Cognition seminars

Recent years have seen a considerable surge of research on interest-based engagement, examining how and why people are engaged in activities without relying on extrinsic rewards. However, the field of inquiry has been somewhat segregated into three different research traditions which have been developed relatively independently -- research on curiosity, interest, and trait curiosity/interest. The current talk sets out an integrative perspective; the reward-learning framework of knowledge acquisition. This conceptual framework takes on the basic premise of existing reward-learning models of information seeking: that knowledge acquisition serves as an inherent reward, which reinforces people’s information-seeking behavior through a reward-learning process. However, the framework reveals how the knowledge-acquisition process is sustained and boosted over a long period of time in real-life settings, allowing us to integrate the different research traditions within reward-learning models. The framework also characterizes the knowledge-acquisition process with four distinct features that are not present in the reward-learning process with extrinsic rewards -- (1) cumulativeness, (2) selectivity, (3) vulnerability, and (4) under-appreciation. The talk describes some evidence from our lab supporting these claims.

The neurobiology of decision-making is informed by neurons capable of representing information over time scales of seconds. Such neurons were initially characterized in studies of spatial working memory, motor planning (e.g., Richard Andersen lab) and spatial attention. For decision-making, such neurons emit graded spike rates, that represent the accumulated evidence for or against a choice. They establish the conduit between the formation of the decision and its completion, usually in the form of a commitment to an action, even if provisional. Indeed, many decisions appear to arise through an accumulation of noisy samples of evidence to a terminating threshold, or bound. Previous studies show that single neurons in the lateral intraparietal area (LIP) represent the accumulation of evidence when monkeys make decisions about the direction of random dot motion (RDM) and express their decision with a saccade to the neuron’s preferred target. The mechanism of termination (the bound) is elusive. LIP is interconnected with other brain regions that also display decision-related activity. Whether these areas play roles in the decision process that are similar to or fundamentally different from that of LIP is unclear. I will present new unpublished experiments that begin to resolve these issues by recording from populations of neurons simultaneously in LIP and one of its primary targets, the superior colliculus (SC), while monkeys make difficult perceptual decisions.

The hippocampus and the amygdala are two structures required for emotional memory. While the hippocampus encodes the contextual part of the memory, the amygdala processes its emotional valence. During Non-REM sleep, the hippocampus displays high frequency oscillations called “ripples”. Our early work shows that the suppression of ripples during sleep impairs performance on a spatial task, underlying their crucial role in memory consolidation. We more recently showed that the joint amygdala-hippocampus activity linked to aversive learning is reinstated during the following Non-REM sleep epochs, specifically during ripples. This mechanism potentially sustains the consolidation of aversive associative memories during Non REM sleep. On the other hand, REM sleep is associated with regular 8 Hz theta oscillations, and is believed to play a role in emotional processing. A crucial, initial step in understanding this role is to unravel sleep dynamics related to REM sleep in the hippocampus-amygdala network

Several studies have showed that face stimuli elicit extremely fast and involuntary saccadic responses toward them, relative to other categories of visual stimuli. In the talk, I will mainly focus on a quite recent research done in our team that investigated to what extent face stimuli influence the programming and execution of saccades. In this research, two experiments were performed using a saccadic choice task: two images (one with a face, one with a vehicle) were simultaneously displayed in the left and right visual fields of participants who had to execute a saccade toward the image (Experiment 1) or toward a cross added in the center of the image (Experiment 2) containing a target stimulus (a face or a vehicle). As expected participants were faster to execute a saccade toward a face than toward a vehicle and did less errors. We also observed shorter saccades toward vehicle than face targets, even if participants were explicitly asked to perform their saccades toward a specific location (Experiment 2). Further analyses, that I will detailed in the talk, showed that error saccades might be interrupted in mid-fight to initiate a concurrently programmed corrective saccade.

The last two decades have seen a rise in online projects such as Wikipedia or OpenStreetMap in which people collaborate to create a common product. Contributors in such projects often work together sequentially. Essentially, the first contributor generates an entry (e.g., a Wikipedia article) independently which is then adjusted in the following by other contributors by adding or correcting information. We refer to this way of working together as sequential collaboration. This process has not yet been studied in the context of judgment and decision making even though research has demonstrated that Wikipedia and OpenStreetMap yield very accurate information. In this talk, I give first insights into the structure of sequential collaboration, how adjusting each other’s judgments can yield more accurate final estimates, which boundary conditions need to be met, and which underlying mechanisms may be responsible for successful collaboration. A preprint is available at https://psyarxiv.com/w4xdk/

Abstract relations are considered the pinnacle of human cognition, allowing analogical and logical reasoning, and possibly setting humans apart from other animal species. Such relations cannot be represented in a perceptual code but can easily be represented in a propositional language of thought, where relations between objects are represented by abstract discrete symbols. Focusing on the abstract relations same and different, I will show that (1) there is a discontinuity along ontogeny with respect to the representations of abstract relations, but (2) young infants already possess representations of same and different. Finally, (3) I will investigate the format of representation of abstract relations in young infants, arguing that those representations are not discrete, but rather built by juxtaposing abstract representations of entities.

During natural behavior animals dynamically switch between different behaviors, yet little is known about how the brain performs behavioral-switches. Navigation is a complex dynamic behavior that enables testing these kind of behavioral switches: It requires the animal to know its own allocentric (world-centered) location within the environment, while also paying attention to incoming sudden events such as obstacles or other conspecifics – and therefore the animal may need to rapidly switch from representing its own allocentric position to egocentrically representing ‘things out-there’. Here we used an ethological task where two bats flew together in a very large environment (130 meters), and had to switch between two behaviors: (i) navigation, and (ii) obstacle-avoidance during ‘cross-over’ events with the other bat. Bats increased their echolocation click-rate before a cross-over, indicating spatial attention to the other bat. Hippocampal CA1 neurons represented the bat’s own position when flying alone (allocentric place-coding); surprisingly, when meeting the other bat, neurons switched very rapidly to jointly representing the inter-bat distance × position (egocentric × allocentric coding). This switching to a neuronal representation of the other bat was correlated on a trial-by-trial basis with the attention signal, as indexed by the bat’s echolocation calls – suggesting that sensory attention is controlling these major switches in neural coding. Interestingly, we found that in place-cells, the different place-fields of the same neuron could exhibit very different tuning to inter-bat distance – creating a non-separable coding of allocentric position × egocentric distance. Together, our results suggest that attentional switches during navigation – which in bats can be measured directly based on their echolocation signals – elicit rapid dynamics of hippocampal spatial coding. More broadly, this study demonstrates that during natural behavior, when animals often switch between different behaviors, neural circuits can rapidly and flexibly switch their core computations.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

Even young children seem to have an early understanding of the world around them, and the people in it. Before children can reliably say "ball", "wall", or "Saul", they expect balls to not go through walls, and for Saul to go right for a ball (if there's no wall). What is the formal conceptual structure underlying this commonsense reasoning about objects and agents? I will raise several possibilities for models underlying core intuitive physics as a way of talking about models of core knowledge and intuitive theories more generally. In particular, I will present some recent ML work trying to capture early expectations about object solidly, cohesion, and permanence, that relies on a rough-derendering approach.

In highly volatile environments, performing actions that address current needs and desires is an ongoing challenge for living organisms. For example, the predictive value of environmental signals needs to be updated when predicted and actual outcomes differ. Furthermore, organisms also need to gain control over the environment through actions that are expected to produce specific outcomes. The data to be presented will show that these processes are highly reliant on thalamocortical circuits wherein thalamic nuclei make a critical contribution to adaptive decision-making, challenging the view that the thalamus only acts as a relay station for the cortical stage. Over the past few years, our work has highlighted the specific contribution of multiple thalamic nuclei in the ability to update the predictive link between events or the causal link between actions and their outcomes via the combination of targeted thalamic interventions (lesion, chemogenetics, disconnections) with behavioral procedures rooted in experimental psychology. We argue that several features of thalamocortical architecture are consistent with a prominent role for thalamic nuclei in shaping mental representations.

Cognitive control involves the flexible allocation of mental resources during goal-directed behaviour and comprises three correlated but distinct domains—inhibition, task shifting, and working memory. Healthy ageing is characterised by reduced cognitive control. Professor Cheryl Grady and her team have been studying the influence of age differences in large-scale brain networks on the three control processes in a sample of adults from 20 to 86 years of age. In this webinar, Professor Cheryl Grady will describe three aspects of this work: 1) age-related dedifferentiation and reconfiguration of brain networks across the sub-domains 2) individual differences in the relation of task-related activity to age, structural integrity and task performance for each sub-domain 3) modulation of brain signal variability as a function of cognitive load and age during working memory. This research highlights the reduction in dynamic range of network activity that occurs with ageing and how this contributes to age differences in cognitive control. Cheryl Grady is a senior scientist at the Rotman Research Institute at Baycrest, and Professor in the departments of Psychiatry and Psychology at the University of Toronto. She held the Canada Research Chair in Neurocognitive Aging from 2005-2018 and was elected as a Fellow of the Royal Society of Canada in 2019. Her research uses MRI to determine the role of brain network connectivity in cognitive ageing.

The ability to recognize someone’s voice spans a broad spectrum with phonagnosia on the low end and super recognition at the high end. Yet there is no standardized test to measure the individual ability to learn and recognize newly-learnt voices with samples of speech-like phonetic variability. We have developed the Jena Voice Learning and Memory Test (JVLMT), a 20 min-test based on item response theory and applicable across different languages. The JVLMT consists of three phases in which participants are familiarized with eight speakers in two stages and then perform a three-alternative forced choice recognition task, using pseudo sentences devoid of semantic content. Acoustic (dis)similarity analyses were used to create items with different levels of difficulty. Test scores are based on 22 Rasch-conform items. Items were selected and validated in online studies based on 232 and 454 participants, respectively. Mean accuracy is 0.51 with an SD of .18. The JVLMT showed high and moderate correlations with convergent validation tests (Bangor Voice Matching Test; Glasgow Voice Memory Test) and a weak correlation with a discriminant validation test (Digit Span). Empirical (marginal) reliability is 0.66. Four participants with super recognition (at least 2 SDs above the mean) and 7 participants with phonagnosia (at least 2 SDs below the mean) were identified. The JVLMT is a promising screen too for voice recognition abilities in a scientific and neuropsychological context.

Analogies are widely used by teachers and coaches of different movement disciplines, serving a role during the learning phase of a new skill, and honing one’s performance to a competitive level. In previous studies, analogies improved motor control in various tasks and across age groups. Our study aimed to evaluate the efficacy of analogies throughout the learning process, using kinematic measures for an in-depth analysis. We tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. The experiment included a drawing task, in which subjects were asked to connect four dots into a closed shape, and a mirror game, in which subjects tracked an oval that moved across the screen. After establishing a baseline, subjects were given an analogy, explicit instructions, or no further instruction. We compared their improvement in overall skill, accuracy, and speed. Subjects in the analogy and explicit groups improved their performance in the drawing task, while significant differences were found in the mirror game only for slow movements between analogy and controls. In conclusion, analogies are an important tool for teachers and coaches, and more research is needed to understand how to apply them for maximum results. They can rapidly change motor control and strategy but may also affect only some aspects of a movement and not others. Careful thought is needed to construct an effective analogy that encompasses relevant movement facets, as well as the practitioner’s personal background and experience.

During primate evolution, prefrontal cortex (PFC) expanded substantially relative to other cortical areas. The expansion of PFC circuits likely supported the increased cognitive abilities of humans and anthropoids to plan, evaluate, and decide between different courses of action. But what do these circuits compute as a decision is being made, and how can they be related to anatomical specialisations within and across PFC? To address this, we recorded PFC activity during value-based decision making using single unit recording in non-human primates and magnetoencephalography in humans. At a macrocircuit level, we found that value correlates differ substantially across PFC subregions. They are heavily shaped by each subregion’s anatomical connections and by the decision-maker’s current locus of attention. At a microcircuit level, we found that the temporal evolution of value correlates can be predicted using cortical recurrent network models that temporally integrate incoming decision evidence. These models reflect the fact that PFC circuits are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. Our findings build upon recent work describing economic decision making as a process of attention-weighted evidence integration across time.