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Institute
"Autonomy is the condition under which what one does reflects who one is" (Weinrib, 2019, p.8). This quote encapsulates the core idea of autonomy, namely the correspondence of one’s inner values with one’s actions. This is a beautiful idea. After all, who wants their actions to be determined or controlled from the outside?
The classical definition of autonomy is precisely about this independence from external circumstances, which Murray (1938) primarily coined. Among other things, Murray characterizes autonomy as resistance to influence and defiance of authority. Similarly, Piaget (1983) describes individuals as autonomous, independent of external influences, in their thinking and actions, and foremost, adult authority. Subsequent work criticized this equation of autonomy with separation or independence (Bekker, 1993; Chirkov et al., 2003; Hmel & Pincus, 2002). In lieu thereof, autonomy is defined as an ability (Chirkov, 2011; Rössler, 2017) and as an essential human need (Ryan & Deci, 2006). Focus is now
on self-governing while relying on rationally determined values to pursue a happy life (Chirkov, 2011). According to Social Determination Theory (SDT), autonomy is about a sense of initiative and responsibility for one’s own actions. The experience of interest and appreciation can strengthen autonomy, whereas experiences of external control, e.g., through rewards or punishments, limit autonomy (Ryan & Deci, 2020). In the psychological discourse of autonomy, SDT is strongly represented (Chirkov et al., 2003; Koestner & Losier, 1996; Weinstein et al., 2012). Notably, SDT distinguishes between autonomy and independence as follows. While a person can autonomously ask for help or rely on others, a person can also be involuntarily alone and independent. Interestingly, these definitions are again closer to its etymological meaning as self-governing, originating from Greek αυτòνoμζ (autonomous).
The two strands of autonomy as independence and autonomy as self-determination are also reflected in the vital differentiation into reactive and reflective autonomy by Koestner and Losier (1996). Resisting external influence, particularly interpersonal in fluence, is what reactive autonomy entails. This interpretation is closely related to the classical concept of autonomy as separation and independence from others (Murray, 1938). On the other hand, reflective autonomy concerns intrapersonal processes, such as self-governing or self-regulation, as defined in Self-Determination Theory (Ryan et al., 2021). In this dissertation, we investigated the concept in three different approaches while focusing on its assessment and operationalization: To begin, in Article 1, we compared the layperson’s and the scientific perspective to each other to gain insight into the characteristics of autonomy. Then, in Articles 2 and 3, we experimentally tested behavioral autonomy as resistance to external influences. Simultaneously, we investigated the link between various autonomy trait measures and autonomous behavior. As a result, in Article 2, we looked at how people reacted to the effects of message framing and sender authority on social distancing behavior during the early COVID-19 pandemic. Finally, in Article 3 we investigated the resistance to a descriptive norm in answering factual questions, in the context of autonomous personality. In our first article, we used a semi-qualitative bottom-up approach to gain insights into the laypersons’ perspective on autonomy and compare it to the scientific notion. We followed a design proposed by Kraft-Todd and Rand (2019) on the term heroism. We derived five components from philosophical and psychological literature: dignity, independence from others, morality, self-awareness, and unconventionality. In three preregistered online studies, we compared these scientific components to the laypersons’ understanding of autonomy. In Study 1, participants (N = 222) listed at least three and up to ten examples of autonomous (self-determined) behaviors. Here, the participants named 807 meaningful examples, which we systematically categorized into 34 representative items for Study 2. Next, new participants (N = 114) rated these regarding their autonomy. Finally, we transferred the five highest-rated autonomy and the five lowest-rated autonomy items to Study 3 (N = 175). We asked participants to rate how strongly the items represented dignity, independence from others, morality, self-awareness, and unconventionality. We found all components to distinguish between high and low autonomy items but not for unconventionality. Thus, we conclude that laypersons’ view corresponds with the scientific characteristics of dignity, independence from others, self-awareness, and morality. A qualitative analysis of the examples also showed that both reactive and reflective definitions of autonomy are prevalent.
Understanding the brain's proactive nature and its ability to anticipate the future has been a longstanding pursuit in philosophy and scientific research. The predictive processing framework explains how the brain generates predictions based on environmental regularities and adapts to both predicted and unpredicted events. Prediction errors (PE) occur when sensory evidence deviates from predictions, triggering cognitive and neural processes that enhance learning and subsequent memory. However, the effects of PE on episodic memory have not been clearly explained. This dissertation aims to address three key questions to advance our understanding of PE and episodic memory. First, how does the degree of PE influence episodic memory, and how do expected and unexpected events interact in this process? Second, what insights can be gained from studying the electrophysiological activity associated with prediction violations, and what role does PE play in subsequent memory benefits? Lastly, how do memory processes change across the lifespan, and how does this impact the brain's ability to remember events? By answering these questions, this dissertation contributes to advancing our understanding of the cognitive and neural mechanisms underlying the relationship PE and episodic memory.
Our mind has the function of representing the physical and social world we are in, so that we can efficiently interact with it. This results in a constant and dynamic interaction between mind and world that produces a balance when representations are at the same time accurate with respect to what the world is communicating to our organism, but also compatible with how our mind works.
A paradigmatic case of this interaction is offered by perception, which is the mental function that represents contingent aspects of the world built from what is captured by our senses. Indeed, the dominant philosophical view in cognitive science is that our perceptual states are representations of the world and not direct access to that world. These representational perceptual states therefor include the aspects of the world they represent and that initiate the perception by stimulating our sensory organs.
Perceptual representations are built using information from the sensory system, i.e., bottom-up information, but are also integrated with information previously acquired, i.e., top-down information, so that perception interacts with memory through language and other mental functions. Such organization is believed to reflect a general mechanism of our mind/brain, which is to acquire and use information to make efficient predictions about the future, continuously updating older information with present information.
This predictive processing works because the world is not random, but shows a regular structure from which reliable expectations can be built. One way that our minds make these predictions is by adapting to the structure of the world in an implicit, automatic and unconscious way, a process that has been called Implicit Statistical Learning (ISL). ISL is a learning process that does not require awareness and happens in an incidental and spontaneous way, with mere exposure to statistical regularities of the world. It is what happens when we learn a language during early childhood, and that allows us to be implicitly sensitive to the phonological structure of speech, or to associate speech patterns with objects and events to learn word meaning.
A specific case of ISL is the learning of spatial configuration in the visual world, which we apply to abstract arrays of items, but most importantly, also to more ecological settings such as the visual scenes we are immersed in during our everyday life. The knowledge we acquire about the structure of visual scenes has been called “Scene Grammar”, because it informs about presence and position of objects in a similar way to what linguistic grammar tells us about the presence and position of words. So, we implicitly acquire the semantics of scenes, learning which objects are consistent with a certain scene, as well as the syntax of scenes, learning where objects are positioned in a consistent way within a certain scene.
More recent developments have proposed that scene grammar knowledge might be organized based on a hierarchical system: objects are arranged in the scene, which offers the more general context, but within a scene we can identify different spatial and functional clusters of objects, called “phrases”, that offer a second level of context; within every phrase, then, objects have different status, with usually one object (“anchor object”) offering strong prediction of where and which are the other objects within the phrase (“local objects”). However, these further aspects of the organization of objects In scenes remain poorly understood.
Another problem relates to the way we measure the structure of scenes to compare the organization of the visual world with the organization in the mind. Typically, to decide if an object appears or not in a certain scene, and whether or not it appears in a certain position within a scene, researchers based their decision on intuition and common-sense, maybe validating those decisions with independent raters. But it has been shown that often these decisions can be limited and more complex information about objects’ arrangement in scenes can be lost.
A potential solution to this problem might be using large set of real-world images, that have annotations and segmentations of objects, to measures statistics about how objects are arranged in the environment. This idea exploits the nowadays larger availability of this kind of datasets due to increasing developments of computer vision algorithms, and also parallels with the established usage of large text corpora in language research.
The goals of the current investigation were to extract object statistics from this image datasets and test if they reliably predict behavioural responses during object processing, as well as to use these statistics to investigate more complex aspects of scene grammar, such as its hierarchical organization, to see if this organization is reflected in the organization of objects in our mind.
Previous research has demonstrated the efficacy of psychological interventions to foster resilience. However, little is known about whether the cultural context in which resilience interventions are implemented affects their efficacy on mental health. Studies performed in Western (k = 175) and Eastern countries (k = 46) regarding different aspects of interventions (setting, mode of delivery, target population, underlying theoretical approach, duration, control group design) and their efficacy on resilience, anxiety, depressive symptoms, quality of life, perceived stress, and social support were compared. Interventions in Eastern countries were longer in duration and tended to be more often conducted in group settings with a focus on family caregivers. We found evidence for larger effect sizes of resilience interventions in Eastern countries for improving resilience (standardized mean difference [SMD] = 0.48, 95% confidence interval [CI] 0.28 to 0.67; p < 0.0001; 43 studies; 6248 participants; I2 = 97.4%). Intercultural differences should receive more attention in resilience intervention research. Future studies could directly compare interventions in different cultural contexts to explain possible underlying causes for differences in their efficacy on mental health outcomes.