The optimality of perception and cognition: the perception-cognition gap explored

The ability to choose wisely is crucial for our survival. Yet, the received wisdom has been that humans choose irrationally and sub-optimally. This conclusion is largely based on studies in which participants are asked to make choices on the basis of explicit numerical information. Lately, our ability to make such high-level choices has been contrasted with our ability to make low-level (perceptual or perceptuo-motor) choices. Remarkably, we seem able to make near-optimal low-level choices. Taken at face value, the discrepancy gives rise to a perception-cognition gap. The gap implies, for example, that our ancestors were much better at choosing where to put their feet on a rocky ridge (a perceptuo-motor task), compared to choosing which prey to hunt (a cognitive task).The work reported herein probes this gap. There are many differences between literatures showing optimal and sub-optimal performance. The main approach taken here was to match low- and high-level tasks as closely as possible to eliminate such differences. When this is done one finds very little evidence for a perception-cognition gap. Moreover, once the standards of performance assessment of the respective literature are applied to data generated under such conditions it becomes apparent that the cause of the gap seems to lie in the standards themselves. When low-level standards are applied, human choice, whether low- or high-level, looks good. When high-level standards are applied, human choice, whether low- or high-level, looks rather poor. It is easy to see then, that applying high-level standards to high-level tasks, and low-level standards to low-level tasks, will give rise to a “gap”, with no or little actual difference in performance

On a theorem of Y. Miyashita

Background: Portion size is an important driver of larger meals. However, effects on food choice remain unclear. Objective: Our aim was to identify how portion size influences the effect of palatability and expected satiety on choice. Methods: In Study 1, adult participants (n = 24, 87.5% women) evaluated the palatability and expected satiety of 5 lunchtime meals and ranked them in order of preference. Separate ranks were elicited for equicaloric portions from 100 to 800 kcal (100-kcal steps). In Study 2, adult participants (n = 24, 75% women) evaluated 9 meals and ranked 100–600 kcal portions in 3 contexts (scenarios), believing that 1) the next meal would be at 1900, 2) they would receive only a bite of one food, and 3) a favorite dish would be offered immediately afterwards. Regression analysis was used to quantify predictors of choice. Results: In Study 1, the extent to which expected satiety and palatability predicted choice was highly dependent on portion size (P < 0.001). With smaller portions, expected satiety was a positive predictor, playing a role equal to palatability (100-kcal portions: expected satiety, β: 0.42; palatability, β: 0.46). With larger portions, palatability was a strong predictor (600-kcal portions: β: 0.53), and expected satiety was a poor or negative predictor (600-kcal portions: β: −0.42). In Study 2, this pattern was moderated by context (P = 0.024). Results from scenario 1 replicated Study 1. However, expected satiety was a poor predictor in both scenario 2 (expected satiety was irrelevant) and scenario 3 (satiety was guaranteed), and palatability was the primary driver of choice across all portions. Conclusions: In adults, expected satiety influences food choice, but only when small equicaloric portions are compared. Larger portions not only promote the consumption of larger meals, but they encourage the adoption of food choice strategies motivated solely by palatability

Beyond Nudging: Generalisable and transferable learning in human decision-making

Canonical work on human decision-making demonstrates consistent, stereotyped and sub-optimal risk-reward trade-offs. However, recent work on experienced-based-, perceptual- and sensori-motor choice appear to severely restrict the scope of the canonical work, demonstrating trade-offs that are either differently sub-optimal, or much closer to optimal. Such dissociations may reflect highly specific mechanisms, or other confounds between domains; impossible to tease apart with current observational methods. We develop a method for strong causal inference based on experimentally manipulating risk preferences. Using a double-blind randomized control design, we trained people in a single domain, evaluate the extent to which training generalizes beyond the training-set, and more importantly the extent to which training transfers to risk domains in which participants were not trained. Participants were trained to maximize their expected earnings (i.e., to be risk-neutral). In total, we tested for transfer to four different risk domains, all of which are known to dissociate. We find that training is necessary and sufficient to cause reliable changes in decision-making. Importantly, training transfers, with participants becoming more risk-neutral also in domains for which they had had no training, showing that risk-reward trade-offs in different domains and tasks share common substrate. Shared mechanisms open up the opportunity to for practical general-purpose training programmes to improve human decision-making

Human value learning and representation reflects rational adaptation to task demands

Supplementary Material, Data and Code for Human value learning and representation reflects rational adaptation to task demands

Data

Data &amp; cod

The Long-Term Impact of Cognitive Training on Risk-Reward Trade-Offs

Decision-making often involves a trade-off between risks and rewards, for which humans are susceptible to biases. Cognitive training could facilitate these processes, but, for applicability outside the lab, it needs to persist over time. In this double-blind study, participants were split into treatment and control groups to complete a decision-making task involving monetary gambles. All participants completed pre-training (Day 1), training (Day 2-7), and several post-training sessions (up to 6 months). During training, one group was given feedback to promote risk-neutral choice (treatment), whereas the other merely practiced the task (control). Following training, choices in the treatment group were significantly more risk-neutral than in the control group (with no improvements), and this pattern was replicated up to 6 months without any top-up training. Computational modeling revealed a complex pattern of change in the feedback group – whereby participants' initial risk preferences partially determined the effect of training on their post-training preferences