Great Minds Think Alike? Spatial Search Processes Can Be More Idiosyncratic When Guided by More Accurate Information.

Existing research demonstrates that pre-decisional information sampling strategies are often stable within a given person while varying greatly across people. However, it remains largely unknown what drives these individual differences, that is, why in some circumstances we collect information more idiosyncratically. In this brief report, we present a pre-registered online study of spatial search.

Eye movement anomalies as a source of diagnostic information in decision process analysis.

As deviations from what is expected, anomalies are typically seen as an obstruction to making good predictions or an impulse to revise the predictive framework. Here, we consider a different possibility-that anomalies, particularly those related to cognitive processing, may be a valuable source of diagnostic information. More specifically, we hypothesize that the extent to which the prechoice information search has been atypical (anomalous) can be used to reverse-infer important latent features of the decision process.

Scanpath similarity measure reveals not only a decreased social preference, but also an increased nonsocial preference in individuals with autism.

We compared scanpath similarity in response to repeated presentations of social and nonsocial images representing natural scenes in a sample of 30 participants with autism spectrum disorder and 32 matched typically developing individuals. We used scanpath similarity (calculated using ScanMatch) as a novel measure of attentional bias or preference, which constrains eye-movement patterns by directing attention to specific visual or semantic features of the image. We found that, compared with the control group, scanpath similarity of participants with autism was significantly higher in response to nonsocial images, and significantly lower in response to social images.

A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task.

Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.

The world as we know it and the world as it is: Eye-movement patterns reveal decreased use of prior knowledge in individuals with autism.

We used a perceptual closure task with Mooney images as stimuli to record eye-movement patterns in response to the same degraded image before and after perceptual learning in 21 adolescents and young adults with ASD and 23 sex-, age-, and IQ-matched typically developing individuals. In the control group, we observed changes in the eye-movement patterns between the first and the last presentation of the degraded stimulus, reflecting top-down optimization of eye-movement patterns, that is, a decrease in the number of fixations and interfixation distance, coupled with an increase in the duration of fixations. This effect was attenuated in individuals with autism, pointing to a decreased rate of perceptual learning.

A novel machine learning analysis of eye-tracking data reveals suboptimal visual information extraction from facial stimuli in individuals with autism.

We propose a new method of quantifying the utility of visual information extracted from facial stimuli for emotion recognition. The stimuli are convolved with a Gaussian fixation distribution estimate, revealing more information in those facial regions the participant fixated on. Feeding this convolution to a machine-learning emotion recognition algorithm yields an error measure (between actual and predicted emotions) reflecting the quality of extracted information.

A valence asymmetry in predecisional distortion of information: Evidence from an eye tracking study with incentivized choices.

Existing research shows that the order in which evidence arrives can bias its evaluation and the resulting decision in favor of information encountered early on. We used eye-tracking to study the underlying cognitive mechanisms in the context of incentivized financial choices based on real world market data. Subjects learned about the presence/absence of a transaction fee, before seeing expert opinions regarding an investment prospect and deciding whether to invest.

Learning From Peers' Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.

Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time-consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision-makers to learn what information to attend to from one another, rather than from an expert.

The right look for the job: decoding cognitive processes involved in the task from spatial eye-movement patterns.

The aim of the study was not only to demonstrate whether eye-movement-based task decoding was possible but also to investigate whether eye-movement patterns can be used to identify cognitive processes behind the tasks. We compared eye-movement patterns elicited under different task conditions, with tasks differing systematically with regard to the types of cognitive processes involved in solving them. We used four tasks, differing along two dimensions: spatial (global vs.

"Economies of Experience"-Disambiguation of Degraded Stimuli Leads to a Decreased Dispersion of Eye-Movement Patterns.

We demonstrate "economies of experience" in eye-movement patterns-that is, optimization of eye-movement patterns aimed at more efficient and less costly visual processing, similar to the priming-induced formation of sparser cortical representations or reduced reaction times. Participants looked at Mooney-type, degraded stimuli that were difficult to recognize without prior experience, but easily recognizable after exposure to their undegraded versions. As predicted, eye-movement dispersion, velocity, and the number of fixations decreased with each stimulus presentation.

The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability.