Biology subjects

Colizoli, O.

Publications and source records attributed to Colizoli, O..

4 recordsLinked to original sources

bioRxiv2024

Pupil dilation offers a time-window on prediction error

Task-evoked pupil dilation has been linked to many cognitive variables, perhaps most notably unexpected events. Zenon (2019) proposed a unifying framework stating that pupil dilation related to cognition should be considered from an information-theory perspective. In the current study, we investigated whether the pupils response to decision outcome in the context of associative learning reflects a prediction error signal defined operationally as an interaction between stimulus-pair frequency and accuracy, while also exploring the time course of this prediction error signal. Thereafter, we tested whether these prediction error signals correlated with information gain, defined formally as the KL divergence between posterior and prior belief distributions of the ideal observer. We reasoned that information gain should be proportional to the (precision-weighted) prediction error signals potentially arising from neuromodulatory arousal networks. To do so, we adapted a simple model of trial-by-trial learning of stimulus probabilities based on information theory from previous literature. We analyzed two data sets in which participants performed perceptual decision-making tasks that required associative learning while pupil dilation was recorded. Our findings consistently showed that a significant proportion of variability in the post-feedback pupil response during decision-making can be explained by a formal quantification of information gain shortly after feedback presentation in both task contexts. In the later time window, the relationship between information-theoretic variables and the post-feedback pupil response differed per task. For the first time, we present evidence that whether the post-feedback pupil dilates or constricts along with information gain was context dependent. This study offers empirical evidence showcasing how the pupils response can offer valuable insights into the process of model updating during learning, highlighting the promising utility of this readily accessible physiological indicator for investigating internal belief states.

neuroscience↗

bioRxiv2022

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

Within predictive processing learning is construed as Bayesian model updating with the degree of certainty for different existing hypotheses changing in light of new evidence. Bayesian model updating, however, cannot explain how new hypotheses are added to a model. Model revision, unlike model updating, makes structural changes to a generative model by altering its causal connections or adding or removing hypotheses. Whilst model updating and model revision have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between model updating and revision on the basis of how they affect prediction errors and predictions over time. To study this, participants took part in a within-subject computer-based learning experiment with two phases: updating and revision. In the updating phase, participants had to predict the relationship between cues and target stimuli and in the revision phase, they had to correctly predict a change in the said relationship. Based on previous research, phasic pupil dilation was taken as a proxy for prediction error. During model updating, we expected that the prediction errors over trials would be gradually decreasing as a reflection of the continuous integration of new evidence. During model revision, in contrast, prediction errors over trials were expected to show an abrupt decrease following the successful integration of a new hypothesis within the existing model. The opposite results were expected for predictions. Our results show that the learning dynamics as reflected in pupil and accuracy data are indeed qualitatively different between the revision and the updating phase, however in the opposite direction as expected. Participants were learning more gradually in the revision phase compared to the updating phase. This could imply that participants first built multiple models from scratch in the updating phase and updated them in the revision phase.

neuroscience↗

bioRxiv2020

Comparing fMRI responses measured at 3 versus 7 Tesla across human cortex, striatum, and brainstem

Significant progress has been made in ultra-high field functional magnetic resonance imaging (fMRI) at 7 Tesla (T). While fMRI at 7 T promises a general increase in sensitivity compared to lower field strengths, the benefits may be most pronounced for specific applications. The current study aimed to evaluate the relative benefit of 7 T over 3 T fMRI for the assessment of task-evoked fMRI responses in different brain regions. We compared the amplitude of task-evoked responses between 3 T and 7 T measured from the same human participants. Participants performed a challenging random dot motion discrimination task with delayed monetary feedback, which animal physiology has linked to several cortical and subcortical structures including extrastriate (dorsal) visual cortical areas, the striatum, and the brainstem including dopaminergic midbrain nuclei. We quantified the evoked fMRI responses in each of these brain regions during the decision interval and the post-feedback interval of the task, and compared them between brain regions and field strengths. The dependence of response amplitudes on field strength during the decision interval differed between cortical, striatal, and brainstem regions, with a generally bigger 7 T vs. 3 T benefit in subcortical (in particular brainstem) structures. We also found stronger differential responses during easy than hard decisions at 7 T for the dopaminergic nuclei, possibly reflecting reward expectation. Our results demonstrate the potential of 7 T fMRI for illuminating the contribution of small brainstem nuclei to the orchestration of cognitive computations in the human brain. HighlightsO_LIWe compared 7 T to 3 T fMRI during perceptual decision-making under uncertainty. C_LIO_LIDifferences between 7 T and 3 T evoked responses and tSNR varied across the brain. C_LIO_LIEvoked responses in dopaminergic brainstem nuclei were bigger at 7 T than 3 T. C_LIO_LIThe responses of dopaminergic nuclei are consistent with reward expectation. C_LIO_LIResults highlight the potential of 7 T fMRI for imaging small brainstem nuclei. C_LI

neuroscience↗

bioRxiv2018

Task-evoked pupil responses reflect internal belief states

Perceptual decisions about the state of the environment are often made in the face of uncertain evidence. Internal uncertainty signals are considered important regulators of learning and decision-making. A growing body of work has implicated the brains arousal systems in uncertainty signaling. Here, we found that two specific computational variables, postulated by recent theoretical work, evoke boosts of arousal at different times during a perceptual decision: decision confidence (the observers internally estimated probability that a choice was correct given the evidence) before feedback, and prediction errors (deviations from expected reward) after feedback. We monitored pupil diameter, a peripheral marker of central arousal state, while subjects performed a challenging perceptual choice task with a delayed monetary reward. We quantified evoked pupil responses during decision formation and after reward-linked feedback. During both intervals, decision difficulty and accuracy had interacting effects on pupil responses. Pupil responses negatively scaled with decision confidence prior to feedback and scaled with uncertainty-dependent prediction errors after feedback. This pattern of pupil responses during both intervals was in line with a model using the observers graded belief about choice accuracy to anticipate rewards and compute prediction errors. We conclude that pupil-linked arousal systems are modulated by internal belief states.

neuroscience↗