Human biases limit cumulative innovation

Abstract

Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

Keywords: Bayesian; cultural evolution; function learning; inductive bias; innovation; optimization.

Figure 1.

Transmission chain design. Participants were organized into transmission chains of 10 generations. A participant at generation t inherited the arrowhead designed by the participant at generation t − 1 of their chain. The principle experimental manipulation was the design of the highest-scoring virtual arrowhead (illustrated above), which varied between treatments (environments E1–E4). Each treatment was replicated in 25 independent chains. (Online version in colour.)

Figure 2.

Optimization problem. (a) Illustration of the design space of possible arrowheads implied by the ability to manipulate base width and length (between 50 and 150 pixels). Arrowheads are regularly sampled points for illustration purposes in a continuous underlying space of possible designs. (b) The objective function (score landscape) f(θ). Yellow denotes higher scores, blue denotes lower scores. The score landscape was a simple, smooth, hill-shaped function (quadratic surface) with maximum at θ* (here θ*_bw = 115, θ*_l = 115, illustrating environment E4). Across environments, the position of the optimum design (θ*) varied but the shape of the landscape remained constant and as displayed here. (Online version in colour.)

Figure 3.

Experimental results (bias estimate). Visualization of the sample-based estimate of the mean $\hat{\mu }$ of the prior distribution p(θ) implicitly assumed by participants. Plot shows arrowheads produced by all n = 250 participants in the serial reproduction treatment faintly overlaid, illustrating $\hat{\mu }$. Inset shows prior mean arrowhead ($\hat{\mu }$, black) positioned in design space relative to optimum arrowheads in optimization treatments E1–E4. (Online version in colour.)

Figure 4.

Experimental results (task success). Task success across optimization treatments. Triangles show mean score for individual chains. Faint dots show individual participant scores. Greater distance between the optimum and the prior mean resulted in worse performance. (Online version in colour.)

Figure 5.

Experimental results (task success baselines and comparisons). Task success overall (a) and over generations (b). Solid box plots show arrowhead score within-treatment distributions. Faded box plots show scores that would have been awarded to arrowheads produced by participants in the serial reproduction treatment if they had been evaluated in against the scoring function. Triangles (a) show mean score. Dot-dashed lines show score expected under a random walk over the scoring function. Dotted lines show maximum score. (c) Optimum arrowhead and the mean design of produced arrowheads positioned within the stimulus design space. Faint dots show individual arrowhead designs in stimulus space. (Online version in colour.)