Age-dependent statistical learning trajectories reveal differences in information weighting.

Abstract

E-mail: steffen.herff[at]epfl.ch.We thank Lauren Fairley, Jon Prince, and Estefanía Cano for constructive comments on an earlier draft, and Arihant Singhai, Ren Jie Tay, and Jing Wen Chai for their support during data collection. We thank Feng Lei for advice on the choice of cognitive assessment tests and 2 organising training on administering the tests. The study was supported by the Singapore Ministry of Education (MOE2016-T2-1-015) awarded to Prof Yu. Steffen A. Herff developed the paradigm, and designed, coded, as well as prepared the experiment. Kat R. Agres helped develop the experimental design and paradigm. Data collection was performed or supervised by Steffen A. Herff and Shanshan Zhen. Data was analyzed and interpreted by Steffen A. Herff. The manuscript was written by Steffen A. Herff with Shanshan Zhen, Rongjun Yu, and Kat R. Agres providing comments. The project and collaboration were initiated by Kat R. Agres, and Rongjun Yu provided lab space and equipment. All authors approved the final version of this manuscript. We have no known conflict of interest to disclose.We archived a preprint of the present work which can be accessed through: https://psyarxiv.com/kuy6p.

Information processing biases: The effects of negative emotional symptoms on sampling pleasant and unpleasant information.

“…All data, code, and models can be found online (https://osf.io/8zy52/). The present analysis closely follows Herff et al (2020). Forty participants provided a total of 5,920 responses across the pleasant and the unpleasant conditions.…”

“…Recent work deployed an information weights approach to investigate age-related changes to information sampling (Herff et al, 2020). This approach models learning as a continuous sampling of information using a confirmatory and disconfirmatory information weight.…”

Brain Structure and Episodic Learning Rate in Cognitively Healthy Ageing

“…As GM and WM properties and memory function both deteriorate with age, future longitudinal studies would be needed to better understand the relationship between learning ability and structural changes as ageing progresses and eliminate age-related confounds in cross-sectional studies (Elliott, 2020). We used the learning rate across trials in an established neuropsychological memory task (FCSRT) as a measure for episodic learning; it would be interesting to examine neural correlates of other forms of learning such as error-driven and statistical learning (Samanez-Larkin et al, 2012; Nassar et al, 2016; Herff et al, 2020), as well as consider learning ability as a potential cognitive phenotype in pathological ageing.…”

“…We used the learning rate across trials in an established neuropsychological memory task (FCSRT) as a measure for episodic learning; it would be interesting to examine neural correlates of other forms of learning such as error-driven and statistical learning (Samanez-Larkin et al, 2012;Nassar et al, 2016;Herff et al, 2020), as well as consider learning ability as a potential cognitive phenotype in pathological ageing.…”

“…Whilst memory performance is usually quantified by the ability to correctly recognise or recall information, a retrieval impairment could be caused by reduced ability to learn information. Indeed, there is evidence for reduced error-driven (Nassar et al, 2016) and probabilistic learning (Samanez-Larkin et al, 2012; Herff et al, 2020) in older adults, but evidence for similar deficits in episodic learning is lacking. Relatedly, the microstructure of WM limbic tracts has been related to episodic and navigational learning across species (Aggleton et al, 1995; Buckley et al, 2008; Hofstetter et al, 2013; Hodgetts et al, 2020), but it is still unclear whether such changes in these tracts can explain memory deficits in ageing.…”