Cognition seminars

Over the last decade, research on curiosity – the desire to seek new information – has been rapidly growing. Several studies have shown that curiosity elicits activity within the dopaminergic circuit and thereby enhances hippocampus-dependent learning. However, given this new field of research, we do not have a good understanding yet of (i) how curiosity-based learning changes across the lifespan, (ii) why some people show better learning improvements due to curiosity than others, and (iii) whether lab-based research on curiosity translates to how curiosity affects information seeking in real life. In this talk, I will present a series of behavioural and neuroimaging studies that address these three questions about curiosity. First, I will present findings on how curiosity and interest affect learning differently in childhood and adolescence. Second, I will show data on how inter-individual differences in the magnitude of curiosity-based learning depend on the strength of resting-state functional connectivity within the cortico-mesolimbic dopaminergic circuit. Third, I will present findings on how the level of resting-state functional connectivity within this circuit is also associated with the frequency of real-life information seeking (i.e., about Covid-19-related news). Together, our findings help to refine our recently proposed framework – the Prediction, Appraisal, Curiosity, and Exploration (PACE) framework – that attempts to integrate theoretical ideas on the neurocognitive mechanisms of how curiosity is elicited, and how curiosity enhances learning and information seeking. Furthermore, our findings highlight the importance of curiosity research to better understand how curiosity can be harnessed to improve learning and information seeking in real life.

Vision and visual cognition is experience-dependent with likely multiple sensitive periods, but we know very little about statistics of visual experience at the scale of everyday life and how they might change with development. By traditional assumptions, the world at the massive scale of daily life presents pretty much the same visual statistics to all perceivers. I will present an overview our work on ego-centric vision showing that this is not the case. The momentary image received at the eye is spatially selective, dependent on the location, posture and behavior of the perceiver. If a perceiver’s location, possible postures and/or preferences for looking at some kinds of scenes over others are constrained, then their sampling of images from the world and thus the visual statistics at the scale of daily life could be biased. I will present evidence with respect to both low-level and higher level visual statistics about the developmental changes in the visual input over the first 18 months post-birth.

Why should psychologists be interested in robots? This talk aims to illustrate how social robots – machines with human-like features and behaviors – can offer interesting insights into the human mind. I will first provide a brief overview of how robots have been used in psychology and cognitive science research focusing on two approaches - Developmental Robotics and Human-Robot Interaction (HRI). We will then delve into recent works in HRI, including my own, in greater detail. We will also address the limitations of research thus far, such as the lack of proper controlled experiments, and discuss how the scientific community should evaluate the use of technology in educational and other social settings.

We are now clearly entering a new age in our relationship with machines. The power of AI natural language processors and image generators has rapidly exceeded the expectations of even those who developed them. Serious questions are now being asked about the extent to which machines could become — or perhaps already are — sentient or conscious. Do AI machines understand the instructions they are given and the answers they provide? In this talk I will consider the prospects for conscious machines, by which I mean machines that have feelings, know about their own existence, and about ours. I will suggest that the recent focus on information processing in models of consciousness, in which the brain is treated as a kind of digital computer, have mislead us about the nature of consciousness and how it is produced in biological systems. Treating the brain as an energy processing system is more likely to yield answers to these fundamental questions and help us understand how and when machines might become minds.

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as 'rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and in turn adaptively affect - prefrontal dynamics.

When listening to music, humans readily perceive and move along with a periodic beat. Critically, perception of a periodic beat is commonly elicited by rhythmic stimuli with physical features arranged in a way that is not strictly periodic. Hence, beat perception must capitalize on mechanisms that transform stimulus features into a temporally recurrent format with emphasized beat periodicity. Here, I will present a line of work that aims to clarify the nature and neural basis of this transformation. In these studies, electrophysiological activity was recorded as participants listened to rhythms known to induce perception of a consistent beat across healthy Western adults. The results show that the human brain selectively emphasizes beat representation when it is not acoustically prominent in the stimulus, and this transformation (i) can be captured non-invasively using surface EEG in adult participants, (ii) is already in place in 5- to 6-month-old infants, and (iii) cannot be fully explained by subcortical auditory nonlinearities. Moreover, as revealed by human intracerebral recordings, a prominent beat representation emerges already in the primary auditory cortex. Finally, electrophysiological recordings from the auditory cortex of a rhesus monkey show a significant enhancement of beat periodicities in this area, similar to humans. Taken together, these findings indicate an early, general auditory cortical stage of processing by which rhythmic inputs are rendered more temporally recurrent than they are in reality. Already present in non-human primates and human infants, this "periodized" default format could then be shaped by higher-level associative sensory-motor areas and guide movement in individuals with strongly coupled auditory and motor systems. Together, this highlights the multiplicity of neural processes supporting coordinated musical behaviors widely observed across human cultures.The experiments herein include: a motor timing task comparing the effects of movement vs non-movement with and without feedback (Exp. 1A & 1B), a transcranial magnetic stimulation (TMS) study on the role of the supplementary motor area (SMA) in transforming temporal information (Exp. 2), and a perceptual timing task investigating the effect of noisy movement on time perception with both visual and auditory modalities (Exp. 3A & 3B). Together, the results of these studies support the Bayesian cue combination framework, in that: movement improves the precision of time perception not only in perceptual timing tasks but also motor timing tasks (Exp. 1A & 1B), stimulating the SMA appears to disrupt the transformation of temporal information (Exp. 2), and when movement becomes unreliable or noisy there is no longer an improvement in precision of time perception (Exp. 3A & 3B). Although there is support for the proposed framework, more studies (i.e., fMRI, TMS, EEG, etc.) need to be conducted in order to better understand where and how this may be instantiated in the brain; however, this work provides a starting point to better understanding the intrinsic connection between time and movement

Mobile organisms must be capable of deciding both where and when to move in order to keep up with a changing environment; therefore, a strong sense of time is necessary, otherwise, we would fail in many of our movement goals. Despite this intrinsic link between movement and timing, only recently has research begun to investigate the interaction. Two primary effects that have been observed include: movements biasing time estimates (i.e., affecting accuracy) as well as making time estimates more precise. The goal of this presentation is to review this literature, discuss a Bayesian cue combination framework to explain these effects, and discuss the experiments I have conducted to test the framework. The experiments herein include: a motor timing task comparing the effects of movement vs non-movement with and without feedback (Exp. 1A & 1B), a transcranial magnetic stimulation (TMS) study on the role of the supplementary motor area (SMA) in transforming temporal information (Exp. 2), and a perceptual timing task investigating the effect of noisy movement on time perception with both visual and auditory modalities (Exp. 3A & 3B). Together, the results of these studies support the Bayesian cue combination framework, in that: movement improves the precision of time perception not only in perceptual timing tasks but also motor timing tasks (Exp. 1A & 1B), stimulating the SMA appears to disrupt the transformation of temporal information (Exp. 2), and when movement becomes unreliable or noisy there is no longer an improvement in precision of time perception (Exp. 3A & 3B). Although there is support for the proposed framework, more studies (i.e., fMRI, TMS, EEG, etc.) need to be conducted in order to better understand where and how this may be instantiated in the brain; however, this work provides a starting point to better understanding the intrinsic connection between time and movement

On Thursday, May 25th we will host Darcy Diesburg and Mark Richardson. Darcy Diesburg, PhD, is a post-doctoral research fellow at Brown University. She will tell us about “Deep brain stimulation and action-stopping: A cognitive neuroscience perspective on the contributions of fronto-basal ganglia circuits to inhibitory control”. Mark Richardson, MD, PhD, is the Director of Functional Neurosurgery at the Massachusetts General Hospital, Charles Pappas Associate Professor of Neurosciences at Harvard Medical School and Visiting Associate Professor of Brain and Cognitive Sciences at MIT. Beside his scientific presentation on “Auditory input to the basal ganglia”, he will give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

Frequency tagging based on fast periodic visual stimulation (FPVS) provides a window into ongoing visual and cognitive processing and can be leveraged to measure rule learning and high-level categorization. In this talk, I will present data demonstrating highly proficient categorization as living and non-living in preschool children, and characterize the development of this ability during infancy. In addition to associating cognitive functions with development, an intriguing question is whether frequency tagging also captures enduring individual differences, e.g. in general cognitive abilities. First studies indicate high psychometric quality of FPVS categorization responses (XU et al., Dzhelyova), providing a basis for research on individual differences. I will present results from a pilot study demonstrating high correlations between FPVS categorization responses and behavioral measures of processing speed and fluid intelligences. Drawing upon this first evidence, I will discuss the potential of frequency tagging for diagnosing cognitive functions across development.

Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. Despite this most studies have considered only on the outcome of, and time taken to make, decisions. Motion is, however, crucial in terms of how space is represented by organisms during spatial decision-making. Employing a range of new technologies, including automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) for animals, experiments with fruit flies, locusts and zebrafish (representing aerial, terrestrial and aquatic locomotion, respectively), I will demonstrate that this time-varying representation results in the emergence of new and fundamental geometric principles that considerably impact decision-making. Specifically, we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (‘critical’) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. Due to the critical nature of these transitions (and the corresponding increase in ‘susceptibility’) even noisy brains are extremely sensitive to very small differences between remaining options (e.g., a very small difference in neuronal activity being in “favor” of one option) near these locations in space-time. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Speech may be characterized as conveying both segmental information (i.e., about vowels and consonants) as well as suprasegmental information - cued through pitch, intensity, and duration - also known as the prosody of speech. In this contribution, I will argue that prosody shapes low-level speech perception, changing which speech sounds we hear. Perhaps the most notable example of how prosody guides word recognition is the phenomenon of lexical stress, whereby suprasegmental F0, intensity, and duration cues can distinguish otherwise segmentally identical words, such as "PLAto" vs. "plaTEAU" in Dutch. Work from our group showcases the vast variability in how different talkers produce stressed vs. unstressed syllables, while also unveiling the remarkable flexibility with which listeners can learn to handle this between-talker variability. It also emphasizes that lexical stress is a multimodal linguistic phenomenon, with the voice, lips, and even hands conveying stress in concert. In turn, human listeners actively weigh these multisensory cues to stress depending on the listening conditions at hand. Finally, lexical stress is presented as having a robust and lasting impact on low-level speech perception, even down to changing vowel perception. Thus, prosody - in all its multisensory forms - is a potent factor in speech perception, determining what speech sounds we hear.

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.

Theories from reinforcement learning have been highly influential for interpreting neural activity in the biological circuits critical for animal and human learning. Central among these is the identification of phasic activity in dopamine neurons as a reward prediction error signal that drives learning in basal ganglia and prefrontal circuits. However, recent findings suggest that dopaminergic prediction error signals have access to complex, structured reward predictions and are sensitive to more properties of outcomes than learning theories with simple scalar value predictions might suggest. Here, I will present recent work in which we probed the identity-specific structure of reward prediction errors in an odor-guided choice task and found evidence for multiple predictive “threads” that segregate reward predictions, and reward prediction errors, according to the specific sensory features of anticipated outcomes. Our results point to an expanded class of neural reinforcement learning algorithms in which biological agents learn rich associative structure from their environment and leverage it to build reward predictions that include information about the specific, and perhaps idiosyncratic, features of available outcomes, using these to guide behavior in even quite simple reward learning tasks.

There is growing recognition that host-associated microbiotas modulate intrinsic neurodevelopmental programs including those underlying human social behavior. Despite this awareness, the fundamental processes are generally not understood. We discovered that the zebrafish microbiota is necessary for normal social behavior. By examining neuronal correlates of behavior, we found that the microbiota restrains neurite complexity and targeting of key forebrain neurons within the social behavior circuitry. The microbiota is also necessary for both localization and molecular functions of forebrain microglia, brain-resident phagocytes that remodel neuronal arbors. In particular, the microbiota promotes expression of complement signaling pathway components important for synapse remodeling. Our work provides evidence that the microbiota modulates zebrafish social behavior by stimulating microglial remodeling of forebrain circuits during early neurodevelopment and suggests molecular pathways for therapeutic interventions during atypical neurodevelopment.

Different prefrontal areas contribute in distinctly different ways to rule-guided behaviour in the context of a Wisconsin Card Sorting Test (WCST) analog for macaques. For example, causal evidence from circumscribed lesions in NHPs reveals that dorsolateral prefrontal cortex (dlPFC) is necessary to maintain a reinforced abstract rule in working memory, orbitofrontal cortex (OFC) is needed to rapidly update representations of rule value, and the anterior cingulate cortex (ACC) plays a key role in cognitive control and integrating information for correct and incorrect trials over recent outcomes. Moreover, recent lesion studies of frontopolar cortex (FPC) suggest it contributes to representing the relative value of unchosen alternatives, including rules. Yet we do not understand how these functional specializations relate to intrinsic neuronal activities nor the extent to which these neuronal activities differ between different prefrontal regions. After reviewing the aforementioned causal evidence I will present our new data from studies using multi-area multi-electrode recording techniques in NHPs to simultaneously record from four different prefrontal regions implicated in rule-guided behaviour. Multi-electrode micro-arrays (‘Utah arrays’) were chronically implanted in dlPFC, vlPFC, OFC, and FPC of two macaques, allowing us to simultaneously record single and multiunit activity, and local field potential (LFP), from all regions while the monkey performs the WCST analog. Rule-related neuronal activity was widespread in all areas recorded but it differed in degree and in timing between different areas. I will also present preliminary results from decoding analyses applied to rule-related neuronal activities both from individual clusters and also from population measures. These results confirm and help quantify dynamic task-related activities that differ between prefrontal regions. We also found task-related modulation of LFPs within beta and gamma bands in FPC. By combining this correlational recording methods with trial-specific causal interventions (electrical microstimulation) to FPC we could significantly enhance and impair animals performance in distinct task epochs in functionally relevant ways, further consistent with an emerging picture of regional functional specialization within a distributed framework of interacting and interconnected cortical regions.