Introduction to Cognitive Science

Introduction to Cognitive Science

Winter Semester 2023/2024

Paper Topics

This is a list of offered topics for this year. Topics in gray have already been taken. Please see also guidelines for writing this paper.

In case you want a custom topic (which has to be approved first), please send us an anotation and a list of basic literature (minimum 3 papers or 1 good book).


Do Intelligent Robots Need Emotion?

(created by Barbora Cimrova)

Is it possible to create real human-like intelligence without emotions? How important the cognitive–emotional integration might be? Brain and behavioural scientists argue emotions are crucial for the types of intelligent behaviours such as problem solving, planning, attention etc.

Literature

  • Pessoa, L. (2017). Do Intelligent Robots Need Emotion?. Trends in Cognitive Sciences. da Silva Simões, A., Colombini, E. L., & Ribeiro, C. H. C. (2017). CONAIM: A Conscious Attention-Based Integrated Model for Human-Like Robots. IEEE Systems Journal, 11(3), 1296-1307.
  • Inzlicht, M., Bartholow, B. D., & Hirsh, J. B. (2015). Emotional foundations of cognitive control. Trends in cognitive sciences, 19(3), 126-132. Moser, D., Thenius, R., & Schmickl, T. (2016, July). First Investigations into Artificial Emotions in Cognitive Robotics. In International Workshop on Medical and Service Robots (pp. 213-227). Springer, Cham.
  • Can, W. S. R., Do, S., & Seibt, J. (2016). Can Artificial Systems Have Genuine Emotions? The Enactive Approach to Affectivity and Artificial Systems. What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016, 290, 206.
  • Arbib, M. A., & Fellous, J. M. (2004). Emotions: from brain to robot. Trends in cognitive sciences, 8(12), 554-561.

Gut bacteria influencing mind

(created by Barbora Cimrova)

Recent evidence suggests numerous types of bacteria in our gut communicate with our brain. This interaction may influence not only our health and mood but also our cognition and behaviour.

Literature

  • Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F., & Tillisch, K. (2014). Gut microbes and the brain: paradigm shift in neuroscience. Journal of Neuroscience, 34(46), 15490-15496.
  • Cryan, J. F., & Dinan, T. G. (2012). Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nature reviews neuroscience, 13(10), 701-712.
  • Clarke, G., Grenham, S., Scully, P., Fitzgerald, P., Moloney, R. D., Shanahan, F., ... & Cryan, J. F. (2013). The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner. Molecular psychiatry, 18(6), 666-674.
  • Foster, J. A., & Neufeld, K. A. M. (2013). Gut–brain axis: how the microbiome influences anxiety and depression. Trends in neurosciences, 36(5), 305-312.
  • Li, W., Dowd, S. E., Scurlock, B., Acosta-Martinez, V., & Lyte, M. (2009). Memory and learning behavior in mice is temporally associated with diet-induced alterations in gut bacteria. Physiology & behavior, 96(4), 557-567.

Trees talking. Communication in plants.

(created by Barbora Cimrova)

Plants are able to transfer resources and stress signals to other plants in their neighbouring environment via large underground root communication. Can this proto-communication constitute the basis of our communication?

Literature

  • Babikova, Z., Gilbert, L., Bruce, T. J., Birkett, M., Caulfield, J. C., Woodcock, C., ... & Johnson, D. (2013). Underground signals carried through common mycelial networks warn neighbouring plants of aphid attack. Ecology letters, 16(7), 835-843.
  • Wu, Q. S., Zhang, Y. C., Zhang, Z. Z., & Srivastava, A. K. (2017). Underground communication of root hormones by common mycorrhizal network between trifoliate orange and white clover. Archives of Agronomy and Soil Science, 63(9), 1187-1197.
  • Gorzelak, M. A., Asay, A. K., Pickles, B. J., & Simard, S. W. (2015). Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities. AoB Plants, 7.
  • Song, Y. Y., Ye, M., Li, C., He, X., Zhu-Salzman, K., Wang, R. L., ... & Zeng, R. S. (2014). Hijacking common mycorrhizal networks for herbivore-induced defence signal transfer between tomato plants. Scientific reports, 4.
  • Huber, A. E., & Bauerle, T. L. (2016). Long-distance plant signaling pathways in response to multiple stressors: the gap in knowledge. Journal of experimental botany, 67(7), 2063-2079.

Why do we cry?

(created by Barbora Cimrova)

Emotional tearing is a poorly understood behaviour that is considered uniquely human. In mice, tears serve as a chemosignal. Some evidence suggests sniffing women‘s negative-emotion–related tears may reduce arousal, levels of testosterone and alter perception of sexual appeal in men.

Literature

  • Vingerhoets, A. J., & Bylsma, L. M. (2016). The riddle of human emotional crying: A challenge for emotion researchers. Emotion Review, 8(3), 207-217.
  • Gelstein, S., Yeshurun, Y., Rozenkrantz, L., Shushan, S., Frumin, I., Roth, Y., & Sobel, N. (2011). Human tears contain a chemosignal. Science, 331(6014), 226-230.
  • Provine, R. R., Krosnowski, K. A., & Brocato, N. W. (2009). Tearing: Breakthrough in human emotional signaling. Evolutionary Psychology, 7(1), 147470490900700107.

Does our brain know we are wrong? Error related negativity (ERN)

(created by Barbora Cimrová)

When an individual makes a behavioral error, typically in a simple cognitive tasks, a specific change in electroencephalographic (EEG) signal can be detected over the central scalp region via event related potential (ERP). The ERN manifests as a negative deflection in the ERP at approximately 80–150 ms following error commission, time-locked to an individual’s response. An ERN is even evident when participants are unaware of having made a mistake.

Literature

  • Nieuwenhuis, S., Ridderinkhof, K. R., Blom, J., Band, G. P., & Kok, A. (2001). Error‐related brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task. Psychophysiology, 38(5), 752-760.
  • Cohen, M. X., Simon van Gaal, K., & Lamme, V. A. (2009). Unconscious errors enhance prefrontal-occipital oscillatory synchrony. Frontiers in human neuroscience, 3.
  • Hirsh, J. B., & Inzlicht, M. (2010). Error‐related negativity predicts academic performance. Psychophysiology, 47(1), 192-196.

Memory enhancement

(created by Barbora Cimrová)

How can we enhance our memory performance? Is it possible to improve it? From basic physiological factors influencing memory processes to (almost unbelievable sci-fi:) manipulating our neural substrate responsible for memory mechanisms via external device.

Literature

  • Stern, S. A., & Alberini, C. M. (2013). Mechanisms of memory enhancement.Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 5(1), 37-53.
  • Diekelmann, S. (2014). Sleep for cognitive enhancement. Frontiers in systems neuroscience, 8.
  • Wing, E. A., Marsh, E. J., & Cabeza, R. (2013). Neural correlates of retrieval-based memory enhancement: An fMRI study of the testing effect.Neuropsychologia, 51(12), 2360-2370.
  • Spiers, H. J., & Bendor, D. (2014). Enhance, delete, incept: Manipulating hippocampus-dependent memories. Brain research bulletin, 105, 2-7.
  • Plattner, F., Hernández, A., Kistler, T. M., Pozo, K., Zhong, P., Yuen, E. Y., ... & Bibb, J. A. (2014). Memory enhancement by targeting Cdk5 regulation of NR2B. Neuron, 81(5), 1070-1083.
  • Hoy, K. E., Emonson, M. R., Arnold, S. L., Thomson, R. H., Daskalakis, Z. J., & Fitzgerald, P. B. (2013). Testing the limits: investigating the effect of tDCS dose on working memory enhancement in healthy controls. Neuropsychologia,51(9), 1777-1784.
  • Blumenfeld, R. S., Lee, T. G., & D’Esposito, M. (2014). The effects of lateral prefrontal transcranial magnetic stimulation on item memory encoding.Neuropsychologia, 53, 197-202.

Time perception

(created by Barbora Cimrová)

Time perception is highly subjective and depends on many factors. It seems like time flies faster when we get older. Perception of time intervals might be distorted by some psychological phenomena (telescoping effect, kappa effect etc.) The ability to estimate the length of a time interval differs between individuals but also within an individual depending on different conditions (mood, age, time of the day, temperature and more). The goal of the paper is to review selected aspects and phenomena of subjective time perception.

Literature

  • Hancock, P. A., & Rausch, R. (2010). The effects of sex, age, and interval duration on the perception of time. Acta psychologica, 133(2), 170-179.
  • Grondin, S. (2010). Timing and time perception: a review of recent behavioral and neuroscience findings and theoretical directions. Attention, Perception, & Psychophysics, 72(3), 561-582.
  • Halberg, F., Sothern, R. B., Cornélissen, G., & Czaplicki, J. (2008). Chronomics, human time estimation, and aging. Clinical interventions in aging,3(4), 749.
  • Friedman, W. J., & Janssen, S. M. (2010). Aging and the speed of time. Acta psychologica, 134(2), 130-141.
  • Draaisma, D. (2009). [Waarom het leven sneller gaat als je ouder wordt. Rubinstein.] preklad Pellar, R. Proč život ubíhá rychleji, když stárneme: o autobiograficke paměti;. Academia

Double espresso at 10 p.m.

(created by Barbora Cimrová)

What is the exact effect of caffeine on our brain? Recent evidence suggests, it works via adenosine receptors and it can restore our decreased response to light after sleep deprivation. But what is the effect of having an evening coffee on our sleep quality?

Literature

  • Kaster, M. P., Machado, N. J., Silva, H. B., Nunes, A., Ardais, A. P., Santana, M., ... & Chen, J. F. (2015). Caffeine acts through neuronal adenosine A2A receptors to prevent mood and memory dysfunction triggered by chronic stress. Proceedings of the National Academy of Sciences, 112(25), 7833-7838.
  • Sanders, L. (2015). Body & brain: Caffeine resets body's circadian clock: After‐dinner coffee could induce 40‐minute delay, study shows. Science News, 188(8), 8-8.
  • Burke, T. M., Markwald, R. R., McHill, A. W., Chinoy, E. D., Snider, J. A., Bessman, S. C., ... & Wright, K. P. (2015). Effects of caffeine on the human circadian clock in vivo and in vitro. Science translational medicine, 7(305), 305ra146-305ra146.

Neurophysiological and psychological effects of Qigong and their measurement

(created by Dezider Kamhal)

Think about a correlation between neurophysiological and psychological effects of Qigong (e.g. changes in brain activity and mood changes)

Literature

  • Yvonne W. Y. Chow: An Analysis of the Neurophysiological Effects of Qigong on the Mind" In: Jenkins, Sullivan (eds): Philosophy of Mind
  • Matthieu Ricard, Antoine Lutz and Richard J. Davidson: Mind of the Meditator. Scientific American (November 2014), 311, 38-45

Free will from a neural point of view

(created by Igor Farkaš)

Only a few issues in neuroscience attract such wide interest as the brain basis of the “free will.” We all have the strong belief that we make choices about what we do and that our conscious decisions initiate our actions. At the same time, our actions are clearly the result of a causal chain of neuronal activity in premotor and motor areas of the brain. Neuroscience has a few convincing experimental methods to study the brain processes that precede voluntary action and interesting views and results have been published recently.

Literature

  • Haggard P.: Decision Time for Free Will. Neuron, 69, 2011.
  • Wegner, D. M. (2004). Précis of the illusion of conscious will. Behavioral and Brain Sciences, 27(5), 649-659.
  • a book provided by prof. Farkaš

Autonomous body exploration

(created by Igor Farkaš)

Self-touching behaviors constitute one of the possible important mechanisms in which infants learn about their bodies through motor-proprioceptive, tactile and visual contingencies, giving rise to the first models or representations (body schema) (Rochat 1998; Thomas et al. 2014). However, random motor babbling will most of the time not result in interesting (self-touching) configurations. To arrive at an effective exploration strategy, variants of goal babbling and active learning (Baranes & Oudeyer 2013) will be a starting point.

Literature

  • Baranes, Adrien, and Pierre-Yves Oudeyer. "Active learning of inverse models with intrinsically motivated goal exploration in robots." Robotics and Autonomous Systems 61.1 (2013): 49-73.
  • Rochat, P. (1998), 'Self-perception and action in infancy', Exp Brain Res 123, 102-109.
  • Thomas, B. L., Karl, J. M., & Whishaw, I. Q. (2014). Independent development of the Reach and the Grasp in spontaneous self-touching by human infants in the first 6 months. Frontiers in psychology, 5.

Imitation learning

(created by Igor Farkaš)

Humans, monkeys and some other species have a unique ability to imitate, i.e. to learn movements or actions based on observation. Some manifestations of imitation are inherent, some are learned early in life. Research in this area of cognitive neuroscience concerns the neural correlates and mechanisms in the brain that enable this ability. In the area of cognitive robotics, the goal is to design algorithms for robots that enable them to learn in such an effective way.

Literature

  • Zentall T.R. (2006). Imitation: Definitions, evidence, and mechanisms. Animal Cognition, 9, 335–53.
  • Meltzoff A.N. (2005). Imitation and Other Minds: The "Like Me" Hypothesis. In: S. Hurley and N. Chater (Eds.), Perspectives on Imitation: From Neuroscience to Social Science (Vol. 2, pp. 55-77). Cambridge, MA: MIT Press.
  • Schaal S., Ijspeert A., Billard A. (2003). Computational approaches to motor learning by imitation, Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358, 1431, pp.537-547.
  • Schaal S. (1999). Is imitation learning the route to humanoid robots?, Trends in Cognitive Sciences, 3(6), pp.233-242.
  • Billard A., Mataric M.J. (2001). Learning human arm movements by imitation: Evaluation of a biologically inspired connectionist architecture. Robotics and Autonomous Systems, 145-160.

Neural correlates of action observation

(created by Igor Farkaš)

While observing actions within one's motor repertoire the human brain reacts as if the action was executed. This phenomenon may be explained by the existence of a system of mirror neurons and its influence on the desynchronization of mu rhythm in the motor cortex. In humans the ability to mirror appears to be more abstract than in animals (e.g. macaques). There are also alternative explanations that do not support the mirroring theories. Make a survey of important and most recent literature on neural correlates of action observation with emphasis on the differences between humans and other animals.

Literature

  • Gatti R. et al.(2013). Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study. Neurosci. Letters.
  • Kilner J., Frith C. (2007). Action Observation: Inferring Intentions without Mirror Neurons. Current Biology, 18(1): R32-33.
  • Press C. (2011). Action observation and robotic agents: learning and anthropomorphism. Neurosci Biobehav Rev., 35(6):1410-18

Representation of space and reference frames

(created by Igor Farkaš)

In order to be able to react effectively and to move in the environment humans must use internal representations of the positions of the objects around them. As neuroscientific research has shown, positions are simultaneously represented in several reference frames (coordinate systems) in different parts of the brain, and humans can switch between them. We can look at the object from its own perspective (egocentric view), from the viewpoint of another observer (allocentric frame), or from the point of view of absolute coordinates (north-south). Even within its own perspective, there are several frames (e.g., in relation to the center of the head or the position of a hand) that we seem to use effectively in sensorimotor coordination. These findings can indeed be inspirational even in the design of artificial robots that must learn to effectively orient themselves in the surrounding environment.

Literature

  • Battaglia-Mayer et al. (2003). Multiple levels of representation of reaching in the parieto-frontal network. Cerebral Cortex, 13, 1009-1022.
  • Cohen Y. (2002). A common reference frame for movement plans in the posterior parietal cortex. Nature Reviews Neuroscience, 3(2), 553-562.
  • Chang S., Papadimitriou C., Snyder L. (2009). Using a compound gain field to compute a reach plan. Neuron, 64, 744-755.
  • Gaspare Galati G. et al. (2010). Multiple reference frames used by the human brain for spatial perception and memory. Experimental Brain Research, doi:10.1007/s00221-010-2168-8.
  • Hoffmann M. et al. (2010). Body schema in robotics: a review. IEEE Transactions on Autonomous Mental Development, 304-324.

Somatosensory target reaching

(created by Igor Farkaš)

Humans are capable of reaching for so-called somatosensory targets - specified by proprioceptive and tactile information - without relying on visual information. That is, when tickled on the skin of one arm, a mapping needs to exist between the proprioceptive and tactile information pertaining to that arm and the motor/proprioceptive information (joint angles) of the contralateral arm, which results in a successful reach for the stimulus. In infants, this mapping is likely to be learned from experience during the first year from self-touching behaviors. Several neural network architectures capable of learning this mapping have been proposed with different properties and architectures.

Literature

  • Hoffmann, M. & Bednárová, N. (2016). The encoding of proprioceptive inputs in the brain: knowns and unknowns from a robotic perspective. In M. Vavrečka; O. Bečev; M. Hoffmann & K. Štěpánová, ed., 'Kognice a umělý život XVI', pp. 55-66.
  • Metohajrová, L. (2016), 'Biologically inspired predictive model of proprioceptive body representations', Bachelor thesis, Comenius University in Bratislava, Faculty of Mathematics, Physics and Informatics.
  • Seelke, A. M. H.; Padberg, J.J.; Disbrow, E.; Purnell, S. M.; Recanzone, G. & Krubitzer, L. (2011), Topographic Maps within Brodmann's Area 5 of Macaque Monkeys, Cerebral Cortex, 22(8):1834-50.
  • Thomas B.L., Karl J.M., Whishaw I.Q. (2014). Independent development of the reach and the grasp in spontaneous self-touching by human infants in the first 6 months. Frontiers in psychology, 5.

Evolutionary Art and Aesthetic Measures

(created by Ivor Uhliarie)

Evolutionary art may be classified into (1) interactive evolutionary computation and (2) unsupervised evolutionary art. The former requires human intervention, while the latter uses the aesthetic measure as a function. When generating vector graphics, the scene may be represented as a tree (SVG), when the evolutionary process becomes one of genetic programming. Can such a unsupervised method have feasible outcomes? Can it reveal anything about aesthetic measures used in the process?

Literature

  • den Heijer, E. & Eiben, A. E. (2014). Investigating aesthetic measures for unsupervised evolutionary art. Swarm and Evolutionary Computation, 16, 52-68.
  • den Heijer, E. & Eiben, A. E. (2016). Using scalable vector graphics to evolve art. International Journal of Arts and Technology, 9(1), 59-85.
  • Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.
  • http://geneticprogramming.com/tutorial/

Detecting sparkles of consciousness in vegetative state.

(created by Jakub Benko)

Every year, thousands of people end up in with severed consciousness due to traumatic brain injury. Be it relatively slight change in one of the cognitive functions or severe coma, there appears to be a grey area of so called vegetative state. By definition, it was thought that people in vegetative state are unable to respond to commands and show rather stereotyped reflexive behaviour often mistaken for complex coordinated actions. However, recent discoveries show more encouraging picture.

Literature

  • Owen, A. M., Coleman, M. R., Boly, M., Davis, M. H., Laureys, S., & Pickard, J. D. (2006). Detecting awareness in the vegetative state. science, 313(5792), 1402-1402.
  • Giacino, J. T., Fins, J. J., Laureys, S., & Schiff, N. D. (2014). Disorders of consciousness after acquired brain injury: the state of the science. Nature Reviews Neurology, 10(2), 99-114.

Religiosity and pathology - is there a connection?

(created by Jakub Benko)

Religiosity has played an indispensable role in every human culture. Usually only chosen ones play a role in formulation and prescription of religious ideas. Recently, various comparisons have been made between several aspects of highly religious people and symptoms expressed in a number of neurological and mental conditions. Often, these occur together and pose an interesting lead to possible explanation of human religious behaviour.

Literature

  • Devinsky O, Lai G. Spirituality and Religion in Epilepsy. 2008;12:636-643. doi:10.1016/j.yebeh.2007.11.011.
  • Wolfradt U, Oubaid V, Straube ER, Bischo N. Thinking styles, schizotypal traits and anomalous experiences. 1999;27:821-830.
  • Woody S. Journal of Anxiety Disorders OCD cognitions and symptoms in different religious contexts. 2009;23:401-406. doi:10.1016/j.janxdis.2008.11.001.

High-functioning autism and empathy deficits

(created by Ján Rybár)

Characterization, cause, measurableness, diagnosis and treatment.

Literature

  • Simon Baron-Cohen: Mindblidness: An Essay on Autism and Theory of Mind. A Bradford Books, 1997
  • Simon Baron-Cohen: The Essential Difference: Male and Female and Truth about Autism. Basic Books, 2004
  • Simon Baron-Cohen: The Science of Evil. Basic Books, 2011

Theory of mind

(created by Ján Rybár / Kristína Malinovská)

To have the theory of mind means to have the ability to attribute mental states to oneself and others and to understand that others have beliefs, desires, intentions, and perspectives that are different from one's own. This ability is not inherent, children master it at a certain age, and it is impaired in people with autism spectrum disorder and some other conditions. There are many directions in the study of theory of mind, developmental aspects, neural correlates, canonical and novel experiments, the paper should cover some of them including the latest advances.

Literature

  • Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. MIT press.
  • Gopnik, A., Meltzoff, A. N., Kuhl, P. K. (2001): What Children Learn About People. In: The scientist in the crib. What early learning tells us about the mind (chap. 2). Harper, New York.
  • Saxe, R., Carey, S. Kanwisher, N. (2004): Understanding other minds. Linking developmental psychology and functional neuroimaging. Annual Review of Psychology, 55.
  • Bašnáková, J., Weber, K., Petersson, K. M., van Berkum, J., & Hagoort, P. (2013). Beyond the language given: the neural correlates of inferring speaker meaning. Cerebral Cortex, 24(10), 2572-2578.

Deep Learning vs. Cognitive Modelling

(created by Kristína Malinovská)

Deep Learning is currently one of the most debated topic in machine learning. Networks designed to perform specified highly advanced tasks have already outsmarted humans. Although they are a very powerful tool, deep learning architectures have their limitations and their performance depends on the expert knowledge with which they are built. In cognitive science related modeling, for instance cognitive robotics, emphasis is given on properties of living organisms such as autonomy or the ability to adapt to new situations. Can such properties be achieved using deep learning architectures?

Literature

  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44.
  • Bengio Y. Learning deep architectures for AI. Foundations and trends® in Machine Learning. 2009 Nov 15;2(1):1-27.
  • Pasquale G, Ciliberto C, Odone F, Rosasco L, Natale L. Teaching iCub to recognize objects using deep Convolutional Neural Networks. In Machine Learning for Interactive Systems 2015 Jun 18 (pp. 21-25).
  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S. Mastering the game of Go with deep neural networks and tree search. Nature. 2016 Jan 28;529(7587):484-9.

Thinking Fast and Slow

(created by Kristína Malinovská)

The dual-process accounts of reasoning posits that there are two systems or minds in one brain. These systems are often referred to as "implicit" and "explicit" or by the more neutral "System 1" and "System 2," as coined by Stanovich and West. These two competing systems, (evolutionary) old and new, fast and slow, automatic and controlled, are involved in cognitive processes related to reasoning and thinking, such as stereotyping, categorization, and judgment and their duality contributes to interesting cognitive phenomena. Even their neural correlates seem to be disjunct.

Literature

  • Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate?. Behavioral and brain sciences, 23(5), 645-665.
  • Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
  • Evans, J. S. B. (2011). Dual-process theories of reasoning: Contemporary issues and developmental applications.Developmental Review, 31(2), 86-102.
  • Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on psychological science, 8(3), 223-241.
  • Palkovics, M. A., & Takáč, M. (2016). Exploration of cognition–affect and Type 1–Type 2 dichotomies in a computational model of decision making. Cognitive Systems Research, 40, 144-160.

Where do mirror neurons come from?

(created by Kristína Malinovská)

Mirror neurons (MN) were originally discovered in monkeys and later in humans. They are motor neurons that respond also to solely visual stimuli. MN were theorized to be involved in action understanding, since they connect perception of an action with its representation in the observer's own motor repertoire, and, in a similar way, to be involved in other cognitive capacities such as empathy or language. There are several different accounts on the origin and role of mirror neurons and fundamental questions still unanswered: are MN an evolutionary advantage (so they are innate) or a mere by-product of associative learning? Are MN facilitating or even mediating imitation learning? Is even imitation and imitation learning inherent or learned?. If the mirroring mechanisms are a product of learning, what is the nature of such learning process?

Literature

  • Rizzolatti G, Sinigaglia C. The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nature reviews. Neuroscience. 2010 Apr 1;11(4):264.
  • Keysers C, Perrett DI. Demystifying social cognition: a Hebbian perspective. Trends in cognitive sciences. 2004 Nov 30;8(11):501-7.
  • Heyes C. Where do mirror neurons come from?. Neuroscience & Biobehavioral Reviews. 2010 Mar 31;34(4):575-83.
  • Cook R, Bird G, Catmur C, Press C, Heyes C. Mirror neurons: from origin to function. Behavioral and Brain Sciences. 2014 Apr;37(2):177-92.
  • Gallese V. From grasping to language: Mirror neurons and the origin of social communication. Towards a science of consciousness. 1999 Oct 29:165-78.
  • Stamenov M, Gallese V, editors. Mirror neurons and the evolution of brain and language. John Benjamins Publishing; 2002.
  • Giudice MD, Manera V, Keysers C. Programmed to learn? The ontogeny of mirror neurons. Developmental science. 2009 Mar 1;12(2):350-63.

Bats, Machines, and Waterlilies: Cognitive Reflection as the Predictor of Rationality?

(created by Lenka Kostovičová)

Cognitive reflection is the ability to suppress automated intuitive responses and engage the analytical system. It is studied using the Cognitive Reflection Test (CRT), based on 3 (in new, extended, version 7) numerical tasks. CRT scores have proven to be a strong predictor of rational (normative) responses in different domains, risk judgements or Bayesian reasoning. The aim of the paper is to provide an overview of the results of empirical studies that support or contradict the hypothesis that the level of cognitive reflection predicts rationality.

Literature

  • Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25–42. doi:10.1257/089533005775196732
  • Sirota, M., & Juanchich, M. (2011). Role of numeracy and cognitive reflection in Bayesian reasoning with natural frequencies. Studia Psychologica, 53, 151–161.
  • Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks. Memory & Cognition, 39, 1275–89. doi:10.3758/s13421-011-0104-1
  • Toplak, M. E., West, R. F., & Stanovich, K. E. (2014). Assessing miserly information processing: An expansion of the Cognitive Reflection Test. Thinking & Reasoning, 20, 147–168. doi:10.1080/13546783.2013.844729

The Effects of Transcranial Direct-Current Stimulation on Neural Cells and Circuitry

(created by Martin Marko)

One promising method for studying various brain mechanisms is transcranial direct current stimulation (tDCS). During tDCS, a mild electrical current is passed between two electrodes placed on the scalp (electrode montage). Although a large number of studies indicate that tDCS may enhance cognitive functioning, the precise neurophysiological mechanism of these effects is not well-understood. Review and evaluate current empirical evidence and theories related to the effects of tDCS on neural substrate and circuits. The student may optionally choose to see or even experience tDCS treatment in laboratory conditions.

Literature

  • Berker, A. O. De, Bikson, M., & Bestmann, S. (2013). Predicting the behavioral impact of transcranial direct current stimulation : issues and limitations, 7(October), 1–6. https://doi.org/10.3389/fnhum.2013.00613
  • Bikson, M., & Rahman, A. (2013). Origins of specificity during tDCS : anatomical , activity-selective , and input-bias mechanisms, 7(October), 1–5. https://doi.org/10.3389/fnhum.2013.00688
  • Rahman, A., Lafon, B., Parra, L. C., & Bikson, M. (2017). Direct current stimulation boosts synaptic gain and cooperativity in vitro, 0, 1–13. https://doi.org/10.1113/JP273005
  • Rahman, A., Reato, D., Arlotti, M., Gasca, F., Datta, A., Parra, L. C., & Bikson, M. (2013). Cellular effects of acute direct current stimulation : somatic and synaptic terminal effects, 10, 2563–2578. https://doi.org/10.1113/jphysiol.2012.247171

Computational psychiatry

(created by Martin Takáč)

Computational psychiatry focuses on building computational models of neural or cognitive phenomena relevant to psychiatric diseases. The goal of the paper would be to provide overview of approaches and computational techniques used in the field, possibly with examples of some models (e.g. depression).

Literature

  • Huys, QJM (2014): Computational psychiatry. In: Encyclopedia of Computational Neuroscience, DOI 10.1007/978-1-4614-7320-6_501-2, Springer Science+Business Media New York.
  • Dayan P (2009): Dopamine, reinforcement learning, and addiction. Pharmacopsychiatry 42 (S1) S56-S65.
  • Huys QJM & Dayan P (2009): A Bayesian formulation of behavioral control. Cognition 113 314-328.
  • Dayan P & Huys QJM (2009): Serotonin in Affective Control. Annual Review of Neuroscience 32 95-126.
  • Huys QJM, Vogelstein J & Dayan P (2008): Psychiatry: Insights into depression through normative decision-making models. NIPS 2008.

Big Five

(created by Martin Takáč)

Big Five has been a widely accepted 5 factor model of personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism). The goal of this paper would be to relate this model to other personality theories, recent findings from neuroscience, developmental psychology and cybernetics.

Literature

  • DeYoung, C. G. (2015). Cybernetic Big Five Theory. Journal of Research in Personality 56, 33–58.
  • Corr, P. J., DeYoung, C. G., & McNaughton, N. (2013). Motivation and personality: A neuropsychological perspective. Social and Personality Psychology Compass, 7, 158–175.
  • Shiner, R. L., & DeYoung, C. G. (2013). The structure of temperament and personality traits: A developmental perspective. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology. New York: Oxford University Press.

Cognitive science of science

(created by Martin Takáč)

Analyse scientific development (nature of explanation, mental models, theory choice, resistance to scientific change, instruments of science, conceptual and paradigm changes) from the point of view of cognitive science.

Literature

  • Thagard, P.: Cognitive Science of Science, MIT Press 2012

Drugs of our brain

(created by Martin Takáč)

Dopamin, Serotonin, Testosterone, Oxytocin and other drugs of our body and their effect on our behaviour and cognitivie functioning. As a primer, watch Helen Fisher's TED talk.

Literature

  • Doya, K (2002): Metalearning and neuromodulation. Neural Netw. 15, 495–506.
  • Dayan P (2012): Twenty-five lessons from computational neuromodulation. Neuron 76.
  • DeYoung, CG (2013): The neuromodulator of exploration: A unifying theory of the role of dopamine in personality. Frontiers in Human Neuroscience, 7, article 762.
  • Marcincakova Husarova V, Lakatosova, S, Pivovarciova, A, Ostatníková, D (2016): Plasma Oxytocin in Children with Autism and Its Correlations with Behavioral Parameters in Children and Parents. Psychiatry investigation 13(2):174
  • Zehentbauer, J. (1992). Körpereigene Drogen: die ungenutzten Fähigkeiten unseres Gehirns. Artemis & Winkler. [in Czech Zehentbauer, J (2012): Drogy lidského těla, PORTÁL.]

Neural correlates of romantic love

(created by Martin Takáč)

Why some people can be happily in love after 20 years of marriage? What does it have to do with their brains? As a primer, watch Helen Fisher's TED talk.

Literature

  • Acevedo, BP, Aron, A, Fisher, HE, Brown, LL (2012): Neural correlates of long-term intense romantic love. Social Cognitive and Affective Neuroscience 7(2).
  • Acevedo, BP, Aron, A, Fisher, HE, Brown, LL (2012):Neural correlates of marital satisfaction and well-being: Reward, empathy, and affect. Clinical Neuropsychiatry 9(1), p. 20-31.

Computation and Artificial Life

(created by Matej Fandl)

Artificial life is a field that deals with building systems that behave similarly to natural living systems. Every attempt to build such system encapsulates a theory of what life is, which is often grounded in recent technological advancements. Now it is computers. Even though we do not think brain is a von Neumann computer built of organic matter anymore, computers still have a lot to offer in building artificial life systems. What is it?

Literature

  • Dorin, A. (2014). Biological Bits. A Brief Guide to the Ideas and Artefacts of Computational Artificial Life. Melbourne: Animaland. (note: a good overview of the field - whole book, but skimming through is a good start)
  • Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH computer graphics, 21(4), 25-34.
  • Navlakha, S., & Bar‐Joseph, Z. (2011). Algorithms in nature: the convergence of systems biology and computational thinking.Molecular systems biology, 7(1), 546.

Theories of concept learning

(created by Matúš Tuna)

Concept learning, also known as category learning, concept attainment, and concept formation is a fundamental feature of human mind that enables us to form mental categories that help us to understand our environment and make decisions. The goal of this project is to explore, summarize and compare the most influential theories of concept learning with a focus on prototype and exemplar theories of concept learning and their significance for artificial intelligence.

Literature

  • Rosch, Eleanor (1988). Principles of categorization. In Allan Collins & Edward E. Smith (eds.), Readings in Cognitive Science, a Perspective From Psychology and Artificial Intelligence. Morgan Kaufmann Publishers. pp. 312-22.
  • Nosofsky, R. (2011). The generalized context model: an exemplar model of classification. Formal Approaches in Categorization, pp.18-39.
  • Gärdenfors, Peter. (2004). Conceptual Spaces as a Framework for Knowledge Representation. Mind and Matter. 2. 9-27.

Can animals mentally time travel?

(created by Zdenko Kohút)

Mental time travel is the ability of being aware of ones future or past i.e. ability to simulate episodes outside present time. There are two borderline views on mental time travel – first claiming that this ability is uniquely human whereas the other suggesting that it is shared across species. Students task is to analyse the evidence and formulate opinion on the question, possibly consider principle or methodological issues.

Literature

  • Corballis, 2013. Mental time travel: Case for evolutionary continuity. Trends in Cognitive Sciences.
  • Roberts & Feeney, 2009. The comparative study of mental time travel. Trends in Cognitive Sciences.
  • Roberts, 2008. The current status of cognitive mental time travel research in animals. Chapter 2.1. Handbook of Episodic memory.
  • Suddendorf & Corballis, 2009. Behavioural evidence for mental time travel in nonhuman animals. Behavioural Brain Research.
  • Zentall, 2006. Mental time travel in animals: A challenging question. Behavioural Processes.

Involuntary music imagery

(created by Zuzana Cenkerová)

Involuntary music imagery (INMI), also known in the literature as earworms, refer to the phenomenon in which a shorter, "catchy" musical snippet repeats itself in the mind of a person without deliberately inducing it, and even though there is no music playing in their acoustic environment. Although there is no scientific consensus as to how this phenomenon emerges, INMI has earned a lot of attention in recent years. Scientists investigate the circumstances under which INMI appears (e.g. listening to music, low attention state), differences in individual INMI experience (pleasant/disturbing), susceptibility to INMI (e.g. individuals with certain neurological deviations, but with distinction between INMI and musical hallucinations or obsessions), INMI coping strategies (e.g. deliberate replay of the entire song, distraction with other music, or non-musical activities requiring focused attention, such as solving crossword puzzles). The subject of the research are also the properties of melodies equipped with INMI (e.g. the faithfulness of it compared to the original) or neurological aspects of INMI.

Literature

  • Farrugia, N., Jakubowski, K., Cusack, R., & Stewart, L. (2015). Tunes stuck in your brain: The frequency and affective evaluation of involuntary musical imagery correlate with cortical structure. Consciousness and cognition, 35, 66-77.
  • Floridou, G. A., Williamson, V. J., Stewart, L., & Müllensiefen, D. (2015). The Involuntary Musical Imagery Scale (IMIS). Psychomusicology: Music, Mind, and Brain, Vol 25(1), 28-36.
  • Sacks, O. (2010). Musicophilia: Tales of music and the brain. Vintage Canada.
  • Williamson, V. J., Jilka, S. R., Fry, J., Finkel, S., Müllensiefen, D., & Stewart, L. (2012). How do “earworms” start? Classifying the everyday circumstances of involuntary musical imagery. Psychology of Music, 40(3), 259-284.
  • Williamson, V. J., Liikkanen, L. A., Jakubowski, K., Stewart, L. (2014). Sticky tunes: how do people react to involuntary musical imagery? PloS one 9.1, e86170.

Neural thermal networks

(created by Elena Mikhina)

Thermoregulation is crucial for a human organism. Temperature affects enzyme reactions and the functioning of bioactive molecules. However, our knowledge about thermoregulatory mechanisms is very limited. We know that the hypothalamus and precisely anterior portion and preoptic area are responsible for temperature control. Similar to a thermostat, the hypothalamus measures deep brain temperature, integrates temperature information from peripheral body sensors, and then makes appropriate changes. But what are the molecular mechanisms of this regulation?

Literature

  • Kamm, Gretel B., et al. "A synaptic temperature sensor for body cooling." Neuron 109.20 (2021): 3283-3297.
  • Muzik, Otto, et al. "Effective connectivity of brain networks controlling human thermoregulation." Brain Structure and Function 227.1 (2022): 299-312.
  • Siemens, Jan, and Gretel B. Kamm. "Cellular populations and thermosensing mechanisms of the hypothalamic thermoregulatory center." Pflügers Archiv-European Journal of Physiology 470.5 (2018): 809-822.
  • Werner, Jürgen. "Book Review:“Thermoregulation: From Basic Neuroscience to Clinical Neurology, Part 1”." (2018): 205-207.

Bilingualism as a cure for aging

(created by Elena Mikhina)

It is believed that bilingual people are "smarter" than monolinguals: allegedly, they have better neuroconnectivity, increased neuroplasticity, and are at less risk of developing dementia and neurodegenerative diseases. How exactly does knowledge of more than one language prevent aging and help cognitive functions development?

Literature

  • Anderson, John AE, et al. "Effects of bilingualism on white matter integrity in older adults." Neuroimage 167 (2018): 143-150.
  • DeLuca, Vincent, et al. "Duration and extent of bilingual experience modulate neurocognitive outcomes." NeuroImage 204 (2020): 116222.
  • Sulpizio, Simone, et al. "Bilingualism as a gradient measure modulates functional connectivity of language and control networks." NeuroImage 205 (2020): 116306.

Total 36 topics. 6 already taken.

Last update November 08 2023 09:43:39.