Experimental Paradigm
experimental paradigm
Distinctive features of experiential time: Duration, speed and event density
William James’s use of “time in passing” and “stream of thoughts” may be two sides of the same coin that emerge from the brain segmenting the continuous flow of information into discrete events. Departing from that idea, we investigated how the content of a realistic scene impacts two distinct temporal experiences: the felt duration and the speed of the passage of time. I will present you the results from an online study in which we used a well-established experimental paradigm, the temporal bisection task, which we extended to passage of time judgments. 164 participants classified seconds-long videos of naturalistic scenes as short or long (duration), or slow or fast (passage of time). Videos contained a varying number and type of events. We found that a large number of events lengthened subjective duration and accelerated the felt passage of time. Surprisingly, participants were also faster at estimating their felt passage of time compared to duration. The perception of duration heavily depended on objective duration, whereas the felt passage of time scaled with the rate of change. Altogether, our results support a possible dissociation of the mechanisms underlying the two temporal experiences.
Beyond Volition
Voluntary actions are actions that agents choose to make. Volition is the set of cognitive processes that implement such choice and initiation. These processes are often held essential to modern societies, because they form the cognitive underpinning for concepts of individual autonomy and individual responsibility. Nevertheless, psychology and neuroscience have struggled to define volition, and have also struggled to study it scientifically. Laboratory experiments on volition, such as those of Libet, have been criticised, often rather naively, as focussing exclusively on meaningless actions, and ignoring the factors that make voluntary action important in the wider world. In this talk, I will first review these criticisms, and then look at extending scientific approaches to volition in three directions that may enrich scientific understanding of volition. First, volition becomes particularly important when the range of possible actions is large and unconstrained - yet most experimental paradigms involve minimal response spaces. We have developed a novel paradigm for eliciting de novo actions through verbal fluency, and used this to estimate the elusive conscious experience of generativity. Second, volition can be viewed as a mechanism for flexibility, by promoting adaptation of behavioural biases. This view departs from the tradition of defining volition by contrasting internally-generated actions with externally-triggered actions, and instead links volition to model-based reinforcement learning. By using the context of competitive games to re-operationalise the classic Libet experiment, we identified a form of adaptive autonomy that allows agents to reduce biases in their action choices. Interestingly, this mechanism seems not to require explicit understanding and strategic use of action selection rules, in contrast to classical ideas about the relation between volition and conscious, rational thought. Third, I will consider volition teleologically, as a mechanism for achieving counterfactual goals through complex problem-solving. This perspective gives a key role in mediating between understanding and planning on the one hand, and instrumental action on the other hand. Taken together, these three cognitive phenomena of generativity, flexibility, and teleology may partly explain why volition is such an important cognitive function for organisation of human behaviour and human flourishing. I will end by discussing how this enriched view of volition can relate to individual autonomy and responsibility.
Disentangling neural correlates of consciousness and task relevance using EEG and fMRI
How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.
A new experimental paradigm to study analogy transfer
Analogical reasoning is one of the most complex cognitive functions in humans that allows abstract thinking, high-level reasoning, and learning. Based on analogical reasoning, one can extract an abstract and general concept (i.e., an analogy schema) from a familiar situation and apply it to a new context or domain (i.e., analogy transfer). These processes allow us to solve problems we never encountered before and generate new ideas. However, the place of analogy transfer in problem solving mechanisms is unclear. This presentation will describe several experiments with three main findings. First, we show how analogy transfer facilitates problem-solving, replicating existing empirical data largely based on the radiation/fortress problems with four new riddles. Second, we propose a new experimental task that allows us to quantify analogy transfer. Finally, using science network methodology, we show how restructuring the mental representation of a problem can predict successful solving of an analogous problem. These results shed new light on the cognitive mechanism underlying solution transfer by analogy and provide a new tool to quantify individual abilities.
“Wasn’t there food around here?”: An Agent-based Model for Local Search in Drosophila
The ability to keep track of one’s location in space is a critical behavior for animals navigating to and from a salient location, and its computational basis is now beginning to be unraveled. Here, we tracked flies in a ring-shaped channel as they executed bouts of search triggered by optogenetic activation of sugar receptors. Unlike experiments in open field arenas, which produce highly tortuous search trajectories, our geometrically constrained paradigm enabled us to monitor flies’ decisions to move toward or away from the fictive food. Our results suggest that flies use path integration to remember the location of a food site even after it has disappeared, and flies can remember the location of a former food site even after walking around the arena one or more times. To determine the behavioral algorithms underlying Drosophila search, we developed multiple state transition models and found that flies likely accomplish path integration by combining odometry and compass navigation to keep track of their position relative to the fictive food. Our results indicate that whereas flies re-zero their path integrator at food when only one feeding site is present, they adjust their path integrator to a central location between sites when experiencing food at two or more locations. Together, this work provides a simple experimental paradigm and theoretical framework to advance investigations of the neural basis of path integration.
An open-source experimental framework for automation of cell biology experiments
Modern biological methods often require a large number of experiments to be conducted. For example, dissecting molecular pathways involved in a variety of biological processes in neurons and non-excitable cells requires high-throughput compound library or RNAi screens. Another example requiring large datasets - modern data analysis methods such as deep learning. These have been successfully applied to a number of biological and medical questions. In this talk we will describe an open-source platform allowing such experiments to be automated. The platform consists of an XY stage, perfusion system and an epifluorescent microscope with autofocusing. It is extremely easy to build and can be used for different experimental paradigms, ranging from immunolabeling and routine characterisation of large numbers of cell lines to high-throughput imaging of fluorescent reporters.
Assessing consciousness in human infants
In a few months, human infants develop complex capacities in numerous cognitive domains. They learn their native language, recognize their parents, refine their numerical capacities and their perception of the world around them but are they conscious and how can we study consciousness when no verbal report is possible? One way to approach this question is to rely on the neural responses correlated with conscious perception in adults (i.e. a global increase of activity in notably frontal regions with top-down amplification of the sensory levels). We can thus study at what age the developing anatomical architecture might be mature enough to allow this type of responses, but moreover we can use similar experimental paradigms than in adults in which we expect to observe a similar pattern of functional responses.
Preschoolers' Comprehension of Functional Metaphors
Previous work suggests that children’s ability to understand metaphors emerges late in development. Researchers argue that children’s initial failure to understand metaphors is due to an inability to reason about shared relational structures between concepts. However, recent work demonstrates that preschoolers, toddlers, and even infants are already capable of relational reasoning. Might preschoolers also be capable of understanding metaphors, given more sensitive experimental paradigms? I explore whether preschoolers (N = 200, ages 4-5) understand functional metaphors, namely metaphors based on functional similarities. In Experiment 1a, preschoolers rated functional metaphors (e.g. “Roofs are hats”; “Clouds are sponges”) as “smarter” than nonsense statements. In Experiment 1b, adults (N = 48) also rated functional metaphors as “smarter” than nonsense statements (e.g. “Dogs are scissors”; “Boats are skirts”). In Experiment 2, preschoolers preferred functional explanations (e.g. “Both hold water”) over perceptual explanations (e.g. “Both are fluffy”) when interpreting a functional metaphor (e.g. “Clouds are sponges”). In Experiment 3, preschoolers preferred functional metaphors over nonsense statements in a dichotomous-choice task. Overall, this work demonstrates preschoolers’ early-emerging ability to understand functional metaphors.
Free will, decision-making and machine learning
The question of free will has been topical for millennia, especially considering its links to moral responsibility and the ownership of that responsibility. Free will, or volition, is an incredibly complex phenomenon - and cannot easily be reduced to a single empirical paradigm. Roskies (2010) proposes that there are five cognitive aspects to be considered when developing a more complete understanding of volition. These are: intention, initiation, feeling, executive control and decision-making. Decision-making will be the focus of this talk, which steps through aspects of the philosophy of free will; highlights experimental paradigms stemming from the seminal work of Benjamin Libet et al., and proposes machine learning as a promising method in progressing the empirical studies of decision-making and free will.