The Number of Unanimous Witnesses who Identify a Suspect from a Lineup Influences Mock Jurors

<p>During a criminal proceeding, jurors need to weigh up the presented evidence and determine a verdict. Research has shown that witness identification evidence is compelling to jurors, despite the fact that it can be unreliable. How reliable are the combined lineup decisions gathered from multiple witnesses? Generally, the more witnesses who identify the same person from a lineup, the more likely that person is guilty. But recent theoretical evidence suggests that a greater number of witnesses identifying the same person from a biased lineup can indicate that person is actually less likely to be guilty than if there were a smaller number of witnesses identifying that person (Gunn et al., 2016). As the number of agreeing witnesses increases, the more likely that agreement is caused by the lineup bias, rather than consistent witness memories of the crime. In this thesis, I examined how unanimity and lineup bias influenced jurors’ perceptions of guilt. Subjects who saw a biased lineup gave lower ratings of guilt compared to subjects that were shown a lineup that had no obvious bias. In addition, warning subjects that a lineup was biased led them to give lower guilt ratings than subjects who did not receive a warning. Subjects who were told there were two witnesses who identified the police suspect gave higher guilt ratings than subjects who were told there was one witness who identified the police suspect, but only when the lineup was clearly not biased. Subjects’ guilt ratings were not significantly greater in conditions with more than two unanimous witnesses identifying the police suspect. It seems subjects had a limit of certainty based on changes in witness numbers alone. We also found that the way in which witness numbers were presented to subjects influenced guilt ratings. When we presented witnesses coming forward in different groups and on different days, subjects shifted their guilt ratings upwards. When the number of witnesses decreased during the experiment, subjects did not decrease their guilt ratings to the same extent as those subjects in conditions in which the number of witnesses increased by the same magnitude. This finding is consistent with the literature on confirmation bias and the story model of juror decision-making—subjects likely formed an initial belief that the identified suspect was guilty and subsequent evidence was evaluated against that belief (Nickerson, 1988; Pennington & Hastie, 1993). The finding that presenting witnesses coming forward in separate groups increased subjects’ guilt ratings adds to the literature showing that jurors are influenced by irrelevant information presented to them during a proceeding. This research also demonstrates that future research should examine strength of evidence manipulations over multiple levels—rather than as dichotomous “strong” and “weak” extremes.</p>

The Number of Unanimous Witnesses who Identify a Suspect from a Lineup Influences Mock Jurors

<p>During a criminal proceeding, jurors need to weigh up the presented evidence and determine a verdict. Research has shown that witness identification evidence is compelling to jurors, despite the fact that it can be unreliable. How reliable are the combined lineup decisions gathered from multiple witnesses? Generally, the more witnesses who identify the same person from a lineup, the more likely that person is guilty. But recent theoretical evidence suggests that a greater number of witnesses identifying the same person from a biased lineup can indicate that person is actually less likely to be guilty than if there were a smaller number of witnesses identifying that person (Gunn et al., 2016). As the number of agreeing witnesses increases, the more likely that agreement is caused by the lineup bias, rather than consistent witness memories of the crime. In this thesis, I examined how unanimity and lineup bias influenced jurors’ perceptions of guilt. Subjects who saw a biased lineup gave lower ratings of guilt compared to subjects that were shown a lineup that had no obvious bias. In addition, warning subjects that a lineup was biased led them to give lower guilt ratings than subjects who did not receive a warning. Subjects who were told there were two witnesses who identified the police suspect gave higher guilt ratings than subjects who were told there was one witness who identified the police suspect, but only when the lineup was clearly not biased. Subjects’ guilt ratings were not significantly greater in conditions with more than two unanimous witnesses identifying the police suspect. It seems subjects had a limit of certainty based on changes in witness numbers alone. We also found that the way in which witness numbers were presented to subjects influenced guilt ratings. When we presented witnesses coming forward in different groups and on different days, subjects shifted their guilt ratings upwards. When the number of witnesses decreased during the experiment, subjects did not decrease their guilt ratings to the same extent as those subjects in conditions in which the number of witnesses increased by the same magnitude. This finding is consistent with the literature on confirmation bias and the story model of juror decision-making—subjects likely formed an initial belief that the identified suspect was guilty and subsequent evidence was evaluated against that belief (Nickerson, 1988; Pennington & Hastie, 1993). The finding that presenting witnesses coming forward in separate groups increased subjects’ guilt ratings adds to the literature showing that jurors are influenced by irrelevant information presented to them during a proceeding. This research also demonstrates that future research should examine strength of evidence manipulations over multiple levels—rather than as dichotomous “strong” and “weak” extremes.</p>

Backfire effects after correcting misinformation are strongly associated with reliability

The backfire effect is when a correction increases belief in the very misconception it is attempting to correct, and it is often used as a reason not to correct misinformation. The current study aimed to test whether correcting misinformation increases belief more than a no-correction control. Furthermore, we aimed to examine whether item-level differences in backfire rates were associated with test-retest reliability or theoretically meaningful factors. These factors included worldview-related attributes, namely perceived importance and strength of pre-correction belief, and familiarity-related attributes, namely perceived novelty and the illusory truth effect. In two nearly identical experiments, we conducted a longitudinal pre/post design with N = 388 and 532 participants. Participants rated 21 misinformation items and were assigned to a correction condition or test-retest control. We found that no items backfired more in the correction condition compared to test-retest control or initial belief ratings. Item backfire rates were strongly negatively correlated with item reliability (⍴ = -.61 / -.73) and did not correlate with worldview-related attributes. Familiarity-related attributes were significantly correlated with backfire rate, though they did not consistently account for unique variance beyond reliability. While there have been previous papers highlighting the non-replicable nature of backfire effects, the current findings provide a potential mechanism for this poor replicability. It is crucial for future research into backfire effects to use reliable measures, report the reliability of their measures, and take reliability into account in analyses. Furthermore, fact-checkers and communicators should not avoid giving corrective information due to backfire concerns.

Ten-year panel data confirm generation gap but climate beliefs increase at similar rates across ages

Accumulating evidence indicates that climate change awareness and concern has increased globally, but commentators suggest a climate change generation gap whereby younger people care more about climate change than older people. Here we use a decade of panel data from 56,513 New Zealanders to test whether belief that “Climate change is real” and “Climate change is caused by humans” increased over the 2009-2018 period; and whether changes are uniform across 12 five-year birth cohorts spanning those born from 1936 to 1995. Results confirm a generation gap in mean (intercept) climate change beliefs but not in over-time increase (slope). The generation gap occurs because older cohorts started from a lower initial belief level (circa 2009), but all age cohorts increased their belief level at a similar rate over the last decade; and these results were not qualified by respondents’ gender. The findings offer hope for collective action that bridges efforts across generations.

You Know What I Know: Interviewer Knowledge Effects in Subjective Expectation Elicitation

Directly eliciting individuals' subjective beliefs via surveys is increasingly popular in social science research, but doing so via face-to-face surveys has an important downside: the interviewer's knowledge of the topic may spill over onto the respondent's recorded beliefs. Using a randomized experiment that used interviewers to implement an information treatment, we show that reported beliefs are significantly shifted by interviewer knowledge. Trained interviewers primed respondents to use the exact numbers used in the training, nudging them away from higher answers; recorded responses decreased by about 0.3 standard deviations of the initial belief distribution. Furthermore, respondents with stronger prior beliefs were less affected by interviewer knowledge. We suggest corrections for this issue from the perspectives of interviewer recruitment, survey design, and experiment setup.

Limit Points of Endogenous Misspecified Learning

We study how an agent learns from endogenous data when their prior belief is misspecified. We show that only uniform Berk–Nash equilibria can be long‐run outcomes, and that all uniformly strict Berk–Nash equilibria have an arbitrarily high probability of being the long‐run outcome for some initial beliefs. When the agent believes the outcome distribution is exogenous, every uniformly strict Berk–Nash equilibrium has positive probability of being the long‐run outcome for any initial belief. We generalize these results to settings where the agent observes a signal before acting.

Targeting the pH Paradigm at the Bedside: A Practical Approach

The inversion of the pH gradient in malignant tumors, known as the pH paradigm, is increasingly becoming accepted by the scientific community as a hallmark of cancer. Accumulated evidence shows that this is not simply a metabolic consequence of a dysregulated behavior, but rather an essential process in the physiopathology of accelerated proliferation and invasion. From the over-simplification of increased lactate production as the cause of the paradigm, as initially proposed, basic science researchers have arrived at highly complex and far-reaching knowledge, that substantially modified that initial belief. These new developments show that the paradigm entails a different regulation of membrane transporters, electrolyte exchangers, cellular and membrane enzymes, water trafficking, specialized membrane structures, transcription factors, and metabolic changes that go far beyond fermentative glycolysis. This complex world of dysregulations is still shuttered behind the walls of experimental laboratories and has not yet reached bedside medicine. However, there are many known pharmaceuticals and nutraceuticals that are capable of targeting the pH paradigm. Most of these products are well known, have low toxicity, and are also inexpensive. They need to be repurposed, and this would entail shorter clinical studies and enormous cost savings if we compare them with the time and expense required for the development of a new molecule. Will targeting the pH paradigm solve the “cancer problem”? Absolutely not. However, reversing the pH inversion would strongly enhance standard treatments, rendering them more efficient, and in some cases permitting lower doses of toxic drugs. This article’s goal is to describe how to reverse the pH gradient inversion with existing drugs and nutraceuticals that can easily be used in bedside medicine, without adding toxicity to established treatments. It also aims at increasing awareness among practicing physicians that targeting the pH paradigm would be able to improve the results of standard therapies. Some clinical cases will be presented as well, showing how the pH gradient inversion can be treated at the bedside in a simple manner with repurposed drugs.

Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory

Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D–S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.

Adaptive social networks promote the wisdom of crowds

Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network “edges” encode the computation); and 2) a local-adaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the “node” in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments.