The cultural evolution of cultural evolution

What makes fast, cumulative cultural evolution work? Where did it come from? Why is it the sole preserve of humans? We set out a self-assembly hypothesis: cultural evolution evolved culturally. We present an evolutionary account that shows this hypothesis to be coherent, plausible, and worthy of further investigation. It has the following steps: (0) in common with other animals, early hominins had significant capacity for social learning; (1) knowledge and skills learned by offspring from their parents began to spread because bearers had more offspring, a process we call CS1 (or Cultural Selection 1); (2) CS1 shaped attentional learning biases; (3) these attentional biases were augmented by explicit learning biases (judgements about what should be copied from whom). Explicit learning biases enabled (4) the high-fidelity, exclusive copying required for fast cultural accumulation of knowledge and skills by a process we call CS2 (or Cultural Selection 2) and (5) the emergence of cognitive processes such as imitation, mindreading and metacognition—‘cognitive gadgets' specialized for cultural learning. This self-assembly hypothesis is consistent with archaeological evidence that the stone tools used by early hominins were not dependent on fast, cumulative cultural evolution, and suggests new priorities for research on ‘animal culture'. This article is part of the theme issue ‘Foundations of cultural evolution’.

The cultural evolution of cultural evolution

What makes fast, cumulative cultural evolution work? Where did it come from? Why is it the sole preserve of humans? We set out a self-assembly hypothesis: cultural evolution evolved culturally. We present an evolutionary account that shows this hypothesis to be coherent, plausible, and worthy of further investigation. It has the following steps: (0) in common with other animals, early hominins had significant capacity for social learning; (1) knowledge and skills learned by offspring from their parents began to spread because bearers had more offspring, a process we call CS1 (or Cultural Selection 1); (2) CS1 shaped attentional learning biases; (3) these attentional biases were augmented by explicit learning biases (judgements about what should be copied from whom). Explicit learning biases enabled (4) the high-fidelity, exclusive copying required for fast cultural accumulation of knowledge and skills by a process we call CS2 (or Cultural Selection 2), and (5) the emergence of cognitive processes such as imitation, mindreading and metacognition – ‘cognitive gadgets’ specialised for cultural learning. This self-assembly hypothesis is consistent with archaeological evidence that the stone tools used by early hominins were not dependent on fast, cumulative cultural evolution, and suggests new priorities for research on ‘animal culture’.

Effect of a context shift on the inverse base rate effect

The Inverse Base Rate Effect (IBRE; Medin and Edelson (1988)) is a non-rational behavioural phenomenon in predictive learning. In the IBRE, participants learn that a stimulus compound AB leads to one outcome and that another compound AC leads to a different outcome. Importantly, AB and its outcome are presented three times as often as AC (and its outcome). On test, when asked which outcome to expect on presentation of the novel compound BC, participants preferentially select the rarer outcome, previously associated with AC. This is irrational because, objectively, the common outcome is more likely. Usually, the IBRE is attributed to greater attention paid to cue C than to cue B, and so is an excellent test for attentional learning models. The current experiment tested a simple model of attentional learning proposed by Le Pelley, Mitchell, Beesley, George, and Wills (2016) where attention paid to a stimulus is determined by its associative strength. This model struggles to capture the IBRE, but a potential solution suggested by the authors appeals to the role of experimental context. In the present paper, we derive three predictions from their account concerning the effect of changing to a novel experimental context at test, and examine these predictions empirically. Only one of the predictions was supported, concerning the effect of a context shift on responding to a novel cue, was supported. In contrast, Kruschke (2001b)’s EXIT model, in which attention and associative strength can vary independently, captured the data with a high degree of quantitative accuracy.

Learning rapidly about the relevance of visual cues requires conscious awareness

Humans have been shown to be capable of performing many cognitive tasks using information of which they are not consciously aware. This raises questions about what role consciousness actually plays in cognition. Here, we explored whether participants can learn cue-target contingencies in an attentional learning task when the cues were presented below the level of conscious awareness and how this differs from learning about conscious cues. Participants’ manual (Experiment 1) and saccadic (Experiment 2) response speeds were influenced by both conscious and unconscious cues. However, participants were only able to adapt to reversals of the cue-target contingencies (Experiment 1) or changes in the reliability of the cues (Experiment 2) when consciously aware of the cues. Therefore, although visual cues can be processed unconsciously, learning about cues over a few trials requires conscious awareness of them. Finally, we discuss implications for cognitive theories of consciousness.

Attention to perceive, to learn and to respond

Mackintosh and his collaborators put forward an account of perceptual learning effects based, in part, on learned changes in stimulus salience. In the workshop held to mark Mackintosh’s retirement, and published as a special issue of this journal, Hall discussed Mackintosh’s theory and proposed his own alternative account. We now want to take the story forward in the light of findings and theoretical perspectives that have emerged since then. Specifically, we will argue that neither Mackintosh nor Hall was correct in his account of the principles that govern how changes in salience occur. Both supposed (in different ways) that such changes depend on the way in which the stimulus (or stimulus element) is predicted by another event. In contrast, theories of attentional learning have stressed the notion that changes in the properties of a stimulus might depend on the way in which it predicts its consequences. These theories have been concerned with attention-for-learning (associability). We now consider how the general principle they both employ might be relevant to the other forms of attention (for perception and for performance) that are, we will argue, critical for the perceptual learning effect.

Link Prediction via Ranking Metric Dual-Level Attention Network Learning

Link prediction is a challenging problem for complex network analysis, arising in many disciplines such as social networks and telecommunication networks. Currently, many existing approaches estimate the proximity of the link endpoints for link prediction from their feature or the local neighborhood around them, which suffer from the localized view of network connections and insufficiency of discriminative feature representation. In this paper, we consider the problem of link prediction from the viewpoint of learning discriminative path-based proximity ranking metric embedding. We propose a novel ranking metric network learning framework by jointly exploiting both node-level and path-level attentional proximity of the endpoints for link prediction. We then develop the path-based dual-level reasoning attentional learning method with recurrent neural network for proximity ranking metric embedding. The extensive experiments on two large-scale datasets show that our method achieves better performance than other state-of-the-art solutions to the problem.