Related Experiment Video
Updated: Apr 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Discounting testimony with the argument ad hominem and a Bayesian congruent prior model
Jaydeep-Singh Bhatia1, Mike Oaksford1
1Department of Psychological Sciences, Birkbeck College, University of London.
Abstract:
When directed to ignore evidence of a witness's previous bad character because of a violation of the rules of evidence, are jurors' beliefs still affected? The intuition is that they will be because in everyday argumentation, fallacies, like the ad hominem, are effective argumentative strategies. An ad hominem argument (against the person) undermines a conclusion by questioning the character of the proposer. This intuition divides current theories of argumentation. According to pragmadialectical theory (e.g., Van Eemeren & Grootendorst, 2004), procedural rules exactly like the rules of evidence are part of our cognitive resources for evaluating arguments. If one of these rules is violated, an argument should be treated as a fallacy and so it should not alter someone's belief in the conclusion. Some recent experiments investigating how reasonable these arguments are perceived to be seem to support this account (van Eemeren, Garssen, & Meuffels, 2009). These experiments are critiqued from the perspective of the relevance (Walton, 2009, 2010) and epistemic (Hahn & Oaksford, 2006, 2007; Oaksford & Hahn, 2004) approaches to argumentation. An experiment investigates the predictions of these approaches for a graded belief change version of van Eemeren et al.'s (2009) experiment, and the results are modeled using a Bayesian congruent prior model. These results cannot be explained by the pragmadialectical approach and show that in everyday argument people are extremely sensitive to the epistemic relevance of evidence. Moreover, it seems highly unlikely that this can be switched off in more formal contexts such as the courtroom.
Related Concept Videos
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Hypothesis: Accept or Fail to Reject?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
Decision Making: Traditional Method
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

