Multiple weak biases support adaptive choices without prior experience: a self-supervised strategy
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The fitness of newborn animals – from approaching social partners to foraging – depends on their ability to make adaptive decisions in the absence of prior experience. Without prior experience, decisions can be guided by biases shaped through evolution. However, innate biases expose individuals to the risk of errors. For instance, a newborn with an innate preference for maternal colours may mistakenly approach irrelevant misleading objects that share the features of target stimuli. Which strategies newborn animals use to minimise the costs of biases remains unknown. To address this issue, we modelled a self-supervised strategy where inexperienced animals leverage their innate internal biases and the information present in the environment to maximise adaptive choices. Our model provides a set of testable predictions. First, innate biases tend to focus on cues that are rare in the background but frequent in the target stimuli (e.g., red colour), thus reducing false positives. Second, the evolution of multiple biases enables animals to benefit from the presence of co-occurring cues (e.g., the co-occurrence of red colour, movement against gravity and a face-like pattern present in mother hen) for more robust identification of relevant stimuli. This combination supports the emergence of weak biases, whose weakness reduces the risk of wrong choices for single stimuli that partially resemble target objects. The presence of multiple weak biases is particularly advantageous in complex environments where multiple stimuli are present. Overall, a strategy to requires the simultaneous co-occurrence of independent and rare stimuli can explain the occurrence of multiple weak biases observed in newborn animals. This simple self-supervised strategy can support effective choices in both biological and artificial minds, with applications from animal cognition to developmental psychology and artificial intelligence.