150 Psychologie
Refine
Year of publication
- 2023 (38) (remove)
Document Type
- Preprint (21)
- Article (11)
- Doctoral Thesis (3)
- Book (1)
- Part of a Book (1)
- Contribution to a Periodical (1)
Has Fulltext
- yes (38)
Is part of the Bibliography
- no (38)
Keywords
- Functional connectivity (2)
- Memory (2)
- Philosophie (2)
- Psychoanalyse (2)
- natural scenes (2)
- neuronal populations (2)
- primary visual cortex (2)
- stimulus encoding (2)
- visual attention (2)
- Aesthetic responsiveness (1)
Institute
Highlights
• Brain connectivity states identified by cofluctuation strength.
• CMEP as new method to robustly predict human traits from brain imaging data.
• Network-identifying connectivity ‘events’ are not predictive of cognitive ability.
• Sixteen temporally independent fMRI time frames allow for significant prediction.
• Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths.
Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Despite the recent popularity of predictive processing models of brain function, the term prediction is often instantiated very differently across studies. These differences in definition can substantially change the type of cognitive or neural operation hypothesised and thus have critical implications for the corresponding behavioural and neural correlates during visual perception. Here, we propose a five-dimensional scheme to characterise different parameters of prediction. Namely, flow of information, mnemonic origin, specificity, complexity, and temporal precision. We describe these dimensions and provide examples of their application to previous work. Such a characterisation not only facilitates the integration of findings across studies, but also helps stimulate new research questions.
The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children’s learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.
The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children’s learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.
The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children’s learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.
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.
"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.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.