Strength and weight: The determinants of choice and confidence

Kvam, Peter D.; Pleskac, Timothy J.

doi:10.1016/j.cognition.2016.04.008

Cited by 41 publications

(57 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study exploited a previously observed effect of evidence variability to dissociate objective accuracy from subjective confidence, without specifically aiming to understand the cause of this dissociation. Nonetheless, in replicating previous findings using the same paradigm (Boldt et al, 2017) and related manipulations (Spence et al, 2015), our results stand in apparent contrast to recent findings that evidence mean more strongly influences choice and confidence than evidence weight (Kvam & Pleskac, 2016). In that study, however, evidence weight was operationalized as the amount of evidence, as distinct from the variability of presented evidence studied here.…”

Section: Models Of Decision Making and Confidencesupporting

confidence: 76%

Subjective Confidence Predicts Information Seeking in Decision Making

2018

View full text Add to dashboard Cite

There is currently little direct evidence regarding the function of subjective confidence in decision making: The tight correlation between objective accuracy and subjective confidence makes it difficult to distinguish each variable's unique contribution. Here, we created conditions in a perceptual decision task that were matched in accuracy but differed in subjective evaluation of accuracy by orthogonally varying the strength versus variability of evidence. Confidence was reduced with variable (vs. weak) evidence, even across conditions matched for difficulty. Building on this dissociation, we constructed a paradigm in which participants ( N = 20) could choose to seek further information before making their decision. The data provided clear support for the hypothesis that subjective confidence predicts information seeking in decision making: Participants were more likely to sample additional information before giving a response in the condition with low confidence, despite matched accuracy. In a preregistered replication ( N = 50), these findings were replicated with increased task difficulty levels.

show abstract

Section: Models Of Decision Making and Confidencesupporting

confidence: 76%

Subjective Confidence Predicts Information Seeking in Decision Making

2018

View full text Add to dashboard Cite

show abstract

“…Median splits were used because the psychological scaling of these quantities is unlikely to be linear. These scaling issues, which are topics of investigation in their own right (Siegler and Opfer, 2003; Kvam and Pleskac, 2016), are beyond the scope of this contribution.…”

Section: Resultsmentioning

confidence: 99%

Social Information Is Integrated into Value and Confidence Judgments According to Its Reliability

et al. 2017

View full text Add to dashboard Cite

How much we like something, whether it be a bottle of wine or a new film, is affected by the opinions of others. However, the social information that we receive can be contradictory and vary in its reliability. Here, we tested whether the brain incorporates these statistics when judging value and confidence. Participants provided value judgments about consumer goods in the presence of online reviews. We found that participants updated their initial value and confidence judgments in a Bayesian fashion, taking into account both the uncertainty of their initial beliefs and the reliability of the social information. Activity in dorsomedial prefrontal cortex tracked the degree of belief update. Analogous to how lower-level perceptual information is integrated, we found that the human brain integrates social information according to its reliability when judging value and confidence.SIGNIFICANCE STATEMENT The field of perceptual decision making has shown that the sensory system integrates different sources of information according to their respective reliability, as predicted by a Bayesian inference scheme. In this work, we hypothesized that a similar coding scheme is implemented by the human brain to process social signals and guide complex, value-based decisions. We provide experimental evidence that the human prefrontal cortex's activity is consistent with a Bayesian computation that integrates social information that differs in reliability and that this integration affects the neural representation of value and confidence.

show abstract

“…These include both the simple random walk models, where a decision maker has a discrete set of states (e.g., 11 confidence levels, 0/10/20/.../100) that they move through over time, shown in Figure 2, and the more common diffusion models where the "states" are a continuously-valued level of evidence (such as 0-100, including all numbers in between). Since the discrete-state random walks approach a diffusion process as the number of states gets very large, we group these two approaches together under the umbrella of Markov process, which have been used to model choices and response times (Emerson, 1970;Luce, 1986;Stone, 1960) as well as probability judgments (Edwards et al, 1963;Wald & Wolfowitz, 1949, 1948Kvam & Pleskac, 2016;Moran et al, 2015;Ratcliff & Starns, 2009;Yu et al, 2015) in domains such as memory (Ratcliff, 1978), categorization (Nosofsky & Palmeri, 1997), and inference (Pleskac & Busemeyer, 2010).…”

Section: Markov and Quantum Views Of Evidence Accumulationmentioning

confidence: 99%

Comparison of Markov versus quantum dynamical models of human decision making

Kvam¹,

Pleskac²,

Busemeyer³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

What kind of dynamic decision process do humans use to make decisions? In this article, two different types of processes are reviewed and compared: Markov and quantum. Markov processes are based on the idea that at any given point in time a decision maker has a definite and specific level of support for available choice alternatives, and the dynamic decision process is represented by a single trajectory that traces out a path across time. When a response is requested, a person's decision or judgment is generated from the current location along the trajectory. By contrast, quantum processes are founded on the idea that a person's state can be represented by a superposition over different degrees of support for available choice options, and that the dynamics of this state form a wave moving across levels of support over time. When a response is requested, a decision or judgment is constructed out of the superposition by 'actualizing' a specific degree or range of degrees of support to create a definite state. The purpose of this article is to introduce these two contrasting theories, review empirical studies comparing the two theories, and identify conditions that determine when each theory is more accurate and useful than the other. Figure 1: Markov and quantum random walk models generate diverging predictions for how evidence evolves over time and how measurements like decisions interact with subsequent responses.

show abstract

Strength and weight: The determinants of choice and confidence

Cited by 41 publications

References 53 publications

Subjective Confidence Predicts Information Seeking in Decision Making

Subjective Confidence Predicts Information Seeking in Decision Making

Social Information Is Integrated into Value and Confidence Judgments According to Its Reliability

Comparison of Markov versus quantum dynamical models of human decision making

Contact Info

Product

Resources

About