Cognition seminars

This talk will focus on our lab's extensive research on understanding and enhancing creative analogical reasoning. I will cover the development of the analogy finding matrix task, evidence for conscious augmentation of creative state during this task, and the real-world implications this ability has for college STEM education. I will also discuss recent research aimed at enhancing performance on this creative analogical reasoning task using both transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS).

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Cognitive models of timing often include a pacemaker analogue whose ticks are accumulated to form an internal representation of time, and a threshold that determines when a target duration has elapsed. However, clear EEG manifestations of these abstract components have not yet been identified. We measured the EEG of subjects while they performed a temporal bisection task in which they were requested to categorize visual stimuli as short or long in duration. We report an ERP component whose amplitude depends monotonically on the stimulus duration. The relation of the ERP amplitude and stimulus duration can be captured by a simple model, adapted from a known drift-diffusion model for time perception. It includes a noisy accumulator that starts with the stimulus onset and a threshold. If the threshold is reached during stimulus presentation, the stimulus is categorized as "long", otherwise the stimulus is categorized as "short". At the stimulus offset, a response proportional to the distance to the threshold is emitted. This simple model has two parameters that fit both the behavior and ERP amplitudes recorded in the task. Two subsequent experiments replicate and extend this finding to another modality (touch) as well as to different time ranges (subsecond and suprasecond), establishing the described ERP component as a useful handle on the cognitive processes involved in temporal decisions.

A key purpose of causal reasoning by individuals and by collectives is to enhance action, to give humans yet more control over their environment. As a result, causal reasoning serves as the infrastructure of both thought and discourse. Humans represent causal systems accurately in some ways, but also show some systematic biases (we tend to neglect causal pathways other than the one we are thinking about). Even when accurate, people’s understanding of causal systems tends to be superficial; we depend on our communities for most of our causal knowledge and reasoning. Nevertheless, we are better causal reasoners than machines. Modern machine learners do not come close to matching human abilities.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

Can we learn without conscious awareness? Numerous evidences in the research of implicit learning have indicated that people can learn the statistical structure of the stimuli but seemingly without any awareness of its underlying rules. However, it remains unclear what types of knowledge can be learned in implicit learning, what is the relationship between conscious and unconscious knowledge, and what are the neural substrates for the acquisition of conscious and unconscious knowledge. In this talk, I will discuss with you about these ongoing questions.

Visual perceptual learning (VPL) is defined as improved performance on a visual task due to visual experience. It was once argued that attention to a visual feature is necessary for VPL of the feature to occur. Contrary to this view, a phenomenon called task-irrelevant VPL demonstrated that VPL can occur due to exposure to a feature which is sub-threshold and task-irrelevant, and therefore, unattended. A series of findings based on task-irrelevant VPL has indicated the following two mechanisms. First, attention to a feature facilitates VPL of the feature while inhibiting VPL of unattended and supra-threshold features. Second, reward paired with a feature enables VPL of the feature irrespective of whether the feature is attended or not. However, we recently found an additional twist; VPL of a task-irrelevant and supra-threshold feature embedded in a natural scene is not subject to the inhibition of attention. This new finding suggests a need to revise the current view or add a new mechanism as to how VPL occurs.

Cognitive control allows us to think and behave flexibly based on our context and goals. Most theories of cognitive control propose a control representation that enables the same input to produce different outputs contingent on contextual factors. In this talk, I will focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. This tradeoff has important implications for cognitive control. In this talk, I will present initial evidence from fMRI and EEG showing that task representations in the human brain leverage both ends of this tradeoff during flexible behavior.

Social learning and analogical reasoning both provide exponential opportunities for learning. These skills have largely been studied independently, but my future research asks how combining skills across previously independent domains could add up to more than the sum of their parts. Analogical reasoning allows individuals to transfer learning between contexts and opens up infinite opportunities for innovation and knowledge creation. Its origins and development, so far, have largely been studied in purely cognitive domains. Constraining analogical development to non-social domains may mistakenly lead researchers to overlook its early roots and limit ideas about its potential scope. Building a bridge between social learning and analogy could facilitate identification of the origins of analogical reasoning and broaden its far-reaching potential. In this talk, I propose that the early emergence of social learning, its saliency, and its meaningful context for young children provides a springboard for learning. In addition to providing a strong foundation for early analogical reasoning, the social domain provides an avenue for scaling up analogies in order to learn to learn from others via increasingly complex and broad routes.

International organizations, governments and companies are increasingly committed to developing measures that encourage adoption of sustainable consumption patterns among the population. However, their success requires a deep understanding of the everyday purchasing decision process and the elements that shape it. Price is an element that stands out. Prior research concluded that the influence of price on purchase decisions varies across consumer profiles. Yet no consumer behavior study to date has assessed the differences of price processing among consumers adopting sustainable habits (prosocial) as opposed to those who have not (non-prosocial). This is the first study to resort to neuroimaging tools to explore the underlying neural mechanisms that reveal the effect of price on prosocial and non-prosocial consumers. Self-reported findings indicate that prosocial consumers place greater value on collective costs and benefits while non-prosocial consumers place a greater weight on price. The neural data gleaned from this analysis offers certain explanations as to the origin of the differences. Non-prosocial (vs. prosocial) consumers, in fact, exhibit a greater activation in brain areas involved with reward, valuation and choice when evaluating price information. These findings could steer managers to improve market segmentation and assist institutions in their design of campaigns fostering environmentally sustainable behaviors

Executing memory-guided behavior requires storage of information about experience and later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, facilitating planning based on evaluation of those experiences, or both. We designed a dynamic spatial task that promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been visited recently, indicating a role in memory storage rather than in directly guiding subsequent behavior.

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. We have used threat-evoked escape behavior in mice to probe the relationship between ethological exploratory behavior and abstract spatial cognition. First, we show that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. Using closed-loop neural manipulations to interrupt running movements during exploration, we next found that blocking runs targeting an obstacle edge abolished subgoal learning. We conclude that mice use an action-driven learning process to identify subgoals, and these subgoals are then integrated into an allocentric map-like representation. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

Vision provides us with a holistic interpretation of the world that is, with very few exceptions, coherent and consistent across multiple levels of abstraction, from scene to objects to features. In this talk I will present results from past and ongoing work in my laboratory that investigates the role top-down signals play in establishing such coherent perceptual experience. Based on the results of several psychophysical experiments I will introduce a theory of “self-consistent inference” and show how it can account for human perceptual behavior. The talk will close with a discussion of how the theory can help us understand more cognitive processes.

Common factors are omnipresent in everyday life, e.g., it is widely held that there is a common factor g for intelligence. In vision, however, there seems to be a multitude of specific factors rather than a strong and unique common factor. In my thesis, I first examined the multidimensionality of the structure underlying visual illusions. To this aim, the susceptibility to various visual illusions was measured. In addition, subjects were tested with variants of the same illusion, which differed in spatial features, luminance, orientation, or contextual conditions. Only weak correlations were observed between the susceptibility to different visual illusions. An individual showing a strong susceptibility to one visual illusion does not necessarily show a strong susceptibility to other visual illusions, suggesting that the structure underlying visual illusions is multifactorial. In contrast, there were strong correlations between the susceptibility to variants of the same illusion. Hence, factors seem to be illusion-specific but not feature-specific. Second, I investigated whether a strong visual factor emerges in healthy elderly and patients with schizophrenia, which may be expected from the general decline in perceptual abilities usually reported in these two populations compared to healthy young adults. Similarly, a strong visual factor may emerge in action video gamers, who often show enhanced perceptual performance compared to non-video gamers. Hence, healthy elderly, patients with schizophrenia, and action video gamers were tested with a battery of visual tasks, such as a contrast detection and orientation discrimination task. As in control groups, between-task correlations were weak in general, which argues against the emergence of a strong common factor for vision in these populations. While similar tasks are usually assumed to rely on similar neural mechanisms, the performances in different visual tasks were only weakly related to each other, i.e., performance does not generalize across visual tasks. These results highlight the relevance of an individual differences approach to unravel the multidimensionality of the visual structure.

When making decision under risk, people often exhibit behaviours that classical economic theories cannot explain. Newer models that attempt to account for these ‘irrational’ behaviours often lack neuroscience bases and require the introduction of subjective and problem-specific constructs. Here, we present a decision-making model inspired by the prediction error signals and introspective neuronal replay reported in the brain. In the model, decisions are chosen based on ‘anticipated surprise’, defined by a nonlinear average of the differences between individual outcomes and a reference point. The reference point is determined by the expected value of the possible outcomes, which can dynamically change during the mental simulation of decision-making problems involving sequential stages. Our model elucidates the contribution of each stage to the appeal of available options in a decision-making problem. This allows us to explain several economic paradoxes and gambling behaviours. Our work could help bridge the gap between decision-making theories in economics and neurosciences.