The scaled target learning model: Revisiting learning in the balloon analogue risk task

Zhou, Ran; Myung, Jay I.; Pitt, Mark A.

doi:10.1016/j.cogpsych.2021.101407

Cited by 10 publications

(10 citation statements)

References 42 publications

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“…Finally, computational models that capture differences in learning as a function of the regularities' deep structure and the type of transition might be considered 15,28,57 , complemented by tracking the electrophysiological correlates of uncertainty and feedback processing 4,58 . Computational modeling would be particularly helpful to directly test the current interpretations of the findings (described in earlier sections) derived from existing theoretical approaches and previous experimental and modeling work.…”

Section: Interpretation Of Resultsmentioning

confidence: 99%

“…As a measure of learning, some studies have quantified trial-by-trial reactivity in the BART (e.g., [23][24][25][26][27] ). Moreover, current computational models have become increasingly successful in capturing the learning aspect of task performance 28,29 . However, with experimental methods, it has scarcely been investigated how the direct manipulation of outcome probabilities alters the learning process and thereby risk-taking behavior in the BART [30][31][32][33][34] .…”

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confidence: 99%

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Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Kóbor¹,

Tóth-Fáber²,

Kardos³

et al. 2021

Preprint

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Beliefs about positive and negative outcome probabilities have been frequently investigated in experience-based risky decision making. However, it has not been clarified how these beliefs emerge and whether they remain persistent if the predictability and complexity of outcome probabilities change across decision contexts. Hence, the present study manipulated these two factors in a variant of the Balloon Analogue Risk Task performed by healthy young adults. In the first and final task phases, outcomes (rewards or balloon bursts) were predictable because of the presence of an underlying regularity. In the middle phase, outcomes were unpredictable because the regularity was absent. The complexity of the regularity differed across the deterministic, probabilistic, and hybrid experimental conditions. In the simple deterministic condition, a repeating sequence of three deterministic regularities perfectly predicted balloon bursts. In the more complex probabilistic condition, a single probabilistic regularity ensured that the probability of balloon bursts increased with each successive pump. In the most complex hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. Even without informing participants about the presence or absence of the regularity, sensitivity to both the simple deterministic and the most complex hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate the acquired sensitivity to these regularities. When only a single probabilistic regularity was present, predictable and unpredictable outcomes were processed similarly. In conclusion, assuming the reappearance of the initially experienced regularity, the robustness of representations might serve fast adaptation in a volatile decision environment.

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Section: Interpretation Of Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Kóbor¹,

Tóth-Fáber²,

Kardos³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, computational models that capture differences in learning as a function of the regularities’ deep structure and the type of transition might be considered 15 , 28 , 57 , complemented by tracking the electrophysiological correlates of uncertainty and feedback processing 4 , 58 . Computational modeling would be particularly helpful to directly test the current interpretations of the findings (described in earlier sections) derived from existing theoretical approaches and previous experimental and modeling work.…”

Section: Discussionmentioning

confidence: 99%

“…As a measure of learning, some studies have quantified trial-by-trial reactivity in the BART (e.g., 23 – 27 ). Moreover, current computational models have become increasingly successful in capturing the learning aspect of task performance 28 , 29 . However, with experimental methods, it has scarcely been investigated how the direct manipulation of outcome probabilities alters the learning process and thereby risk-taking behavior in the BART 30 – 34 .…”

Section: Introductionmentioning

confidence: 99%

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Kóbor

Tóth-Fáber

Kardos

et al. 2023

Sci Rep

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Predictions supporting risky decisions could become unreliable when outcome probabilities temporarily change, making adaptation more challenging. Therefore, this study investigated whether sensitivity to the temporal structure in outcome probabilities can develop and remain persistent in a changing decision environment. In a variant of the Balloon Analogue Risk Task with 90 balloons, outcomes (rewards or balloon bursts) were predictable in the task’s first and final 30 balloons and unpredictable in the middle 30 balloons. The temporal regularity underlying the predictable outcomes differed across three experimental conditions. In the deterministic condition, a repeating three-element sequence dictated the maximum number of pumps before a balloon burst. In the probabilistic condition, a single probabilistic regularity ensured that burst probability increased as a function of pumps. In the hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. In every condition, the regularity was absent in the middle 30 balloons. Participants were not informed about the presence or absence of the regularity. Sensitivity to both the deterministic and hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate this sensitivity. In conclusion, humans can adapt their risky choices in a changing decision environment by exploiting the statistical structure that controls how the environment changes.

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“…A lower COV indicates consistent and strategic decision-making, whereas a higher COV indicates less consistent and less strategic decision-making. This assumption is based on the general notion that during sequential decision-making in risk tasks, efficient strategies typically involve consistent decisions that are continuously adjusted in small steps based on previous outcomes (Pleskac, 2008;Wallsten et al, 2005;Zhou et al, 2021). In the BART, a consistent strategy would therefore be reflected by starting with an arbitrary number of pumps in the first trial, which would be slowly increased if balloons would not explode (e.g., 8 pumps à 9 pumps à 10 pumps à 11 pumps …), resulting in a low COV and thus a high strategic consistency.…”

Section: Introductionmentioning

confidence: 99%

Neural signatures of heterogeneity in risk‐taking and strategic consistency

Leota

Kleinert

Tran

et al. 2021

Eur J of Neuroscience

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People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

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The scaled target learning model: Revisiting learning in the balloon analogue risk task

Cited by 10 publications

References 42 publications

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Neural signatures of heterogeneity in risk‐taking and strategic consistency

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