Developmental Changes in Learning: Computational Mechanisms and Social Influences

Bolenz, Florian; Reiter, Andrea; Eppinger, Ben

doi:10.3389/fpsyg.2017.02048

Cited by 43 publications

(47 citation statements)

References 107 publications

(142 reference statements)

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“…Evidence from earlier computational studies of aging neuromodulation (Li et al, 2001;Li, von Oertzen & Lindenberger, 2006) suggest that older adult's deficient neuromodulation The current findings also add to the emerging literature on adult development of social cognition. Accumulating evidence points to the fact that social cognitive function differs between younger and older adults (Bolenz, Reiter, & Eppinger, 2017;Henry et al, 2013;Reiter et al, 2017). A limitation of many of the previously applied paradigms might be that they did not explicitly test social inference evolving over the course of time.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Human aging alters Bayesian social inference about others’ changing intentions

Reiter¹,

Diaconescu²,

Eppinger³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Instead, participants only learned how many chips they sorted correctly at the end of the task. We did not include trial‐by‐trial feedback in our task both to mitigate the influence of age‐related change in learning to select actions based on explicit feedback (Bolenz, Reiter, & Eppinger, 2017; Nussenbaum & Hartley, 2019) and to more closely resemble real‐world causal learning contexts in which explicit feedback is often absent.…”

Section: Methodsmentioning

confidence: 99%

Causal Information‐Seeking Strategies Change Across Childhood and Adolescence

et al. 2020

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Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three‐ or four‐node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information‐seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

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“…Instead, participants only learned how many chips they sorted correctly at the end of the task. We did not include trial-by-trial feedback in our task both to mitigate the influence of age-related change in learning to select actions based on explicit feedback (Bolenz, Reiter, & Eppinger, 2017;Nussenbaum & Hartley, 2019) and to more closely resemble real-world causal learning contexts in which explicit feedback is often absent.…”

Section: Taskmentioning

confidence: 99%

Causal information-seeking strategies change across childhood and adolescence

Nussenbaum¹,

Cohen²,

Davis³

et al. 2019

Preprint

View full text Add to dashboard Cite

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip’s hidden wires and had to select a single node to activate. After observing whether their intervention caused other nodes to turn on, participants selected a wire configuration, and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures vs. those that elicited evidence in favor of one of the two causal hypotheses, but did not necessarily discriminate between them. Our model estimates revealed that the use of discriminatory strategies increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that their interventions revealed, as indexed by participants’ confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

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Developmental Changes in Learning: Computational Mechanisms and Social Influences

Cited by 43 publications

References 107 publications

Human aging alters Bayesian social inference about others’ changing intentions

Human aging alters Bayesian social inference about others’ changing intentions

Causal Information‐Seeking Strategies Change Across Childhood and Adolescence

Causal information-seeking strategies change across childhood and adolescence

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