When Eyewitnesses Talk

When two people witness an event, they often discuss it. Because memory is not perfect, sometimes this discussion includes errors. One person's errors can become part of another person's account, and this proliferation of error can lead to miscarriages of justice. In this article, we describe the social and cognitive processes involved. Research shows how people combine information about their own memory with other people's memories based on factors such as confidence, perceived expertise, and the social cost of disagreeing with other people. We describe the implications of this research for eyewitness testimony

Measuring Empathizing and Systemizing with a Large US Sample

A large number of people completed one of two versions of the empathizing quotient (EQ) and systemizing quotient (SQ). One version had the negatively phrased items all re-worded. These re-worded items were answered more rapidly than the original items, and for the SQ produced a more reliable scale. Subjects gave self-assessments of empathizing and systemizing, and these were moderately correlated, r≈.6, with their respective quotients. Females had on average higher empathizing scores and males had on average higher systemizing scores. If a female-male pair was chosen at random, the female would have the higher empathizing score about two-thirds of the time, and the males would have the higher systemizing score about two-thirds of the time

Factors influencing memory conformity

The topic of co-witness discussions has become an important focus in eyewitness research due to the acknowledgment that these discussions can lead to transfers of misinformation. One person's memory of an event can influence that of another in a process often referred to as "memory conformity". The aim of this thesis was to try to fill some of the gaps in the memory conformity literature.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Manipulating power can affect memory conformity

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Examining mixed unknown distributions (mud)

A function, written in R, for testing whether the distribution of responses in one condition can be considered a combination of the distributions from two other conditions is described. The important aspect of this function is that it does not make any assumptions about the shape of the distributions. It is based on the Kolmogorov—Smirnov D statistic. The function also allows the user to test more specific and, hence, more statistically powerful hypotheses. One hypothesis, that the mixture does not capture the middle third of the distribution, is included as a built-in option, and code is provided so that other alternatives can easily be run. A power analysis reveals that the function is most likely to detect a difference between the combined conditions’ distribution and the other distribution when the center of the other distribution is near the midpoint of the two original distributions. Critical p values are estimated for each set of distributions, using bootstrap methods. An example from human memory research, exploring the blending hypothesis of the misinformation effect, is used for illustrative purposes

The co-witness misinformation effect: memory blends or memory conformity?

For 30 years hundreds of researchers have shown participants videos and slide-sequences of events, presented the participants with misleading information, and found that this misinformation distorted their memories. The purpose of this study was to establish whether those misled participants are reporting a memory blend of the two sources of information or whether they are simply complying with the post-event information. A total of 92 participants were shown one of two versions of six different videos, which included some subtle differences. After having watched each video individually, participants were paired with someone who had seen the other version and they discussed the clips together. They then individually answered questions about the videos, and their responses showed that some of the distorted memories were blends of the original information and the post-event information. The implications of these findings are discussed

Memory Conformity Between Eyewitnesses

More than a century of psychology research has shown that memory is fallible. People’s memory can be influenced by information encountered after an incident has been witnessed—so-called postevent information, or PEI. In everyday life, one of the most common ways to encounter PEI is when individuals who have shared the same experience discuss this with one another. In the case of witnessing a crime, individuals might be particularly motivated to discuss what happened, and who was involved, because of the significance of the event. The PEI encountered during this discussion with a co-witness might be largely consistent with one’s own memories of the event. However, some details may differ either because one witness has remembered something differently, has paid attention to different details, or has simply made an honest mistake in his or her own account. A common finding within eyewitness-memory literature is that exposure to PEI that is inconsistent with a person’s own memory can affect the ability to subsequently report details of the originally encoded event

Examining mixed unknown distributions (mud)

A function, written in R, for testing whether the distribution of responses in one condition can be considered a combination of the distributions from two other conditions is described. The important aspect of this function is that it does not make any assumptions about the shape of the distributions. It is based on the Kolmogorov—Smirnov D statistic. The function also allows the user to test more specific and, hence, more statistically powerful hypotheses. One hypothesis, that the mixture does not capture the middle third of the distribution, is included as a built-in option, and code is provided so that other alternatives can easily be run. A power analysis reveals that the function is most likely to detect a difference between the combined conditions’ distribution and the other distribution when the center of the other distribution is near the midpoint of the two original distributions. Critical p values are estimated for each set of distributions, using bootstrap methods. An example from human memory research, exploring the blending hypothesis of the misinformation effect, is used for illustrative purposes

The distribution of mean responses for males and females for the AQ.

<p>The distributions are drawn using a Gaussian kernel estimation method (the R default).</p