Evidence for entropy maximisation in human free choice behaviour

Rens, Natalie; Lancia, Gian Luca; Eluchans, Mattia; Schwartenbeck, Philipp; Cunnington, Ross; Pezzulo, Giovanni

doi:10.1016/j.cognition.2022.105328

Cited by 5 publications

(7 citation statements)

References 69 publications

(105 reference statements)

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“…Under those specific circumstances of low uncertainty about the structure of your world, and no pressing prior preferences to attend to, 'surprise can be minimized when an agent selects a policy [action sequence] that increases the likelihood of visiting new states' [100]. Empirically, Rens et al [101] recently showed that human choice behaviour was indeed better accounted for by a model in which humans were not only maximizing expected utility but also increasing the availability of options. This availability of options also caused greater feelings of freedom in people.…”

Section: Freedom Forwardmentioning

confidence: 99%

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Order and change in art: towards an active inference account of aesthetic experience

Van de Cruys,

Frascaroli,

Friston

2023

Phil. Trans. R. Soc. B

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How to account for the power that art holds over us? Why do artworks touch us deeply, consoling, transforming or invigorating us in the process? In this paper, we argue that an answer to this question might emerge from a fecund framework in cognitive science known as predictive processing (a.k.a. active inference). We unpack how this approach connects sense-making and aesthetic experiences through the idea of an ‘epistemic arc’, consisting of three parts (curiosity, epistemic action and aha experiences), which we cast as aspects of active inference. We then show how epistemic arcs are built and sustained by artworks to provide us with those satisfying experiences that we tend to call ‘aesthetic’. Next, we defuse two key objections to this approach; namely, that it places undue emphasis on the cognitive component of our aesthetic encounters—at the expense of affective aspects—and on closure and uncertainty minimization (order)—at the expense of openness and lingering uncertainty (change). We show that the approach offers crucial resources to account for the open-ended, free and playful behaviour inherent in aesthetic experiences. The upshot is a promising but deflationary approach, both philosophically informed and psychologically sound, that opens new empirical avenues for understanding our aesthetic encounters. This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’.

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Section: Freedom Forwardmentioning

confidence: 99%

“…Empirically, Rens et al . [ 101 ] recently showed that human choice behaviour was indeed better accounted for by a model in which humans were not only maximizing expected utility but also increasing the availability of options. This availability of options also caused greater feelings of freedom in people.…”

Section: Freedom Forwardmentioning

confidence: 99%

Order and change in art: towards an active inference account of aesthetic experience

Van de Cruys,

Frascaroli,

Friston

2023

Phil. Trans. R. Soc. B

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“…By analysing entropy in human behaviour, researchers can gain a deeper understanding of the underlying patterns, motivations, and dynamics that influence human decision making. For example, a study in [ 39 ] found evidence for entropy maximisation in human free choice behaviour. The authors found that individuals have a preference for choosing options that provide further choices, even when the additional choice provides no gain in reward or even a sacrifice of reward [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

“…For example, a study in [ 39 ] found evidence for entropy maximisation in human free choice behaviour. The authors found that individuals have a preference for choosing options that provide further choices, even when the additional choice provides no gain in reward or even a sacrifice of reward [ 39 ]. This behaviour has been difficult to account for with classical economic decision models [ 40 ] that consider decisions on the basis of expected utility alone.…”

Section: Related Workmentioning

confidence: 99%

“…This behaviour has been difficult to account for with classical economic decision models [ 40 ] that consider decisions on the basis of expected utility alone. Inferential accounts of decision making have considered the value of occupying states with more options available as a means of entropy maximisation, in addition to utility maximisation [ 39 ]. In [ 41 ], the authors applied a method using the concept of transfer entropy [ 42 ] to estimate synchronised behaviour and interpersonal relationships in human communication.…”

Section: Related Workmentioning

confidence: 99%

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Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Mai,

Crane,

Bezbradica

2023

Entropy

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The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.

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