In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Three studies conducted in the context of PhD work (UNIL) aimed at proving evidence to address the question of potential gender differences in trait anxiety and executive control biases on behavioral efficacy. In scope were male and female non-clinical samples of adult young age that performed non-emotional tasks assessing basic attentional functioning (Attention Network Test – Interactions, ANT-I), sustained attention (Test of Variables of Attention, TOVA), and visual recognition abilities (Object in Location Recognition Task, OLRT). Results confirmed the intricate nature of the relationship between gender and health trait anxiety through the lens of their impact on processing efficacy in males and females. The possibility of a gendered theory in trait anxiety biases is discussed.

There are many mysteries in the universe. One of the most significant, often considered the final frontier in science, is understanding how our subjective experience, or consciousness, emerges from the collective action of neurons in biological systems. While substantial progress has been made over the past decades, a unified and widely accepted explanation of the neural mechanisms underpinning consciousness remains elusive. The field is rife with theories that frequently provide contradictory explanations of the phenomenon. To accelerate progress, we have adopted a new model of science: adversarial collaboration in team science. Our goal is to test theories of consciousness in an adversarial setting. Adversarial collaboration offers a unique way to bolster creativity and rigor in scientific research by merging the expertise of teams with diverse viewpoints. Ideally, we aim to harness collective intelligence, embracing various perspectives, to expedite the uncovering of scientific truths. In this talk, I will highlight the effectiveness (and challenges) of this approach using selected case studies, showcasing its potential to counter biases, challenge traditional viewpoints, and foster innovative thought. Through the joint design of experiments, teams incorporate a competitive aspect, ensuring comprehensive exploration of problems. This method underscores the importance of structured conflict and diversity in propelling scientific advancement and innovation.

In the current virtual journal club Dr Di Luca will present findings from a series of psychophysical investigations where he measured sensitivity and bias in the perception of the timing of stimuli. He will present how improved detection with longer sequences and biases in reporting isochrony can be accounted for by optimal statistical predictions. Among his findings was also that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted to appear more regular. Such change depends on whether the context these sequences are presented is also regular. Dr Di Luca will present a Bayesian model for the combination of dynamically updated expectations, in the form of a priori probability, with incoming sensory information. These findings contribute to the understanding of how the brain processes temporal information to shape perceptual experiences.

Voluntary actions are actions that agents choose to make. Volition is the set of cognitive processes that implement such choice and initiation. These processes are often held essential to modern societies, because they form the cognitive underpinning for concepts of individual autonomy and individual responsibility. Nevertheless, psychology and neuroscience have struggled to define volition, and have also struggled to study it scientifically. Laboratory experiments on volition, such as those of Libet, have been criticised, often rather naively, as focussing exclusively on meaningless actions, and ignoring the factors that make voluntary action important in the wider world. In this talk, I will first review these criticisms, and then look at extending scientific approaches to volition in three directions that may enrich scientific understanding of volition. First, volition becomes particularly important when the range of possible actions is large and unconstrained - yet most experimental paradigms involve minimal response spaces. We have developed a novel paradigm for eliciting de novo actions through verbal fluency, and used this to estimate the elusive conscious experience of generativity. Second, volition can be viewed as a mechanism for flexibility, by promoting adaptation of behavioural biases. This view departs from the tradition of defining volition by contrasting internally-generated actions with externally-triggered actions, and instead links volition to model-based reinforcement learning. By using the context of competitive games to re-operationalise the classic Libet experiment, we identified a form of adaptive autonomy that allows agents to reduce biases in their action choices. Interestingly, this mechanism seems not to require explicit understanding and strategic use of action selection rules, in contrast to classical ideas about the relation between volition and conscious, rational thought. Third, I will consider volition teleologically, as a mechanism for achieving counterfactual goals through complex problem-solving. This perspective gives a key role in mediating between understanding and planning on the one hand, and instrumental action on the other hand. Taken together, these three cognitive phenomena of generativity, flexibility, and teleology may partly explain why volition is such an important cognitive function for organisation of human behaviour and human flourishing. I will end by discussing how this enriched view of volition can relate to individual autonomy and responsibility.

Young children have sophisticated representations of their visual and linguistic environment. Where do these representations come from? How much knowledge arises through generic learning mechanisms applied to sensory data, and how much requires more substantive (possibly innate) inductive biases? We examine these questions by training neural networks solely on longitudinal data collected from a single child (Sullivan et al., 2020), consisting of egocentric video and audio streams. Our principal findings are as follows: 1) Based on visual only training, neural networks can acquire high-level visual features that are broadly useful across categorization and segmentation tasks. 2) Based on language only training, networks can acquire meaningful clusters of words and sentence-level syntactic sensitivity. 3) Based on paired visual and language training, networks can acquire word-referent mappings from tens of noisy examples and align their multi-modal conceptual systems. Taken together, our results show how sophisticated visual and linguistic representations can arise through data-driven learning applied to one child’s first-person experience.

With the increasing use of automated face recognition (AFR) technologies, it is important to consider whether these systems not only perform accurately, but also equitability or without “bias”. Despite rising public, media, and scientific attention to this issue, the sources of bias in AFR are not fully understood. This talk will explore how human cognitive biases may impact our assessments of performance differentials in AFR systems and our subsequent use of those systems to make decisions. We’ll also show how, if we adjust our definition of what a “biased” AFR algorithm looks like, we may be able to create algorithms that optimize the performance of a human+algorithm team, not simply the algorithm itself.

Observational data have become a popular source of evidence for causal effects when no randomized controlled trial exists, or to supplement information provided by those. In practice, a wide range of designs and analytical choices exist, and one recent approach relies on the target trial emulation framework. This framework is particularly well suited to mimic what could be obtained in a specific randomized controlled trial, while avoiding time-related selection biases. In this abstract, we present how this framework could be useful to emulate trials in malignant melanoma, and the challenges faced when planning such a study using longitudinal observational data from a cohort study. More specifically, two questions are envisaged: duration of immune checkpoint inhibitors, and trials comparing treatment strategies for BRAF V600-mutant patients (targeted therapy as 1st line, followed by immunotherapy as 2nd line, vs. immunotherapy as 2nd line followed by targeted therapy as 1st line). Using data from 1027 participants to the MELBASE cohort, we detail the results for the emulation of a trial where immune checkpoint inhibitor would be stopped at 6 months vs. continued, in patients in response or with stable disease.

Our perception of time isn’t like a clock; it varies depending on other aspects of experience, such as what we see and hear in that moment. However, in everyday life, the properties of these simple features can change frequently, presenting a challenge to understanding real-world time perception based on simple lab experiments. We developed a computational model of human time perception based on tracking changes in neural activity across brain regions involved in sensory processing, using fMRI. By measuring changes in brain activity patterns across these regions, our approach accommodates the different and changing feature combinations present in natural scenarios, such as walking on a busy street. Our model reproduces people’s duration reports for natural videos (up to almost half a minute long) and, most importantly, predicts whether a person reports a scene as relatively shorter or longer–the biases in time perception that reflect how natural experience of time deviates from clock time

Research into pitch perception and time perception has typically treated the two as independent processes. However, previous studies of music and speech perception have suggested that pitch and timing information may be processed in an integrated manner, such that the pitch of an auditory stimulus can influence a person’s perception, expectation, and memory of its duration and tempo. Typically, higher-pitched sounds are perceived as faster and longer in duration than lower-pitched sounds with identical timing. We conducted a series of experiments to better understand the limits of this pitch-time integrality. Across several experiments, we tested whether the higher-equals-faster illusion generalizes across the broader frequency range of human hearing by asking participants to compare the tempo of a repeating tone played in one of six octaves to a metronomic standard. When participants heard tones from all six octaves, we consistently found an inverted U-shaped effect of the tone’s pitch height, such that perceived tempo peaked between A4 (440 Hz) and A5 (880 Hz) and decreased at lower and higher octaves. However, we found that the decrease in perceived tempo at extremely high octaves could be abolished by exposing participants to high-pitched tones only, suggesting that pitch-induced timing biases are context sensitive. We additionally tested how the timing of an auditory stimulus influences the perception of its pitch, using a pitch discrimination task in which probe tones occurred early, late, or on the beat within a rhythmic context. Probe timing strongly biased participants to rate later tones as lower in pitch than earlier tones. Together, these results suggest that pitch and time exert a bidirectional influence on one another, providing evidence for integrated processing of pitch and timing information in auditory perception. Identifying the mechanisms behind this pitch-time interaction will be critical for integrating current models of pitch and tempo processing.

In this talk, we will examine human behavior from the perspective of the choices we make every day. We will study the role of the brain in enabling these decisions and discuss some simple computational models of decision making and the neural basis. Towards the end, we will have a short, interactive session to engage in some easy decisions that will help us discover our own biases.

Self-motion through a visual world creates a pattern of expanding visual motion called optic flow. Heading estimation from the optic flow is accurate in rigid environments. But it becomes challenging when other humans introduce an independent motion to the scene. The biological motion of human walkers consists of translation through space and associated limb articulation. The characteristic motion pattern is regular, though complex. A world full of humans moving around is nonrigid, causing heading errors. But limb articulation alone does not perturb the global structure of the flow field, matching the rigidity assumption. For heading perception from optic flow analysis, limb articulation alone should not impair heading estimates. But we observed heading biases when participants encountered a group of point-light walkers. Our research investigates the interactions between optic flow perception and biological motion perception. We further analyze the impact of environmental information.

Magnitude estimation and stimulus discrimination tasks are affected by perceptual biases that cause the stimulus parameter to be perceived as shifted toward the mean of its distribution. These biases have been extensively studied in psychophysics and, more recently and to a lesser extent, with neural activity recordings. New computational techniques allow us to train spiking recurrent neural networks on the tasks used in the experiments. This provides us with another valuable tool with which to investigate the network mechanisms responsible for the biases and how behavior could be modeled. As an example, in this talk I will consider networks trained to discriminate the durations of temporal intervals. The trained networks presented the contraction bias, even though they were trained with a stimulus sequence without temporal correlations. The neural activity during the delay period carried information about the stimuli of the current trial and previous trials, this being one of the mechanisms that originated the contraction bias. The population activity described trajectories in a low-dimensional space and their relative locations depended on the prior distribution. The results can be modeled as an ideal observer that during the delay period sees a combination of the current and the previous stimuli. Finally, I will describe how the neural trajectories in state space encode an estimate of the interval duration. The approach could be applied to other cognitive tasks.

One elementary brain function that underlies many of our cognitive behaviors is the ability to maintain parametric information briefly in mind, in the time scale of seconds, to span delays between sensory information and actions. This component of working memory is fragile and quickly degrades with delay length. Under the assumption that behavioral delay-dependencies mark core functions of the working memory system, our goal is to find a neural circuit model that represents their neural mechanisms and apply it to research on working memory deficits in neuropsychiatric disorders. We have constrained computational models of spatial working memory with delay-dependent behavioral effects and with neural recordings in the prefrontal cortex during visuospatial working memory. I will show that a simple bump attractor model with weak inhomogeneities and short-term plasticity mechanisms can link neural data with fine-grained behavioral output in a trial-by-trial basis and account for the main delay-dependent limitations of working memory: precision, cardinal repulsion biases and serial dependence. I will finally present data from participants with neuropsychiatric disorders that suggest that serial dependence in working memory is specifically altered, and I will use the model to infer the possible neural mechanisms affected.

Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In addition, we have shown that this bias is stable over periods of up to 8 s. At the end, I will show ongoing analysis of a recent experiment and argue that serial dependence may rely on distributed memory representations of stimuli and task relevant features.

In this webinar, the ALBA Network & World Women in Neuroscience partner to address equity, inclusion & diversity issues across the Sub-Saharan African neuroscience community. The panel discussion will explore the challenges and biases faced by African neuroscientists while establishing their careers - focusing on a lack of mentoring and networking but also on the difficulties to raise funding - as well as display the strengths present in the region, which can be exploited to find solutions. Registration is free but required: https://www.alba.network/alba-wwn-webinar-africa

A key purpose of causal reasoning by individuals and by collectives is to enhance action, to give humans yet more control over their environment. As a result, causal reasoning serves as the infrastructure of both thought and discourse. Humans represent causal systems accurately in some ways, but also show some systematic biases (we tend to neglect causal pathways other than the one we are thinking about). Even when accurate, people’s understanding of causal systems tends to be superficial; we depend on our communities for most of our causal knowledge and reasoning. Nevertheless, we are better causal reasoners than machines. Modern machine learners do not come close to matching human abilities.

As part of its webinar series on region-specific diversity issues, the ALBA Network is organizing a panel discussion to explore the challenges and biases faced by neuroscientists while establishing their research groups and careers in the MENA region, from an academic and cultural perspective. This will be followed by highlights of success stories, unique region-specific opportunities for research collaborations and recommendations to improve representation of MENA neuroscientists in the global stage.

Predictive coding represents a promising framework for understanding brain function, postulating that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view on cortical computation is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency, a fundamental requirement of neural processing. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. We demonstrate that prediction units can reliably be identified through biases in their median preactivation, pointing towards a fundamental property of prediction units in the predictive coding framework. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time. Our results, which replicate across two separate data sets, suggest that predictive coding can be interpreted as a natural consequence of energy efficiency. More generally, they raise the question which other computational principles of brain function can be understood as a result of physical constraints posed by the brain, opening up a new area of bio-inspired, machine learning-powered neuroscience research.

Recently, the field of computational neuroscience has seen an explosion of the use of trained recurrent network models (RNNs) to model patterns of neural activity. These RNN models are typically characterized by tuned recurrent interactions between rate 'units' whose dynamics are governed by smooth, continuous differential equations. However, the response of biological single neurons is better described by all-or-none events - spikes - that are triggered in response to the processing of their synaptic input by the complex dynamics of their membrane. One line of research has attempted to resolve this discrepancy by linking the average firing probability of a population of simplified spiking neuron models to rate dynamics similar to those used for RNN units. However, challenges remain to account for complex temporal dependencies in the biological single neuron response and for the heterogeneity of synaptic input across the population. Here, we make progress by showing how to derive dynamic rate equations for a population of spiking neurons with multi-timescale adaptation properties - as this was shown to accurately model the response of biological neurons - while they receive independent time-varying inputs, leading to plausible asynchronous activity in the network. The resulting rate equations yield an insightful segregation of the population's response into dynamics that are driven by the mean signal received by the neural population, and dynamics driven by the variance of the input across neurons, with respective timescales that are in agreement with slice experiments. Further, these equations explain how input variability can shape log-normal instantaneous rate distributions across neurons, as observed in vivo. Our results help interpret properties of the neural population response and open the way to investigating whether the more biologically plausible and dynamically complex rate model we derive could provide useful inductive biases if used in an RNN to solve specific tasks.

In a famous philosophical paradox, Buridan's ass perishes because he is equally hungry and thirsty, and cannot make up his mind whether to first drink or eat. We are faced daily with the need to pick between alternatives that are equally attractive (or not) to us. What are the processes that allow us to avoid paralysis and to rapidly select between such equal options when there are no preferences or rational reasons to rely on? One solution that was offered is that although on a higher cognitive level there is symmetry between the alternatives, on a neuronal level the symmetry does not maintain. What is the nature of this asymmetry of the neuronal level? In this talk I will present experiments addressing this important phenomenon using measures of human behavior, EEG, EMG and large scale neural network modeling, and discuss mechanisms involved in the process of intention formation and execution, in the face of alternatives to choose from. Specifically, I will show results revealing the temporal dynamics of rapid intention formation and, moreover, ‘change of intention’ in a free choice picking scenario, in which the alternatives are on a par for the participant. The results suggest that even in arbitrary choices, endogenous or exogenous biases that are present in the neural system for selecting one or another option may be implicitly overruled; thus creating an implicit and non-conscious ‘change of mind’. Finally, the question is raised: in what way do such rapid implicit ‘changes of mind’ help retain one’s self-control and free-will behavior?

Macroscopic brain organisation emerges early in life, even prenatally, and continues to develop through adolescence and into early adulthood. The emergence and continual refinement of large-scale brain networks, connecting neuronal populations across anatomical distance, allows for increasing functional integration and specialisation. This process is thought crucial for the emergence of complex cognitive processes. But how and why is this process so diverse? We used structural neuroimaging collected from a large diverse cohort, to explore how different features of macroscopic brain organisation are associated with diverse cognitive trajectories. We used diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected 'hubs'. The more children's brains were critically dependent on hubs, the better their cognitive skills. Conversely, having poorly integrated hubs was a very strong risk factor for cognitive and learning difficulties across the sample. We subsequently developed a computational framework, using generative network modelling (GNM), to model the emergence of this kind of connectome organisation. Relatively subtle changes within the wiring rules of this computational framework give rise to differential developmental trajectories, because of small biases in the preferential wiring properties of different nodes within the network. Finally, we were able to use this GNM to implicate the molecular and cellular processes that govern these different growth patterns.

Methodology for understanding the basic phenomena of life can be done in vitro or in vivo, under tightly-controlled experimental conditions designed to limit variability. However stringent the protocol, these experiments do not occur in a cultural vacuum and they are often subject to the same societal biases as other research disciplines. Many researchers uphold the status quo of biased basic research by not questioning the characteristics of their experimental animals, or the people from whom their tissue samples were collected. This means that our fundamental understanding of life has been built on biased models. This session will explore the ways in which basic life sciences research can be biased and the implications of this. We will discuss practical ways to assess your research design and how to make sure it is representative.

During communication, alignment between signals and sensors can be critical. Signals are often best perceived from specific angles, and sensory systems can also exhibit strong directional biases. However, we know little about how animals establish and maintain such signaling alignment during communication. To investigate this, we characterized the spatial dynamics of visual courtship signal- ing in the jumping spider Habronattus pyrrithrix. The male performs forward-facing displays involving complex color and movement patterns, with distinct long- and short-range phases. The female views displays with 2 distinct eye types and can only perceive colors and fine patterns of male displays when they are presented in her frontal field of view. Whether and how courtship interactions pro- duce such alignment between male display and female field of view is unknown. We recorded relative positions and orientations of both actors throughout courtship and established the role of each sex in maintaining signaling alignment. Males always oriented their displays toward the female. However, when females were free to move, male displays were consistently aligned with female princi- pal eyes only during short-range courtship. When female position was fixed, signaling alignment consistently occurred during both phases, suggesting that female movement reduces communication efficacy. When female models were experimentally rotated to face away during courtship, males rarely repositioned themselves to re-align their display. However, males were more likely to present cer- tain display elements after females turned to face them. Thus, although signaling alignment is a function of both sexes, males appear to rely on female behavior for effective communication

Visual attention refers to a set of processes allowing selection of relevant and filtering out of irrelevant information in the visual environment. A large amount of research on visual attention has involved visual search paradigms, where observers are asked to report whether a single target is present or absent. However, recent studies have revealed that these classic single-target visual search tasks only provide a snapshot of how attention is allocated in the visual environment, and that multitarget visual foraging tasks may capture the dynamics visual attention more accurately. In visual foraging, observers are asked to select multiple instances of multiple target types, as fast as they can. A critical question in foraging research concerns the factors driving the next target selection. Most likely, this would require two steps: (1) identifying a set of candidates for the next selection, and (2) selecting the best option among these candidates. After having briefly described the advantage of visual foraging over visual search, I will review recent visual foraging studies testing the influence of several manipulations (e.g., target crypticity, number of items, selection modality) on foraging behaviour. Overall, these studies revealed that the next target selection during visual foraging is determined by the competition between three factors: target value, target proximity, and priming of features. I will explain how the analysis of individual differences in foraging behaviour can provide important information, with the idea that individuals show by-default internal biases toward value, proximity and priming that determine their search strategy and behaviour.

Mahzarin Banaji and her colleague coined the term “implicit bias” in the mid-1990s to refer to behavior that occurs without conscious awareness. Today, Professor Banaji is Cabot Professor of Social Ethics in the Department of Psychology at Harvard University, a member of the American Academy of Arts and Sciences, the National Academy of Sciences and has received numerous awards for her scientific contributions. The purpose of the seminar, Blindspot: Hidden Biases of Good People, is to reveal the surprising and even perplexing ways in which we make errors in assessing and evaluating others when we recruit and hire, onboard and promote, lead teams, undertake succession planning, and work on behalf of our clients or the public we serve. It is Professor Banaji’s belief that people intend well and that the inconsistency we see, between values and behavior, comes from a lack of awareness. But because implicit bias is pervasive, we must rely on scientific evidence to “outsmart” our minds. If we do so, we will be more likely to reach the life goals we have chosen for ourselves and to serve better the organizations for which we work.

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

ALBA is organizing a special event on ‘Bias in Indian STEM’ at the online conference NeuroFemIndia 2021. Prof Shubha Tole (Tata Institute of Fundamental Research), ALBA Advisor, will be moderating and leading the discussion on biases in Indian STEM academia. The panel will discuss the main biases that women and minorities in India face as they navigate the academic system. This event is part of the NeuroFemIndia Online Conference 2021.

It has long been appreciated (and celebrated) that certain species have sensory capabilities that humans do not share, for example polarization, ultraviolet, and infrared vision. What is less appreciated however, is that our position as terrestrial human scientists can significantly affect our study of animal senses and signals, even within modalities that we do share. For example, our acute vision can lead us to over-interpret the relevance of fine patterns in animals with coarser vision, and our Cartesian heritage as scientists can lead us to divide sensory modalities into orthogonal parameters (e.g. hue and brightness for color vision), even though this division may not exist within the animal itself. This talk examines two cases from marine visual ecology where a reconsideration of our biases as sharp-eyed Cartesian land mammals can help address questions in visual ecology. The first case examines the enormous variation in visual acuity among animals with image-forming eyes, and focuses on how acknowledging the typically poorer resolving power of animals can help us interpret the function of color patterns in cleaner shrimp and their client fish. The second case examines the how the typical human division of polarized light stimuli into angle and degree of polarization is problematic, and how a physiologically relevant interpretation is both closer to the truth and resolves a number of issues, particularly when considering the propagation of polarized light

My research focuses, broadly speaking, on how emotions impact decision making. Specifically, I am interested in affective biases, a phenomenon known to be important in depression. Using a rodent decision-making task, combined with computational modelling I have investigated how different antidepressant and pro-depressant manipulations that are known to alter mood in humans alter judgement bias, and provided insight into the decision processes that underlie these behaviours. I will also highlight how the combination of behaviour and modelling can provide a truly translation approach, enabling comparison and interpretation of the same cognitive processes between animal and human research.

From robots to humans, the ability to learn from experience turns a rigid response system into a flexible, adaptive one. In this talk, I will discuss emerging findings regarding the neural and cognitive mechanisms by which learning shapes decisions. The lecture will focus on how multiple brain regions interact to support learning, what this means for how memories are built, and the consequences for how decisions are made. Results emerging from this work challenge the traditional view of separate learning systems and advance understanding of how memory biases decisions in both adaptive and maladaptive ways.

Interval timing is a fundamental component action, and is susceptible to motor-related temporal distortions. Previous studies have shown that movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes. https://www.biorxiv.org/content/10.1101/2020.10.26.355396v1

Cognitive bias is a "subjective reality" that is uniquely created in the brain and affects our various behaviors. It may lead to what is widely called irrationality in behavioral economics, such as inaccurate judgment and illogical interpretation, but it also has an adaptive aspect in terms of mental hygiene. When such cognitive bias is regarded as a product of information processing in the brain, the approach to clarify the mechanism in the brain will play a part in finding the direct relations between the brain and the mind. In my talk, I will introduce our studies investigating the neural and molecular bases of cognitive biases, especially focusing on the role of dopamine.

Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction or supervision. Deciphering the overlap between human and unsupervised machine reasoning may aid in eliminating biases and improving automation and accountability for artificial intelligence-assisted vision tasks and decision-making. Aleksandar Ivanov Title:

Poor air quality in cities is a significant threat to health and life expectancy, with over 80% of people living in urban areas exposed to air quality levels that exceed World Health Organisation limits. In this session, I present a multi-resolution multi-task framework that handles evidence integration under varying spatio-temporal sampling resolution and noise levels. We have developed both shallow Gaussian Process (GP) mixture models and deep GP constructions that naturally handle this evidence integration, as well as biases in the mean. These models underpin our work at the Alan Turing Institute towards providing spatio-temporal forecasts of air pollution across London. We demonstrate the effectiveness of our framework on both synthetic examples and applications on London air quality. For further information go to: https://www.turing.ac.uk/research/research-projects/london-air-quality. Collaborators: Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang and Mark Girolami.