attentional load reduced the reliability of current motion directions (Experiment 2), the serial dependence increased, meaning that the biases of motion direction estimates toward previous form orientations were increased. These trends were well consistent with the prediction of the Bayesian inference theory. Therefore, the current study revealed the involvement of attention in the serial dependence of current motion direction estimation on the previous form orientation, demonstrating that the serial dependence was cognitive and the attentional effect can be a Bayesian inference process, initially revealing its computational mechanism.

Resource-rational account of sequential effects in human prediction

An abundant literature reports on 'sequential effects' observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects' predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this 'attractive effect', but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a 'repulsive effect', observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

A power law describes the magnitude of adaptation in neural populations of primary visual cortex

How do neural populations adapt to the time-varying statistics of sensory input? We used two-photon imaging to measure the activity of neurons in mouse primary visual cortex adapted to different sensory environments, each defined by a distinct probability distribution over a stimulus set. We find that two properties of adaptation capture how the population response to a given stimulus, viewed as a vector, changes across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response direction to a stimulus is largely invariant. These rules could be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

Serial dependence bias can predict the overall estimation error in visual perception

Although visual feature estimations are accurate and precise, overall estimation errors (i.e., the difference between estimates and actual values) tend to show systematic patterns. For example, estimates of orientations are systematically biased away from horizontal and vertical orientations, showing an oblique illusion. Additionally, many recent studies have demonstrated that estimations of current visual features are systematically biased toward previously seen features, showing a serial dependence. However, no study examined whether the overall estimation errors were correlated with the serial dependence bias. To address this question, we enrolled three groups of participants to estimate orientation, motion speed, and point-light-walker direction. The results showed that the serial dependence bias explained over 20% of overall estimation errors in the three tasks, indicating that we could use the serial dependence bias to predict the overall estimation errors. The current study first demonstrated that the serial dependence bias was not independent from the overall estimation errors. This finding could inspire researchers to investigate the neural bases underlying the visual feature estimation and serial dependence.

Influences of local and global context on local orientation perception

Visual context modulates perception of local orientation attributes. These spatially very localised effects are considered to correspond to specific excitatory-inhibitory connectivity patterns of early visual areas as V1, creating perceptual tilt repulsion and attraction effects. Here, orientation misperception of small Gabor stimuli was used as a probe of this computational structure by sampling a large spatio-orientation space to reveal expected asymmetries due to the underlying neuronal processing. Surprisingly, the results showed a regular iso-orientation pattern of nearby location effects whose reference point was globally modulated by the spatial structure, without any complex interactions between local positions and orientation. This pattern of results was confirmed by the two perceptual parameters of bias and discrimination ability. Furthermore, the response times to stimulus configuration displayed variations that further provided evidence of how multiple early visual stages affect perception of simple stimuli.

Neural tuning instantiates prior expectations in the human visual system

Perception is often modelled as a process of active inference, whereby prior expectations are combined with noisy sensory measurements to estimate the structure of the world. This mathematical framework has proven critical to understanding perception, cognition, motor control, and social interaction. While theoretical work has shown how priors can be computed from environmental statistics, their neural instantiation could be realised through multiple competing encoding schemes. Using a data-driven approach, here we extract the brain's representation of visual orientation and compare this with simulations from different sensory coding schemes. We found that the tuning of the human visual system is highly conditional on stimulus-specific variations in a way that is not predicted by previous proposals. We further show that the adopted encoding scheme effectively embeds an environmental prior for natural image statistics within the sensory measurement, providing the functional architecture necessary for optimal inference in the earliest stages of cortical processing.

A power law of cortical adaptation

How do neural populations adapt to the time-varying statistics of sensory input? To investigate, we measured the activity of neurons in primary visual cortex adapted to different environments, each associated with a distinct probability distribution over a stimulus set. Within each environment, a stimulus sequence was generated by independently sampling form its distribution. We find that two properties of adaptation capture how the population responses to a given stimulus, viewed as vectors, are linked across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response directions are largely invariant. These rules can be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

Natural statistics of human head orientation constrain models of vestibular processing

Head orientation relative to gravity determines how gravity-dependent environmental structure is sampled by the visual system, as well as how gravity itself is sampled by the vestibular system. Therefore, both visual and vestibular sensory processing should be shaped by the statistics of head orientation relative to gravity. Here we report the statistics of human head orientation during unconstrained natural activities in humans for the first time, and we explore implications for models of vestibular processing. We find that the distribution of head pitch is more variable than head roll and that the head pitch distribution is asymmetrical with an over-representation of downward head pitch, consistent with ground-looking behavior. We further suggest that pitch and roll distributions can be used as empirical priors in a Bayesian framework to explain previously measured biases in perception of both roll and pitch. Gravitational and inertial acceleration stimulate the otoliths in an equivalent manner, so we also analyze the dynamics of human head orientation to better understand how knowledge of these dynamics can constrain solutions to the problem of gravitoinertial ambiguity. Gravitational acceleration dominates at low frequencies and inertial acceleration dominates at higher frequencies. The change in relative power of gravitational and inertial components as a function of frequency places empirical constraints on dynamic models of vestibular processing, including both frequency segregation and probabilistic internal model accounts. We conclude with a discussion of methodological considerations and scientific and applied domains that will benefit from continued measurement and analysis of natural head movements moving forward.

Efficient sensory encoding predicts robust averaging

Not every item in a stimulus ensemble equally contributes to the perceived ensemble average. Rather, items with feature values close to the ensemble mean (inlying items) contribute stronger compared to those items whose feature values are further away from the mean (outlying items). This nonuniform weighting process, named robust averaging, has been interpreted as evidence against an optimal integration of sensory information. Here, however, we show that robust averaging naturally emerges from an optimal integration process when sensory encoding is efficiently adapted to the ensemble statistics in the experiment. We demonstrate that such a model can accurately fit several existing datasets showing robust perceptual averaging in discriminating low-level stimulus features such as orientation. Across various feature domains, our model accurately predicts subjects' decision accuracy and nonuniform weighting profile, and both their dependency on the specific stimulus distribution in the experiments. Our results suggest that the human visual system forms efficient sensory representations on short time-scales to improve overall decision performance.

Perceptual comparisons modulate memory biases induced by new visual inputs

It is well-established that stimulus-specific information in visual working memory (VWM) can be systematically biased by new perceptual inputs. These memory biases are commonly attributed to interference that arises when perceptual inputs are physically similar to VWM contents. However, recent work has suggested that explicitly comparing the similarity between VWM contents and new perceptual inputs modulates the size of memory biases above and beyond stimulus-driven effects. Here, we sought to directly investigate this modulation hypothesis by comparing the size of memory biases following explicit comparisons to those induced when new perceptual inputs are ignored (Experiment 1) or maintained in VWM alongside target information (Experiment 2). We found that VWM reports showed larger attraction biases following explicit perceptual comparisons than when new perceptual inputs were ignored or maintained in VWM. An analysis of participants' perceptual comparisons revealed that memory biases were amplified after perceptual inputs were endorsed as similar-but not dissimilar-to one's VWM representation. These patterns were found to persist even after accounting for variability in the physical similarity between the target and perceptual stimuli across trials, as well as the baseline memory precision between the distinct task demands. Together, these findings illustrate a causal role of perceptual comparisons in modulating naturally-occurring memory biases.

Natural statistics of human head orientation constrain models of vestibular processing

Head orientation relative to gravity determines how gravity-dependent environmental structure is sampled by the visual system, as well as how gravity itself is sampled by the vestibular system. Therefore, both visual and vestibular sensory processing should be shaped by the statistics of head orientation relative to gravity. Here we report the statistics of human head orientation during unconstrained natural activities in humans for the first time, and we explore implications for models of vestibular processing. We find that the distribution of head pitch is more variable than head roll and that the head pitch distribution is asymmetrical with an over-representation of downward head pitch, consistent with ground-looking behavior. We further show that pitch and roll distributions can be used as empirical priors in a Bayesian framework to explain previously measured biases in perception of both roll and pitch. We also analyze the dynamics of human head orientation to better understand how gravitational and inertial acceleration are processed by the vestibular system. Gravitational acceleration dominates at low frequencies and inertial acceleration dominates at higher frequencies. The change in relative power of gravitational and inertial components as a function of frequency places empirical constraints on dynamic models of vestibular processing. We conclude with a discussion of methodological considerations and scientific and applied domains that will benefit from continued measurement and analysis of natural head movements moving forward.

Perceptual bias contextualized in visually ambiguous stimuli

The visual appearance of an object is a function of stimulus properties as well as perceptual biases imposed by the observer. The context-specific trade-off between both can be measured accurately in a perceptual judgment task, involving grouping by proximity in ambiguous dot lattices. Such grouping depends lawfully on a stimulus parameter of the dot lattices known as their aspect ratio (AR), whose effect is modulated by a perceptual bias representing the preference for a cardinal orientation. In two experiments, we investigated how preceding context can lead to bias modulation, either in a top-down fashion via visual working memory (VWM) or bottom-up via sensory priming. In Experiment 1, we embedded the perceptual judgment task in a change detection paradigm and studied how the factors of VWM load (complexity of the memory array) and content (congruency in orientation to the ensuing dot lattice) affect the prominence of perceptual bias. A robust vertical orientation bias was observed, which was increased by VWM load and modulated by congruent VWM content. In Experiment 2, dot lattices were preceded by oriented primes. Here, primes regardless of orientation elicited a vertical orientation bias in dot lattices compared to a neutral baseline. Taken together, the two experiments demonstrate that top-down context (VWM load and content) effectively controls orientation bias modulation, while bottom-up context (i.e., priming) merely acts as an undifferentiated trigger to perceptual bias. These findings characterize the temporal context sensitivity of Gestalt perception, shed light on the processes responsible for different perceptual outcomes of ambiguous stimuli, and identify some of the mechanisms controlling perceptual bias.

Perception of length and orientation in dry immersion

Introduction

How does gravity (or lack thereof) affect sensory-motor processing? We analyze sensorimotor estimation dynamics for line segments with varying direction (orientation) in a 7-day dry immersion (DI), a ground-based model of gravitational unloading.

Methods

The measurements were carried out before the start of the DI, on the first, third, fifth and seventh days of the DI, and after its completion. At the memorization stage, the volunteers led the leading hand along the visible segment on a touchscreen display, and at the reproduction stage they repeated this movement on an empty screen. A control group followed the same procedure without DI.

Results

Both in the DI and control groups, when memorizing, the overall error in estimating the lengths and directions of the segments was small and did not have pronounced dynamics; when reproducing, an oblique effect (higher variability of responses to oblique orientations compared to cardinal ones) was obtained. We then separated biases (systematic error) and uncertainty (random error) in subjects' responses. At the same time, two opposite trends were more pronounced in the DI group during the DI. On the one hand the cardinal bias (a repulsion of orientation estimates away from cardinal axes) and, to a small extent, the variability of direction estimates decreased. On the other hand, the overestimation bias in length estimates increased.

Discussion

Such error pattern strongly supports the hypotheses of the vector encoding, in which the direction and length of the planned movement are encoded independently of each other when the DI disrupts primarily the movement length encoding.

A General Framework for Inferring Bayesian Ideal Observer Models from Psychophysical Data

A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.

Efficient neural codes naturally emerge through gradient descent learning

Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural networks trained to recognize objects also have patterns of sensitivity that match the statistics of features in images. To interpret these findings, we show mathematically that learning with gradient descent in neural networks preferentially creates representations that are more sensitive to common features, a hallmark of efficient coding. This effect occurs in systems with otherwise unconstrained coding resources, and additionally when learning towards both supervised and unsupervised objectives. This result demonstrates that efficient codes can naturally emerge from gradient-like learning.

Idiosyncratic biases in the perception of medical images

Introduction

Radiologists routinely make life-altering decisions. Optimizing these decisions has been an important goal for many years and has prompted a great deal of research on the basic perceptual mechanisms that underlie radiologists' decisions. Previous studies have found that there are substantial individual differences in radiologists' diagnostic performance (e.g., sensitivity) due to experience, training, or search strategies. In addition to variations in sensitivity, however, another possibility is that radiologists might have perceptual biases-systematic misperceptions of visual stimuli. Although a great deal of research has investigated radiologist sensitivity, very little has explored the presence of perceptual biases or the individual differences in these.

Methods

Here, we test whether radiologists' have perceptual biases using controlled artificial and Generative Adversarial Networks-generated realistic medical images. In Experiment 1, observers adjusted the appearance of simulated tumors to match the previously shown targets. In Experiment 2, observers were shown with a mix of real and GAN-generated CT lesion images and they rated the realness of each image.

Results

We show that every tested individual radiologist was characterized by unique and systematic perceptual biases; these perceptual biases cannot be simply explained by attentional differences, and they can be observed in different imaging modalities and task settings, suggesting that idiosyncratic biases in medical image perception may widely exist.

Discussion

Characterizing and understanding these biases could be important for many practical settings such as training, pairing readers, and career selection for radiologists. These results may have consequential implications for many other fields as well, where individual observers are the linchpins for life-altering perceptual decisions.

Heading perception from optic flow is affected by heading distribution

Recent studies have revealed a central tendency in the perception of physical features. That is, the perceived feature was biased toward the mean of recently experienced features (i.e., previous feature distribution). However, no study explored whether the central tendency was in heading perception or not. In this study, we conducted three experiments to answer this question. The results showed that the perceived heading was not biased toward the mean of the previous heading distribution, suggesting that the central tendency was not in heading perception. However, the perceived headings were overall biased toward the left side, where headings rarely appeared in the right-heavied distribution (Experiment 3), suggesting that heading perception from optic flow was affected by previously seen headings. It indicated that the participants learned the heading distributions and used them to adjust their heading perception. Our study revealed that heading perception from optic flow was not purely perceptual and that postperceptual stages (e.g., attention and working memory) might be involved in the heading perception from optic flow.

Spatial Representations Without Spatial Computations

Cognitive maps are assumed to be fundamentally spatial and grounded only in perceptual processes, as supported by the discovery of functionally dedicated cell types in the human brain, which tile the environment in a maplike fashion. Challenging this view, we demonstrate that spatial representations-such as large-scale geographical maps-can be as well retrieved with high confidence from natural language through cognitively plausible artificial-intelligence models on the basis of nonspatial associative-learning mechanisms. More critically, we show that linguistic information accounts for the specific distortions observed in tasks when college-age adults have to judge the geographical positions of cities, even when these positions are estimated on real maps. These findings indicate that language experience can encode and reproduce cognitive maps without the need for a dedicated spatial-representation system, thus suggesting that the formation of these maps is the result of a strict interplay between spatial- and nonspatial-learning principles.

An adaptive perspective on visual working memory distortions

When holding multiple items in visual working memory, representations of individual items are often attracted to, or repelled from, each other. While this is empirically well-established, existing frameworks do not account for both types of distortions, which appear to be in opposition. Here, we demonstrate that both types of memory distortion may confer functional benefits under different circumstances. When there are many items to remember and subjects are near their capacity to accurately remember each item individually, memories for each item become more similar (attraction). However, when remembering smaller sets of highly similar but discernible items, memory for each item becomes more distinct (repulsion), possibly to support better discrimination. Importantly, this repulsion grows stronger with longer delays, suggesting that it dynamically evolves in memory and is not just a differentiation process that occurs during encoding. Furthermore, both attraction and repulsion occur even in tasks designed to mitigate response bias concerns, suggesting they are genuine changes in memory representations. Together, these results are in line with the theory that attraction biases act to stabilize memory signals by capitalizing on information about an entire group of items, whereas repulsion biases reflect a tradeoff between maintaining accurate but distinct representations. Both biases suggest that human memory systems may sacrifice veridical representations in favor of representations that better support specific behavioral goals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Attractive serial dependence overcomes repulsive neuronal adaptation

Sensory responses and behavior are strongly shaped by stimulus history. For example, perceptual reports are sometimes biased toward previously viewed stimuli (serial dependence). While behavioral studies have pointed to both perceptual and postperceptual origins of this phenomenon, neural data that could elucidate where these biases emerge is limited. We recorded functional magnetic resonance imaging (fMRI) responses while human participants (male and female) performed a delayed orientation discrimination task. While behavioral reports were attracted to the previous stimulus, response patterns in visual cortex were repelled. We reconciled these opposing neural and behavioral biases using a model where both sensory encoding and readout are shaped by stimulus history. First, neural adaptation reduces redundancy at encoding and leads to the repulsive biases that we observed in visual cortex. Second, our modeling work suggest that serial dependence is induced by readout mechanisms that account for adaptation in visual cortex. According to this account, the visual system can simultaneously improve efficiency via adaptation while still optimizing behavior based on the temporal structure of natural stimuli.

The coding principals of early sensory regions are in constant flux due to adaptation, but how does the brain interpret these labile signals from early sensory areas? This study finds that a visual illusion known as serial dependence can be explained by a model where readout of these early areas also changes dynamically; the model is supported by neuroimaging and behavioral data.

State transitions in the statistically stable place cell population correspond to rate of perceptual change

The hippocampus occupies a central role in mammalian navigation and memory. Yet an understanding of the rules that govern the statistics and granularity of the spatial code, as well as its interactions with perceptual stimuli, is lacking. We analyzed CA1 place cell activity recorded while rats foraged in different large-scale environments. We found that place cell activity was subject to an unexpected but precise homeostasis—the distribution of activity in the population as a whole being constant at all locations within and between environments. Using a virtual reconstruction of the largest environment, we showed that the rate of transition through this statistically stable population matches the rate of change in the animals’ visual scene. Thus, place fields near boundaries were small but numerous, while in the environment’s interior, they were larger but more dispersed. These results indicate that hippocampal spatial activity is governed by a small number of simple laws and, in particular, suggest the presence of an information-theoretic bound imposed by perception on the fidelity of the spatial memory system.

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Neural activity in rodent CA1 place cell populations is homeostatically balanced

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Hippocampal place field size and frequency are governed by proximity to boundaries

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Transition rate through place cell population matches rate of change in visual scene

Beyond language: The unspoken sensory-motor representation of the tongue in non-primates, non-human and human primates

The English idiom "on the tip of my tongue" commonly acknowledges that something is known, but it cannot be immediately brought to mind. This phrase accurately describes sensorimotor functions of the tongue, which are fundamental for many tongue-related behaviors (e.g., speech), but often neglected by scientific research. Here, we review a wide range of studies conducted on non-primates, non-human and human primates with the aim of providing a comprehensive description of the cortical representation of the tongue's somatosensory inputs and motor outputs across different phylogenetic domains. First, we summarize how the properties of passive non-noxious mechanical stimuli are encoded in the putative somatosensory tongue area, which has a conserved location in the ventral portion of the somatosensory cortex across mammals. Second, we review how complex self-generated actions involving the tongue are represented in more anterior regions of the putative somato-motor tongue area. Finally, we describe multisensory response properties of the primate and non-primate tongue area by also defining how the cytoarchitecture of this area is affected by experience and deafferentation.

Toward a unified account of nonsymbolic and symbolic representations of number: Insights from a combined psychophysical-computational approach

There is an ongoing, vibrant debate about whether numerical information in both nonsymbolic and symbolic notations would be supported by different neurocognitive systems or rather by a common preverbal approximate number system, which is ratio dependent and follows Weber's law. Here, we propose that the similarities between nonsymbolic and symbolic number processing can be explained based on the principle of efficient coding. To probe this hypothesis we employed a new empirical approach, by predicting the behavioural performance in number comparison tasks with symbolic (i.e., number words) and nonsymbolic (i.e., arrays of dots) information not only from numerical ratio, but for the first time also from natural language data. That is, we used data extracted from vector-space models that are informative about the distributional pattern of number-words usage in natural language. Results showed that linguistic estimates predicted the behavioural performance in both symbolic and nonsymbolic tasks. However, and critically, our results also showed a task-dependent dissociation: linguistic data better predicted the performance in the symbolic task, whereas real numerical ratio better predicted the performance in the nonsymbolic task. These findings indicate that efficient coding of environmental regularities is an explanatory principle of human behavior in tasks involving numerical information. They also suggest that the ability to discriminate a stimulus from similar ones varies as a function of the specific statistical structure of the considered learning environment.

Prior Expectations in Visual Speed Perception Predict Encoding Characteristics of Neurons in Area MT

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate because of its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well explains human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian observer model to psychophysical data ("behavioral prior") to the point that the two priors are indistinguishable in a cross-validation model comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.SIGNIFICANCE STATEMENT Statistical regularities of the environment play an important role in shaping both neural representations and perceptual behavior. Most previous work addressed these two aspects independently. Here we present a quantitative validation of a theoretical framework that makes joint predictions for neural coding and behavior, based on the assumption that neural representations of sensory information are efficient but also optimally used in generating a percept. Specifically, we demonstrate that the neural tuning characteristics for visual speed in brain area MT are precisely predicted by the statistical prior expectations extracted from psychophysical data. As such, our results provide a normative link between perceptual behavior and the neural representation of sensory information in the brain.

Rational inattention in mice

Behavior exhibited by humans and other organisms is generally inconsistent and biased and, thus, is often labeled irrational. However, the origins of this seemingly suboptimal behavior remain elusive. We developed a behavioral task and normative framework to reveal how organisms should allocate their limited processing resources such that sensory precision and its related metabolic investment are balanced to guarantee maximal utility. We found that mice act as rational inattentive agents by adaptively allocating their sensory resources in a way that maximizes reward consumption in previously unexperienced stimulus-reward association environments. Unexpectedly, perception of commonly occurring stimuli was relatively imprecise; however, this apparent statistical fallacy implies "awareness" and efficient adaptation to their neurocognitive limitations. Arousal systems carry reward distribution information of sensory signals, and distributional reinforcement learning mechanisms regulate sensory precision via top-down normalization. These findings reveal how organisms efficiently perceive and adapt to previously unexperienced environmental contexts within the constraints imposed by neurobiology.

The implicit sense of agency is not a perceptual effect but is a judgment effect

The sense of agency (SoA) is characterized as the sense of being the causal agent of one's own actions, and it is measured in two forms: explicit and implicit. In the explicit SoA experiments, the participants explicitly report whether they have a sense of control over their actions or whether they or somebody else is the causal agent of seen actions; the implicit SoA experiments study how do participants' agentive or voluntary actions modify perceptual processes (like time, vision, tactility, and audition) without directly asking the participants to explicitly think about their causal agency or sense of control. However, recent implicit SoA literature reported contradictory findings of the relationship between implicit SoA reports and agency states. Thus, I argue that the purported implicit SoA reports are not agency-driven perceptual effects per se but are judgment effects, by showing that (a) the typical operationalizations in implicit SoA domain lead to perceptual uncertainty on the part of the participants, (b) under uncertainty, participants' implicit SoA reports are due to heuristic judgments which are independent of agency states, and (c) under perceptual certainty, the typical implicit SoA reports might not have occurred at all. Thus, I conclude that the instances of implicit SoA are judgments (or response biases)-under uncertainty-rather than perceptual effects.

Neural tuning and representational geometry

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

Normative Principles for Decision-Making in Natural Environments

The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Grasp aperture corrections in reach-to-grasp movements do not reliably alter size perception

When grasping an object, the opening between the fingertips (grip aperture) scales with the size of the object. If an object changes in size, the grip aperture has to be corrected. In this study, it was investigated whether such corrections would influence the perceived size of objects. The grasping plan was manipulated with a preview of the object, after which participants initiated their reaching movement without vision. In a minority of the grasps, the object changed in size after the preview and participants had to adjust their grasping movement. Visual feedback was manipulated in two experiments. In experiment 1, vision was restored during reach and both visual and haptic information was available to correct the grasp and lift the object. In experiment 2, no visual information was provided during the movement and grasps could only be corrected using haptic information. Participants made reach-to-grasp movements towards two objects and compared these in size. Results showed that participants adjusted their grasp to a change in object size from preview to grasped object in both experiments. However, a change in object size did not bias the perception of object size or alter discrimination performance. In experiment 2, a small perceptual bias was found when objects changed from large to small. However, this bias was much smaller than the difference that could be discriminated and could not be considered meaningful. Therefore, it can be concluded that the planning and execution of reach-to-grasp movements do not reliably affect the perception of object size.

Horses show individual level lateralisation when inspecting an unfamiliar and unexpected stimulus

Animals must attend to a diverse array of stimuli in their environments. The emotional valence and salience of a stimulus can affect how this information is processed in the brain. Many species preferentially attend to negatively valent stimuli using the sensory organs on the left side of their body and hence the right hemisphere of their brain. Here, we investigated the lateralisation of visual attention to the rapid appearance of a stimulus (an inflated balloon) designed to induce an avoidance reaction and a negatively valent emotional state in 77 Italian saddle horses. Horses’ eyes are laterally positioned on the head, and each eye projects primarily to the contralateral hemisphere, allowing eye use to be a proxy for preferential processing in one hemisphere of the brain. We predicted that horses would inspect the novel and unexpected stimulus with their left eye and hence right hemisphere. We found that horses primarily inspected the balloon with one eye, and most horses had a preferred eye to do so, however, we did not find a population level tendency for this to be the left or the right eye. The strength of this preference tended to decrease over time, with the horses using their non-preferred eye to inspect the balloon increasingly as the trial progressed. Our results confirm a lateralised eye use tendency when viewing negatively emotionally valent stimuli in horses, in agreement with previous findings. However, there was not any alignment of lateralisation at the group level in our sample, suggesting that the expression of lateralisation in horses depends on the sample population and testing context.

Response Bias Reflects Individual Differences in Sensory Encoding

Humans exhibit substantial biases in their decision making even in simple two-choice tasks, but the origin of these biases remains unclear. I hypothesized that one source of bias could be individual differences in sensory encoding. Specifically, if one stimulus category gives rise to an internal-evidence distribution with higher variability, then responses should optimally be biased against that stimulus category. Therefore, response bias may reflect a previously unappreciated subject-to-subject difference in the variance of the internal-evidence distributions. I tested this possibility by analyzing data from three different two-choice tasks (ns = 443, 443, and 498). For all three tasks, response bias moved in the direction of the optimal criterion determined by each subject's idiosyncratic internal-evidence variability. These results demonstrate that seemingly random variations in response bias can be driven by individual differences in sensory encoding and are thus partly explained by normative strategies.

Individuals with autism spectrum disorder have altered visual encoding capacity

Perceptual anomalies in individuals with autism spectrum disorder (ASD) have been attributed to an imbalance in weighting incoming sensory evidence with prior knowledge when interpreting sensory information. Here, we show that sensory encoding and how it adapts to changing stimulus statistics during feedback also characteristically differs between neurotypical and ASD groups. In a visual orientation estimation task, we extracted the accuracy of sensory encoding from psychophysical data by using an information theoretic measure. Initially, sensory representations in both groups reflected the statistics of visual orientations in natural scenes, but encoding capacity was overall lower in the ASD group. Exposure to an artificial (i.e., uniform) distribution of visual orientations coupled with performance feedback altered the sensory representations of the neurotypical group toward the novel experimental statistics, while also increasing their total encoding capacity. In contrast, neither total encoding capacity nor its allocation significantly changed in the ASD group. Across both groups, the degree of adaptation was correlated with participants’ initial encoding capacity. These findings highlight substantial deficits in sensory encoding—independent from and potentially in addition to deficits in decoding—in individuals with ASD.

It is increasingly recognized that individuals with Autism Spectrum Disorder (ASD) show anomalies in perception, and these have been recently attributed to altered decoding (i.e. interpretation of sensory signals). This study reveals that independent of these changes, individuals with ASD show upstream deficits in sensory encoding (i.e., how samples are drawn from the environment).

An analysis of the Kozak consensus in retinal genes and its relevance to gene therapy

PURPOSE

The classic Kozak consensus is a critical genetic element included in gene therapy transgenes to encourage the translation of the therapeutic coding sequence. Despite optimizations of other transgene elements, the Kozak consensus has not yet been considered for potential tissue-specific sequence refinement. We screened the -9 to -1 region relative to the AUG start codon of retina-specific genes to identify whether a Kozak consensus that is different from the classic sequence may be more appropriate for inclusion in gene therapy transgenes that treat inherited retinal disease.

METHODS

Sequences for 135 genes known to cause nonsyndromic inherited retinal disease were extracted from the NCBI database, and the -9 to -1 nucleotides were compared. This panel was then refined to 75 genes with specific retinal functions, for which the -9 to -1 nucleotides were placed in front of a GFP transcript sequence and RNAfold predictions performed. These were compared with a GFP sequence with the classic Kozak consensus (GCCGCCACC), and sequences from retinal genes with minimum free energy (MFE) predictions greater than the reference sequence were selected to generate an optimized Kozak consensus sequence. The original Kozak consensus and the refined retina Kozak consensus were placed upstream of the Renilla luciferase coding sequence, which were used to transfect retinoblastoma cell lines Y-79 and WERI-RB-1 and HEK 293T/17 cells.

RESULTS

The nucleotide frequencies of the original panel of genes were determined to be comparable to the classic Kozak consensus. RNAfold analysis of a GFP transcript with the classic Kozak sequence in the 5' untranslated region (UTR) generated an MFE prediction of -503.3 kcal/mol. RNAfold analysis was then performed with a GFP transcript containing each -9 to -1 Kozak sequence of 75 retinal genes. Thirty-eight of the 75 genes provided a greater MFE value than -503.3 kcal/mol and exhibited an absence of stable secondary structures before the AUG codon. The -9 to -1 nucleotide frequencies of these genes identified a Kozak consensus of ACCGAGACC, differing from the classic Kozak consensus at positions -9, -5, and -4. Applying this sequence to the GFP transcript increased the MFE prediction to -500.1 kcal/mol. The newly identified retina Kozak sequence was also applied to Renilla luciferase plus the REP1 and RPGR transcripts used in current clinical trials. In all examples, the predicted transcript MFE score increased when compared with the current transcript sequences containing classic Kozak consensus sequences. In vitro transfections identified a 7%-9% increase in Renilla activity when incorporating the optimized Kozak sequence.

CONCLUSIONS

The Kozak consensus is a critical element of eukaryotic genes; therefore, it is a required feature of gene therapy transgenes. To date, the classic sequence of GCCRCC (-6 to -1) has typically been incorporated in gene therapy transgenes, but the analysis described here suggests that, for vectors targeting the retina, using a Kozak consensus derived from retinal genes can provide increased expression of the target product.

Optimizing perception: Attended and ignored stimuli create opposing perceptual biases

Humans have remarkable abilities to construct a stable visual world from continuously changing input. There is increasing evidence that momentary visual input blends with previous input to preserve perceptual continuity. Most studies have shown that such influences can be traced to characteristics of the attended object at a given moment. Little is known about the role of ignored stimuli in creating this continuity. This is important since while some input is selected for processing, other input must be actively ignored for efficient selection of the task-relevant stimuli. We asked whether attended targets and actively ignored distractor stimuli in an odd-one-out search task would bias observers' perception differently. Our observers searched for an oddly oriented line among distractors and were occasionally asked to report the orientation of the last visual search target they saw in an adjustment task. Our results show that at least two opposite biases from past stimuli influence current perception: A positive bias caused by serial dependence pulls perception of the target toward the previous target features, while a negative bias induced by the to-be-ignored distractor features pushes perception of the target away from the distractor distribution. Our results suggest that to-be-ignored items produce a perceptual bias that acts in parallel with other biases induced by attended items to optimize perception. Our results are the first to demonstrate how actively ignored information facilitates continuity in visual perception.

Individual differences in the perception of probability

In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases.

Serial reproduction reveals the geometry of visuospatial representations

A primary function of human vision is to encode and recall spatial information about visual scenes. We developed an experimental paradigm that reveals the structure of human spatial memory priors in unprecedented detail. We ran a series of 85 large-scale online experiments with 9,202 participants that paint an intricate picture of these priors. Our results suggest a way to understand visuospatial representations as reflecting the efficient allocation of coding resources. In a radical departure from traditional theory, we introduce a model that reinterprets spatial memory priors as reflecting an optimal allocation of perceptual resources. We validate the predictions of the model experimentally by showing that perceptual biases are correlated with variations in discrimination accuracy.

An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding (“efficient encoding”). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations.