Just like awareness of incoming sensory input, awareness of our own knowledge, abilities and memories is often biased and incomplete. During psychophysical detection tasks, it is possible to distinguish between objective accuracy (“type 1” sensitivity), and how accurate one is in subsequently judging their own performance, usually in the form of confidence reports (metacognitive or “type 2” sensitivity)1,2. Recently, these constructs have been modelled and measured within the framework of signal detection theory (SDT)3. Both perceptual and metacognitive sensitivity have been shown to differ widely across participants with important functional implications, as the level of metacognitive insight has been shown to modulate learning, adaptive decision making, error monitoring, collective decision making and optimal exploration behaviours 4,5. At the more extreme end of the spectrum, marked alterations in the ability to reflect accurately on decisions represents a pathological characteristic associated with a range of neurological and mental health disorders.

Perceptual experience, decision-making and metacognition are not only influenced by the immediately available sensory evidence but also by factors related to situational context and endogenous state. In everyday life, our decisions do not occur in isolation but rather as part of a countless chain of preceding decisions, and with myriad sources of internal and external evidence influencing the outcome. For instance, both perceptual decisions and confidence reports are modulated by spontaneous fluctuations in neural excitability and arousal even prior to the presentation of task related evidence 6,7 and perceptual decisions are often biased away from or towards preceding sensory experiences, a recently discovered phenomenon known as serial dependence 8. The neural correlates of serial dependence and how ongoing endogenous neural activity in general interacts with incoming sensory information to influence perception and metacognition remain unknown. Moreover, we know very little about the neurophysiological determinants of whether sensory evidence will reach conscious awareness or not, and how this in turn influences not just the decision itself but also the metacognitive judgement of decision accuracy.

Ground-breaking recent studies employing EEG recordings and analysis techniques grounded in SDT have revealed neural correlates of perceptual decision-related evidence accumulation 9,10. For instance, a post-stimulus component in the EEG signal (the centro-parietal positivity (CPP)) has been identified which predicts the timing and accuracy of perceptual decisions 10. Some studies investigating neural substrates of metacognition have supported the notion that our sense of confidence emerges from the same neural activity which implements the decision itself 11. However, others have found evidence that additional higher-order signals (particularly in the prefrontal cortex (PFC)) specifically contribute to the generation of decision confidence 12.

In my lab, we aim to build on these important research lines in order to contribute to understanding of the neural mechanisms underlying intra- and inter-individual differences in perceptual decision making and metacognition. Utilising visual psychophysics, EEG and advanced signal processing techniques, we map the encoding of sensory input in the brain, investigate how this encoding relates to what is consciously experienced, and to what extent evidence encoding processes influence metacognitive judgements. By exploiting inter-trial and inter-subject variability in behavioural responses and pre- and post-stimulus neurophysiological activity, we can identify activity patterns which are sensitive to experimentally controlled visual features and which predict participants’ responses. Using this approach, the processing of visual features and decision-relevant activity can be precisely tracked in time, frequency and space in the brain. The key questions which we aim to address include the following:

  1. When and where is sensory evidence from separate decision-relevant sources encoded and combined during decision formation? For instance, are different sources of visual evidence encoded in separate brain regions, spectral characteristics (i.e. oscillatory power, phase) and/or frequencies (multiplexing of evidence encoding)?
  2. How does the fidelity of this encoding, and the relative weight of evidence, influence the level of confidence reported in perceptual decisions both within- and across-participants?
  3. Do patterns of neural activity related to decision formation and confidence judgements, and connectivity between them, correlate with inter-individual differences in psychological trait dimensions such as anxiety and impulsiveness?
  4. Is a certain ‘threshold’ or pattern of neural activation required, or does a certain neural region need to be engaged, in order for sensory evidence to be consciously reported by the participant?
  5. How do spontaneous fluctuations in endogenous arousal and neural activity prior to stimulus onset interact with post-stimulus evidence encoding to influence both perceptual and metacognitive decisions?
  6. What are the neural correlates of serial dependence in perceptual and metacognitive decisions?

If you are interested in joining or collaborating with my lab, or if you have any questions regarding the research, then please do not hesitate to get in touch.

References: 1. Galvin SJ, Podd JV, et al. (2003) Psychon Bull Rev. 10:843-76. 2. Maniscalco B, Lau H (2012) Conscious. Cogn. 21:422-30 3. Green DM, Swets JA (1966) Signal Detection Theory and Psychophysics. Wiley, New York. 4. Yeung N, Summerfield C (2012) Phil. Trans. R. Soc. B. 367: 1310-1321. 5. Bahrami B, Olsen K, et al. (2012) Phil. Trans. R. Soc. B. 367:1350-65. 6. Benwell C, Tagliabue C, et al. (2017) eNeuro 4(6). 7. van Ede F, Chekroud SR, et al. (2018) Nat Comm. 9:1449. 8. Urai A, Braun A, Donner T (2017) Nat Comm. 8:14637. 9. Philiastides M, Sajda P (2006) Cerebral Cortex 16:509-18. 10. O’Connell R, Dockree P, Kelly S (2012) Nat Neurosci. 15:1729-35. 11. Kiani R, Shadlen M (2009) Science 324:759-764. 12. Fleming SM, Huijgen J, Dolan RJ (2012) J Neurosci. 32:6117-125.


Blog at

Up ↑

%d bloggers like this: