Noise in Cognition: Bug or Feature?

Sanborn, Adam N.; Zhu, Jian-Qiao; Spicer, Jake; León-Villagrá, Pablo; Castillo, Lucas; Falbén, Johanna K.; LI, Yun-Xiao; Tee, Aidan; Chater, Nick

doi:10.31234/osf.io/438nd

Cited by 4 publications

(3 citation statements)

References 69 publications

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“…We have argued our complex concepts are discovered through the combination of stochastic search (as in MCMC) and commitment (as in bootstrapping). We think this gels with the recent suggestion that noise in cognition is better thought of as an essential feature than as a bug (Sanborn et al, 2022), unlocking our capacity for creative thought and growth.…”

Section: What We Are Doing When We Are "Thinking"supporting

confidence: 51%

Local search and the evolution of world models

Bramley,

Zhao,

Quillien

et al. 2023

Preprint

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An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a ``global optimum'', or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases including anchoring, probability matching and confirmation bias.

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Section: What We Are Doing When We Are "Thinking"supporting

confidence: 51%

Local search and the evolution of world models

Bramley,

Zhao,

Quillien

et al. 2023

Preprint

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show abstract

“…Recent work has, however, raised the possibility that a single mechanism—local sampling—might explain both the excessive noisiness of many aspects of human behavior and the excessive predictability of random sequence generation. It turns out that randomness in other tasks is also typically not iid [ 25 ]. Instead, the noise in people’s behavior has a rich structure, including long-term autocorrelations [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Explaining the flaws in human random generation as local sampling with momentum

Castillo,

León-Villagrá,

Chater

et al. 2024

PLoS Comput Biol

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In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people’s sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.

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“…Recent work has, however, raised the possibility that a single mechanism-local sampling-might explain both the excessive noisiness of many aspects of human behavior and the excessive predictability of random sequence generation. It turns out that randomness in other tasks is also typically not iid (Sanborn et al, 2022). Instead, the noise in people's behavior has a rich structure, including longterm autocorrelations (Gilden et al, 1995;Zhu et al, 2022).…”

mentioning

confidence: 99%

Explaining the Flaws in Human Random Generation as Local Sampling with Momentum

Castillo¹,

León-Villagrá²,

Chater³

et al. 2023

Preprint

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In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by the standard account -- which suggests that randomness is produced by switches of deterministic ``schemas''. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.

show abstract

Noise in Cognition: Bug or Feature?

Cited by 4 publications

References 69 publications

Local search and the evolution of world models

Local search and the evolution of world models

Explaining the flaws in human random generation as local sampling with momentum

Explaining the Flaws in Human Random Generation as Local Sampling with Momentum

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