Confirmation Bias in Simulated Child Sexual Abuse (CSA) Interviews: Abuse Assumption Influences Interviewing and Decision-making Processes

PurposeResearch has shown that confirmation bias plays a role in legal and forensic decision-making processes. However, to our knowledge, no study has examined how it manifests itself when interviewing an allegedly abused child. MethodIn the present study, we used data from a series of experiments in which participants interviewed child avatars to examine how an assumption of abuse based on preliminary information influenced decision-making and interviewing style. Interview training data from eight studies with students, psychologists and police officers were included in the analyses.ResultsWe found that interviewers’ preliminary assumption of sexual abuse having taken place predicted 1) a conclusion of abuse by the interviewers after the interview; 2) higher confidence in their judgment; 3) more frequent use of not recommended question types and 4) a decreased likelihood of reaching a correct conclusion given the same number of available relevant details. ConclusionThe importance of considering how preliminary assumptions of abuse affect interview behaviour and outcomes and the implications for the training of investigative interviewers were discussed.

Cervical Spine Inhomogeneously Enhancing Lesion: Avoiding Confirmation Bias

Confirmation bias is the tendency to seek information and evidence in order to confirm a preexisting hypothesis while giving less importance and overlook an alternative solution. This report describes the case of a 52-year-old man with a long history of neck pain and bilateral upper limbs paresthesias with a cervical intracanal inhomogeneously enhancing lesion. Despite all the preoperative radiological findings, a spinal meningioma an anterior approach was performed. The mass ended up being a large migrated hernia with the involvement of two levels. Before suggesting treatment, especially surgery, physicians and practitioners need to evaluate all of the possible alternatives in order to optimize patient outcome.

Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

A large body of research attributes learning deficits in schizophrenia (SZ) to the systems involved in value representation (prefrontal cortex, PFC) and reinforcement learning (basal ganglia, BG) as well as to the compromised connectivity of these regions. In this study, we employed learning tasks hypothesized to probe the function and interaction of the PFC and BG in patients with SZ-spectrum disorders in comparison to healthy control (HC) subjects. In the Instructed Probabilistic Selection task (IPST), participants received false instruction about one of the stimuli used in the course of probabilistic learning which creates confirmation bias, whereby the instructed stimulus is overvalued in comparison to its real experienced value. The IPST was administered to 102 patients with SZ and 120 HC subjects. We have shown that SZ patients and HC subjects were equally influenced by false instruction in reinforcement learning (RL) probabilistic task (IPST) (p-value = 0.441); however, HC subjects had significantly higher learning rates associated with the process of overcoming cognitive bias in comparison to SZ patients (p-value = 0.018). The behavioral results of our study could be hypothesized to provide further evidence for impairments in the SZ-BG circuitry; however, this should be verified by neurofunctional imaging studies.

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

Addressing confirmation bias in middle school data science education

<p style='text-indent:20px;'>More research is needed involving middle school students' engagement in the statistical problem-solving process, particularly the beginning process steps: formulate a question and make a plan to collect data/consider the data. Further, the increased availability of large-scale electronically accessible data sets is an untapped area of study. This interpretive study examined middle school students' understanding of statistical concepts involved in making a plan to collect data to answer a statistical question within a social issue context using data available on the internet. Student artifacts, researcher notes, and audio and video recordings from nine groups of 20 seventh-grade students in two gifted education pull-out classes at a suburban middle school were used to answer the study research questions. Data were analyzed using a priori codes from previously developed frameworks and by using an inductive approach to find themes.</p><p style='text-indent:20px;'>Three themes that emerged from data related to confirmation bias. Some middle school students held preconceptions about the social issues they chose to study that biased their statistical questions. This in turn influenced the sources of data students used to answer their questions. Confirmation bias is a serious issue that is exacerbated due to endless sources of data electronically available. We argue that this type of bias should be addressed early in students' educational experiences. Based on the findings from this study, we offer recommendations for future research and implications for statistics and data science education.</p>

Bayes' theorem: scientific assessment of experience

Homeopathy is based on experience and this is a scientific procedure if we follow Bayes' theorem. Unfortunately this is not the case at the moment. Symptoms are added to our materia medica based on absolute occurrence, while Bayes theorem tells us that this should be based on relative occurrence. Bayes theorem can be applied on prospective research, but also on retrospective research and consensus based on a large number of cases. Confirmation bias is an important source of false data in experience based systems like homeopathy. Homeopathic doctors should become more aware of this and longer follow-up of cases could remedy this. The existing system of adding symptoms to our materia medica is obsolete.

Compliance and confirmation bias on the emergence of cultural conventions

In the present study we develop a co-evolutionary model of cardinal preferences and institutions to explore how the dynamics of cultural diversity in populations with different levels of compliance and confirmation bias evolve. This is the first attempt to formalise these two types of bias in a single learning algorithm for agents learning in iterative chains without access to completeinformation. Results show that, in some regions of the parameter space, institutional influence facilitates the emergence of shared cultural conventions when compliance biases increase. In general, a compliance bias pushes diversity up when institutions are diverse, and pushes diversity down when institutions convey value systems with strong dominance of one or few cultural variants. Interestingly, in some scenarios, a decrease in institutional influence and compliance bias allows theemergence of cultural conventions from the mutual reinforcement of local interactions and institutional values. We asses the robustness of these results by examining how sensitively they depend on different initial conditions of variant assignment, population sizes and alpha diversity indexes.

Characterizing the Influence of Confirmation Bias on Web Search Behavior

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.

Confirmation Bias and Auditor Risk Assessments: Archival Evidence

Judgment and decision making research suggests that auditors’ judgments are negatively affected by the use of heuristics. However, there is little research investigating whether such biases survive the quality control processes that regulators and audit firms implement to mitigate them. We investigate this by identifying a setting where one such bias – confirmation bias – is likely to manifest. Consistent with confirmation bias influencing observable audit outcomes, we find that auditors with previous experience auditing a client with a history of low risk followed by an increase in risk do not adequately respond to the higher level of risk. This effect is mitigated when the risk increase is likely large enough to violate auditors’ reasonableness constraint and when the client is highly visible or has strong external monitors. Our study complements prior experimental research by providing archival evidence that auditors’ use of heuristics has a significant effect on auditor judgments.