Effects of cognitive noise on the temporal dynamics of risky choices
Poster No:
721
Submission Type:
Late-Breaking Abstract Submission
Authors:
Dragan Rangelov1, Andrew McKay2, Jason Mattingley2
Institutions:
1Swinburne University of Technology, Hawthorn, VIC, Australia, 2The University of Queensland, St Lucia, QLD, Australia
First Author:
Co-Author(s):
Introduction:
Real-life choices often require striking a balance between the value of choice outcomes and their likelihood. When taking out flood insurance, for example, it is important to consider both the cost of any damage and the probability of flooding. 'Rational choice theory' assumes that risky choices rely on an optimal integration of choice values with their probabilities. The literature, however, is replete with examples of irrational, biased choices, and these observations have motivated piecemeal modifications of the theory. A recently proposed 'cognitive imprecision theory' (Khaw et al., 2021; Woodford, 2020) can account for most reported biases in a principled fashion by assuming that noisy subjective representations of choice value and probability are integrated optimally. Neurobiological support for these assumptions has come from work showing that estimates of the noise in neural representations of value correlate with risky choices (Barretto-García et al., 2023; de Hollander et al., 2020). It is unclear, however, whether neural noise plays a causal role in choice biases.
Methods:
Here, we developed a novel perceptual game to test a key prediction that risky choices and their neural correlates should co-vary with cognitive noise. Forty healthy, adult humans were briefly shown a circular array of twelve differently oriented gratings around central fixation (Figure 1A). The gratings were always presented at maximum contrast to minimise any effect of sensory noise. Participants had to estimate the average orientation of the all the gratings in the array, and then choose to either play the game or not (risky and safe choices). If participants chose to play, they could either gain or lose points proportional to the average orientation in that trial. Functional brain activity was recorded using electroencephalography (EEG). Cognitive noise was experimentally manipulated by randomly switching between high and low variability (i.e., noise) in the orientations of the displayed gratings (Figure 1A). Higher variability should yield a noisier representation of the average orientation (Figure 1B), which was experimentally confirmed using a separate, perceptual task in which participants reproduced the average orientation instead of playing the game.
Results:
Behavioural choices were modelled using a cumulative asymmetric Laplace distribution which showed that cognitive noise increased risk aversion and decreased loss aversion (Figure 1C). Analyses of the EEG data focused on estimating orientation-specific neural responses using multivariate computational modelling to quantify the noise in neural representations of the average orientation. There were robust, feature-specific neural responses to the average, despite the fact that the 'average' orientation was never physically displayed. Further, the precision of neural representations co-varied with the expected value, mimicking behavioural choices (Figure 1D). Critically, cognitive noise impacted the neural value-curves as predicted by the cognitive imprecision theory.
Conclusions:
Our results suggest that experimentally manipulated cognitive noise impacts both behavioural choices and the noise of neural value representations in an analogous manner. The findings support the notion that representational noise, rather than the choice mechanism, is the main source of suboptimality in value-based decision making.
Higher Cognitive Functions:
Decision Making 1
Perception, Attention and Motor Behavior:
Perception: Visual 2
Keywords:
Cognition
Electroencephaolography (EEG)
Perception
Other - risky choices
1|2Indicates the priority used for review
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Provide references using APA citation style.
de Hollander, G., Garcia, M., Grueschow, M., Polanía, R., Woodford, M., & Ruff, C. C. (2020). Predicting risk aversion from the precision of neural magnitude representations. [Talk]. 18th Annual Meeting of the Society for Neuroeconomics.
Khaw, M. W., Li, Z., & Woodford, M. (2021). Cognitive Imprecision and Small-Stakes Risk Aversion. The Review of Economic Studies, 88(4), 1979–2013. https://doi.org/10.1093/restud/rdaa044
Woodford, M. (2020). Modeling Imprecision in Perception, Valuation, and Choice. Annual Review of Economics, 12(Volume 12, 2020), 579–601. https://doi.org/10.1146/annurev-economics-102819-040518