Abstract
Whereas previous studies suggest that individuals with high implicit fear of failure (FF) perform worse on various indicators of general performance, the underlying mechanisms of this effect have not yet been understood. In our experimental study, 280 participants worked on a binary color discrimination task. Half of the participants were frustrated by means of negative performance feedback, while the control group received mainly positive feedback. We employed a diffusion model analysis (Ratcliff in Psychol Rev 85(2):59–108, 1978) to disentangle the different components involved in the execution of the task. Results revealed that participants in the frustration condition adopted more conservative decision settings (threshold separation parameter of the diffusion model). Besides, high implicit FF was related to slow information accumulation (drift), and this relation was stronger in the frustration condition. Participants with higher FF further showed reduced learning rates during the task. Task related intrusive thoughts are discussed as mechanism for reduced performance of high FF individuals. We conclude that diffusion model analyses can contribute to a better understanding of the mechanisms underlying the effects of psychological motives.
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Notes
In addition to these four main diffusion model parameters (ν, a, t0, z), the model is often expanded by assuming intertrial variabilities of drift rate, starting point and non-decision time (e.g., Ratcliff and Rouder 1998; Ratcliff and Tuerlinckx 2002). These variability parameters, however, cannot be estimated as accurately as the main diffusion model parameters, and they are typically of less psychological interest (especially, the intertrial variabilities of starting point and drift rate; Lerche and Voss 2016; Lerche et al. 2017).
Study 3 differed from studies 1 and 2 in that a daily-diary part preceded the laboratory session.
Due to the intended heterogeneity of the ambiguous pictures used in implicit motive measures, it is common to find lower internal consistencies in comparison with explicit motive measures. It has been demonstrated that the lower reliability does not compromise the construct validity of the measures and it has been argued that the assumptions of classical test theory are not appropriate for projective motive measures (e.g., Atkinson 1981; Reuman 1982). The stability of overall sum-scores, which is satisfactory, is seen as more important than internal consistency (e.g., Schultheiss et al. 2008). Furthermore, recent studies that applied dynamic Thurstonian item response theory have shown good reliability of both the PSE (Lang 2014) and the Operant Motive Test (Runge et al. 2016).
Fast-dm-30 can be downloaded from http://www.psychologie.uni-heidelberg.de/ae/meth/fast-dm/index-en.html. Note that in addition to the command line program, recently, also a graphical user interface developed by Stefan Radev is available.
We are aware that n = 50 is a small trial number for diffusion model analyses. However, recent simulations have shown that the diffusion model can, under certain conditions, supply reliable results even for such small trials numbers (Lerche et al. 2017).
The focus of our study was on the FF component of the achievement motive that we aimed to arouse with the false feedback manipulation. Thus, we did not have any hypotheses regarding the influence of the hope component of the achievement motive (hope for success; HS). Nevertheless we cannot definitely exclude that HS was also aroused by our manipulation. Therefore, in a set of further analyses, we conducted all regression analyses with HS instead of FF. For one dependent variable, namely the difference in accuracy rates between the two trial blocks, we found a significant effect of the condition × HS interaction (b = − 0.02, p = .016). More specifically, in the frustration group, individuals higher in HS improved more in terms of accuracy rate from the first to the second block (b = 0.010, p = .029) whereas in the control group there was a tendency for the other way round (i.e., less improvement for the individuals higher in HS, b = − 0.006, p = .216).
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Lerche, V., Neubauer, A.B. & Voss, A. Effects of implicit fear of failure on cognitive processing: A diffusion model analysis. Motiv Emot 42, 386–402 (2018). https://doi.org/10.1007/s11031-018-9691-5
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DOI: https://doi.org/10.1007/s11031-018-9691-5