Research Interests for Tobias Egner

Research Interests: Cognitive & Affective Control; Selective Attention; Predictive Coding

A main interest of our lab centers on neural mechanisms underpinning cognitive and affective control processes. ‘Cognitive control’ refers to the ability to flexibly adapt one’s behavior in the pursuit of an internal goal. This includes the generation and implementation of a set of processing strategies ( a 'task-set') to obtain a current goal; maintaining and ‘shielding’ that goal and associated task-set in working memory; monitoring the outcome of one’s actions to ascertain that the stated goal is being achieved; and adjusting one’s behavior if the chosen task strategy is not successful. In our Lab, we typically combine functional magnetic resonance imaging (fMRI) with experimental protocols that manipulate aspects of classic selective attention ‘interference’ tasks (such as the Stroop task) in order to isolate neural correlates of different components of cognitive control. We are also interested in 'affective control’, that is, the processes involved in regulating emotional responses. Stimuli that signal potential threats to the organism (such as a nearby fearful face) tend to trigger an emotional physiological response and attract our attention. This process is not entirely automatic, however, since we can override these reactions under certain conditions. The neural mechanisms that allow us to overcome quasi-instinctual emotional reactions may be crucial to understanding a number of psychopathologies, particularly the Anxiety Disorders. We are investigating two main aspects of the interaction between emotion and attention. First, we are interested in delineating the neural mechanisms by which motivational factors (both aversive and appetitive) guide spatial attention, and second, we investigate the way in which attentional control mechanisms ‘shield’ ongoing task performance from intrusion by affective responses to task-irrelevant emotional stimuli. Finally, another area of interest in the lab surrounds the concept of 'predictive coding' in visual perception.Object recognition is no simple feat, since the human brain has no direct knowledge of the outside world, but rather needs to interpret patterns of light that are reflected by objects onto the retina. Perceptual inference therefore is akin to deducing the cause of a particular percept. This deductive process is faced with an ‘inverse problem’: there is no unique solution (interpretation) to any one pattern of light that falls onto the retina. This turns the process of perceptual inference into one of weighing up the relative probabilities of a given percept being caused by a number of possible stimuli. How do human observers solve this puzzle? Theoretically, an ideal solution to the problem of perceptual inference is offered by a ‘Bayesian’ statistical framework: in ‘Empirical Bayes’, unknown causes (the so-called ‘posterior probability’) are inferred by integrating the observed evidence (the so-called ‘likelihood’) with a learned and context-sensitive probability distribution of possible causes (the ‘prior probability’). Put simply, knowledge about the context in which a given percept occurs informs its interpretation. A plausible neurobiological implementation of this scheme is provided by ‘predictive coding’ models of perceptual inference. Here, each level in the visual processing hierarchy feeds back context-sensitive predictions (priors) about the probable causes of sensation to the next lower level, where they are matched against the incoming sensory data. Mismatches (‘prediction error’) between expected and observed data are fed forward from each level of the hierarchy to the level above. The priors at each level are dynamically adjusted in order to eliminate prediction error at the level below; once all prediction errors are minimized, a stimulus has been ‘recognized’, that is, the system has settled on a unique ‘best guess’ interpretation of the stimulus. In our Lab, we are interested in testing whether predictive coding mechanisms are indeed the brain’s solution to the problem of perceptual inference (and possibly apply to other decision-making domains as well). We pursue this question by combining fMRI with perceptual decision-making tasks, where we can manipulate the sensory evidence (i.e., the ‘likelihood’) as well as the task-context, for instance by varying task instructions or the probability of a particular stimulus category occurring (thus affecting the ‘priors’).

Recent Publications   (search)
  1. Egner, T., Multiple conflict-driven control mechanisms in the human brain, Trends in Cognitive Sciences, vol. 12 (2008), pp. 374-380.
  2. Summerfield, C., Trittschuh, E.H., Monti, J.M., Mesulam, M.-M., Egner, T., Neural repetition suppression reflects fulfilled perceptual expectations, Nature Neuroscience, vol. 11 (2008), pp. 1004-1006.
  3. Egner, T., Monti, J.M., Trittschuh, E.H., Wieneke, C.A., Hirsch, J., Mesulam, M.M., Neural integration of top-down spatial and feature-based information in visual search, Journal of Neuroscience, vol. 28 (2008), pp. 6141-6151.
  4. Egner, T., Congruency sequence effects and cognitive control, Cognitive, Affective, & Behavioral Neuroscience, vol. 7 (2007), pp. 380-390.
  5. Egner, T., Hirsch, J., Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information, Nature Neuroscience, vol. 8 (2005), pp. 1784-1790.