Study Description: 

The aim of the study was to investigate how the ability to trade off the benefits of visual information against the costs of this information develops during childhood adolescence and adulthood. We tested this by asking participants to sample dot location cues to find a fish hidden on a touchscreen for points. However, each location cue came at a cost, so the more cues you gather, the less points you can win for finding the fish. By measuring in a separate (fixed) condition how the likelihood of finding the fish increased with more dot-cues, we were able to predict the score-maximising strategy for each individual. We then tested if children, adolescents, and adults were able to identify this ideal strategy - we found that while adults did, children tended to sample fewer dots then they should have to maximise score, and also used more inconsistent sampling strategies.

For further details, see: Jones, Peter R., et al. "Efficient visual information sampling develops late in childhood." Journal of Experimental Psychology: General 148.7 (2019): 1138.


We also used these data to analyse whether children use the same visual averaging strategies as adults do. As part of localising the target participants are asked to locate the middle of a cloud of dot location cues. To test whether children use the same strategy to compute the middle (in the current task the best strategy that will yield most points is the arithmetic mean) we compared their estimates of the mean with the output of various different mean computation strategies.

For further details, see: Jones, Pete R., and Tessa M. Dekker. "The development of perceptual averaging: Learning what to do, not just how to do it." Developmental science 21.3 (2018): e12584.


Details on data collection and design: 

Sample: We include data from 5 age groups: 27 6 to 7-year-olds, 30 8 to 9-year-olds, 27 10 to 11-year-olds, 25 13 to 15-year-olds, and 29 adults. All participants had normal or corrected to normal vision and motor abilities, and had no known neurological abnormalities. Participants were recruited from local schools and databases so this was an opportunity sample. 

Design and procedure: Each participant completed a fixed condition and a free condition. We varied location cue reliability between subjects, by changing the width of the distribution from which the dot location cues were sampled from sigma=12mm to sigma=28mm (see below for details). Most adults did both cue reliability conditions, but we only analysed the first cue reliability condition they took part in. All subject completed the free condition, to measure sampling choices, and the fixed condition to model the ideal strategy that should be used to maximise score in the free condition (see below for details).

In the free conditions participants sampled probabilistic location cues (dots) drawn from a bivariate Gaussian distribution with sigma=12 or sigma=28, centred on a target (max Ndots was 20). They bought between 1 to 20 dots by pressing space bar, with the score won for a hit depreciating with 1 point for each dot (starting from 20 points). Because the middle of the dot-cues is more likely to overlap with the target/distribution mean as the number of dots increases (as variance around this location follows sigma/sqrt(Ndots)), sampling more dots increased the chance of locating the target. However, purchasing more dots also reduced the value of the target, so participants had to trade-off the benefits of more information against the cost. Once the participant felt they had bought a sufficient numbers of dots, they placed the cursor on a touchscreen in the estimated mean location of all the dots to find the target. They then were able to adjust their response until they confirmed their choice by pressing enter, upon which they got feedback about whether they had hit the target, and about their score. Each participant completed 100 trials of this condition. 

In the fixed conditions, the design was identical except that rather than purchasing a number of dots of their choice, participants were presented with a fixed number of dots on each trial, and then located the mean of the dots to find the target following the same procedure as in the free condition. This condition was used to model the ideal strategy in the free condition; Specifically we used it to estimate for each individual, the hit probability for a given Ndots, and multiplied this by the points that could be won for this ndots to obtain the expected gain (hit probability x value) for that given Ndots. The ideal strategy is to sample the Ndots with the highest expected outcome. Each participant completed 25 trials with a fixed number of dots. Most children were presented with Ndots = [2 3 7 15]. Some adults were presented with all Ndots in this condition.

Measures: We recorded participants' aiming location on a touchscreen, as well as their hits (i.e., whether the touched location fell within a circular target area with a 12.761 pixel radius), and reaction times, and tested by now much their reaches deviated from the ideal location that would maximise score according to our predictions from the fixed condition. For further experimental details, please see publication.

Details on data collection and processing: The experimental paradigm was programmed and run in Matlab Psychtoolbox. Results from individual trials were exported to multiple .cvs files for each subject - creating a file per experimental block. The Config ID reported in the .csv file indicates whether the file contains trails in the fixed or the free experimental condition. These data read into and further analysed in matlab to compute model predictions and individual participant specific variables of interest. The results of these analyses are listed in SPSS .sav file.


EXPLANATION OF THE EXCEL DATA FILES

Participant ID: subjectID (see also SPSS file with further personal details)

Session ID: different sessions of experiment

Block Num: number of task block within session (can accumulate across the fixed and free condition within a session)

Config ID: lists 4 possible configurations for the different experimental conditions:

pilot_var28_free.expConfig.xml.
pilot_var12_free.expConfig.xml
pilot_var28_fixed_no_practice.expConfig.xml
pilot_var12_fixed_no_practice.expConfig.xml

var12 and var28 were between subject conditions (they manipulate cue reliability).

Config: print-out of the config file settings, including for example, which fixed number of dots were shown during the fixed condition and some screen and stimulus properties.


EXPLANATION OF VARIABLES STORED IN DATA

ID = trialID within block
expID = dotcost
partID = participant numerical ID
nDots = number of dots gathered during the trial
minNDots = the lowest number of dots that could have been sampled on the trial
maxNDots = the hightest number of dots that could have been sampled on the trial
anscorrect = hit or miss the target
nPoints  = points won for hitting the target
nPointsTotal = points that could have been won for hitting the target (i.e., 20-Ndots)
obsX = X coordinates of individual dot cues in mm
obsY = Y coordinates of individual dot cues in mm
respXY = [X Y] coordinate of of the guessed target location pointed out and selected by the participant in mm 
targXY = [X Y] coordinate of mean of the dot-sampling distribution & centre of the target circle in mm
dist2targ = distance between response and target in mm
targRadius = target radius mm
respTime_fromTrialStart = response time from trial onset to confirmed guessed screen location in sec
respTime_fromLastDot = response time from last shown dot to confirmed guessed screen location in sec
ppmm = pixels per mm.
respXY_px = [X Y] coordinate of of the guessed target location pointed out and selected by the participant in pixels
targXY_px = [X Y] coordinate of mean of the dot-sampling distribution & centre of the target circle in pixels
targRadius_px = target radius pixels


EXPLANATION OF THE SPSS FILE

subId = subject number
AgeGroup = age group 1= youngest, 5 = oldest
Age = subject age in years
Condition = cue reliability as determined by dot sampling distribution with (sigma = 12 or sigma = 28)
Gender = gender (male/female)
IntVarX = variance of localisation responses around the objective mean of the dot cues along the X-direction
IntVarY = variance of localisation responses around the objective mean of the dot cues along the X-direction
Ndots_mu = mean Ndots sampled in the free condition (over 100 trials)
Ndots_med = median Ndots sampled in the free condition (over 100 trials)
Ndots_serr = standard deviation of the Ndots sampled in the free condition (over 100 trials)
OptNDots_Obs = the ideal Ndots estimated to yield the maximum expected gain based on hit probabilities in the fixed condition.
DevOptDots_Obs_mu = the deviation between the participants mean selected Ndots and their predicted optimal Ndots
DevOptDots_Obs_abs_mu = the absolute deviation between the participants mean selected Ndots and their predicted optimal Ndots
ScoreEfficiency_obs = the proportion of the predicted optimal score that was actually obtained