Cognition seminars

In this talk, I will present the results of two recent systematic reviews and meta-analyses related to analogical reasoning and metaphor processing in autism, together with the results of a study that investigated verbal analogical reasoning and metaphor processing in the same sample of participants. Both metaphors and analogies rely on exploiting similarities, and they necessitate contextual processing. Nevertheless, our findings relating to metaphor processing and analogical reasoning showed distinct patterns. Whereas analogical reasoning emerged as a relative strength in autism, metaphor processing was found to be a relative weakness. Additionally, both meta-analytic studies investigated the relations between the level of intelligence of participants included in the studies, and the effect size of group differences between the autistic and typically developing (TD) samples. These analyses suggested in the case of analogical reasoning that the relative advantage of ASD participants might only be present in the case of individuals with lower levels of intelligence. By contrast, impairments in metaphor processing appeared to be more pronounced in the case of individuals with relatively lower levels of (verbal) intelligence. In our experimental study, we administered both verbal analogies and metaphors to the same sample of high-functioning autistic participants and TD controls. The two groups were matched on age, verbal IQ, working memory and educational background. Our aim was to understand better the similarities and differences between processing analogies and metaphors, and to see whether the advantage in analogical reasoning and disadvantage in metaphor processing is universal in autism.

Animals possess the ability to effortlessly and precisely time their actions even though information received from the world is often ambiguous and is inadvertently transformed as it passes through the nervous system. With such uncertainty pervading through our nervous systems, we could expect that much of human and animal behavior relies on inference that incorporates an important additional source of information, prior knowledge of the environment. These concepts have long been studied under the framework of Bayesian inference with substantial corroboration over the last decade that human time perception is consistent with such models. We, however, know little about the neural mechanisms that enable Bayesian signatures to emerge in temporal perception. I will present our work on three facets of this problem, how Bayesian estimates are encoded in neural populations, how these estimates are used to generate time intervals, and how prior knowledge for these tasks is acquired and optimized by neural circuits. We trained monkeys to perform an interval reproduction task and found their behavior to be consistent with Bayesian inference. Using insights from electrophysiology and in silico models, we propose a mechanism by which cortical populations encode Bayesian estimates and utilize them to generate time intervals. Thereafter, I will present a circuit model for how temporal priors can be acquired by cerebellar machinery leading to estimates consistent with Bayesian theory. Based on electrophysiology and anatomy experiments in rodents, I will provide some support for this model. Overall, these findings attempt to bridge insights from normative frameworks of Bayesian inference with potential neural implementations for the acquisition, estimation, and production of timing behaviors.

Neocortical-hippocampal interactions during off-line periods such as slow-wave sleep are implicated in memory processing. In particular, recent memory traces are replayed in hippocampus during some sharp-wave ripple (SWR) events, and these replay events are positively correlated with neocortical memory trace reactivation. A prevalent model is that SWR arise ‘spontaneously’ in CA3 and propagate recent memory ‘indices’ outward to the neocortex to enable memory consolidation there; however, the spatiotemporal distribution of neocortical activation relative to SWR is incompletely understood. We used wide-field optical imaging to study voltage and glutamate release transients in dorsal neocortex in relation to CA1 multiunit activity (MUA) and SWR of sleeping and urethane anesthetized mice. Modulation of voltage and glutamate release signals in relation to SWRs varied across superficial neocortical regions, and it was largest in posteromedial regions surrounding retrosplenial cortex (RSC), which receives strong hippocampal output connections. Activity tended to spread sequentially from more medial towards more lateral regions. Contrary to the unidirectional hypothesis, activation exhibited a continuum of timing relative to SWRs, varying from neocortex leading to neocortex lagging the SWRs (± ~250 msec). The timing continuum was correlated with the skewness of peri-SWR hippocampal MUA and with a tendency for some SWR to occur in clusters. Thus, contrary to the model in which SWRs arise spontaneously in hippocampus, neocortical activation often precedes SWRs and may thus constitute a trigger event in which neocortical information seeds associative reactivation of hippocampal ‘indices’.

With the Donders Inclusion Seminars, we celebrate diversity. Please join us on Apr. 21st 2021 at 15.00 (CET) as we next welcome Dr. Silvy Collin of Tilburg University on Crowdcast. Her seminar is entitled "Schemas and schema-mediated memory". To read the abstract and register for the event visit: https://www.crowdcast.io/e/donders-inclusion-3

When listening to music, we typically lock onto and move to a beat (1-6 Hz). Behavioral studies on such synchronization (Repp 2005) abound, yet the neural mechanisms remain poorly understood. Some models hypothesize an array of self-sustaining entrainable neural oscillators that resonate when forced with rhythmic stimuli (Large et al. 2010). In contrast, our formulation focuses on event time estimation and plasticity: a neuronal beat generator that adapts its intrinsic frequency and phase to match the extermal rhythm. The model quickly learns new rhythms, within a few cycles as found in human behavior. When the stimulus is removed the beat generator continues to produce the learned rhythm in accordance with a synchronization continuation task.

Interpretation of interval timing data generated from animal models is complicated by ostensible motivational effects which arise from the delay-to-reward imposed by interval timing tasks, as well as overlap between timed and non-timed responses. These factors become increasingly prevalent at longer intervals. To address these concerns, two adjustments to long interval timing tasks are proposed. First, subjects should be afforded with reinforced non-timing behaviors concurrent with timing. Second, subjects should initiate the onset of timed stimuli. Under these conditions, interference by extraneous behavior would be detected in the rate of concurrent non- timing behaviors, and changes in motivation would be detected in the rate at which timed stimuli are initiated. In a task with these characteristics, rats initiated a concurrent fixed-interval (FI) random-ratio (RR) schedule of reinforcement. This design facilitated response-initiated timing behavior, even at increasingly long delays. Pre-feeding manipulations revealed an effect on the number of initiated trials, but not on the timing peak function.

Pain is a major clinical problem affecting 1 in 5 people in the world. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. Computational models of pain consider how the brain processes noxious information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual and/or predictive inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. I will discuss how they may comprise a parallel hierarchical architecture that combines pain inference, information-seeking, and adaptive value-based control. Finally, I will discuss whether and how these learning processes might contribute to chronic pain.

The study of altered states of consciousness has long had the potential to provide important insight into the nature of consciousness. In recent years there has been a resurgence of research and public interest in atypical or altered states of consciousness. These have focused both on conditions in which consciousness is considered to be impaired due to brain trauma or enhanced in some way through mediation practices or ingestion of psychedelics. The talk will begin with a brief overview of recent scientific approaches to understanding these different types of altered consciousness. The remainder of the talk will focus on lab-based experiments conducted by myself and others looking at the effects of serotoninergic hallucinogens (i.e. psilocybin and LSD) on perceptual and cognitive function. Together this body of research provides important new insights for the scientific study of consciousness and an initial understanding of the neuropharmacological mechanisms underlying conscious experience.

In this talk I’ll discuss translation of findings of multisensory facilitation of learning to cognitive training. I’ll first review some early findings of multisensory facilitation of learning and then discuss how we have been translating these basic science approaches into gamified training interventions to improve cognitive functions. I’ll touch on approaches to training vision, hearing and working memory that we are developing at the UCR Brain Game Center for Mental Fitness and Well-being. I look forward to discussing both the basic science but also the complexities of how to translate approaches from basic science into the more complex frameworks often used in interventions.

Mental simulation—the capacity to imagine what will or what could be—is a salient feature of human cognition, playing a key role in a wide range of cognitive abilities. In artificial intelligence, the last few years have seen the development of methods which are analogous to mental models and mental simulation. In this talk, I will discuss recent methods in deep learning for constructing such models from data and learning to use them via reinforcement learning, and compare such approaches to human mental simulation. While a number of challenges remain in matching the capacity of human mental simulation, I will highlight some recent progress on developing more compositional and efficient model-based algorithms through the use of graph neural networks and tree search.

How is it that some memories fade in a day while others last forever? The formation of long-lasting (remote) memories depends on the coordinated activity between the hippocampus and frontal cortices, but the timeline of these interactions is debated. Astrocytes, star-shaped glial cells, sense and modify neuronal activity, but their role in remote memory is scarcely explored. We manipulated the activity of hippocampal astrocytes during memory acquisition and discovered it impaired remote, but not recent, memory retrieval. We also revealed a massive recruitment of cortical-projecting hippocampal neurons during memory acquisition, a process that is specifically inhibited by astrocytic manipulation. Finally, we directly inhibited this projection during memory acquisition to prove its necessity for the formation of remote memory. Our findings reveal that the foundation of remote memory can be established during acquisition with projection-specific effect of astrocytes.