Unflattering computer feedback was rated as less fair than human feedback

When humans and computers induce social stress through negative feedback: Effects on performance and subjective state. S. Thuillard et al. Computers in Human Behavior, March 16 2022, 107270. https://doi.org/10.1016/j.chb.2022.107270

Highlights

• Negative performance feedback from both human and computer did not impair subsequent performance on a wide range of tasks.

• Negative feedback led to increased levels of stress and negative affect but did not influence state self-esteem.

• Computer feedback was rated as less fair than human feedback.

Abstract: People increasingly work with autonomous systems, which progressively take over functions previously performed exclusively by humans. This may lead to situations in which automated agents give negative performance feedback, which represents an important work-related social stressor. Little is known about how negative feedback provided by computers (as opposed to humans) affects human performance and subjective state. A first experiment (N = 60) focused on the influence of human feedback on performance. After participants had performed a cognitive task, they received a manipulated performance feedback (either positive or negative) from a human (comparing to a control with no feedback) and subsequent performance on several cognitive tasks and the participants' subjective state was measured. The results showed that while negative feedback had a negative influence on several subjective state measures, performance remained unimpaired. In a second experiment (N = 89), participants received manipulated negative feedback by a human or by a computer (or no feedback at all) after having completed an ability test. Subsequent performance was measured on attention tasks and creativity tasks and participants' subjective state was assessed. Although participants felt stressed by both negative computer and human feedback, subsequent performance was again not impaired. However, computer feedback was rated as being less fair than human feedback. Overall, our findings show that there are costs of protecting one's performance against negative feedback and they call for caution regarding the use of negative feedback by both human and automated agents in work settings.

Keywords: social stressNegative feedbackPerformanceComputer feedbackAutomationInterpersonal fairness

The claim that personality is more important than intelligence in predicting important life outcomes has been greatly exaggerated

The claim that personality is more important than intelligence in predicting important life outcomes has been greatly exaggerated. Chen Zisman, Yoav Ganzach. Intelligence, Volume 92, May–June 2022, 101631. https://doi.org/10.1016/j.intell.2022.101631

Highlights

• We conduct a replication of Borghans, Golsteyn, Heckman and Humphries (PNAS, 2016).

•We show personality as less important than intelligence in predicting life outcomes.

•For pay the predictive validity of intelligence twice as high as this of personality.

• For educational attainment and grades it was 4.4 and 5.2 as high.

• This finding contradict BGHH who argued that personality is more important.

Abstract: We conduct a replication of Borghans, Golsteyn, Heckman and Humphries (PNAS, 2016) who suggested that personality is more important than intelligence in predicting important life outcomes. We focus on the prediction of educational (educational attainment, GPA) and occupational (pay) success, and analyze two of the databases that BGHH used (the NLSY79, n = 5594 and the MIDUS, n = 2240) as well as four additional databases, (the NLSY97, n = 2962, the WLS, n = 7646, the PIAAC, n = 3605 and the ADD health, n = 3553; all databases are American except of the PIAAC which is German). We found that for educational attainment the average R2 of intelligence was .232 whereas for personality it was .053. For GPA it was .229 and .024, respectively and for pay it was .080 and .040, respectively.

Keywords: IntelligencePersonalityThe big-fiveLife outcomesEducational attainmentIncome

What Are the Necessary Conditions for Wisdom? Examining Intelligence, Creativity, Meaning-Making, and the Big-Five Traits

What Are the Necessary Conditions for Wisdom? Examining Intelligence, Creativity, Meaning-Making, and the Big-Five Traits . Mengxi Dong, Marc A. Fournier. Collabra: Psychology (2022) 8 (1): 33145. https://doi.org/10.1525/collabra.33145

Abstract: We investigated whether intelligence, creativity, meaning-making, and the Big-Five traits are necessary conditions for wisdom. We used Amazon’s TurkPrime to recruit 298 participants who ranged from 20 to 73 years of age. Participants completed measures of intelligence, creativity, meaning-making, and the Big-Five traits, along with a battery of self-report and performance wisdom measures. We used principal component analyses to reduce the wisdom battery into self-report and performance wisdom components, followed by necessary condition analysis and segmented regressions to examine whether the cognitive and personality variables under consideration here were necessary conditions for each wisdom component. We found that intelligence was necessary for the performance wisdom component whereas the Big-Five traits were necessary for the self-report wisdom component. This study is the first to demonstrate that high levels of wisdom are unlikely without some level of intelligence and adaptive personality traits.

Keywords:wisdom, intelligence, personality, necessary condition analysis, segmented regression

Necessary Conditions for Wisdom

We found that the necessary conditions for wisdom largely depended on the form of wisdom in question. Intelligence was the only necessary condition for wisdom performance. Specifically, a score above 20 on the WPT-Q, which was close to the population average on the test (Wonderlic, Inc., 2004), was necessary for scoring above average on the performance wisdom component (i.e., a component score above 1.0). However, although the association between wisdom and intelligence was positive before the estimated breakpoint at 21 and negative after it, this breakpoint was not statistically significant, possibly due to inadequate sample size. The threshold hypothesis was thus not supported. We conclude that while intelligence is a necessary condition for the kind of wisdom captured by the performance wisdom component, more empirical evidence is needed before any conclusions can be drawn about the threshold hypothesis. For self-reported wisdom, however, intelligence was not necessary. Although a significant breakpoint existed in the relation between intelligence and the self-report wisdom component, the slopes before and after the breakpoint were not significantly different from zero. It is possible that the slopes would be statistically significant with larger sample sizes; alternatively, the statistical significance of the breakpoint could indicate a Type I error. Future research with larger sample sizes should thus be conducted to cross-validate the results. We concluded that while intelligence might be required for wisdom performance, it was not necessary for self-reported wisdom.

In contrast to intelligence, the Big-Five personality traits were necessary for self-reported wisdom, but not for wisdom performance. The threshold hypothesis was not supported for any of the traits, suggesting that the relationships between these traits and the self-report wisdom component were linear. There are at least two ways to interpret the finding that the Big-Five personality traits were necessary for high scores on the self-report wisdom component. First, the findings might corroborate the proposition that wisdom is an adaptive configuration of personality characteristics (e.g., Ardelt et al., 2019). This proposition has mainly been espoused by researchers who have developed and routinely used self-report measures of wisdom. If this proposition is true, it would explain our findings.

Alternatively, the strong correlations between the Big-Five traits and the self-report wisdom component could be due to common method variance, which would suggest that necessity effects pertained to the self-report method (i.e., it was necessary to score high on one self-report measure in order to score high on another) rather than to the constructs (i.e., it was necessary to be high on a trait in order to be high on wisdom). Although similarity in measurement method does not automatically lead to inflated correlations (e.g., Spector, 2006), measures sharing similar methods can be prone to similar systematic biases, which in turn can inflate the correlation between them. For instance, meta-analytic studies have demonstrated that social desirability is a systematic response bias that is correlated with emotional stability, extraversion, conscientiousness (Ones et al., 1996) and self-report wisdom measures (Dong et al., 2022), suggesting that it could have contributed to the differences between the wisdom components in the current study. However, as social desirability was not measured, we could not confirm whether it had indeed led to inflated correlations. Future studies should thus re-examine whether the Big-Five personality traits constitute necessary conditions for self-reported wisdom while ruling out the effect of common method variance. This can be achieved in at least two ways. First, common method variance can be statistically controlled. One way to achieve this is by measuring systematic response biases (e.g., social desirability) that affect both self-report wisdom measures and measures of the Big-Five personality traits. Systematic response biases (e.g., halo) can also be modelled and controlled for using statistical techniques such as structural equation modeling. Alternatively, methods other than self-report, such as informant reports, can be used to assess the Big-Five personality traits.

Non-Necessary Predictors of Wisdom

Our findings further suggest that while some characteristics, such as creativity and meaning-making, are correlated with wisdom, they are not necessary conditions for it. Of these constructs, meaning-making has been theorized as a resource for wisdom (e.g., Glück et al., 2019). It is important to note that the findings of the current study do not rule out this possibility, as not all resources are necessary conditions. For instance, it is possible that the absence of meaning-making can be compensated by the presence of another resource, or that rather than being a necessary condition for wisdom, meaning-making may be a sufficient condition (i.e., it is impossible to be unwise if one has a strong tendency to make meaning). Future studies should therefore explore the ways in which meaning-making serves as a resource for wisdom.

Interpreting the Wisdom Components

It is important to note that although we interpreted the two wisdom components as representing performance and self-report wisdom, there are alternative interpretations. One such interpretation is to consider the components as representing general wisdom and personal wisdom. General wisdom refers to insights into life in general; it is the kind of wisdom that manifests when advising others. Personal wisdom refers to insights into one’s own life. The measures constituting the self-report wisdom component are all personal wisdom measures, whereas the measures constituting the performance wisdom component are all general wisdom measures. This perfect overlap makes it difficult to evaluate the appropriateness of either interpretation. In favor of the personal vs. general wisdom interpretation are the componential loadings of the Bremen wisdom paradigm and the 3DWS, the two measures that did not meet the .40 cut-off to be included in either component. Specifically, both measures assess personal wisdom and loaded more strongly on the self-report wisdom component (.39 and .38, respectively) than on the performance wisdom component (.16 and .09, respectively). However, as these loadings were low, we concluded that the evidence for the two components representing general and personal wisdom was not strong. Furthermore, if the self-report wisdom component actually represented personal wisdom, then it should have been more strongly correlated with meaning-making, as the lessons and insights learnt through one’s experiences should lead to more personal wisdom by transforming how one interacts with the world. However, meaning-making was instead more strongly correlated with the performance wisdom component and had no significant correlation with the self-report wisdom component, a pattern of results that is more in line with the self-report vs. performance interpretation of the components than with the personal vs. general wisdom interpretation.

Limitations and Future Directions

The current study has several limitations, all of which can inform directions for future investigations. First, the current study only offers preliminary insights that should be replicated. Specifically, the current study’s frequentist approach to statistical inferences necessitates replications to ensure that the Type I error rate is on par with the alpha level (e.g., Mayo, 2018). Furthermore, the current study might be underpowered to detect the necessity effects and changes in slope, as the sample size was planned based on the magnitude of small-to-medium effect sizes commonly found in personality and social psychology, rather than on the magnitudes of necessity effects and changes in slopes, as we had no way to reasonably estimate the latter beforehand. Future replications of the current study could use simulations to determine the sample size needed to detect the effect sizes found in the current study.

Second, the results of the current study might be dependent on the principal components extracted, suggesting that replication studies will have different results if different wisdom components are extracted. Of concern is the fact that two commonly used measures of wisdom, the 3DWS and the Bremen wisdom paradigm, were excluded from the analyses that informed the key conclusions due to low componential loadings. As the 3DWS and the Bremen wisdom paradigm are prominent wisdom measures that meaningfully contribute to the discourse on the definition and operationalization of wisdom, not including these measures may limit the generalizability of the current findings to the construct of wisdom. Findings of the current study should thus be corroborated by other datasets before more definite conclusions can be drawn regarding the necessary conditions for wisdom.

Third, the current study measured intelligence using the WPT-Q, which could not distinguish between crystallized and fluid intelligence. The WPT-Q was chosen as it was the only reliable, valid, and cost-effective instrument suitable for online, unsupervised administration. However, as crystallized intelligence, or the knowledge of the world and learnt operations, has been shown to be more strongly associated with wisdom than fluid intelligence, or the general ability to solve novel problems that is independent of learning (e.g., Dong et al., 2022; Glück et al., 2013; Grossmann et al., 2012; Mickler & Staudinger, 2008; Pasupathi et al., 2001; Staudinger et al., 1997), the inability to distinguish between the two aspects of intelligence limits the scope of the current study. Future studies should further explore the necessity of intelligence for wisdom by examining fluid and crystallized intelligence separately.

Fourth, in order to limit the length of the study protocol and avoid participant fatigue, meaning-making was only measured for one specific situation. It is possible that this one state measure of meaning-making might not accurately reflect participants’ general tendencies to make meaning out of life experiences or represent individual differences in the construct. This may then affect our ability to detect significant necessity effects of meaning-making on wisdom. Future studies should thus re-examine the necessity of meaning-making for wisdom using measures that can better reflect individuals’ general tendencies to make meaning and individual differences in the construct.

Fifth, findings of the current study should be interpreted as probabilistic and not categorical. Given that the current study examined a sample drawn from the population, not the population itself, significant necessity effects indicated that high levels of wisdom were relatively unlikely, but not impossible, with low levels of certain cognitive and personality characteristics. It is thus incorrect to conclude based on the present findings that low levels of these characteristics categorically preclude one from being wise.

Finally, the cross-sectional nature of the data and the statistical analyses employed dictate that the current study is unable to offer any insights into the causal relationships between the cognitive and personality variables on the one hand and wisdom on the other hand. Specifically, neither the NCA nor the segmented regression analysis make any causal assumptions and their results cannot be used to draw causal conclusions. Furthermore, in logic, the statement that one variable is a necessary condition for another variable is not a statement of causal relations. Given the nature of its data and analytical techniques, therefore, the results of the current study should not be interpreted as indicating that the possession of certain cognitive and personality characteristics causes, or even temporally proceeds, wisdom attainment. Instead, results of the present study simply suggest that low levels of certain cognitive and personality characteristics are associated with a low (but not zero) probability of having high levels of wisdom. We acknowledge, however, that when researchers discuss intelligence and certain personality traits as necessary conditions for wisdom, the implication is often that these conditions are necessary because they are resources that can facilitate wisdom development and manifestation. While findings of the current study are consistent with this view, they cannot speak to the causal implications of it.