Efficient visual information sampling develops late in childhood.

Abstract

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The computational cost of active information sampling before decision-making under uncertainty

“…Cognitive effort needs to confer an instrumental advantage for it to be traded against other resources 40 . It is worth emphasising, however, that in the simpler instance where the information extraction rate is constant (i.e., fixed α), our active sampling model actually reduces to previous accounts 5,17 (Fig. 4).…”

“…Accordingly, informative samples are worth acquiring only when the subjective benefits they provide outweigh the cost of obtaining them. This simple axiom has proven extremely powerful in explaining how much information people seek before decision-making 1,5,6,[12][13][14][15][16][17] , and provides a theoretical bridge to other types of motivated behaviours such as foraging 4,35,36 . However, a natural key question is: What do people treat as costs and benefits during active information sampling?…”

“…This instrumental benefit is the increase in EU that results from making the final decision with the information, compared to without it (∆EU ). Such an approach has proven useful in understanding why people seek more knowledge when sampling is cheap, when errors are strongly penalised, or when information content of individual samples is low 1,5,6,[12][13][14][15][16][17] .…”

Structural and contextual priors affect visual search in children with and without autism

“…However, a large change in the baseline response times may be indicative of a substantial difference in the way participants conduct the task, namely in a more serial, less efficient way. Indeed, at least in tasks that require the judgment of average location of sets of dots, children have been found to use less efficient and more variable sampling strategies (Jones et al, 2019;Jones & Dekker, 2018). Other studies confirm that averages can be extracted from very early in development (Manning et al, 2014;Sweeny et al, 2015), but children pooled over a lower number of items compared to adults.…”

Autistic traits influence the strategic diversity of information sampling: Insights from two-stage decision models

“…Following Jones et al [80], we identified three “likely noncompliant” outlier observations in the number of bead samples for each condition based on nonparametric boxplot statistics, that is, those whose values were lower than the 1st quartile or higher than the 3rd quartile of all the observations in the condition by more than 1.5 times of the interquartile range (see S10 Fig). These noncompliant observations (not participants per se) were excluded from LMMs 1–3.…”