The selection balance: Contrasting value, proximity and priming in a multitarget foraging task

The effect of target scarcity on visual foraging

Previous studies have investigated the effect of target prevalence in combination with the effect of explicit target value on human visual foraging strategies, though the conclusions have been mixed. Some find that individuals have a bias towards high-value targets even when these targets are scarcer, while other studies find that this bias disappears when those targets are scarcer. In this study, we tested for a bias for scarce targets using standard feature versus conjunction visual foraging tasks, without an explicit value being given. Based on the idea of commodity theory and implicit value, we hypothesized that participants would show a scarcity bias. The bias was investigated using a Bayesian statistical model which has been developed for predicting target-by-target foraging behaviours. However, we found no evidence of a scarcity bias in our experiment, suggesting that participants did not inherently find rarer targets more rewarding.

How does color distribution learning affect goal-directed visuomotor behavior?

While the visual world is rich and complex, importantly, it nevertheless contains many statistical regularities. For example, environmental feature distributions tend to remain relatively stable from one moment to the next. Recent findings have shown how observers can learn surprising details of environmental color distributions, even when the colors belong to actively ignored stimuli such as distractors in visual search. Our aim was to determine whether such learning influences orienting in the visual environment, measured with saccadic eye movements. In two visual search experiments, observers had to find an odd-one-out target. Firstly, we tested cases where observers selected targets by fixating them. Secondly, we measured saccadic eye movements when observers made judgments on the target and responded manually. Trials were structured in blocks, containing learning trials where distractors came from the same color distribution (uniform or Gaussian) while on subsequent test trials, the target was at different distances from the mean of the learning distractor distribution. For both manual and saccadic measures, performance improved throughout the learning trials and was better when the distractor colors came from a Gaussian distribution. Moreover, saccade latencies during test trials depended on the distance between the color of the current target and the distractors on learning trials, replicating results obtained with manual responses. Latencies were slowed when the target color was within the learning distractor color distribution and also revealed that observers learned the difference between uniform and Gaussian distributions. The importance of several variables in predicting saccadic and manual reaction times was studied using random forests, revealing similar rankings for both modalities, although previous distractor color had a higher impact on free eye movements. Overall, our results demonstrate learning of detailed characteristics of environmental color distributions that affects early attentional selection rather than later decisional processes.

Target selection during "snapshot" foraging

While previous foraging studies have identified key variables that determine attentional selection, they are affected by the global statistics of the tasks. In most studies, targets are selected one at a time without replacement while distractor numbers remain constant, steadily reducing the ratios of targets to distractors with every selection. We designed a foraging task with a sequence of local "snapshots" of foraging displays, with each snapshot requiring a target selection. This enabled tighter control of local target and distractor type ratios while maintaining the flavor of a sequential, multiple-target foraging task. Observers saw only six items for each target selection during a "snapshot" containing varying numbers of two target types and two distractor types. After each selection, a new six-item array (the following snapshot) immediately appeared, centered on the locus of the last selected target. We contrasted feature-based and conjunction-based foraging and analyzed the data by the proportion of different target types in each trial. We found that target type proportion affected selection, with longer response times during conjunction foraging when the number of the alternate target types was greater than the repeated target types. In addition, the choice of target in each snapshot was influenced by the relative positions of selected targets and distractors during preceding snapshots. Importantly, this shows to what degree previous findings on foraging can be attributed to changing global statistics of the foraging array. We propose that "snapshot foraging" can increase experimental control in understanding how people choose targets during continuous attentional orienting.

Bayesian approximations to the theory of visual attention (TVA) in a foraging task

Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Á. Kristjánsson et al. Bundesen proposed the theory of visual attention (TVA) as a computational model of attentional function that divides the selection process into filtering and pigeonholing. The theory describes a mechanism by which the strength of sensory evidence serves to categorise elements. We combined these ideas to train augmented Naïve Bayesian classifiers using data from Á. Kristjánsson et al. as input. Specifically, we attempted to answer whether it is possible to predict how frequently observers switch between different target types during consecutive selections (switches) during feature and conjunction foraging using Bayesian classifiers. We formulated 11 new parameters that represent key sensory and bias information that could be used for each selection during the foraging task and tested them with multiple Bayesian models. Separate Bayesian networks were trained on feature and conjunction foraging data, and parameters that had no impact on the model's predictability were pruned away. We report high accuracy for switch prediction in both tasks from the classifiers, although the model for conjunction foraging was more accurate. We also report our Bayesian parameters in terms of their theoretical associations with TVA parameters, πj (denoting the pertinence value), and βi (denoting the decision-making bias).

A Bayesian Statistical Model Is Able to Predict Target-by-Target Selection Behaviour in a Human Foraging Task

Foraging refers to search involving multiple targets or multiple types of targets, and as a model task has a long history in animal behaviour and human cognition research. Foraging behaviour is usually operationalized using summary statistics, such as average distance covered during target collection (the path length) and the frequency of switching between target types. We recently introduced an alternative approach, which is to model each instance of target selection as random selection without replacement. Our model produces estimates of a set of foraging biases, such as a bias to select closer targets or targets of a particular category. Here we apply this model to predict individual target selection events. We add a new start position bias to the model, and generate foraging paths using the parameters estimated from individual participants' pre-existing data. The model predicts which target the participant will select next with a range of accuracy from 43% to 69% across participants (chance is 11%). The model therefore explains a substantial proportion of foraging behaviour in this paradigm. The situations where the model makes errors reveal useful information to guide future research on those aspects of foraging that we have not yet explained.

Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in 'runs' of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.