Eye pupil signals information gain

Abstract

Keywords: cognitive neuroscience, pupillometry, information theory, computational neuroscience, psychopysiology, arousalIn conditions of constant illumination, the eye pupil diameter indexes the modulation of arousal state and responds to a large breadth of cognitive processes, including mental effort, attention, surprise, decision processes, decision biases, value beliefs, uncertainty, volatility, exploitation/exploration trade-off or learning rate. Here, I propose an information theoretic framework that has the potential to explain the ensemble of these findings as reflecting pupillary response to information processing. In short, updates of brain internal model, quantified formally as the Kullback-Leibler (KL) divergence between prior and posterior beliefs, are the common denominator to all these instances of pupillary dilation to cognition. I show that stimulus presentation leads to pupillary response that is proportional to the amount of information the stimulus carries about itself and to the quantity of information it provides about other task variables. In the context of decision making, pupil dilation in relation to uncertainty is explained by the wandering of the evidence accumulation process, leading to large summed KL divergences. Finally, pupillary response to mental effort and variations in tonic pupil size are also formalized in terms of information theory. On the basis of this framework, I compare pupillary data from past studies to simple information theoretic simulations of task designs and show good correspondance with data across studies. The present framework has the potential to unify the large set of results reported on pupillary dilation to cognition and to provide a theory to guide future research.

Passive visual stimulation induces fatigue or improvement depending on cognitive load

“…Analysis of the arousal-related pupil response showed increased dilation in the hard version of the n-back and side tasks, confirming the increased demand associated with these task conditions (van der Wel & van Steenbergen, 2018; Zénon, 2019). However, surprisingly, we found the opposite effect in the pitch-sequence task, the easier group displaying larger average pupil size.…”

Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions

“…It should be noted that a recent review (Zenon, 2019), in an attempt to give a unified explanation to the pupil effects of a wide range of cognitive processes (e.g. mental effort, attention, exploration/exploitation trade-off, decision making, surprise), related pupil dilation to information gain.…”

“…J. Friston, Stephan, Montague, & Dolan, 2014;Lawson, Rees, & Friston, 2014) To explore learning about precision, we make use of the widely replicated finding that non-luminance related pupil dilation indexes the surprise associated with incoming sensory stimuli (Alamia, VanRullen, Pasqualotto, Mouraux, & Zenon, 2019;Damsma & van Rijn, 2017;De Berker et al, 2016;Friedman, Hakerem, Sutton, & Fleiss, 1973;Kloosterman et al, 2015;Lavín, Martín, & Jubal, 2014;Nassar et al, 2012;O'Reilly et al, 2013;Preuschoff, 't Hart, & Einhäuser, 2011;Qiyuan, Richer, Wagoner, & Beatty, 1985;Raisig, Welke, Hagendorf, & van der Meer, 2010;Reinhard & Lachnit, 2002). (Here we define surprise as the negative log probability of an event occurring, though see (Baldi & Itti, 2010;Philipp Schwartenbeck, FitzGerald, & Dolan, 2016;Zenon, 2019) for an important alternative). This permits one to make inferences about participants' implicit beliefs about the statistics of their environment, without the necessity for an explicit probe or decision, and thus provides a means to characterise statistical learning processes (Alamia, VanRullen, Pasqualotto, Mouraux, & Zenon, 2019;Vincent, Parr, Benrimoh, & Friston, 2019).…”