scholarly journals The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments

2018 ◽  
Author(s):  
Jian-Qiao Zhu ◽  
Adam N Sanborn ◽  
Nick Chater
Keyword(s):  
Probabilistic Models ◽  
Conjunction Fallacy ◽  
Probability Judgment ◽  
Noise Model ◽  
Probability Judgments ◽  
Probability Estimates ◽  
Bayesian Models Of Cognition ◽  
Improved Accuracy ◽  
Biases And Heuristics ◽  
The Brain

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities, and we show in a new experiment that this model better captures these judgments both qualitatively and quantitatively.

scholarly journals Gender fluidity as affordance negotiation

2020 ◽  
Author(s):  
Mahault Albarracin ◽  
Pierre Poirier
Keyword(s):  
Prediction Error ◽  
Probabilistic Models ◽  
Predictive Processing ◽  
Gender Fluidity ◽  
Social Scripts ◽  
Sensorimotor Interaction ◽  
Binary State ◽  
Gender Scripts ◽  
Cultural Niche ◽  
The Brain

Gender is often viewed as static binary state for people to embody, based on the sex they were assigned at birth. However, cultural studies increasingly understand gender as neither binary nor static, a view supported both in psychology and sociology. On this view, gender is negotiated between individuals, and highly dependent on context. Specifically, individuals are thought to be offered culturally gendered social scripts that allow them and their interlocutors the ability to predict future actions, and to understand the scene being set, establishing roles and expectations. We propose to understand scripts in the framework of enactive-ecological predictivism, which integrates aspects of ecological enactivism, notably the importance of dynamical sensorimotor interaction with an environment conceived as a field of affordances, and predictive processing, which views the brain as a predictive engine that builds its probabilistic models in an effort to reduce prediction error. Under this view, script-based negotiation can be linked to the enactive neuroscience concept of a cultural niche, as a landscape of cultural affordances. Affordances are possibilities for action that constrain what actions are pre-reflectively felt possible based on biological, experiential and cultural multisensorial cues. With the shifting landscapes of cultural affordances brought about by a number of recent social, technological and epistemic developments, the gender scripts offered to individuals are less culturally rigid, which translates in an increase in the variety of affordance fields each individual can negotiate. This entails that any individual has an increased possibility for gender fluidity, as shown by the increasing number of people currently identifying outside the binary.

scholarly journals Effects of experience-based frequency learning on posterior probability judgments

2021 ◽  
Author(s):  
Bonnie A. Armstrong
Keyword(s):  
Older Adults ◽  
Posterior Probability ◽  
Natural Frequencies ◽  
Screening Tests ◽  
Conditional Probabilities ◽  
Medical Decisions ◽  
Predictive Values ◽  
Probability Judgments ◽  
Medical Tests ◽  
Probability Estimates

Aging is associated with an increase in the frequency of medical screening tests. Bayesian inference is used to estimate posterior probabilities of medical tests such as positive or negative predictive values (PPVs or NPVs). Both laypeople and experts are typically poor at estimating PPVs and NPVs when relevant probabilities are communicated descriptively. Decision making research has revealed dissociations between described and experience-based judgments. This study examined the accuracy of posterior probability estimates of 80 younger and 81 older adults when statistical information was presented through description or experience. Results show that both younger and older adults can make more accurate posterior probability estimates if they experience probabilities compared to when probabilities are described as either natural frequencies or conditional probabilities. Results also indicate that most people prefer to rely on physicians to make their medical decisions regardless of how confident they are in their judgments of probabilities.

Facilitating Normative Judgments of Conditional Probability: Frequency or Nested Sets?

2003 ◽  
Vol 50 (2) ◽  
pp. 97-106 ◽  
Author(s):  
Kimihiko Yamagishi
Keyword(s):  
Conditional Probability ◽  
Probabilistic Reasoning ◽  
Probability Judgment ◽  
Probability Judgments ◽  
Normative Reasoning ◽  
Roulette Wheel ◽  
Probability Task ◽  
Nested Sets ◽  
Probabilistic Structure ◽  
Inspection Machine

Abstract. Recent probability judgment research contrasts two opposing views. Some theorists have emphasized the role of frequency representations in facilitating probabilistic correctness; opponents have noted that visualizing the probabilistic structure of the task sufficiently facilitates normative reasoning. In the current experiment, the following conditional probability task, an isomorph of the “Problem of Three Prisoners” was tested. “A factory manufactures artificial gemstones. Each gemstone has a 1/3 chance of being blurred, a 1/3 chance of being cracked, and a 1/3 chance of being clear. An inspection machine removes all cracked gemstones, and retains all clear gemstones. However, the machine removes ½ of the blurred gemstones. What is the chance that a gemstone is blurred after the inspection?” A 2 × 2 design was administered. The first variable was the use of frequency instruction. The second manipulation was the use of a roulette-wheel diagram that illustrated a “nested-sets” relationship between the prior and the posterior probabilities. Results from two experiments showed that frequency alone had modest effects, while the nested-sets instruction achieved a superior facilitation of normative reasoning. The third experiment compared the roulette-wheel diagram to tree diagrams that also showed the nested-sets relationship. The roulette-wheel diagram outperformed the tree diagrams in facilitation of probabilistic reasoning. Implications for understanding the nature of intuitive probability judgments are discussed.

Probabilistic Models of the Brain.

2002 ◽  
Vol 12 (4) ◽  
pp. 312
Author(s):  
Donlin M. Long
Keyword(s):  
Probabilistic Models ◽  
The Brain

scholarly journals Effortless integration of probabilistic visual input

2021 ◽  
Author(s):  
Andrey Chetverikov ◽  
Árni Kristjánsson
Keyword(s):  
Probabilistic Models ◽  
Spatial Scales ◽  
Building Blocks ◽  
Visual Input ◽  
Summary Statistics ◽  
Visual Inputs ◽  
The World ◽  
Mean And Variance ◽  
Scene Processing ◽  
The Brain

Prominent theories of perception suggest that the brain builds probabilistic models of the world, assessing the statistics of the visual input to inform this construction. However, the evidence for this idea is often based on simple impoverished stimuli, and the results have often been discarded as an illusion reflecting simple "summary statistics" of visual inputs. Here we show that the visual system represents probabilistic distributions of complex heterogeneous stimuli. Importantly, we show how these statistical representations are integrated with representations of other features and bound to locations, and can therefore serve as building blocks for object and scene processing. We uncover the organization of these representations at different spatial scales by showing how expectations for incoming features are biased by neighboring locations. We also show that there is not only a bias, but also a skew in the representations, arguing against accounts positing that probabilistic representations are discarded in favor of simplified summary statistics (e.g., mean and variance). In sum, our results reveal detailed probabilistic encoding of stimulus distributions, representations that are bound with other features and to particular locations.

Sign in / Sign up

Export Citation Format

Share Document