Cognition seminars

The hippocampus is implicated in memory formation, and neurons in the hippocampus take part in replay sequences, time-compressed reactivations of trajectories through space the animal has previously explored. These replay sequences have been proposed to be a form of memory for previously experienced places. I will present work exploring how these replays appear and change with experience. By recording from large ensembles of hippocampal neurons as rats explored novel and familiar linear tracks in various experiments, we found that hippocampal replays appear after a single experience and slow down with subsequent experience as greater detail is incorporated. We also investigated hover-and-jump dynamics within replays that are associated with the slow gamma (25-50Hz) oscillation in the LFP and found that replays slow down by adding more hover locations, corresponding to depiction of the behavioral trajectory with increased resolution. Thus, replays can reflect single experiences, and be rapidly modified by subsequent experience to incorporate more detail, consistent with their proposed role as a basic mechanism of hippocampally dependent memory.

David Chalmers’s (1995) hard problem famously states: “It is widely agreed that experience arises from a physical basis, but we have no good explanation of why and how it so arises.” Thomas Nagel (1974) wrote something similar: “If we acknowledge that a physical theory of mind must account for the subjective character of experience, we must admit that no presently available conception gives us a clue about how this could be done.” This presentation will point the way towards the long-sought “good explanation” -- or at least it will provide “a clue”. I will make three points: (1) It is unfortunate that cognitive science took vision as its model example when looking for a ‘neural correlate of consciousness’ because cortical vision (like most cognitive processes) is not intrinsically conscious. There is not necessarily ‘something it is like’ to see. (2) Affective feeling, by contrast, is conscious by definition. You cannot feel something without feeling it. Moreover, affective feeling, generated in the upper brainstem, is the foundational form of consciousness: prerequisite for all the higher cognitive forms. (3) The functional mechanism of feeling explains why and how it cannot go on ‘in the dark’, free of any inner feel. Affect enables the organism to monitor deviations from its expected self-states in uncertain situations and thereby frees homeostasis from the limitations of automatism. As Nagel says, “An organism has conscious mental states if and only if there is something that it is like to be that organism—something it is like for the organism.” Affect literally constitutes the sentient subject.

I will present work on a novel fMRI analysis method that allows us to investigate sequential reactivation in the hippocampus. Our method focuses on analysing the time courses of probabilistic multivariate classifiers and allows us to infer the presence and frequency of fast sequential reactivation events. Using a paradigm in which we controlled the speed of sequential visually elicited activations, we validated the method in visual cortex for event sequences with only 32 ms between items. We show that detectability remains possible if low signal-to-noise ratio and when sequence events occur at unknown times. In a preliminary analysis, we show that even the exposure to our visual paradigm elicits reactivations in visual cortex at rest following the task. I then present work in which we tested how representations influence replay by asking whether transitions between task-state representations are reactivated at rest during hippocampal replay events. Participants learned to make decisions about ambiguous stimuli that depended on past events and attentionally filtered stimulus processing. FMRI signals during rest periods following this task indicated sequential reactivation of task states. These results indicate that adaptive task state representations are computed and replayed at different cortical sites. In combination with other methods, fMRI may allow us to unravel this coordinated nature of replay.

Learning the consequences our choices have as we interact with our world is critical for flexible behavior. Relational knowledge of one’s environment gives structure to otherwise-individual one-to-one stimulus-outcome mappings, providing a substrate to globally update behavioral contingencies in the face of changes in the landscape of reward. In the brain, this relational knowledge is thought to be encoded in the hippocampus (HPC) in the form of a cognitive map, while prefrontal regions, such as orbitofrontal cortex (OFC), are thought to instantiate subjective estimates of location on the map, though direct neurophysiological evidence is lacking. In this talk, I will present recent work demonstrating the causal relationship between HPC and OFC as nonhuman primates perform a reward learning task requiring them to learn and maintain knowledge of changing stimulus-outcome associations. I will then provide direct evidence that single primate hippocampal neurons represent an abstract map of the value space defined by the task. Finally, I use behavioral modeling to highlight one possible strategy by which knowledge of value space is exploited by animals to detect changes in choice-outcome mappings and proactively update their behavior in response.

Humans’ ability to perceive and abstract relational structure is fundamental to our learning. It allows us to acquire knowledge all the way from linguistic grammar to spatial knowledge to social structures. How does a learner begin to perceive structure in the world? Why do we sometimes fail to see structural commonalities across events? To begin to answer these questions, I attempt to bridge two large, yet somewhat separate research traditions in understanding human’s structural abstraction: rule learning (Marcus et al., 1999) and analogical learning (Gentner, 1989). On the one hand, rule learning research has shown humans’ domain-general ability and ease—as early as 7-month-olds—to abstract structure from a limited experience. On the other hand, analogical learning works have shown robust constraints in structural abstraction: young learners prefer object similarity over relational similarity. To understand this seeming paradox between ease and difficulty, we conducted a series of studies using the classic rule learning paradigm (Marcus et al., 1999) but with an analogical (object vs. relation) twist. Adults were presented with 2-minute sentences or events (syllables or shapes) containing a rule. At test, they had to choose between rule abstraction and object matches—the same syllable or shape they saw before. Surprisingly, while in the absence of object matches adults were perfectly capable of abstracting the rule, their ability to do so declined sharply when object matches were present. Our initial results suggest that rule learning ability may be subject to the usual constraints and signatures of analogical learning: preference to object similarity can dampen rule generalization. Humans’ abstraction is also concrete at the same time.

From a developmental standpoint, it has been argued that two major complementary factors contribute to the development of analogy comprehension: world knowledge and executive functions. Here I will provide evidence in support of the second view. Beyond paradigms that manipulate task difficulty (e.g., number and types of distractors and semantic distance between domains) we will provide eye-tracking data that describes differences in the way children and adults compare the base and target domains in analogy problems. We will follow the same approach with ageing people. This latter population provides a unique opportunity to disentangle the contribution of knowledge and executive processes in analogy making since knowledge is (more than) preserved and executive control is decreasing. Using this paradigm, I will show the extent to which world knowledge (assessed through vocabulary) compensates for decreasing executive control in older populations. Our eye-tracking data suggests that, to a certain extent, differences between younger and older adults are analogous to the differences between younger adults and children in the way they compare the base and the target domains in analogy problems.

In this talk, I will present some data showing that people’s similarity judgments among word pairs reflect distinctions between abstract semantic relations like contrast, cause-effect, or part-whole. Further, the extent that individual participants’ similarity judgments discriminate between abstract semantic relations was linearly associated with both fluid and crystallized verbal intelligence, albeit more strongly with fluid intelligence. Finally, I will compare three models according to their ability to predict these similarity judgments. All models take as input vector representations of individual word meanings, but they differ in their representation of relations: one model does not represent relations at all, a second model represents relations implicitly, and a third model represents relations explicitly. Across the three models, the third model served as the best predictor of human similarity judgments suggesting the importance of explicit relation representation to fully account for human semantic cognition.

Learning depends on both the learning mechanism and the regularities in the training material, yet most research on human and machine learning focus on the discovering the mechanisms that underlie powerful learning. I will present evidence from our research focusing on the statistical structure of infant visual learning environments. The findings suggest that the statistical structure of those learning environments are not like those used in laboratory experiments on visual learning, in machine learning, or in our adult assumptions about how teach visual categories. The data derive from our use of head cameras and head-mounted eye trackers capturing FOV experiences in the home as well as in simulated home environments in the laboratory. The participants range from 1 month of age to 24 months. The observed statistical structure offers new insights into the developmental foundations of visual object recognition and suggest a computational rethinking of the problem of visual category formation. The observed environmental statistics also have direct implications for understanding the development of cortical visual systems.

The stream of consciousness refers to ideas, images, and memories that meander across the mind when we are otherwise unoccupied. The standard view is that these thoughts are associationistic in character and they arise from subpersonal processes—we are for the most part passive observers of them. Drawing on a series of laboratory studies we have conducted as well as Buddhist models of mind, I argue that these views are importantly incorrect. On the alternative view I put forward, these thoughts arise from minimal decision processes, which lie in a grey zone: They are both manifestations of agency as well as obstacles to it.

The Neural Engineering Framework has been used to create a wide variety of biologically realistic brain simulations that are capable of performing simple cognitive tasks (remembering a list, counting, etc.). This includes the largest existing functional brain model. This talk will describe this method, and show some examples of using it to take high-level cognitive algorithms and convert them into a neural network that implements those algorithms. Overall, this approach gives us new ways of thinking about how the brain works and what sorts of algorithms it is capable of performing.

Understanding and remembering realistic experiences in our everyday lives requires activating many kinds of structured knowledge about the world, including spatial maps, temporal event scripts, and semantic relationships. My recent projects have explored the ways in which we build up this schematic knowledge (during a single experiment and across developmental timescales) and can strategically deploy them to construct event representations that we can store in memory or use to make predictions. I will describe my lab's ongoing work developing new experimental and analysis techniques for conducting functional MRI experiments using narratives, movies, poetry, virtual reality, and "memory experts" to study complex naturalistic schemas.

The neural mechanisms underlying decision-making are typically examined by statistical analysis of large numbers of trials from sequentially recorded single neurons. Averaging across sequential recordings, however, obscures important aspects of decision-making such as variations in confidence and 'changes of mind' (CoM) that occur at variable times on different trials. I will show that the covert decision variables (DV) can be tracked dynamically on single behavioral trials via simultaneous recording of large neural populations in prefrontal cortex. Vacillations of the neural DV, in turn, identify candidate CoM in monkeys, which closely match the known properties of human CoM. Thus simultaneous population recordings can provide insight into transient, internal cognitive states that are otherwise undetectable.

Cognitive tasks typically require the integration of working memory, contextual processing, and planning to be carried out in close coordination. However, these computations are typically studied within neuroscience as independent modular processes in the brain. In this talk I will present an alternative view, that neural representations of mappings between expected stimuli and contingent goal actions can unify working memory and planning computations. We term these stored maps contingency representations. We developed a "conditional delayed logic" task capable of disambiguating the types of representations used during performance of delay tasks. Human behaviour in this task is consistent with the contingency representation, and not with traditional sensory models of working memory. In task-optimized artificial recurrent neural network models, we investigated the representational geometry and dynamical circuit mechanisms supporting contingency-based computation, and show how contingency representation explains salient observations of neuronal tuning properties in prefrontal cortex. Finally, our theory generates novel and falsifiable predictions for single-unit and population neural recordings.