Older adults fail to form stable task representations during model-based reversal inference

Hämmerer, Dorothea; Schwartenbeck, Philipp; Gallagher, Maria; FitzGerald, Thomas H. B.; Düzel, Emrah; Dolan, Raymond J.

doi:10.1016/j.neurobiolaging.2018.10.009

Cited by 33 publications

(57 citation statements)

References 37 publications

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“…Interestingly, these results are in accord with previous studies in the non-social domain and extend them to social inference. Our finding of older adults' stronger prior beliefs about volatility is consistent with very recent findings of age-related differences in learning in uncertain environments (Hämmerer et al, 2018). In this study, OA were similarly found to overestimate reversal probabilities (and thus volatility) in a reversal learning paradigm.…”

Section: Discussionsupporting

confidence: 92%

“…Such social skills might even become more essential the more we rely on the support of others in older age. However, there is reason to hypothesize that social inference in dynamic social contexts changes over the course of the lifespan: Evidence from studies investigating learning in non-social environments indicates that older adults compute and represent uncertainty in changing environments differently than younger adults (Hämmerer et al, 2018;Nassar et al, 2016). It is well conceivable that these age-related differences play an important role also when older adults navigate social environments, especially in light of evidence showing compromised socio-cognitive skills in human aging (Henry, Phillips, Ruffman, & Bailey, 2013;Reiter, Kanske, Eppinger, & Li, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Human aging alters Bayesian social inference about others’ changing intentions

Reiter¹,

Diaconescu²,

Eppinger³

et al. 2019

Preprint

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Decoding others’ intentions accurately in order to adapt one’s own behavior is pivotal throughout life. Yet, it is a process that is imbued with uncertainty since others’ intentions are not directly observable and may change over time. In this study, we asked the question of how younger and older adults deal with uncertainty in dynamic social environments. We used an advice-taking paradigm together with biologically plausible hierarchical Bayesian modelling to characterize effects and mechanisms of aging on learning about others’ time-varying intentions. We observed age differences when comparing learning on two levels of social uncertainty: the fidelity of the adviser and the stability of intentions. We found that, prior to having any experience with the adviser, older adults expected the adviser to change his/her intentions more frequently. They also showed higher confidence in such beliefs and were less willing to change their beliefs over the course of the experiment. This led them to update their predictions about observable outcomes (i.e., advice correctness) more quickly. Potentially indicative of stereotype effects, we also observed that older advisers were perceived as more volatile, but at the same time, more faithful than younger advisers. Together these findings offer new insights into the behavioral and algorithmic mechanisms underlying adult age differences in response to social uncertainty, putatively driven by aging-related changes in neuromodulation to be tested in future studies.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Human aging alters Bayesian social inference about others’ changing intentions

Reiter¹,

Diaconescu²,

Eppinger³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Across different task domains, performance limitations in children and older adults are often either due to preservation or an increased impact of environmental information on behavior (Alarcón and Bonardi, 2020;Carroll et al, 2007;Cepeda and Munakata, 2007;Chatham et al, 2009;Craik and Bialystok, 2006;Crone et al, 2004a,b;De Boer et al, 2017;Gonthier et al, 2019;Head et al, 2009;Lindenberger and Mayr, 2014;Mayr et al, 2015;Munakata et al, 2012;Ridderinkhof et al, 2002;Rutledge et al, 2009;Troller-Renfree et al, 2020). Previous work has identified lifespan differences in learning (Eppinger et al, 2008(Eppinger et al, , 2009(Eppinger et al, , 2013Chowdhury et al, 2013;Crone and Van der Molen, 2004;Hämmerer et al, 2011;Rutledge et al, 2009;Samanez-Larkin et al, 2010, 2014Van den Bos et al, 2012;Van Der Schaaf et al, 2011) and suggested that they might relate to normative impairments in belief updating (De Boer et al, 2017;Hämmerer et al, 2019;. However, it is currently unclear how computational differences across age groups are linked to performance limitations and, particularly, to the seemingly contradictory tendencies to either perseverate on previous behavior or to rely more strongly on the environment.…”

Section: Introductionmentioning

confidence: 99%

Default beliefs guide learning under uncertainty in children and older adults

Bruckner¹,

Nassar²,

Li³

et al. 2020

Preprint

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Across the lifespan, humans rely on the ability to learn from new experiences to adapt to uncertain and changing environments. Here we investigated age-related differences in the reliance on default-belief settings during learning in these environments. We collected behavioral data with a predictive-inference task in children, adolescents as well as younger and older adults. Using a Bayesian belief-updating model, we first showed that age-related learning differences might be due to a reduced ability to adjust learning according to dissociable normative factors. The results revealed a reduced consideration of uncertainty in older adults and increased perseveration in both children and older adults. Simulations indicated that interventions to reduce perseveration might lead to more similar performance levels between the age groups. In a follow-up experiment, we found that one such intervention which randomly distorted participants' initial predictions strongly reduced perseveration, but led to increased performance differences. This counter-intuitive effect resulted from an environmental control of learning in children and older adults through random information from the intervention to reduce perseveration. Across the two experiments, our findings show that age-related learning impairments can be explained with insufficient updating from a default belief. In stable environments, this results in perseverative behavior, while in the presence of random environmental information, it leads to environmental control. We formalized the emergence of these belief-updating behaviors with a model that updated beliefs only to an acceptable level of plausibility, suggesting that children and older adults are more quickly satisfied to report beliefs that reflect their default than younger adults. This model not only accounted for our own findings but might also provide a new perspective on a wide variety of previous findings in the developmental and aging literature.

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“…These findings demonstrate the possibility of boosting model-based control in older adults. Indeed, normative aging is associated with modelbased learning deficits such as a failure to maintain stable task representations (Hämmerer et al, 2018) and a diminished capacity to integrate uncertainty into decision-making (Eppinger et al, 2013;Nassar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Boosting model-based control in older adults

Patzelt¹,

Kool²,

Gershman³

2019

Preprint

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The tension between habits and plans is reflected in everyday decision-making. Habits are computationally cheap, but fail to flexibly adapt to changes in the environment. Planning is a flexible decision-making strategy, but requires greater resources. Arbitration between habits and plans has been formalized using reinforcement learning algorithms that distinguish between model-free control (habits) and model-based control (plans). Evidence about these two decision-making approaches suggests model-based control follows a developmental trajectory, emerging during adolescence, strengthening during young adulthood, and declining in older adulthood. The normative decline in planning (model-based control) presents the opportunity to develop interventions to increase flexible decision-making. Therefore, we asked if incentives could be used to increase model-based control in older adults. We expected older adults would fail to increase model-based control in response to incentives. This prediction was based upon prior research suggesting older adulthood is associated with deficits in representing and updating the expected value of rewards. Contrary to our expectations, in Experiment 1 we found that incentives could be used to boost model-based control in older adults sampled from an online population. We hypothesized this may be due to previous experience with the task (or with similar tasks). In Experiment 2, a naïve sample of older adults did not boost model-based control in response to incentives. These results suggest that incentives may be a useful intervention to increase model-based planning in older adulthood, but this may require extensive experience with the incentive structure.

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Older adults fail to form stable task representations during model-based reversal inference

Cited by 33 publications

References 37 publications

Human aging alters Bayesian social inference about others’ changing intentions

Human aging alters Bayesian social inference about others’ changing intentions

Default beliefs guide learning under uncertainty in children and older adults

Boosting model-based control in older adults

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