Effects of implicit fear of failure on cognitive processing: A diffusion model analysis

Abstract

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Dynamic Reciprocal Relations of Achievement Goals with Daily Experiences of Academic Success and Failure: An Ambulatory Assessment Study

“…Alternatively, a resource depletion account (Ellis & Ashbrook, 1988) might also explain the association of a performance-avoidance goal orientation and subsequent experiences of failure. Sepcifically, fear of failure (a postulated person-level antecedent of performance-avoidance goal orientations; see Dinger et al, 2013) has been suggested to reduce the speed of information processing, in particular after negative performance feedback (Lerche et al, 2018). A similar mechanism might be responsible for the effect of a performance-avoidance goal orientation on daily failure: A more negative selfconcern on days with a higher performance-avoidance goal orientation might interfere with cognitive resources available throughout the day.…”

Financial incentives facilitate the neural computation of prosocial decisions stronger in low empathic individuals

“…Outside the domain of prosocial decisions, there is evidence that the efficiency of decisions (captured by the v -parameter) is affected by individual differences in emotions (Lerche et al ., 2018; Roberts and Hutcherson, 2019; Aylward et al ., 2020; Thompson and Steinbeis, 2021). For example, according to the results of Thompson and Steinbeis (2021), individuals with greater state anxiety show increased v -parameter on fearful face trials.…”

“…The analyses were conducted using the python implementation of HDDM (Wiecki et al ., 2013). Based on previous studies showing changes in drift rate (Lerche et al ., 2018; Roberts and Hutcherson, 2019; Aylward et al ., 2020; Thompson and Steinbeis, 2021) and the starting point (White et al ., 2018) if decisions are made in different affective states, we assumed that these two parameters might also be affected by motivational states. However, given that the modulation of affect and motivation is not the same, effects on the third parameter (the a -parameter) are also possible.…”

“…Extending the classical DDM approach, a recent model has proposed that the evidence in favour of one or another choice alternative might be shaped by affective and motivational states (Roberts and Hutcherson, 2019). Supporting this assumption, affective states have been found to change central parameter of the choice process such as the drift rate (v-parameter; (Lerche et al, 2018;Roberts and Hutcherson, 2019;Aylward et al, 2020;Thompson and Steinbeis, 2021)) and the starting point (z-parameter, (White et al, 2018)). Inspired by these results, we assumed that the evidence in favor of a prosocial choice might be different in different motivational states (i.e., induced by empathy and its potential interaction with the incentive), reflected by a changes in the drift rate and/ or the starting point.…”

“…If this was true, the frequency and efficiency of prosocial decisions should be higher in the empathy-bonus compared to the empathy-alone condition. Specifying the potential effect of the incentive on the prosocial choice process, the DDM proposes that an incentive-related facilitation of prosocial choices may originate A) from an increased speed of information accumulation, i.e., an increased drift rate ( v -parameter (Lerche et al ., 2018; Roberts and Hutcherson, 2019; Aylward et al ., 2020; Thompson and Steinbeis, 2021), an enhancement of participants’ initial preference to choose the prosocial option, i.e, a shift of the starting point towards the prosocial decision boundary ( z -parameter (White et al ., 2018)), or from an enhancement of the v - as well as the z -parameter in the empathy-bonus compared to the empathy-alone condition,…”