Emotion and odor scales (EOS) measuring odor-related affective feelings were recently developed for three different countries (Switzerland, United Kingdom, and Singapore). The first aim of this study was to investigate gender and cultural differences in verbal affective response to odors, measured with EOS and the usual pleasantness scale. To better understand this variability, the second aim was to investigate the link between affective reports and olfactory knowledge (familiarity and identification). Responses of 772 participants smelling 56-59 odors were collected in the three countries. Women rated odors as more intense and identified them better in all countries, but no reliable sex differences were found for verbal affective responses to odors. Disgust-related feelings revealed odor-dependent sex differences, due to sex differences in identification and categorization. Further, increased odor knowledge was related to more positive affects as reported with pleasantness and odor-related feeling evaluations, which can be related to top-down influences on odor representation. These top-down influences were thought, for example, to relate to beliefs about odor properties or to categorization (edible vs. nonedible). Finally, the link between odor knowledge and olfactory affect was generally asymmetrical and significant only for pleasant odors, not for unpleasant ones that seemed to be more resistant to cognitive influences. This study, for the first time using emotional scales that are appropriate to the olfactory domain, brings new insights into the variability of affective responses to odors and its relationship to odor knowledge.

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned.

The free-choice paradigm is a widely used paradigm in psychology. It has been used to show that after a choice between two similarly pleasant stimuli, the pleasantness of the chosen one tends to increase, whereas the pleasantness of the rejected one tends to decrease-a spreading of alternatives. However, the methodological validity of the free-choice paradigm to study choice-induced preference change has recently been seriously questioned [Chen, K. M., & Risen, J. L. (2010). How choice affects and reflects preferences: Revisiting the free-choice paradigm. Journal of Personality and Social Psychology, 99, 573-594. doi:10.1037/a0020217]. According to this criticism, the classically reported spreading of alternatives between the first and second rating sessions cannot be unambiguously interpreted to reflect a true change in preferences and can be observed even for completely static preferences. Here, we used two measurement sequences, a classical Rating 1-choice-Rating 2 sequence and a control Rating 1-Rating 2-choice sequence, to disentangle the spreading of alternatives driven by the effect of choice from the artefactual effect highlighted by Chen and Risen. In two studies using different stimulus material (faces and odours), we find that choice has a robust modulatory impact on preferences for rejected odours, but not for chosen odours and not for faces.

In the context of emotion information processing, several studies have demonstrated the involvement of the amygdala in emotion perception, for unimodal and multimodal stimuli. However, it seems that not only the amygdala, but several regions around it, may also play a major role in multimodal emotional integration. In order to investigate the contribution of these regions to multimodal emotion perception, five patients who had undergone unilateral anterior temporal lobe resection were exposed to both unimodal (vocal or visual) and audiovisual emotional and neutral stimuli. In a classic paradigm, participants were asked to rate the emotional intensity of angry, fearful, joyful, and neutral stimuli on visual analog scales. Compared with matched controls, patients exhibited impaired categorization of joyful expressions, whether the stimuli were auditory, visual, or audiovisual. Patients confused joyful faces with neutral faces, and joyful prosody with surprise. In the case of fear, unlike matched controls, patients provided lower intensity ratings for visual stimuli than for vocal and audiovisual ones. Fearful faces were frequently confused with surprised ones. When we controlled for lesion size, we no longer observed any overall difference between patients and controls in their ratings of emotional intensity on the target scales. Lesion size had the greatest effect on intensity perceptions and accuracy in the visual modality, irrespective of the type of emotion. These new findings suggest that a damaged amygdala, or a disrupted bundle between the amygdala and the ventral part of the occipital lobe, has a greater impact on emotion perception in the visual modality than it does in either the vocal or audiovisual one. We can surmise that patients are able to use the auditory information contained in multimodal stimuli to compensate for difficulty processing visually conveyed emotion.

Subthalamic nucleus (STN) deep brain stimulation in Parkinson’s disease induces modifications in the recognition of emotion from voices (or emotional prosody). Nevertheless, the underlying mechanisms are still only poorly understood, and the role of acoustic features in these deficits has yet to be elucidated. Our aim was to identify the influence of acoustic features on changes in emotional prosody recognition following STN stimulation in Parkinson’s disease. To this end, we analysed the performances of patients on vocal emotion recognition in pre-versus post-operative groups, as well as of matched controls, entering the acoustic features of the stimuli into our statistical models. Analyses revealed that the post-operative biased ratings on the Fear scale when patients listened to happy stimuli were correlated with loudness, while the biased ratings on the Sadness scale when they listened to happiness were correlated with fundamental frequency (F0). Furthermore, disturbed ratings on the Happiness scale when the post-operative patients listened to sadness were found to be correlated with F0. These results suggest that inadequate use of acoustic features following subthalamic stimulation has a significant impact on emotional prosody recognition in patients with Parkinson’s disease, affecting the extraction and integration of acoustic cues during emotion perception.

In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable and dichotomous indicators to mark an event of interest. Consequently, joint modeling of longitudinal and time-to-event data has generated much interest in these disciplines over the previous decade. In behavioural sciences, too, often we are interested in relating individual trajectories and discrete events. Yet, joint modeling is rarely applied in behavioural sciences more generally. This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral study with the JMbayes R package. In particular, the tutorial discusses practical topics, such as model selection and comparison, choice of joint modeling parameterization and interpretation of model parameters. In the end, this tutorial aims at introducing didactically the theory related to joint modeling and to introduce novice analysts to the use of the JMbayes package.

Over the past 30 years, a large body of research has accrued demonstrating that video games are capable of placing substantial demands on the human cognitive, emotional, physical, and social processing systems. Within the cognitive realm, playing games belonging to one particular genre, known as the action video game genre, has been consistently linked with demands on a host of cognitive abilities including perception, top-down attention, multitasking, and spatial cognition. More recently, a number of new game genres have emerged that, while different in many ways from “traditional” action games, nonetheless seem likely to load upon similar cognitive processes. One such example is the multiplayer online battle arena genre (MOBA), which involves a mix of action and real-time strategy characteristics. Here, a sample of over 500 players of the MOBA game League of Legends completed a large battery of cognitive tasks. Positive associations were observed between League of Legends performance (quantified by participants’ in-game match-making rating) and a number of cognitive abilities consistent with those observed in the existing action video game literature, including speed of processing and attentional abilities. Together, our results document a rich pattern of cognitive abilities associated with high levels of League of Legends performance and suggest similarities between MOBAs and action video games in terms of their cognitive demands.

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.