Research questions will be chosen from a range of topics in theoretical/computational neuroscience and cognitive science, involving either data analysis or theory, and drawing on recent machine learning approaches.

Research questions will be chosen from a range of topics in theoretical/computational neuroscience and cognitive science, involving either data analysis or theory, and drawing on recent machine learning approaches.

The lab of Rava Azeredo da Silveira invites applications for PhD positions at the Institute for Molecular and Clinical Ophthalmology Basel (IOB), an associated institute of the University of Basel. Research questions will be chosen from a broad range of topics in theoretical/computational neuroscience and cognitive science (see the description of the lab’s activity, below). Candidates with backgrounds in mathematics, statistics, artificial intelligence, physics, computer science, engineering, biology, and psychology are welcome. Experience with data analysis and proficiency with numerical methods, in addition to familiarity with neuroscience topics and mathematical and statistical methods, are desirable. Equally desirable are a spirit of intellectual adventure, eagerness, and drive. The positions will come with highly competitive work conditions and salaries. Application deadline: We will start reviewing applications on 20 December 2020. How to apply: Please send the following information in one single PDF, to silveira@iob.ch: 1. letter on motivation and interests; 2. curriculum vitæ; 3. transcripts of undergraduate and Master grades. In addition, please arrange for three letters of recommendations to be sent to the same email address. In all email correspondence, please include the mention “APPLICATION-PHD” in the subject header, otherwise the application will not be considered. *** The Silveira Lab focuses on a range of topics, which, however, are tied together through a central question: How does the brain represent and manipulate information? Among the more concrete approaches to this question, the lab analyses and models neural activity in circuits that can be identified, recorded from, and perturbed experimentally, such as visual neural circuits in the retina and the cortex. Establishing links between physiological specificity and the structure of neural activity yields an understanding of circuits as building blocks of cerebral information processing. On a more abstract level, the lab investigates the representation of information in populations of neurons, from a statistical and algorithmic—rather than mechanistic—point of view, through theories of coding and data analyses. These studies aim at understanding the statistical nature of high-dimensional neural activity in different conditions, and how this serves to encode and process information from the sensory world. In the context of cognitive studies, the lab investigates mental processes such as inference, learning, and decision-making, through both theoretical developments and behavioral experiments. A particular focus is the study of neural constraints and limitations and, further, their impact on mental processes. Neural limitations impinge on the structure and variability of mental representations, which in turn inform the cognitive algorithms that produce behavior. The lab explores the nature of neural limitations, mental representations, and cognitive algorithms, and their interrelations.

Several postdoctoral openings in the lab of Rava Azeredo da Silveira (Paris & Basel) The lab of Rava Azeredo da Silveira invites applications for Postdoctoral Researcher positions at ENS, Paris, and IOB, an associated institute of the University of Basel. Research questions will be chosen from a broad range of topics in theoretical/computational neuroscience and cognitive science (see the description of the lab’s activity, below). One of the postdoc positions to be filled in Basel will be part of a collaborative framework with Michael Woodford (Columbia University) and will involve projects relating the study of decision making to models of perception and memory. Candidates with backgrounds in mathematics, statistics, artificial intelligence, physics, computer science, engineering, biology, and psychology are welcome. Experience with data analysis and proficiency with numerical methods, in addition to familiarity with neuroscience topics and mathematical and statistical methods, are desirable. Equally desirable are a spirit of intellectual adventure, eagerness, and drive. The positions will come with highly competitive work conditions and salaries. Application deadline: Applications will be reviewed starting on 1 November 2020. How to apply: Please send the following information in one single PDF, to silveira@iob.ch: 1. letter of motivation; 2. statement of research interests, limited to two pages; 3. curriculum vitæ including a list of publications; 4. any relevant publications that you wish to showcase. In addition, please arrange for three letters of recommendations to be sent to the same email address. In all email correspondence, please include the mention “APPLICATION-POSTDOC” in the subject header, otherwise the application will not be considered. * ENS, together with a number of neighboring institutions (College de France, Institut Curie, ESPCI, Sorbonne Université, and Institut Pasteur), offers a rich scientific and intellectual environment, with a strong representation in computational neuroscience and related fields. * IOB is a research institute combining basic and clinical research. Its mission is to drive innovations in understanding vision and its diseases and develop new therapies for vision loss. IOB is an equal-opportunity employer with family-friendly work policies. * The Silveira Lab focuses on a range of topics, which, however, are tied together through a central question: How does the brain represent and manipulate information? Among the more concrete approaches to this question, the lab analyses and models neural activity in circuits that can be identified, recorded from, and perturbed experimentally, such as visual neural circuits in the retina and the cortex. Establishing links between physiological specificity and the structure of neural activity yields an understanding of circuits as building blocks of cerebral information processing. On a more abstract level, the lab investigates the representation of information in populations of neurons, from a statistical and algorithmic—rather than mechanistic—point of view, through theories of coding and data analyses. These studies aim at understanding the statistical nature of high-dimensional neural activity in different conditions, and how this serves to encode and process information from the sensory world. In the context of cognitive studies, the lab investigates mental processes such as inference, learning, and decision-making, through both theoretical developments and behavioral experiments. A particular focus is the study of neural constraints and limitations and, further, their impact on mental processes. Neural limitations impinge on the structure and variability of mental representations, which in turn inform the cognitive algorithms that produce behavior. The lab explores the nature of neural limitations, mental representations, and cognitive algorithms, and their interrelations.

The M.Sc. program 'Modeling for Neuronal and Cognitive Systems' at Université Côte d'Azur is recruiting new students. This international Master of science is interdisciplinary and related to computational approaches in neuroscience/cognitive science.

The main goal of the project is to extend and improve on our VBMC framework for efficient probabilistic inference with moderately-to-very expensive models, published in multiple papers, available in MATLAB and recently released for Python. We aim to perform Bayesian inference for parameters of complex, expensive state-of-the-art models in fields such as cognitive science and AI. An example is the AI-inspired model of human gameplay from Wei Ji Ma's group (van Opheusden et al., Nature 2023). The project includes funding for research visits to international collaborators such as Wei Ji Ma at New York University and Michael Osborne at the University of Oxford. We also have many local collaborators, such as Antti Honkela for applications of sample-efficient inference to privacy, and our team is highly involved in the thriving & highly collaborative community of probabilistic ML/AI researchers — PhDs, postdocs, PIs — in the Finnish Center for Artificial Intelligence FCAI, on top of many ongoing national and international collaborations in cognitive science and computational neuroscience.

The position holder will be a member of the Hessian Center for Artificial Intelligence - hessian.AI and provides research at the Center and will also be a member of the Centre for Cognitive Science. The scientific focus of the position is on the computational and algorithmic modeling of behavioral data to understand the human mind. Exemplary research topics include computational level models of perception, cognition, decision making, action, and learning as well as extended behavior and social interactions in humans, algorithmic models that are able to simulate, predict, and explain human behavior, model-driven behavioral research on human cognition. The professorship is expected to strengthen the Hessian Center for Artificial Intelligence and TU Darmstadt’s Human Science department’s research focus on Cognitive Science. Depending on the candidate’s profile there is the opportunity to participate in joint research projects currently running at TU Darmstadt. This in particular includes the state funded cluster projects “The Adaptive Mind (TAM)” and “The Third Wave of Artificial Intelligence (3AI)”. In addition to excellent scientific credentials, we seek a strong commitment to teaching in the department’s Bachelor and Masters programs in Cognitive Science. Experience in attracting third-party funding as well as participation in academic governance is expected.

There is a fully funded PhD position available at the Institute of Neural Information Processing, Ulm University, Germany. At the institute we are interested in the mathematical foundations of intelligent behaviour in biological and artificial systems. The PhD topic will revolve around the fundamental question of how the abstraction capabilities of classic symbolic knowledge systems can be combined with the sub-symbolic pattern recognition capabilities of neural networks in order to allow neural networks to take existing knowledge into account when making predictions. The PhD position will be part of the newly established DFG graduate school KEMAI (Knowledge Infusion and Extraction for Explainable Medical AI). The structured PhD programme has a duration of 3 years with the possibility of extending for one more year. The candidate will have the opportunity both to make contributions to fundamental questions in AI and cognitive science and to apply their work directly in the context of medical imaging through collaboration with Ulm University Clinic. Within the same broad topic area there is a second PhD position available at the Institute of Medical Systems Biology that includes investigation of genetic markers.

The Faculty of Psychology and Educational Sciences of the University of Coimbra (FPCE-UC) Portugal invites applications from rising and aspiring leaders in Cognitive Science and Neuroscience for 2 tenure-track positions at the Assistant (1) and Associate (1) Professor level. These positions are part of a transformative ERA Chair grant CogBooster from the European Union to FPCE-UC led by Dr. Alfonso Caramazza. The goal of CogBooster is to implement a strong and international line of research in Cognitive Science/Neuroscience, so as to contribute to the ongoing renewal of the Psychological and Brain Sciences in Portugal over the next decade.

We are seeking postdoctoral fellows with interest and experience in computational approaches for quantifying human social behavior. This research is conducted at the University of Pennsylvania and the Center for Autism Research at Children’s Hospital of Philadelphia, as a part of multiple NIH grants. The applicant will be part of a big multidisciplinary team that develops AI tools to study human behavior (facial and bodily movements) during social interactions. Our research is a unique blend of machine learning, computer vision, cognitive science, bioinformatics, and mental health conditions. The fellow will be responsible for all or some of the following tasks, depending on the expertise: - Developing computer vision techniques (e.g., face analysis, body movement analysis, gesture analysis) - Developing signal processing methodologies to analyze biological and behavioral signals (e.g., head movements, joint movements) - Developing time series analysis techniques to extract patterns in biological and behavioral signals (e.g., coordination and causality in movements of multiple people) - Validating developed tools using in-house clinical data, as well as publicly available datasets - Performing pattern recognition on collected data (i.e., classification, regression, clustering, feature learning)

We are seeking postdoctoral fellows with interest and experience in computational approaches for quantifying human social behavior. This research is conducted at the University of Pennsylvania and the Center for Autism Research at Children’s Hospital of Philadelphia, as a part of multiple NIH grants. The applicant will be part of a big multidisciplinary team that develops AI tools to study human behavior (facial and bodily movements) during social interactions. Our research is a unique blend of machine learning, computer vision, cognitive science, bioinformatics, and mental health conditions. The fellow will be responsible for all or some of the following tasks, depending on the expertise: Developing computer vision techniques (e.g., face analysis, body movement analysis, gesture analysis), Developing signal processing methodologies to analyze biological and behavioral signals (e.g., head movements, joint movements), Developing time series analysis techniques to extract patterns in biological and behavioral signals (e.g., coordination and causality in movements of multiple people), Validating developed tools using in-house clinical data, as well as publicly available datasets, Performing pattern recognition on collected data (i.e., classification, regression, clustering, feature learning)

Our Deep NeuroCognition Lab in NTU and A*STAR, Singapore is currently recruiting multiple PhD students and postdocs. Research experiences in neuroscience, cognitive science, and AI are preferred. Students and staff members will receive competitive monthly salaries/scholarships and other benefits (e.g. medical insurance, annual leave, sick leave).

The Gatsby Unit seeks to appoint a new principal investigator with an outstanding record of research achievement and an innovative research programme in theoretical neuroscience or machine learning at any academic rank. In theoretical neuroscience, we are particularly interested in candidates who focus on the mathematical underpinnings of adaptive intelligent behaviour in animals, or develop mathematical tools and models to understand how neural circuits and systems function. In machine learning, we seek candidates who focus on the mathematical foundations of learning from data and experience, addressing fundamental questions in probabilistic or statistical machine learning and understanding; areas of particular interest include generative or probabilistic modelling, causal discovery, reinforcement learning, theory of deep learning, and links between these areas and neuroscience or cognitive science.

The Santa Fe Institute seeks applications for postdoctoral fellows for the 2024 cohort. The fellowships offer early-career scholars the opportunity to undertake their own independent research within a collaborative research community that nurtures creative, transdisciplinary thought in pursuit of key insights about the complex systems that matter most for science and society. Postdoctoral Fellows spend up to three years in residence at SFI, where they contribute to SFI’s research in the sciences of complexity and are trained to become leaders in interdisciplinary science. The fellowships offer a competitive salary, generous benefits, and discretionary research funding. The city of Santa Fe offers a remarkable quality of life and year-round opportunities for outdoor activities. Interdisciplinary scholars with broad interests with interests in any scientific discipline, including AI, machine learning, and cognitive science, are encouraged to apply.

The Faculty of Psychology and Educational Sciences of the University of Coimbra (FPCE-UC) Portugal invites applications from rising and aspiring leaders in Cognitive Science and Neuroscience for 2 tenure-track positions at the Assistant (1) and Associate (1) Professor level. These positions are part of a transformative ERA Chair grant CogBooster from the European Union to FPCE-UC led by Dr. Alfonso Caramazza. The goal of CogBooster is to implement a strong and international line of research in Cognitive Science/Neuroscience, so as to contribute to the ongoing renewal of the Psychological and Brain Sciences in Portugal over the next decade.

The Department of Psychological and Brain Sciences at the University of Massachusetts, Amherst is inviting applications for a tenure track, academic year, faculty position at the Assistant Professor level in its Cognition and Cognitive Neuroscience Psychology program, starting in Fall 2024. We are seeking outstanding applicants with expertise in any area of cognitive psychology or cognitive neuroscience, including interdisciplinary fields connected to cognitive psychology, whose work complements and broadens existing strengths in our program. The program has current strengths in attention, decision-making, psycholinguistics, and mathematical modeling, with connections to our Behavioral Neuroscience, Clinical Psychology, Developmental Science, and Social Psychology programs. Across the university, our faculty have strong connections to Linguistics, Information and Computer Sciences, and Speech, Language, and Hearing Sciences, as well as the Initiative in Cognitive Science, the Computational and Social Science Institute, the Institute for Diversity Sciences, and the Institute for Applied Life Sciences.

The Department of Psychology at the University of Miami invites applications for two full-time, tenure-eligible, or tenure-track faculty members to join our department in August 2024. One position is in the department’s Adult Division, and the other is the Cognitive & Behavioral Neuroscience division. The specific area for both positions is open. For the Adult Division, areas of focus could include basic research on affect, cognitive science, and/or mechanistic studies related to mental health or the impact of disparities. Scholars with expertise in lab-based experimental, neurophysiological, computational, and/or mobile health/digital phenotyping methods are welcome. Individuals with interests in data science, including advanced quantitative techniques, big data, and machine learning are also encouraged to apply. For the Cognitive & Behavioral Neuroscience Division, we are particularly interested in individuals who incorporate innovative and sophisticated cognitive, affective, or social neuroscience methods into their research program.

The University of Coimbra invites applications from rising and aspiring leaders in Cognitive Science and Neuroscience for 2 tenure-track positions at the Assistant (1) and Associate (1) Professor level. These positions are part of a transformative ERA Chair grant CogBooster from the European Union to FPCE-UC led by Dr. Alfonso Caramazza. The goal of CogBooster is to implement a strong and international line of research in Cognitive Science/Neuroscience, so as to contribute to the ongoing renewal of the Psychological and Brain Sciences in Portugal over the next decade. The positions are tenure-track at the rank of Assistant (1) and Associate (1) Professor. The start date should be around November/December, 2023 (but potentially negotiable). Applicants for the Associate Professor position should have completed their PhD before September 2018.

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Why should psychologists be interested in robots? This talk aims to illustrate how social robots – machines with human-like features and behaviors – can offer interesting insights into the human mind. I will first provide a brief overview of how robots have been used in psychology and cognitive science research focusing on two approaches - Developmental Robotics and Human-Robot Interaction (HRI). We will then delve into recent works in HRI, including my own, in greater detail. We will also address the limitations of research thus far, such as the lack of proper controlled experiments, and discuss how the scientific community should evaluate the use of technology in educational and other social settings.

Cognitive science and cognitive neuroscience researchers have agreed that the replication of findings is important for establishing which ideas (or theories) are integral to the study of cognition across the lifespan. Recently, high-profile papers have called into question findings that were once thought to be unassailable. Much attention has been paid to how p-hacking, publication bias, and sample size are responsible for failed replications. However, much less attention has been paid to the fidelity by which researchers enact study protocols. Researchers conducting education or clinical trials are aware of the importance in fidelity – or the extent to which the protocols are delivered in the same way across participants. Nevertheless, this idea has not been applied to cognitive contexts. This seminar discusses factors that impact the replicability of findings alongside recent models suggesting that even small fidelity deviations have real impacts on the data collected.

In this lecture, I would like to share with the broad audience the empirical results gathered and the theoretical advancements made in the framework of the Lendület project entitled ’The cognitive basis of human sociality’. The main objective of this project was to understand the mechanisms that enable the unique sociality of humans, from the angle of cognitive science. In my talk,  I will focus on recent empirical evidence in the study of three fundamental social cognitive functions (social categorization, theory of mind and social learning; mainly from the empirical lenses of developmental psychology) in order to outline a theory that emphasizes the need to consider their interconnectedness. The proposal is that the ability to represent the social world along categories and the capacity to read others’ minds are used in an integrated way to efficiently assess the epistemic states of fellow humans by creating a shared representational space. The emergence of this shared representational space is both the result of and a prerequisite to efficient learning about the physical and social environment.

In this talk I will introduce the central notions of the theory of autonomy, as it is being currently developed in biology and cognitive science. The theory of autonomy puts forward the capacity of self-determination of organisms as whole systems, and constitutes thereby an alternative to more reductionist and mechanistic approaches. I will discuss how the theory of autonomy provides a justification for the scientific use of notions as function, norm, agency and teleology, whose epistemological legitimacy is highly debated. I will conclude by describing the difficult challenges that poses the transition from biological to cognitive autonomy.

The Interdisciplinary College is an annual spring school which offers a dense state-of-the-art course program in neurobiology, neural computation, cognitive science/psychology, artificial intelligence, machine learning, robotics and philosophy. It is aimed at students, postgraduates and researchers from academia and industry. This year's focus theme "Flexibility" covers (but not be limited to) the nervous system, the mind, communication, and AI & robotics. All this will be packed into a rich, interdisciplinary program of single- and multi-lecture courses, and less traditional formats.

Abstract: There is no doubt that the study of brain damaged individuals has contributed greatly to our understanding of the mind/brain. Within this broad approach, cognitive neuropsychology accentuates the cognitive dimension: it investigates the structure and organization of perceptual, motor, cognitive, and language systems – prerequisites for understanding the functional organization of the brain – through the analysis of their dysfunction following brain damage. Significant insights have come specifically from this paradigm. But progress has been slow and enthusiasm for this approach has waned somewhat in recent years, and the use of existing findings to constrain new theories has also waned. What explains the current diminished status of cognitive neuropsychology? One reason may be failure to calibrate expectations about the effective contribution of different subfields of the study of the mind/brain as these are determined by their natural peculiarities – such factors as the types of available observations and their complexity, opportunity of access to such observations, the possibility of controlled experimentation, and the like. Here, I also explore the merits and limitations of cognitive neuropsychology, with particular focus on the role of intellectual, pragmatic, and societal factors that determine scientific practice within the broader domains of cognitive science/neuroscience. I conclude on an optimistic note about the continuing unique importance of cognitive neuropsychology: although limited to the study of experiments of nature, it offers a privileged window into significant aspects of the mind/brain that are not easily accessible through other approaches. Biography: Alfonso Caramazza's research has focussed extensively on how words and their meanings are represented in the brain. His early pioneering studies helped to reformulate our thinking about Broca's aphasia (not limited to production) and formalised the logic of patient-based neuropsychology. More recently he has been instrumental in reconsidering popular claims about embodied cognition.

Understanding how our brains process language is one of the fundamental issues in cognitive science. In order to reach such understanding, it is critical to cover the full spectrum of manners in which humans acquire and experience language. However, due to a myriad of socioeconomic factors, research has disproportionately focused on monolingual English speakers. In this talk, I present a series of studies that systematically target fundamental questions about bilingual language use across a range of conversational contexts, both in production and comprehension. The results lay the groundwork to propose a more inclusive theory of the neurobiology of language, with an architecture that assumes a common selection principle at each linguistic level and can account for attested features of both bilingual and monolingual speech in, but crucially also out of, experimental settings.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. We have used threat-evoked escape behavior in mice to probe the relationship between ethological exploratory behavior and abstract spatial cognition. First, we show that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. Using closed-loop neural manipulations to interrupt running movements during exploration, we next found that blocking runs targeting an obstacle edge abolished subgoal learning. We conclude that mice use an action-driven learning process to identify subgoals, and these subgoals are then integrated into an allocentric map-like representation. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

Tom Froese is Assistant Professor at the Okinawa Institute of Science and Technology Graduate University (OIST), where he heads the Embodied Cognitive Science Unit. He is a cognitive scientist with a background in phenomenological philosophy, human-computer interaction, and complex systems theory. His interdisciplinary research centers on the role of agent-environment interaction in shaping cognition and consciousness, specifically when the interaction process involves sociality and technology. In this talk he will present current work in progress on “irruption theory”, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.

The assumed data generating models (response distributions) of experimental or observational data in cognitive science have become increasingly complex over the past decades. This trend follows a revolution in model estimation methods and a drastic increase in computing power available to researchers. Today, higher-level cognitive functions can well be captured by and understood through computational cognitive models, a common example being drift diffusion models for decision processes. Such models are often expressed as the combination of two modeling layers. The first layer is the response distribution with corresponding distributional parameters tailored to the cognitive process under investigation. The second layer are latent models of the distributional parameters that capture how those parameters vary as a function of design, stimulus, or person characteristics, often in an additive manner. Such cognitive models can thus be understood as special cases of distributional regression models where multiple distributional parameters, rather than just a single centrality parameter, are predicted by additive models. Because of their complexity, distributional models are quite complicated to estimate, but recent advances in Bayesian estimation methods and corresponding software make them increasingly more feasible. In this talk, I will speak about the specification, estimation, and post-processing of Bayesian distributional regression models and how they can help to better understand cognitive processes.

Comparison is a powerful learning process that improves learning in many domains. For over 10 years, my colleagues and I have researched how we can use comparison to support better learning of school mathematics within classroom settings. In 5 short-term experimental, classroom-based studies, we evaluated comparison of solution methods for supporting mathematics knowledge and tested whether prior knowledge impacted effectiveness. We next developed supplemental Algebra I curriculum and professional development for teachers to integrate Comparison and Explanation of Multiple Strategies (CEMS) in their classrooms and tested the promise of the approach when implemented by teachers in two studies. Benefits and challenges emerged in these studies. I will conclude with evidence-based guidelines for effectively supporting comparison and explanation in the classroom. Overall, this program of research illustrates how cognitive science research can guide the design of effective educational materials as well as challenges that occur when bridging from cognitive science research to classroom instruction.

The Brain Awareness Week by the Centre for Cognitive and Brain Sciences, IIT Gandhinagar spans across 7 days and invites you for a series of talks, panel discussions, competitions and workshops on topics ranging from 'Using songbirds to understand how the brain initiates movements' to 'Cognitive Science and UX in Game Design' by speakers from prestigious Indian and International institutes. Explore the marvels of the brain by joining us on 15th March. Free Registration.

Analogies, broadly defined, map novel concepts onto familiar concepts, making them essential for perception, reasoning, and communication. We argue that analogy-building served a critical role in the evolution of cumulative culture, by allowing humans to learn and transmit complex behavioural sequences that would otherwise be too cognitively demanding or opaque to acquire. The emergence of a protolanguage consisting of simple labels would have provided early humans with the cognitive tools to build explicit analogies and to communicate them to others. This focus on analogy-building can shed new light on the coevolution of cognition and culture, and addresses recent calls for better integration of the field of cultural evolution with cognitive science. This talk will address what cumulative cultural evolution is, how we define analogy-building, how analogy-building applies to cumulative cultural evolution, how analogy-building fits into language evolution, and the implications of analogy-building for causal understanding and cognitive evolution.

Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will argue that cognitive science can turn its lens to more complex scenarios to study exploration in real-world domains and online games. I will show in one large data set of online food delivery orders and across many online games how current cognitive theories of learning and exploration can describe human behavior in the wild, but also how these tasks demand us to expand our theoretical toolkit to describe a rich repertoire of real-world behaviors such as empowerment and fun.

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.

The last quarter century has provided extensive evidence that some regions of the human cortex are selectively engaged in processing a single specific domain of information, from faces, places, and bodies to language, music, and other people’s thoughts. This work dovetails with earlier theories in cognitive science highlighting domain specificity in human cognition, development, and evolution. But many questions remain unanswered about even the clearest cases of domain specificity in the brain, the selective engagement of the FFA, PPA, and EBA in the perception of faces, places, and bodies, respectively. First, these claims lack precision, saying little about what is computed and how, and relying on human judgements to decide what counts as a face, place, or body. Second, they provide no account of the reliably varying responses of these regions across different “preferred” images, or across different “nonpreferred” images for each category. Third, the category selectivity of each region is vulnerable to refutation if any of the vast set of as-yet-untested nonpreferred images turns out to produce a stronger response than preferred images for that region. Fourth, and most fundamentally, they provide no account of why, from a computational point of view, brains should exhibit this striking degree of functional specificity in the first place, and why we should have the particular visual specializations we do, for faces, places, and bodies, but not (apparently) for food or snakes. The advent of convolutional neural networks (CNNs) to model visual processing in the ventral pathway has opened up many opportunities to address these long-standing questions in new ways. I will describe ongoing efforts in our lab to harness CNNs to do just that.