Imagination inflation: Imagining a childhood event inflates confidence that it occurred

Counterfactual imaginings are known to have far-reaching implications. In the present experiment, we ask if imagining events from one’s past can affect memory for childhood events. We draw on the social psychology literature showing that imagining a future event increases the subjective likelihood that the event will occur. The concepts of cognitive availability and the source-monitoring framework provide reasons to expect that imagination may inflate confidence that a childhood event occurred. However, people routinely produce myriad counterfactual imaginings (i.e., daydreams and fantasies) but usually do not confuse them with past experiences. To determine the effects of imagining a childhood event, we pretested subjects on how confident they were that a number of childhood events had happened, asked them to imagine some of those events, and then gathered new confidence measures. For each of the target items, imagination inflated confidence that the event had occurred in childhood. We discuss implications for situations in which imagination is used as an aid in searching for presumably lost memories.     

Personality for free: Psychometric properties of a public domain Australian measure of the five‐factor model

Fifty items from Goldberg's International Personality Item Pool were compiled to form a public‐domain measure of personality, the Australian Personality Inventory (API). Data from a random community sample (N = 7615) and a university‐based sample (N = 271) were used to explore psychometric properties of this 50‐item measure of the five‐factor model of personality (FFM). In both samples, internal reliabilities were adequate. In the university‐based sample an appropriate pattern of convergent and divergent relationship was found between scale scores and domain scores from the NEO Five‐Factor Inventory. After adjusting for an apparent response set (mean response across items), exploratory factor analyses clearly retrieved the FFM in both samples. It is provisionally concluded that raw scale scores from the API provide reliable estimates of the FFM, but adjustment for mean response across the 50 items might clarify the five‐factor structure, especially in less educated samples.     

When Photographs Create False Memories

ABSTRACT— Photographs help people illustrate the stories of their lives and the significant stories of their society. However, photographs can do more than illustrate events; in this article, we show that photographs can distort memory for them. We describe the course of our "false-memory implantation" research, and review recent work showing that photographs can sometimes increase—while other times decrease—false memories. First, we discuss research showing that a doctored photo, showing subjects taking a completely fictitious hot-air-balloon ride, can cultivate false memories for that experience. We hypothesize that the photograph helps subjects to imagine details about the event that they later confuse with reality. Second, we show that although photographs are indeed powerful sources of influence on memory, they are not necessarily as powerful as narrative. In fact, in certain circumstances, photographs might constrain imagination. Third, we discuss research showing that true photographs can also cultivate false memories. Finally, we present recent work showing that photographs can create false memories for current events.     

People consider reliability and cost when verifying their autobiographical memories

Because memories are not always accurate, people rely on a variety of strategies to verify whether the events that they remember really did occur. Several studies have examined which strategies people tend to use, but none to date has asked why people opt for certain strategies over others. Here we examined the extent to which people's beliefs about the reliability and the cost of different strategies would determine their strategy selection. Subjects described a childhood memory and then suggested strategies they might use to verify the accuracy of that memory. Next, they rated the reliability and cost of each strategy, and the likelihood that they might use it. Reliability and cost each predicted strategy selection, but a combination of the two ratings provided even greater predictive value. Cost was significantly more influential than reliability, which suggests that a tendency to seek and to value “cheap” information more than reliable information could underlie many real-world memory errors.     

Building false memories without suggestions

People can come to remember doing things they have never done. The question we asked in this study is whether people can systematically come to remember performing actions they never really did, in the absence of any suggestion from the experimenter. People built LEGO vehicles, performing some steps but not others. For half the people, all the pieces needed to assemble each vehicle were laid out in order in front of them while they did the building; for the other half, the pieces were hidden from view. The next day, everyone returned for a surprise recognition test. People falsely and confidently remembered having carried out steps they did not; those who saw all the pieces while they built each vehicle were more likely to correctly remember performing steps they did perform but equally likely to falsely remember performing steps they did not. We explain our results using the source monitoring framework: People used the relationships between actions to internally generate the missing, related actions, later mistaking that information for genuine experience.     

Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by  Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach.     

Network models for social influence processes

This paper generalizes thep* class of models for social network data to predict individual-level attributes from network ties. Thep* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so constructp* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.This research was supported by grants from the Australian Research Council. The authors would like to acknowledge the help of Stanley Wasserman, Janice Langan-Fox and Larry Hubert, and would like to thank four anonymous reviewers for their helpful comments. Earlier versions of this article were presented at the North American Conference of the Psychometric Society, Lawrence, Kansas, June, 1999, and at the Australasian Mathematical Psychology Conference, Brisbane, Australia, December 1999.