Noise Increases Anchoring Effects

We introduce a theoretical framework distinguishing between anchoring effects, anchoring bias, and judgmental noise: Anchoring effects require anchoring bias, but noise modulates their size. We tested this framework by manipulating stimulus magnitudes. As magnitudes increase, psychophysical noise due to scalar variability widens the perceived range of plausible values for the stimulus. This increased noise, in turn, increases the influence of anchoring bias on judgments. In 11 preregistered experiments ( N = 3,552 adults), anchoring effects increased with stimulus magnitude for point estimates of familiar and novel stimuli (e.g., reservation prices for hotels and donuts, counts in dot arrays). Comparisons of relevant and irrelevant anchors showed that noise itself did not produce anchoring effects. Noise amplified anchoring bias. Our findings identify a stimulus feature predicting the size and replicability of anchoring effects—stimulus magnitude. More broadly, we show how to use psychophysical noise to test relationships between bias and noise in judgment under uncertainty.

Categorical judgments do not modify sensory representations in working memory

Categorical judgments can systematically bias the perceptual interpretation of stimulus features. However, it remained unclear whether categorical judgments directly modify working memory representations or, alternatively, generate these biases via an inference process down-stream from working memory. To address this question we ran two novel psychophysical experiments in which human subjects had to reverse their categorical judgments about a stimulus feature, if incorrect, before providing an estimate of the feature. If categorical judgments indeed directly altered sensory representations in working memory, subjects’ estimates should reflect some aspects of their initial (incorrect) categorical judgment in those trials. We found no traces of the initial categorical judgment. Rather, subjects seemed to be able to flexibly switch their categorical judgment if needed and use the correct corresponding categorical prior to properly perform feature inference. A cross-validated model comparison also revealed that feedback may lead to selective memory recall such that only memory samples that are consistent with the categorical judgment are accepted for the inference process. Our results suggest that categorical judgments do not modify sensory information in working memory but rather act as top-down expectations in the subsequent sensory recall and inference process.

Feature-based attention induces surround suppression during the perception of visual motion

Attention to a stimulus feature prioritizes its processing while strongly suppressing the processing of similar features, a non-linear phenomenon called surround suppression. Here we investigated this phenomenon using neurophysiology and psychophysics. We recorded responses of motion direction-selective neurons in area MT/MST of monkeys in different conditions. When attention was allocated to a stimulus moving in the neurons’ preferred direction responses to a distractor were strongly suppressed for directions nearby the preferred direction. These effects were modeled as the interaction between two Gaussian fields representing narrowly-tuned excitatory and widely-tuned inhibitory inputs into a neuron, with attention more strongly modulating the gain of the inhibitory inputs. We additionally demonstrated a corresponding behavioral effect in humans: Feature-based attention strongly reduced motion repulsion in the vicinity of the attended motion direction. Our results demonstrate that feature-based attention can induce non-linear changes in neuronal tuning curves via unbalanced gain changes to excitatory and inhibitory inputs into neurons ultimately translating into similar effects during behavior.

Perceptual decisions are biased toward relevant prior choices

Perceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.

Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits

The brain performs probabilistic inference to interpret the external world, but the underlying neuronal mechanisms remain not well understood. The stimulus structure of natural scenes exists in a high-dimensional feature space, and how the brain represents and infers the joint posterior distribution in this rich, combinatorial space is a challenging problem. There is added difficulty when considering the neuronal mechanics of this representation, since many of these features are computed in parallel by distributed neural circuits. Here, we present a novel solution to this problem. We study continuous attractor neural networks (CANNs), each representing and inferring a stimulus attribute, where attractor coupling supports sampling-based inference on the multivariate posterior of the high-dimensional stimulus features. Using perturbative analysis, we show that the dynamics of coupled CANNs realizes Langevin sampling on the stimulus feature manifold embedded in neural population responses. In our framework, feedforward inputs convey the likelihood, reciprocal connections encode the stimulus correlational priors, and the internal Poisson variability of the neurons generate the correct random walks for sampling. Our model achieves high-dimensional joint probability representation and Bayesian inference in a distributed manner, where each attractor network infers the marginal posterior of the corresponding stimulus feature. The stimulus feature can be read out simply with a linear decoder based only on local activities of each network. Simulation experiments confirm our theoretical analysis. The study provides insight into the fundamental neural mechanisms for realizing efficient high-dimensional probabilistic inference.

Categorical judgments do not modify sensory information in working memory

Categorical judgments can systematically bias the perceptual interpretation of stimulus features. However, it remained unclear whether categorical judgments directly modify working memory representations or, alternatively, generate these biases via an inference process down-stream from working memory. To address this question we ran two novel psychophysical experiments in which human subjects had to revert their categorical judgments about a stimulus feature, if incorrect based on feedback, before providing an estimate of the feature. If categorical judgments indeed directly altered sensory representations in working memory, subjects’ estimates should reflect some aspects of their initial (incorrect) categorical judgment in those trials.We found no traces of the initial categorical judgment. Rather, subjects seem to be able to flexibly switch their categorical judgment if needed and use the correct corresponding categorical prior to properly perform feature inference. A cross-validated model comparison also revealed that feedback may lead to selective memory recall such that only memory samples that are consistent with the categorical judgment are accepted for the inference process. Our results suggest that categorical judgments do not modify sensory information in working memory but rather act as top-down expectation in the subsequent sensory recall and inference process down-stream from working memory.

Perceptual decisions are biased toward relevant prior choices

Perceptual decisions are biased by recent perceptual history, a phenomenon termed 'serial dependence.' Using a visual location discrimination task, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions on subsequent perceptual decisions. Following several biased (prior) location discriminations, subsequent (test) discriminations were biased toward the prior choices, even when reported via different motor actions, and when prior and test stimuli differed in color. By contrast, biased discriminations about an irrelevant stimulus feature did not substantially influence subsequent location discriminations. Additionally, biased stimulus locations, when color was discriminated, no longer substantially influenced subsequent location decisions. Hence, the degree of relevance between prior and subsequent perceptual decisions is a key factor for serial dependence. This suggests that serial-dependence reflects a high-level mechanism by which the brain predicts and interprets incoming sensory information in accordance with relevant prior choices.

A robust anchoring effect in linear ordering

The robustness of effects indicating a spatial component associated with abstract reasoning is tested. Judgements regarding hierarchical orderings tend to be faster and more accurate when the dominant element in any pair from the order (e.g., the older, richer) is presented on the left of the screen as compared with the right ( left-anchoring effect). This signature effect is investigated in three conditions (Experiment 1), each implementing a different timing regime for the elements in each pair, during learning. Thereby, the construction of a mental representation of the ordering was exposed to a potentially competing spatial simulation, that is, the well-known “mental timeline” with orientation from left (present) to right (future). First, the left-anchoring effect for order representations remained significant when timeline information was congruent with the presumed left-anchoring process, that is, the dominant element in a pair was always presented first. Second, the same effect remained also significant when the timeline-related information was random, that is, the dominant element being presented either first or second. Third, the same effect was found to be still significant, when the timeline-related information was contrary to the left-anchoring process, that is, the dominant element being presented always second. Experiment 2 replicates the target effect under random timeline information, controlling for colour as a stimulus feature. The results are discussed in the context of a theoretical model that integrates basic assumptions about acquired reading/writing habits as a scaffold for spatial simulation and primacy/dominance representation within such spatial simulations.