Noise Increases Anchoring Effects

Psychological Science ◽

10.1177/09567976211024254 ◽

2021 ◽

pp. 095679762110242

Author(s):

Chang-Yuan Lee ◽

Carey K. Morewedge

Keyword(s):

Theoretical Framework ◽

Stimulus Feature ◽

Stimulus Magnitude ◽

Point Estimates ◽

Plausible Values ◽

Anchoring Effects ◽

Anchoring Bias ◽

Reservation Prices ◽

Scalar Variability

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.

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One Swallow Doesn't Make a Summer: New Evidence on Anchoring Effects

The American Economic Review ◽

10.1257/aer.104.1.277 ◽

2014 ◽

Vol 104 (1) ◽

pp. 277-290 ◽

Cited By ~ 122

Author(s):

Zacharias Maniadis ◽

Fabio Tufano ◽

John A. List

Keyword(s):

Statistical Power ◽

Theoretical Framework ◽

False Positives ◽

Significance Level ◽

Anchoring Effects ◽

New Evidence

Some researchers have argued that anchoring in economic valuations casts doubt on the assumption of consistent and stable preferences. We present new evidence that explores the strength of certain anchoring results. We then present a theoretical framework that provides insights into why we should be cautious of initial empirical findings in general. The model importantly highlights that the rate of false positives depends not only on the observed significance level, but also on statistical power, research priors, and the number of scholars exploring the question. Importantly, a few independent replications dramatically increase the chances that the original finding is true. (JEL D12, C91)

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A Short Note on Obtaining Point Estimates of the IRT Ability Parameter With MCMC Estimation in Mplus: How Many Plausible Values Are Needed?

Educational and Psychological Measurement ◽

10.1177/0013164418777569 ◽

2018 ◽

Vol 79 (2) ◽

pp. 272-287 ◽

Cited By ~ 2

Author(s):

Yong Luo ◽

Dimiter M. Dimitrov

Keyword(s):

Posterior Distribution ◽

Population Level ◽

Posterior Mean ◽

Ability Estimates ◽

Point Estimates ◽

Level Statistics ◽

Plausible Values ◽

Minimum Number ◽

Individual Ability ◽

Ability Parameter

Plausible values can be used to either estimate population-level statistics or compute point estimates of latent variables. While it is well known that five plausible values are usually sufficient for accurate estimation of population-level statistics in large-scale surveys, the minimum number of plausible values needed to obtain accurate latent variable point estimates is unclear. This is especially relevant when an item response theory (IRT) model is estimated with MCMC (Markov chain Monte Carlo) methods in Mplus and point estimates of the IRT ability parameter are of interest, as Mplus only estimates the posterior distribution of each ability parameter. In order to obtain point estimates of the ability parameter, a number of plausible values can be drawn from the posterior distribution of each individual ability parameter and their mean (the posterior mean ability estimate) can be used as an individual ability point estimate. In this note, we conducted a simulation study to investigate how many plausible values were needed to obtain accurate posterior mean ability estimates. The results indicate that 20 is the minimum number of plausible values required to obtain point estimates of the IRT ability parameter that are comparable to marginal maximum likelihood estimation(MMLE)/expected a posteriori (EAP) estimates. A real dataset was used to demonstrate the comparison between MMLE/EAP point estimates and posterior mean ability estimates based on different number of plausible values.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Adaptive and Maladaptive Neural Plasticity Due to Facial Nerve Palsy

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000244 ◽

2016 ◽

Vol 224 (2) ◽

pp. 102-111 ◽

Cited By ~ 1

Author(s):

Carsten M. Klingner ◽

Stefan Brodoehl ◽

Gerd F. Volk ◽

Orlando Guntinas-Lichius ◽

Otto W. Witte

Keyword(s):

Facial Nerve ◽

Neural Plasticity ◽

Cortical Plasticity ◽

Brain Plasticity ◽

Motor Nerve ◽

Predictive Coding ◽

Theoretical Framework ◽

Cortical Reorganization ◽

Nerve Lesion ◽

Peripheral Facial Palsy

Abstract. This paper reviews adaptive and maladaptive mechanisms of cortical plasticity in patients suffering from peripheral facial palsy. As the peripheral facial nerve is a pure motor nerve, a facial nerve lesion is causing an exclusive deefferentation without deafferentation. We focus on the question of how the investigation of pure deefferentation adds to our current understanding of brain plasticity which derives from studies on learning and studies on brain lesions. The importance of efference and afference as drivers for cortical plasticity is discussed in addition to the crossmodal influence of different competitive sensory inputs. We make the attempt to integrate the experimental findings of the effects of pure deefferentation within the theoretical framework of cortical responses and predictive coding. We show that the available experimental data can be explained within this theoretical framework which also clarifies the necessity for maladaptive plasticity. Finally, we propose rehabilitation approaches for directing cortical reorganization in the appropriate direction and highlight some challenging questions that are yet unexplored in the field.

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