Kogan et al., 2014 ; Stellar et al., 2015 ). Agreeableness is a personality trait that is closely related to compassion and compassion is a personality trait that is closely related to empathetic concern ( Goetz et al., 2010 ), a distinct dimension of empathy that has been regarded as an important precursor of prosocial behavior ( Batson and Shaw, 1991 ). Inter-individualdifferences in vmHRV may, thus, be differentially associated with distinct empathy dimensions, implying the possibility of positive associations with empathy dimensions that facilitate prosocial behavior, such as empathetic concern (e.g., Toi and Batson, 1982 ; Batson et al., 1983 ), and negative associations with empathy dimensions that impair prosocial behavior, such as empathetic distress (e.g., Toi and Batson, 1982 ; Batson et al., 1983 ). In the present study, we were unable to test this possibility because the psychometric properties of our empathy questionnaire argued against the use of the questionnaire’s subscales in the respective analyses. Future studies should, thus, employ empathy questionnaires with psychometrically sound subscales to further elucidate the association between inter-individualdifferences in vmHRV and inter-individualdifferences in empathy. With respect to alexithymia it is noteworthy that individuals with high vmHRV report fewer difficulties in identifying or describing their own emotional and mental states than individuals with low vmHRV (e.g.,
Understanding the basic mechanisms and dynamics of motivational-affective cue processing is highly relevant for understanding more complex phenomena of motivation and affect. For example, variations in the sensitivity of appetitive and aversive affective-motivational systems may predispose individuals to respond with higher motivational intensity to affective cues, thus giving rise to personality traits like positive and negative affectivity, respectively (Watson & Tellegen, 1985; Watson, Wiese, Vaidya, & Tellegen, 1999). Individuals may differ in their trait anxiety or fearfulness because of differences in previous aversive learning experiences (Barlow, Ellard, Sauer-Zavala, Bullis, & Carl, 2014). These putative relationships between processing of motivational-affective cues and stable dispositions do not only stress the importance of understanding mechanisms in order to explain individualdifferences, but also imply that individualdifferences can be used to investigate mechanisms. Moreover, maladaptive alterations in these mechanisms may be associated with various psychopathologies. For example, a core symptom of major depression is anhedonia, i.e. reduced sensitivity to appetitive stimuli (L. A. Clark, Watson, & Mineka, 1994; E. M. Mueller, Panitz, Pizzagalli, Hermann, & Wacker, 2015). A wide variety of anxiety disorders are characterized by maladaptive processing of motivational-affective cues. The intensity of fear and anxiety reactions may be exaggerated if non-threatening stimuli, e.g. small, non-venomous spiders in spider phobia (Michalowski, Pané-Farré, Löw, & Hamm, 2015) or heart palpitations in panic disorder (D. M. Clark et al., 1997; Ehlers & Margraf, 1989), are falsely interpreted as threat cues.
The present thesis presents a synopsis of studies investigating the relationship between individualdifferences, in particular sex differences, in cognitive performance and brain structure and function. The first study, “Sex differences in neural efficiency: Are they due to the stereotype threat effect?” was published in Personality and IndividualDifferences and examines EEG data from adolescent girls and boys with varying intellectual abilities. It does not only demonstrate correlations between cortical activation and intelligence measures, but also implicitly activated stereotype-related changes in brain activity when working on a visuo- spatial task. The study showed that stereotype threat cannot explain sex differences in neural efficiency, but that it influences the brain activity during visuo-spatial tasks. The second study, “Neural efficiency as a function of task demands” was published in Intelligence, and presents fMRI data from 58 persons with lower versus higher intelligence respectively working on easy and difficult inductive reasoning tasks having the same person-specific task difficulty. No differences in task performance and brain activation were found between more and less intelligent individuals when performing tasks with the same person-specific task difficulty. Interestingly, when individuals with lower versus higher intelligence worked on the same tasks (same sample-based task difficulty), differences in task performance and brain activation were found, in line with the neural efficiency hypothesis. The third study, “Sex differences in the IQ-white matter microstructure relationship: A DTI study” was published in Brain and Cognition, and presents DTI data from 63 women and men with lower or
The main idea behind these association studies could be summed up with the term “candidate gene approach”. In this approach, it is assumed that individualdifferences in regions of the genetic code that play a role in the manifold processes linked to neurotransmitter metabolism might also have an influence on personality and human behavior. For example, as selective serotonin reuptake inhibitors (SSRIs) improve depressive symptoms, genetic variations of the gene coding for the serotonin transporter (called SLC6A4), where SSRIs bind, might also play a role in understanding individualdifferences in personality traits linked to negative emotionality (having higher neuroticism is linked with a higher likelihood of also suffering from depression, Lahey, 2009 ).
ric analysis was based on the CC outline in a midsagittal slice from the 3D FLASH image volume. Each 3D data set was subject to AC- PC (anterior commisure – posterior commisure) alignment using the AFNI software package. This was applied to eliminate variability in CC shape and cross-sectional area because of individualdifferences in head position in the scanner and the orientation of the scan plane used to generate the midsagittal image. After determining the mid- sagittal slice, the outline of the CC was manually traced using the specially designed program in the XITE package for outlining regions of interest. The total area (in mm²) of the CC region was calculated from the number of pixels inside the closed contour multiplied by the pixel size. The total midsagittal callosal area was subdivided into seven subregions after having manually chosen the inflexion point at the anterior point of the inner convexity. This subdivision was identi- cal to that of Witelson . The maximal CC length was measured parallel to the AC-PC line to allow for inter-individual comparisons. The CC was then divided into halves, thirds and the posterior fifth with the following regions: R1 (rostrum), R2 (genu), R3 (rostral body), R4 (anterior midbody), R5 (posterior midbody), R6 (isthmus), R7 (splenium), as shown in Figure 3.
Implicit learning broadly describes learning without awareness (Cleeremans, Destrebecqz, & Boyer, 1998). More precisely, implicit information acquisition characterizes learning processes that do not require intention or conscious awareness of what is learned. Implicit learning paradigms generate incidental experiences with associated stimuli that participants behaviorally adapt to despite usually not being able to report the underlying principle. The serial reaction time task (SRTT) is a commonly used paradigm that infers visuomotor learning from reaction time differences to random versus sequential stimuli (Robertson, 2007). As opposed to cognitive tasks that require higher cognitive processing, implicit information acquisition has been characterized as a complex form of priming (Cleeremans et al., 1998). Based on rather small empirical associations with intelligence measures, implicit learning has been dissociated from explicit cognitive abilities (Danner, Hagemann, Schankin, Hager, & Funke, 2011; Gebauer & Mackintosh, 2007; Kaufman, DeYoung, Gray, Jiménez, Brown, & Mackintosh, 2010; Reber, Walkenfeld, & Hernstadt, 1991). Instead, Kaufman et al. (2010) found implicit sequence learning more strongly correlated with self-reported intuition and openness. Individualdifferences in openness refer to engagement with perceptual information and the ability to detect and utilize patterns of sensory information (DeYoung, Quilty, & Peterson, 2007). A second aspect of the broader openness to experience domain has been labeled intellect and refers to engagement with abstract information (e.g., scientific insights or philosophical ideas; DeYoung et al. 2007). Finding intellect more strongly correlated with intelligence and openness with implicit learning, Kaufman et al. (2010) argued for a dissociation between openness and intellect in predicting implicit and explicit cognitive abilities. However, a recent study that was published during our ongoing data collection failed to replicate the association between openness and implicit sequence learning (Sobkow, Traczyk, Kaufman, & Nosal, 2018). Thus, trait levels of openness have been theoretically
Study Objectives: Environmental noise exposure disturbs sleep and impairs recuperation, and may contribute to the increased risk for (cardiovascular) disease. Noise policy and regulation are usually based on average responses despite potentially large inter-individualdifferences in the effects of traffic noise on sleep. In this analysis, we investigated what percentage of the total variance in noise-induced awakening reactions can be explained by stable inter- individualdifferences.
There has been substantial research to predict public goods contributions under the VCM using individualdifferences, which can be either distal or proximal to overt be- havior (Kanfer and Ackerman, 1989). While the former include broad factors, such as general personality structure (Hilbig et al., 2014; Hilbig and Zettler, 2009), the latter comprises more specific determinants of behavior like social value orientation (SVO) (Murphy et al., 2011) or risk preferences (Fung et al., 2012). As mentioned before, the game-theoretic predictions are fundamentally different under the PPM, yielding two types of pure-strategy Nash equilibria and thus a coordination problem to solve. Hence, the role of determining factors is less clear and evidence is scarcer. While us- ing the real-time protocol of play should mitigate coordination failure, the availability of real-time information about contributors adds an additional strategic dimension to the game: moving early can be used to signal a cooperative social norm, but waiting for others to move first and updating beliefs about the probability of implementation with and without one’s own contribution on a rolling basis is also possible. In contrast, without real-time information about contributions available, subjects have no way of updating beliefs and less need to behave strategically, i.e., based on what others are do- ing. In essence, all group members base their decision on less information and thus take less time to choose whether to contribute or not. Therefore, we propose the following hypothesis:
connection between creativity and intelligence might be closer than previously described in the creativity literature. Working memory shows similar results: It only weakly predicts creativity (Benedek et al. 2014). The sum of these ﬁndings indicates that working memory and intelligence are important to be creative and that individualdifferences in creativity go back to differences in working memory (Págan Cánovas 2020) or in knowledge and vo- cabulary (Bergs and Kompa 2020; Hoffmann 2020; Págan Cánovas 2020; Trousdale 2020; Uhrig 2020). However, it also implies that, in order to be creative, we need more than intelligence (knowledge) or working memory and that these are not the same — even though one might require the other, especially in linguistics. In- vestigations in the so-called threshold theory have shown that there is arguably a nonlinear relation between intelligence and creativity (Holling and Kuhn 2008). There is a great debate between researchers reporting a threshold for creativity: Intelligence is proposed as a pre-condition for creativity when under this threshold value, whereas neither are as connected when over the threshold (Jauk et al. 2013).
This finding is only partially in line with the higher rater-consistent genetic variance reported by other multi-rater twin studies on personality characteristics. Riemann et al. (1997) reported higher rater-consistent heritability estimates for personality traits, while Kandler et al. (2016) did not find substantial differences in estimated genetic effects between composite scores (aggregates of self- and peer reports), true scores, and self-reports for reported right- wing authoritarianism and social dominance orientation. Considering that the self-rater agreement did not markedly differ between our and the other studies (except for self-other agreements on social dominance orientation in Kandler et al., 2016), these differences indicate that genetic effects found through multi-methods do not merely reflect an increased reliability of the measurement itself. Rather, they provide a more accurate estimation of the sources of the investigated characteristic, namely individualdifferences in homophobia. This is also supported by the finding that most genetic variation (62%) and a portion of nonshared environmental variation (16%) in self-reported homophobia was also reflected in informant-reported homophobia. Potential explanations for the results include individualdifferences in
Keywords: individualdifferences; choice prediction; I-SAW; modeling correlation
Are individualdifferences correlated and can modeling them as such increase the accuracy of a model’s predictions? Correlations between individualdifferences was one of the features of the model I entered in the Games choice prediction competition (CPC). This note briefly summarizes the consequences of adding correlated individualdifferences to the I-SAW model, the best baseline model in the CPC. It is assumed the reader is familiar with the experiments, I-SAW model, and competition described by Erev et al. .
Thirdly, for each subject the normalized eigenvector scores were integrated into a vector sum (see 4. in the supplement), which provided an index of psychophys- iological arousal (Psychophysiological Arousal Value, PAV). The lack of differences between the ARP-groups in the PAV-scores demonstrated that the present method is able to compensate for individualdifferences in physiological reactivity. 2.5. Study set-up
flashed either at the target or the distractor locations (dot probe array). On the other half of the trials, no dot probe occurred. The logic behind this procedure was that electrophysiological markers should show enhanced evoked responses at locations to which attention was allocated. As electrophysiological index for attentional selection the P1/N1 complex was used, components which are assumed to be sensitive to spatial attention (Luck & Hillyard, 1994). They examined P1/N1 attention effects to the probe array, which measured the ability to resist attentional capture from distractors, and they varied the SOAs between target and probe display to test for individual variations in the time needed to disengage attention. If disengaging attention takes time and individuals with high and low WM capacity differ in disengagement speed, the electrophysiological responses to targets and distractors should not only be a function of SOA but also be related to WM capacity. They observed that at the shortest SOA (50 ms) distractors captured attention for high and low WM capacity individuals, as indexed by an equal P1/N1 amplitude elicited by dots at target or distractor locations. However, while the P1/N1 measures suggested that at the longer 100 ms SOA attention of low WM capacity individuals was still captured by distractors, the focus of attention of high WM capacity individuals was employed only onto targets. Therefore, the individual’s ability to disengage attention may be a critical trait which determines WM capacity, and it steps in when distractors have already been selected for processing. Low WM capacity individuals seem to need more time to do so, and therefore distractors might be processed and unnecessarily represented in WM, competing with relevant items for storage space. Taken together, individualdifferences in the efficiency to orient attention on relevant information and to disengage attention once it has been captured seem to be an important factor when trying to explain variations in WM capacity. Apparently, the amount of cognitive resources at the moment of target selection determines at which stage of processing selective attention mechanisms for low WM capacity are efficient.
The results of the present study do not provide evidence that at the inter-individual level entrainment to the 10 Hz distracter stream typical for the AB paradigm or larger power during the distracter stream period are negatively related to AB task performance. On the contrary, the results indicate that performance in the AB paradigm benefits from entrainment. Though in contrast to a number of AB-specific findings and theories [ 7 , 14 , 15 ], the observation of a positive correlation between entrainment and performance is in line with findings from detec- tion tasks. Here it has been reported that a visual target presented in phase with an entraining sequence—as is the case for targets in the AB paradigm—is more easily detected than a target presented with a phase shift, with the difference being the more pronounced the longer the entraining sequence [ 22 ]. The positive effect of a longer period of entrainment for identifica- tion and processing of a single target has also been demonstrated in the RSVP-based Atten- tional Awakening paradigm [ 23 – 25 ]. But also several findings from AB studies are difficult to integrate with the idea that entrainment or power increases to the RSVP stream are detrimen- tal to AB task performance: For instance, taking an intra-individualdifferences approach Jan- son and colleagues [ 4 ] observed an interaction between correct report of T2 and total power at the RSVP and the IAF frequencies: while high power in the RSVP frequency in the pre-target distracter stream was linked to correct report of T2, the opposite pattern emerged for IAF power. As another example, the positive effect of introducing temporal jitter in the RSVP stream on AB task performance [ 15 ] could not be replicated in a later study [ 16 ].
Noise-vocoded speech is commonly used to simulate the sensation after cochlear implantation as it consists of spectrally degraded speech. High individual variability exists in learning to understand both noise-vocoded speech and speech perceived through a cochlear implant (CI). This variability is partly ascribed to differing cognitive abilities like working memory, verbal skills or attention. Although clinically highly relevant, up to now, no consensus has been achieved about which cognitive factors exactly predict the intelligibility of speech in noise-vocoded situations in healthy subjects or in patients after cochlear implantation. We aimed to establish a test battery that can be used to predict speech understanding in patients prior to receiving a CI. Young and old healthy listeners completed a noise-vocoded speech test in addition to cognitive tests tapping on verbal memory, working memory, lexicon and retrieval skills as well as cognitive flexibility and attention. Partial-least-squares analysis revealed that six variables were important to significantly predict vocoded-speech performance. These were the ability to perceive visually degraded speech tested by the Text Reception Threshold, vocabulary size assessed with the Multiple Choice Word Test, working memory gauged with the Operation Span Test, verbal learning and recall of the Verbal Learning and Retention Test and task switching abilities tested by the Comprehensive Trail-Making Test. Thus, these cognitive abilities explain individualdifferences in noise-vocoded speech understanding and should be considered when aiming to predict hearing-aid outcome.
Knowledge of the linguistic structure of language has been shown to shape speech perception, but how individualdifferences in language ability could be linked to benefits in SRM in children has not been explored. In the case of two competing talkers, successful language processing relies on the ability to attend to one talker over the other (i.e., successful auditory stream segregation), and although the boundary between language processing and speech per- ception is not entirely clear, auditory stream segregation is theorized to occur as a precursor to language processing (Cooke, Garcia Lecumberri, & Barker, 2008). This would indicate that language processing would not affect auditory stream segregation of speech. However, a native language benefit for speech reception masked by informational maskers has been shown in the area of second language listening and referred to as the “foreign language cocktail party problem” (Cooke et al., 2008), indicating that language ability and familiarity somehow assists with speech-in-noise perception. Indeed, the latter is more difficult for bilinguals than mono- linguals, but results indicate that SRTs are lower (better) the earlier a language is acquired (Mayo, Florentine, & Buus, 1997). Johnson (2011) states that the exact manner in which knowledge-driven processes assist speech percep- tion is not entirely agreed upon, but the interaction of language-related knowledge-driven processes with speech processing might explain why individualdifferences in lan- guage ability modulate speech perception under acoustically challenging conditions. Evidence indicates that linguistic experience begins to shape speech perception in infancy, at the time that speech sounds begin to acquire meaning from approximately 6 months of age (Kuhl, Williams, Lacerda, Stevens, & Lindblom, 1992). Findings by Ganong (1980) indicate that linguistic knowledge shapes speech perception in various ways, as listeners are more likely to hear acousti- cally and linguistically similar words in place of the non- words they are presented with (Ganong, 1980). Furthermore, when parts of words are replaced with noise, listeners tend to still hear the missing phoneme (called phoneme restora- tion; Samuel, 1991). Therefore, those with better knowledge of the language they are listening to are likely to fill in the gaps in perception more effectively.
Our findings regarding sex-differences in self-reported emotion regulation abilities are consistent with those of
previous studies revealing more self-reported reappraisal and suppression use in male as compared to female participants ( Graser et al., 2012 ; Erreygers and Spooren, 2017 ; Totzeck et al., 2018 ). Moreover, our findings complement findings of other studies indicating that female participants report and show more emotionality than male participants ( Grossman and Wood, 1993 ; Kring and Gordon, 1998 ; Bradley et al., 2001 ), implying that sex-differences in emotion regulation may account for sex-differences in emotional sensitivity and emotional expressivity. Notwithstanding the role of sex-differences in emotion regulation, it is interesting to note that our findings converge with the findings of a study that investigated the association of inter-individualdifferences in HRV with inter- individualdifferences in self-reports regarding the inability rather than ability to regulate emotions ( Williams et al., 2015 ). In that study, participants with high vmHRV reported fewer difficulties to understand and control emotions than participants with low vmHRV ( Williams et al., 2015 ). As an understanding and control of emotions is more relevant for reappraisal than suppression use ( Hofmann and Kashdan, 2010 ; Totzeck et al., 2018 ), the findings of that study indicate a similar association of inter-individualdifferences in vmHRV with inter-individualdifferences in emotion regulation like the one that has been found in the present study. Although the findings of these studies suggest that inter-individualdifferences in vmHRV are more associated with inter-individualdifferences in self-reported reappraisal than self-reported suppression use, it is important to note that both studies relied on self-report measures that lack ecologic validity in comparison to performance measures. Studies that used performance measures, however, revealed similar
Between-individualdifferences in mating preferences 55 other than the mate and consequently lead to higher promiscuity in explorative individ- uals. The link between personality traits and sexual selection is not highly obvious, but has been suggested repeatedly (e.g. Dingemanse & R´eale 2005; R´eale et al. 2007). For example, R´eale et al. (2007) suggest that boldness as measured in novel-object experi- mental setups influences mating success (an aspect of sexual selection) via dominance structures in the population. Empirical data, in particular for the link between personal- ity and sexual selection, is lacking. This makes it difficult, to make clear-cut predictions. The first aim of our study was to estimate proximate sources of variation in person- ality traits by quantifying additive genetic, maternal and early-environmental effects using animal models (Kruuk 2004). The second aim was to explore the covariance be- tween personality traits in both sexes and several components of fitness, including a measure of brood quality that reflects parental quality. We did this by calculating stan- dardized linear and non-linear selection differentials (Arnold & Wade 1984; Brodie et al. 1995). Linear selection differentials measure the directional selection effects on the phenotype (for larger or smaller trait values), while non-linear selection differentials capture selection on the variance in the trait value (disruptive versus stabilizing selec- tion). Finally, we explored how personality traits influence levels of extra-pair paternity (number of genetic partners and proportion of extra-pair eggs laid/sired). For all traits we tested whether the sex-ratio treatments changed the relationship between personal- ity traits and fitness, i.e. whether the performance of personality phenotypes depended on the social environment.
There are vast individualdifferences in the responses to sexual stimuli (e.g. pictures, videos, imagery, fantasies, and touch). These responses can be assessed on a subjective level using questionnaires and on a physiological level using peripheral physiological measures (e.g., genital reactions) or neural responses. Yet, each of these measures has its limitations. Subjective responses - most frequently used - can be influenced by social desirability and cultural standards, which can lead to answer distortions. Periph- eral physiological measures have a rather low specificity because they reflect mainly general arousal. Physiological measures such as penis-plethysmography are physically intrusive. Neural measures are hampered by the fact that the functional meaning of the observed brain responses is unclear and findings are still partly inconclusive (cf. [1,2]).
Which cognitive processes might underlie criterion-based dropout learning? Dropout learning is characterized by successively smaller amounts of the to-be-learned material. According to the item noise approach (Criss & Shiffrin, 2004), learning successively smaller amounts is easier because less interference among items exists. Recently, it was shown that such list length effects (Ratcliff et al., 1990) may not only result from less interference, but also be due to other factors, such as a shorter retention interval typically resulting from dropout schedules, a smaller amount of attention required, and a more consistent context of study and test context due to less time between studying and testing (Kinnell & Dennis, 2011). One may assume that persons differ regarding these cognitive correlates. Thus, persons might also differ regarding criterion-based dropout learning trajectories. These differences might be even more pronounced in old age, after a life-long history of individualdifferences in cognitive development. In line with a position advocated by Hofer and Sliwinski (2001), the goal of our study was thus not to demonstrate age-related differences in dropout learning between, e.g., young and old adults. Rather, our focus was on individualdifferences and possible predictor variables that might account for these individualdifferences with respect to dropout learning in one age group---that of older individuals.