The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex

By Celeste Kidd, S. Piantadosi, Richard N. Aslin. 2012

664 citations recorded by the source corpus.

Human infants, like immature members of any species, must be highly selective in sampling information from their environment to learn efficiently. Failure to be selective would waste precious computational resources on material that is already known (too simple) or unknowable (too complex). In two experiments with 7- and 8-month-olds, we measure infants’ visual attention to sequences of events varying in complexity, as determined by an ideal learner model. Infants’ probability of looking away was greatest on stimulus items whose complexity (negative log probability) according to the model was either very low or very high. These results suggest a principle of infant attention that may have broad applicability: infants implicitly seek to maintain intermediate rates of information absorption and avoid wasting cognitive resources on overly simple or overly complex events.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Developmental and Educational Psychology, Cognitive Science & Psychology

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Citations in the corpus

Contrastive Multiview Coding (2019, background): “This suggests a “Goldilocks principle” [39]: a good collection of views is one that shares some information but not too much.”
What can the brain teach us about building artificial intelligence? (2016, background): “Humans learn complex skills in a domain after previously learning simpler ones, gradually building structured knowledge as they learn.”
How Evolution May Work Through Curiosity-Driven Developmental Process (2016, methodology): “In general, learning in these curiosity driven activities progresses to yield an improvement of prediction or control over a repeated activity and thus a reduction of uncertainty (Friston, 2012; Kidd et al, 2012; Oudeyer and Kaplan, 2007; Schmidhuber, 1991).”
A nice surprise? Predictive processing and the active pursuit of novelty (2018, background): “Compelling empirical evidence for such a profile includes work by (Kidd et al. 2012) who conducted a series of experiments with 7- and 8-month-old infants measuring attention to sequences of events of varying (and well-controlled) complexity.”
Process Account of Curiosity and Interest: A Reward-Learning Perspective (2018, background): “We speculate that the classical optimal arousal model of curiosity proposed by Berlyne (1960) (see Kidd et al. 2012 for a recent empirical investigation), which claims that people avoid stimuli that are too boring (easy) or too complex (difficult), can also be explained by this learning progress…”
Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies. (2016, background): “For example, some hypotheses approach curiosity as a mechanism maximizing surprise (Itti and Baldi, 2009), or intermediate complexity (Kidd et al., 2012), or learning progress (Oudeyer et al., 2007).”
The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration (2014, background): “ORIGINAL RESEARCH ARTICLE published: 14 October 2014 doi: 10.3389/fnins.2014.00317 The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration”
Explanation-seeking curiosity in childhood (2020, background): “Introduction From early in development, children exhibit curiosity about the properties [1] and names [2–4] of objects, and about visual and auditory patterns [5,6].”
What do we learn about development from baby robots? (2015, background): “what the brain finds interesting to practice is what is just beyond the current level of knowledge or competencies, and as observed in recent experiments with infants.(28) Such a model also leads to a concrete definition of epistemic curiosity as a motivational mechanism that pushes an organism to e”
Affective Value in the Predictive Mind (2017, background): “This preference for optimal level of complexity is corroborated in experiments with infants that looked longer at stimulus items that were neither very simple nor very complex (Kidd et al. 2012).”