Cognition seminars

We must constantly adapt the rules we use to guide our attention. To understand how the brain learns these rules, we designed a novel task that required monkeys to learn which color is the most rewarded at a given time (the current rule). However, just as in real life, the monkey was never explicitly told the rule. Instead, they had to learn it through trial and error by choosing a color, receiving feedback (amount of reward), and then updating their internal rule. After the monkeys reached a behavioral criterion, the rule changed. This change was not cued but could be inferred based on reward feedback. Behavioral modeling found monkeys used rewards to learn the rules. After the rule changed, animals adopted one of two strategies. If the change was small, reflected in a small reward prediction error, the animals continuously updated their rule. However, for large changes, monkeys ‘reset’ their belief about the rule and re-learned the rule from scratch. To understand the neural correlates of learning new rules, we recorded neurons simultaneously from the prefrontal and parietal cortex. We found that the strength of the rule representation increased with the certainty about the current rule, and that the certainty about the rule was represented both implicitly and explicitly in the population.

As Socrates said that "I know that I know nothing," our mind's function to be aware of our ignorance is essential for abstract and conceptual reasoning. However, the biological mechanism to enable such a hierarchical thought, or meta-cognition, remained unknown. In the first part of the talk, I will demonstrate our studies on the neural mechanism for metacognition on memory in macaque monkeys. In reality, awareness of ignorance is essential not only for the retrospection of the past but also for the exploration of novel unfamiliar environments for the future. However, this proactive feature of metacognition has been understated in neuroscience. In the second part of the talk, I will demonstrate our studies on the neural mechanism for prospective metacognitive matching among uncertain options prior to perceptual decision making in humans and monkeys. These studies converge to suggest that higher-order processes to self-evaluate mental state either retrospectively or prospectively are implemented in the primate neural networks.

Computer AI thinks differently from us, which is why it's such a useful tool. Thanks to the ingenuity of human programmers, AI's different method of thinking has made humans redundant at certain human tasks, such as chess. Yet there are mechanical limits to how far AI can replicate the products of human thinking. In this talk, we'll trace one such limit by exploring how AI and humans create differently. Humans create by reverse-engineering tools or behaviors to accomplish new actions. AI creates by mix-and-matching pieces of preexisting structures and labeling which combos are associated with positive and negative results. This different procedure is why AI cannot (and will never) learn to innovate technology or tactics and why it also cannot (and will never) learn to generate narratives (including novels, business plans, and scientific hypotheses). It also serves as a case study in why there's no reason to believe in "general intelligence" and why computer AI would have to partner with other mechanical forms of AI (run on non-computer hardware that, as of yet, does not exist, and would require humans to invent) for AI to take over the globe.

The literature suggests that ensemble coding, the ability to represent the gist of sets, may be an underlying mechanism for becoming familiar with newly encountered faces. This phenomenon was investigated by introducing a new training paradigm that involves incidental learning of target identities interspersed among distractors. The effectiveness of this training paradigm was explored in Study 1, which revealed that unfamiliar observers who learned the faces incidentally performed just as well as the observers who were instructed to learn the faces, and the intervening distractors did not disrupt familiarization. Using the same training paradigm, ensemble coding was investigated as an underlying mechanism for face familiarization in Study 2 by measuring familiarity with the targets at different time points using average images created either by seen or unseen encounters of the target. The results revealed that observers whose familiarity was tested using seen averages outperformed the observers who were tested using unseen averages, however, this discrepancy diminished over time. In other words, successful recognition of the target faces became less reliant on the previously encountered exemplars over time, suggesting an exemplar-independent representation that is likely achieved through ensemble coding. Taken together, the results from the current experiment provide direct evidence for ensemble coding as a viable underlying mechanism for face familiarization, that faces that are interspersed among distractors can be learned incidentally.

In my talk I will discuss the concept of a cognitive ontology, which defines the parts of the mind that psychologists and neuroscientsts aim to study. I will discuss the way in which ontologies have traditionally been defined, and then discuss ways in which ontology might be reconsidered in the context of computational approaches to cognition.

In this talk, Dr. Quandt will share results from behavioral and cognitive neuroscience studies from the past few years of her work in the Action & Brain Lab, an EEG lab at Gallaudet University, the world's premiere university for deaf and hard-of-hearing students. These results will center upon the question of how extensive knowledge of signed language changes, and in some cases enhances, people's perception and cognition. Evidence for this effect comes from studies of human biological motion using point light displays, self-report, and studies of action perception. Dr. Quandt will also discuss some of the lab's efforts in designing and testing a virtual reality environment in which users can learn American Sign Language from signing avatars (virtual humans).

Tom Froese is Assistant Professor at the Okinawa Institute of Science and Technology Graduate University (OIST), where he heads the Embodied Cognitive Science Unit. He is a cognitive scientist with a background in phenomenological philosophy, human-computer interaction, and complex systems theory. His interdisciplinary research centers on the role of agent-environment interaction in shaping cognition and consciousness, specifically when the interaction process involves sociality and technology. In this talk he will present current work in progress on “irruption theory”, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.

One challenge in exploring the internal representation of faces is the lack of controlled stimuli transformations. Researchers are often limited to verbalizable transformations in the creation of a dataset. An alternative approach to verbalization for interpretability is finding image-based measures that allow us to quantify image transformations. In this study, we explore whether PCA could be used to create controlled transformations to a face by testing the effect of these transformations on human perceptual similarity and on computational differences in Gabor, Pixel and DNN spaces. We found that perceptual similarity and the three image-based spaces are linearly related, almost perfectly in the case of the DNN, with a correlation of 0.94. This provides a controlled way to alter the appearance of a face. In experiment 2, the effect of familiarity on the perception of multidimensional transformations was explored. Our findings show that there is a positive relationship between the number of components transformed and both the perceptual similarity and the same three image-based spaces used in experiment 1. Furthermore, we found that familiar faces are rated more similar overall than unfamiliar faces. That is, a change to a familiar face is perceived as making less difference than the exact same change to an unfamiliar face. The ability to quantify, and thus control, these transformations is a powerful tool in exploring the factors that mediate a change in perceived identity.

The limited capacity of visual working memory constrains the quantity and quality of the information we can store in mind for ongoing processing. Research from our lab has demonstrated that verbal labeling/categorization of visual inputs increases its retention and fidelity in visual working memory. In this talk, I will outline the hypotheses that explain the interaction between visual and verbal inputs in working memory, leading to the boosts we observed. I will further show how manipulations of the categorical distinctiveness of the labels, the timing of their occurrence, to which item labels are applied, as well as their validity modulate the benefits one can draw from combining visual and verbal inputs to alleviate capacity limitations. Finally, I will discuss the implications of these results to our understanding of working memory and its interaction with prior knowledge.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.

Prosopometamorphopsia (PMO) is a disorder characterized by face perception distortions. People with PMO see facial features that appear to melt, stretch, and change size and position. I'll discuss research on PMO carried out by my lab and others that sheds light on the cognitive and neural organization of face perception. https://facedistortion.faceblind.org/

At any given moment the brain receives more sensory information than it can use to guide adaptive behaviour, creating the need for mechanisms that promote efficient processing of incoming sensory signals. One way in which the brain might reduce its sensory processing load is to encode successive presentations of the same stimulus in a more efficient form, a process known as neural adaptation. Conversely, when a stimulus violates an expected pattern, it should evoke an enhanced neural response. Such a scheme for sensory encoding has been formalised in predictive coding theories, which propose that recent experience establishes expectations in the brain that generate prediction errors when violated. In this webinar, Professor Jason Mattingley will discuss whether the encoding of elementary visual features is modulated when otherwise identical stimuli are expected or unexpected based upon the history of stimulus presentation. In humans, EEG was employed to measure neural activity evoked by gratings of different orientations, and multivariate forward modelling was used to determine how orientation selectivity is affected for expected versus unexpected stimuli. In mice, two-photon calcium imaging was used to quantify orientation tuning of individual neurons in the primary visual cortex to expected and unexpected gratings. Results revealed enhanced orientation tuning to unexpected visual stimuli, both at the level of whole-brain responses and for individual visual cortex neurons. Professor Mattingley will discuss the implications of these findings for predictive coding theories of sensory encoding. Professor Jason Mattingley is a Laureate Fellow and Foundation Chair in Cognitive Neuroscience at The University of Queensland. His research is directed toward understanding the brain processes that support perception, selective attention and decision-making, in health and disease.

The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of the excitatory synaptic plasticity remains largely understudied. Recent experiments showed that repeated cholinergic activation of 𝛼7 nACh receptors expressed in oriens-lacunosum-moleculare (OLM𝛼2) interneurons could induce LTP in SC-CA1 synapses. We used a biophysically realistic computational model to examine mechanistically how cholinergic activation of OLMa2 interneurons increases SC to CA1 transmission. Our results suggest that, when properly timed, activation of OLMa2 interneurons cancels the feedforward inhibition onto CA1 pyramidal cells by inhibiting fast-spiking interneurons that synapse on the same dendritic compartment as the SC, i.e., by disinhibiting the pyramidal cell dendritic compartment. Our work further describes the pairing of disinhibition with SC stimulation as a general mechanism for the induction of synaptic plasticity. We found that locally-reduced GABA release (disinhibition) paired with SC stimulation could lead to increased NMDAR activation and intracellular calcium concentration sufficient to upregulate AMPAR permeability and potentiate the excitatory synapse. Our work suggests that inhibitory synapses critically modulate excitatory neurotransmission and induction of plasticity at excitatory synapses. Our work also shows how cholinergic action on OLM interneurons, a mechanism whose disruption is associated with memory impairment, can down-regulate the GABAergic signaling into CA1 pyramidal cells and facilitate potentiation of the SC-CA1 synapse.

Although analogical reasoning has been assumed to involve insight and its associated “aha!” experience, the relationship between these phenomena has never been directly probed empirically. In this study we investigated the relationship between representational change and the “aha!” experience during analogical reasoning. A novel set of verbal analogy stimuli were developed for use as an insight task. Across two experiments, participants reported significantly stronger aha moments and showed greater evidence of representational change on trials with more semantically distant analogies. Further, the strength of reported aha moments was correlated with the degree to which participants’ descriptions of the analogies changed over the course of each trial. Lastly, we probed the individual differences associated with a tendency to report stronger "aha" experiences, particularly related to mood, curiosity, and reward responsiveness. The findings shed light on the affective components of analogical reasoning and suggest that measuring affective responses during such tasks may elucidate novel insights into the mechanisms of creative analogical reasoning.

The integrated conflict monitoring theory of Botvinick introduced cognitive demand into conflict monitoring research. We investigated effects of individual differences of cognitive demand and another determinant of conflict monitoring entitled reinforcement sensitivity on conflict monitoring. We showed evidence of differential variability of conflict monitoring intensity using the electroencephalogram (EEG), functional magnet resonance imaging (fMRI) and behavioral data. Our data suggest that individual differences of anxiety and reasoning ability are differentially related to the recruitment of proactive and reactive cognitive control (cf. Braver). Based on previous findings, the team of the Leue-Lab investigated new psychometric data on conflict monitoring and proactive-reactive cognitive control. Moreover, data of the Leue-Lab suggest the relevance of individual differences of conflict monitoring for the context of deception. In this respect, we plan new studies highlighting individual differences of the functioning of the Anterior Cingulate Cortex (ACC). Disentangling the role of individual differences in working memory-related cognitive demand, mental effort, and reinforcement-related processes opens new insights for cognitive-motivational approaches of information processing (Passcode to rewatch: 0R8v&m59).

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising and this typically evokes prediction error signatures in animal brains. In humans such mismatched expectations are often associated with an emotional response as well. Appropriate emotional responses are understood to be important for memory consolidation, suggesting that valence cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolishing error detection and surprise entirely is probably also maladaptive, as this might undermine the very mechanism that brains use to become better prediction machines. This paradoxical view of brain functions as an ongoing tug-of-war between prediction and surprise suggests a compelling new way to study and understand the evolution of consciousness in animals. I will present approaches to studying attention and prediction in the tiny brain of the fruit fly, Drosophila melanogaster. I will discuss how an ‘active’ sleep stage (termed rapid eye movement – REM – sleep in mammals) may have evolved in the first animal brains as a mechanism for optimizing prediction in motile creatures confronted with constantly changing environments. A role for REM sleep in emotional regulation could thus be better understood as an ancient sleep function that evolved alongside selective attention to maintain an adaptive balance between prediction and surprise. This view of active sleep has some interesting implications for the evolution of subjective awareness and consciousness.