Cognitive Psychology seminars

Multisensory responses are ubiquitous in so-called unisensory cortex. However, despite their prevalence, we have very little understanding of what – if anything - they contribute to perception. In this talk I will focus on audio-visual integration in auditory cortex. Anatomical tracing studies highlight visual cortex as one source of visual input to auditory cortex. Using cortical cooling we test the hypothesis that these inputs support audiovisual integration in ferret auditory cortex. Behavioural studies in humans support the idea that visual stimuli can help listeners to parse an auditory scene. This effect is paralleled in single units in auditory cortex, where responses to a sound mixture can be determined by the timing of a visual stimulus such that sounds that are temporally coherent with a visual stimulus are preferentially represented. Our recent data therefore support the idea that one role for the early integration of auditory and visual signals in auditory cortex is to support auditory scene analysis, and that visual cortex plays a key role in this process.

The seminar will first outline the architecture of the human mind, specifying general and domain-specific mental processes. The place of causal reasoning and its relations with the other processes will be specified. Experimental, psychometric, developmental, and brain-based evidence will be summarized. The main message of the talk is that causal thought involves domain-specific core processes rooted in perception and served by special brain networks which capture interactions between objects. With development, causal reasoning is increasingly associated with a general abstraction system which generates general principles underlying inductive, analogical, and deductive reasoning and also heuristics for specifying causal relations. These associations are discussed in some detail. Possible implications for artificial intelligence and educational implications are also discussed.

Selection is the hallmark of attention: processing improves for attended items but is relatively impaired for unattended items. It is well known that visual spatial attention changes sensory signals and perception in this selective fashion. In the work I will present, we asked whether and how attentional selection happens across time. First, our experiments revealed that voluntary temporal attention (attention to specific points in time) is selective, resulting in perceptual tradeoffs across time. Second, we measured small eye movements called microsaccades and found that directing voluntary temporal attention increases the stability of the eyes in anticipation of an attended stimulus. Third, we developed a computational model of dynamic attention, which proposes specific mechanisms underlying temporal attention and its selectivity. Lastly, I will mention how we are testing predictions of the model with MEG. Altogether, this research shows how precisely timed voluntary attention helps manage inherent limits in visual processing across short time intervals, advancing our understanding of attention as a dynamic process.

Working memory is the sketchpad of consciousness, the fundamental mechanism the brain uses to gain volitional control over its thoughts and actions. For the past 50 years, working memory has been thought to rely on cortical neurons that fire continuous impulses that keep thoughts “online”. However, new work from our lab has revealed more complex dynamics. The impulses fire sparsely and interact with brain rhythms of different frequencies. Higher frequency gamma (>35 Hz) rhythms help carry the contents of working memory while lower frequency alpha/beta (~8-30 Hz) rhythms act as control signals that gate access to and clear out working memory. In other words, a rhythmic dance between brain rhythms may underlie your ability to control your own thoughts.

Episodic memory performance decreases with advancing age. According to theoretical models, such memory decline might be a consequence of age-related reductions in the ability to form distinct neural representations of our past. In this talk, I want to present our new age-comparative fMRI study investigating age-related neural dedifferentiation across different representational levels. By combining univariate analyses and searchlight pattern similarity analyses, we found that older adults show reduced category selective processing in higher visual areas, less specific item representations in occipital regions and less stable item representations. Dedifferentiation on all these representational levels was related to memory performance, with item specificity being the strongest contributor. Overall, our results emphasize that age-related dedifferentiation can be observed across the entire cortical hierarchy which may selectively impair memory performance depending on the memory task.

Natural soundscapes such as from a forest, a busy restaurant, or a busy intersection are generally composed of a cacophony of sounds that the brain needs to interpret either independently or collectively. In certain instances sounds - such as from moving cars, sirens, and people talking - are perceived in unison and are recognized collectively as single sound (e.g., city noise). In other instances, such as for the cocktail party problem, multiple sounds compete for attention so that the surrounding background noise (e.g., speech babble) interferes with the perception of a single sound source (e.g., a single talker). I will describe results from my lab on the perception and neural representation of auditory textures. Textures, such as a from a babbling brook, restaurant noise, or speech babble are stationary sounds consisting of multiple independent sound sources that can be quantitatively defined by summary statistics of an auditory model (McDermott & Simoncelli 2011). How and where in the auditory system are summary statistics represented and the neural codes that potentially contribute towards their perception, however, are largely unknown. Using high-density multi-channel recordings from the auditory midbrain of unanesthetized rabbits and complementary perceptual studies on human listeners, I will first describe neural and perceptual strategies for encoding and perceiving auditory textures. I will demonstrate how distinct statistics of sounds, including the sound spectrum and high-order statistics related to the temporal and spectral correlation structure of sounds, contribute to texture perception and are reflected in neural activity. Using decoding methods I will then demonstrate how various low and high-order neural response statistics can differentially contribute towards a variety of auditory tasks including texture recognition, discrimination, and categorization. Finally, I will show examples from our recent studies on how high-order sound statistics and accompanying neural activity underlie difficulties for recognizing speech in background noise.

Did we eat spaghetti for lunch because we saw our colleague eat spaghetti? What drives a risk decision? How can our breakfast impact our decisions throughout the day? Research from different disciplines such as economics, psychology and neuroscience have attempted to investigate the motives and modulators of human decision making. Human decisions can be flexibly modulated by the different experiences we have in our daily lives, at the same time, bodily processes, such as metabolism can also impact economic behavior. These modulations can occur through our social networks, through the impact of our own behavior on the social environment, but also simply by the food we have eaten. Here, I will present a series of recent studies from my lab in which we shed light on the psychological, neural and metabolic motives and modulators of human decision making.

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.

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.

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.

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.

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans – which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

I will begin by describing recent research taking a new, model-based approach to perceptual development. This approach uncovers fundamental changes in information processing underlying the protracted development of perception, action, and decision-making in childhood. For example, integration of multiple sensory estimates via reliability-weighted averaging – widely used by adults to improve perception – is often not seen until surprisingly late into childhood, as assessed by both behaviour and neural representations. This approach forms the basis for a newer question: the scope for the nervous system to deploy useful computations (e.g. reliability-weighted averaging) to optimise perception and action using newly-learned sensory signals provided by technology. Our initial model system is augmenting visual depth perception with devices translating distance into auditory or vibro-tactile signals. This problem has immediate applications to people with partial vision loss, but the broader question concerns our scope to use technology to tune in to any signal not available to our native biological receptors. I will describe initial progress on this problem, and our approach to operationalising what it might mean to adopt a new signal comparably to a native sense. This will include testing for its integration (weighted averaging) alongside the native senses, assessing the level at which this integration happens in the brain, and measuring the degree of ‘automaticity’ with which new signals are used, compared with native perception.

Initial social perceptions are often thought to reflect direct “read outs” of facial features. Instead, we outline a perspective whereby initial perceptions emerge from an automatic yet gradual process of negotiation between the perceptual cues inherent to a person (e.g., facial cues) and top-down social cognitive processes harbored within perceivers. This perspective argues that perceivers’ social-conceptual knowledge in particular can have a fundamental structuring role in perceptions, and thus how we think about social groups, emotions, or personality traits helps determine how we visually perceive them in other people. Integrative evidence from real-time behavioral paradigms (e.g., mouse-tracking), multivariate fMRI, and computational modeling will be discussed. Together, this work shows that the way we use facial cues to categorize other people into social groups (e.g., gender, race), perceive their emotion (e.g., anger), or infer their personality (e.g., trustworthiness) are all fundamentally shaped by prior social-conceptual knowledge and stereotypical assumptions. We find that these top-down impacts on initial perceptions are driven by the interplay of higher-order prefrontal regions involved in top-down predictions and lower-level fusiform regions involved in face processing. We argue that the perception of social categories, emotions, and traits from faces can all be conceived as resulting from an integrated system relying on domain-general cognitive properties. In this system, both visual and social cognitive processes are in a close exchange, and initial social perceptions emerge in part out of the structure of social-conceptual knowledge.

Concerns about the impact of social media use on adolescent well-being and mental health are common. While the amount of research in this area has increased rapidly over the last 5 years, most outputs are still marred by a multitude of limitations. These shortcomings have left our understanding of social media effects severely limited, holding back both scientific discovery and policy interventions. This talk discusses how developmental, cognitive and neuroscientific approaches might provide a new and improved way of studying social media effects. It will detail new studies in support of this idea, and raise potential avenues for collaborative work across the Cambridge Neuroscience community. As the digital world now (re)shapes what it means for us to live, communicate and develop, only an interdisciplinary approach will allow us to truly understand its impacts.

In a few months, human infants develop complex capacities in numerous cognitive domains. They learn their native language, recognize their parents, refine their numerical capacities and their perception of the world around them but are they conscious and how can we study consciousness when no verbal report is possible? One way to approach this question is to rely on the neural responses correlated with conscious perception in adults (i.e. a global increase of activity in notably frontal regions with top-down amplification of the sensory levels). We can thus study at what age the developing anatomical architecture might be mature enough to allow this type of responses, but moreover we can use similar experimental paradigms than in adults in which we expect to observe a similar pattern of functional responses.

Our senses are constantly bombarded with a myriad of diverse signals. Transforming this sensory cacophony into a coherent percept of our environment relies on solving two computational challenges: First, we need to solve the causal inference problem - deciding whether signals come from a common cause and thus should be integrated, or come from different sources and be treated independently. Second, when there is a common cause, we should integrate signals across the senses weighted in proportion to their sensory reliabilities. I discuss recent research at the behavioural, computational and neural systems level that investigates how the brain addresses these two computational challenges in multisensory perception.