Developing a model of guilty plea decision-making: Fuzzy-trace theory, gist, and categorical boundaries.

Abstract

Objectives: To date, most research on plea bargaining has used some form of the shadow of the trial (SOT) model to frame defendant decisions. In this research, we proposed and tested a new conceptual model of plea decision-making, based on fuzzy-trace theory (FTT), for the context in which a nondetained, guilty defendant chooses between a guilty plea or trial, where both the plea and potential trial sentence entail incarceration. Hypotheses: We predicted that plea decisions would be affected by (a) meaningful, categorical changes in conviction probability (e.g., low to moderate, moderate to high), as opposed to more granular changes within categories and (b) the presence and magnitude of categorical distinctions between plea offer and potential trial sentence rather than fine-grained differences between individual offers. Method: We conducted three vignette-based experiments (Study 1: N = 1,701, Study 2: N = 1,098, Study 3: N = 1,232), using Mechanical Turk participants. In Studies 1 and 2, we manipulated potential trial sentence and conviction probability, asking participants to indicate either the maximum plea sentence they would accept (Study 1) or whether they would plead guilty to a specific offer (Study 2). In Study 3, we manipulated plea discount and potential trial sentence and measured plea acceptance. Results: Maximum acceptable plea sentences were similar within and different between "groupings" of meaningfully similar conviction probabilities (Study 1). Plea rates were similar within and different between groupings that comprised plea offers of similarly meaningful distance from the potential trial sentence (Study 3). The results also provide insight into the plea rates that might be expected under different combinations of the independent variables (Studies 2 and 3). Conclusions: These results support a new conceptual model of plea decision-making that may be better suited to explaining case-level differences in plea outcomes than the SOT model and suggest that future research extending this model to a wider range of contexts would be fruitful. Public Significance StatementIn this research, we proposed and tested a psychologically informed model of guilty plea decisionmaking in factually guilty (mock) defendants. In three studies, we found support for the model, which suggests that defendants make decisions on the basis of broad categorical representations of perceived probability of conviction and magnitude of the differences between plea and trial sentences, rather than on more fine-grained, numerical distinctions.

Attorneys' experiences, perceptions, and plea recommendations in child sexual abuse cases.

“…Attorneys’ ratings of the strength of evidence predicted their perceived likelihood that the defendant would be convicted, which predicted whether they were likely to recommend a plea or trial. These findings are consistent with past research showing that the shadow of the trial model has some value in predicting attorneys’ decisions (Bushway et al, 2014; Kramer et al, 2007) even if it has documented limitations in predicting defendants’ decisions themselves (Wilford et al, 2021; Zottoli et al, 2023). However, two large pieces of the puzzle are still missing regarding these findings.…”

“…Differences in defendants’ guilt status (Wilford et al, 2021), their ability to make accurate mathematical calculations (Petersen et al, 2022), their conceptualization of the plea as a gain or loss (Garnier-Dykstra & Wilson, 2021), and the amount of time and location where they have to wait until trial (Clatch & Borgida, 2021) affect defendants’ plea decisions in ways traditionally unaccounted for in the model. Thus, scholars have called for expanded versions of the model and proposed alternatives that may better account for the variation in defendants’ plea decisions (Bartlett & Zottoli, 2021; Helm & Reyna, 2017; Wilford et al, 2021; Zottoli et al, 2023).…”

Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language

“…The study's results show that communications about vaccines are more widespread when they express a clear gist explaining the bottom-line meaning of the statistics rather than just the data themselves. Likewise, scholars have also used FTT as a theoretical framework to examine people's behavior across diverse contexts, such as law, medicine, public health, systems engineering, and HCI [61,88,124]. For example, in HCI, researchers have used FTT to examine people's behavior in online social tagging [93] and to improve speech-to-text interface design through gist-based communication [65].…”