Cognition seminars

Holding information in working memory is essential for cognition, but removing unwanted thoughts is equally important. There is great flexibility in how we can manipulate information in working memory, but the processes and consequences of these operations are poorly understood. In this talk I will discuss our recent findings using multivariate pattern analyses of fMRI brain data to demonstrate the successful removal of information from working memory using three different strategies: suppressing a specific thought, replacing a thought with a different one, and clearing the mind of all thought. These strategies are supported by distinct brain regions and have differential consequences on the encoding of new information. I will discuss implications of these results on theories of memory and I will highlight some new directions involving the use of real-time neurofeedback to investigate causal links between brain and behavior.

Analogical reasoning remains foundational to the human ability to forge meaningful patterns within the sea of information that continually inundates the senses. Yet, meaningful patterns rely not only on the recognition of attributional similarities but also dissimilarities. Just as the perception of images rests on the juxtaposition of lightness and darkness, reasoning relationally requires systematic attention to both similarities and dissimilarities. With that awareness, my colleagues and I have expanded the study of relational reasoning beyond analogous reasoning and attributional similarities to highlight forms based on the nature of core dissimilarities: anomalous, antinomous, and antithetical reasoning. In this presentation, I will delineate the character of these relational reasoning forms; summarize procedures and measures used to assess them; overview key research findings; and describe how the forms of relational reasoning work together in the performance of complex problem solving. Finally, I will share critical next steps for research which has implications for instructional practice.

Predictive learning hypotheses posit that the neocortex learns a hierarchical model of the structure of features in the environment. Under these hypotheses, expected or predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of data, with unexpected features then driving changes in the representation of incoming stimuli. This is supported by numerous studies in early sensory cortices showing that pyramidal neurons respond particularly strongly to unexpected stimulus events. However, it remains unknown how their responses govern subsequent changes in stimulus representations, and thus, govern learning. Here, I present results from our study of layer 2/3 and layer 5 pyramidal neurons imaged in primary visual cortex of awake, behaving mice using two-photon calcium microscopy at both the somatic and distal apical planes. Our data reveals that individual neurons and distal apical dendrites show distinct, but predictable changes in unexpected event responses when tracked over several days. Considering existing evidence that bottom-up information is primarily targeted to somata, with distal apical dendrites receiving the bulk of top-down inputs, our findings corroborate hypothesized complementary roles for these two neuronal compartments in hierarchical computing. Altogether, our work provides novel evidence that the neocortex indeed instantiates a predictive hierarchical model in which unexpected events drive learning.

Sensory stimuli are often predictable consequences of one’s actions, and behavior exerts a correspondingly strong influence over sensory responses in the brain. Closed-loop experiments with the ability to control the sensory outcomes of specific animal behaviors have revealed that neural responses to self-generated sounds are suppressed in the auditory cortex, suggesting a role for prediction in local sensory processing. However, it is unclear whether this phenomenon derives from a precise movement-based prediction or how it affects the neural representation of incoming stimuli. We address these questions by designing a behavioral paradigm where mice learn to expect the predictable acoustic consequences of a simple forelimb movement. Neuronal recordings from auditory cortex revealed suppression of neural responses that was strongest for the expected tone and specific to the time of the sound-associated movement. Predictive suppression in the auditory cortex was layer-specific, preceded by the arrival of movement information, and unaffected by behavioral relevance or reward association. These findings illustrate that expectation, learned through motor-sensory experience, drives layer-specific predictive processing in the mouse auditory cortex.

Given the complex and dynamic nature of our social relationships, the human brain needs to quickly learn and adapt to new social situations. The breakdown of any of these computations could lead to social deficits, as observed in many psychiatric disorders. In this talk, I will present our recent neurocomputational and intracranial work that attempts to model both 1) how humans dynamically adapt beliefs about other people and 2) how individuals can exert influence over social others through model-based forward thinking. Lastly, I will present our findings of how impaired social computations might manifest in different disorders such as addiction, delusion, and autism. Taken together, these findings reveal the dynamic and proactive nature of human interactions as well as the clinical significance of these high-order social processes.

In an ever-changing environment, uncertainty is omnipresent. To deal with this, organisms have evolved mechanisms that allow them to take advantage of environmental regularities in order to make decisions robustly and adjust their behavior efficiently, thus maximizing their chances of survival. In this talk, I will present behavioral evidence that animals perform model-based state inference to predict environmental state changes and adjust their behavior rapidly, rather than slowly updating choice values. This model-based inference process can be described using Bayesian change-point models. Furthermore, I will show that neural populations in the prefrontal cortex accurately predict behavioral switches, and that the activity of these populations is associated with Bayesian estimates. In addition, we will see that learning leads to the emergence of a high-dimensional representational subspace that can be reused when the animals re-learn a previously learned set of action-value associations. Altogether, these findings highlight the role of the PFC in representing a belief about the current state of the world.

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.

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.

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.

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/

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.