A self-regulatory approach to rational decisions: The implemental mindset optimizes economic decision making in situations requiring belief updating

Abstract

We examined the effects of the deliberative and implemental mindset on decision behavior when decisions require Bayesian updating of beliefs. In the decision task, in some situations Bayes' rule conflicts with a simple reinforcement‐based heuristic. Previous research showed that in these situations individuals rely on the faulty heuristic, hence committing many decision errors. We investigated deliberative and implemental mindset effects on the reinforcement heuristic. Results showed that the implemental mindset improved rational decision making. Participants in an implemental mindset exhibited decreased rates of reinforcement errors compared to the deliberative mindset, the neutral mindset, and the baseline condition. We further found that, compared to the deliberative mindset, the implemental mindset was also more beneficial for decision performance in situations where Bayes' rule and reinforcement were aligned.

Sensitivity to Sunk Costs Depends on Attention to the Delay

“…Importantly, while deliberating, an individual is contemplating the pros and cons of an option, and is more receptive to all kinds of information; in the implemental state they become goal-oriented and become less attentive to new information (see Gollwitzer, 2012 ; Achtziger and Gollwitzer, 2018 for overview). Recent studies in this area of research have found that individuals who are manipulated into a deliberative mindset were slower in their decision making in a gamble ( Ludwig et al, 2020 ), while those in an implemental mindset were better suited to learning during a reinforcement learning task ( Li et al, 2019 ). No prior research in mindset theory has specifically tested sunk cost sensitivity, but it is possible that these states may also inform decisions to quit after re-evaluating less valuable options.…”

The role of motivation and volition in economic decisions: Evidence from eye movements and pupillometry

“…We refrained from inducing a neutral mindset in which participants work on a simple, generic task prior to the measurement of dependent variables (e.g., report on everyday activities; Harmon‐Jones & Harmon‐Jones, ; Li et al, ). The purpose of a neutral mindset is to control for the potentially confounding impact of the increased workload prior to the measurement of the dependent variables and to account for the time that it takes to induce the mindsets before participants proceed to the main task.…”

“…Moreover, the neutral mindset was included in a recent study as a control condition in addition to a baseline measurement to test potential effects of the neutral mindset on economic decision making. It was discovered that the neutral mindset condition and the baseline did not differ in terms of performance in the decision task (see Li et al, ). These previous studies demonstrated that mindset effects on several dependent measures were qualitatively different from a neutral mindset and a baseline measurement.…”

“…Some studies have already used the deliberative and implemental mindsets to explore the effects of motivational and volitional processes on decisions (V. Brandstätter, Giesinger, Job, & Frank, 2015;Büttner et al, 2014;Hügelschäfer & Achtziger, 2014;Keller & Gollwitzer, 2017;Li, Hügelschäfer, & Achtziger, 2019;Rahn et al, 2016a;Rahn, Jaudas, & Achtziger, 2016b). For instance, Li et al (2019) observed that rational decision making was increased in the implemental mindset compared with a baseline and a deliberative mindset condition. Similarly, Rahn et al (2016a) investigated mindset effects on risk taking in an incentivized ring toss task (Atkinson & Litwin, 1960).…”

“…Moreover, the neutral mindset was included in a recent study as a control condition in addition to a baseline measurement to test potential effects of the neutral mindset on economic decision making. It was discovered that the neutral mindset condition and the baseline did not differ in terms of performance in the decision task (see Li et al, 2019).…”