The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as "if the dispersion of gaze samples is lower than a particular threshold, then code as a fixation; otherwise code as a saccade." More recent approaches incorporate additional eye-movement categories in automated parsing algorithms by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process vector auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye-movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson et al. (Behavior Research Methods 49(2), 1-22 2016) and to low-sampling rate data from the DIEM project (Mital et al., Cognitive Computation 3(1), 5-24 2011). Driven by the data properties, the BP-AR-HMM identified over five categories of movements, some which clearly mapped on to fixations and saccades, and others potentially captured post-saccadic oscillations, smooth pursuit, and various recording errors. The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.
Naturalistic surveillance tasks provide a rich source of eye-tracking data. It can be challenging to make meaningful comparisons using standard eye-tracking analysis techniques such as saccade frequency or blink rate in surveillance studies due to the temporal irregularity of events of interest. Naturalistic research environments present unique challenges, such as requiring specialized or expert analysts, small sample size, and long data collection sessions. These constraints demand rich data and sophisticated analyses, particularly in prescriptive naturalistic environments where problems must be thoroughly understood to implement effective and practical solutions. Using a small sample of expert surveillance analysts and an equalsized sample of novices, we computed scanpath similarity on a variety of surveillance data using the ScanMatch Matlab tool. ScanMatch implements an algorithm initially developed for DNA protein sequence comparisons and provides a similarity score for two scanpaths based on their morphology and, optionally, duration in an area of interest. Both experts and novices showed equal dwell time on targets regardless of identification accuracy and both samples showed higher scanpath consistency across participants as a function of target type rather than individual subjects showing a particular scanpath preference. Our results show that scanpath analysis can be leveraged as a highly effective computer-based methodology to characterize surveillance identification errors and guide the implementation of solutions. Similarity scores can also provide insight into processes guiding visual search. Keywords Visual search • Scanpath analysis • Applied research Background Variables and Data Collection Tools in Surveillance Research Naturalistic research provides many opportunities to understand cognitive phenomena in real-life working environments. By examining cognition as it naturally unfolds, it becomes easier to develop a fuller understanding of applied research problems and implement reasonable solutions, but there are challenges that are not typical in laboratory studies. Naturalistic environments require laboratory tasks that are high fidelity to the environment where software and Mary E. Frame
Contemporary theories of decision-making often seek to specify the emotional, motivational, and cognitive processes that underlie observable decision behaviors. This requires us as researchers to pursue more sophisticated means of empirically verifying hypothesized processes. To that end, we present 3 experiments that used the lateralized readiness potential (LRP) to establish a neurological basis for response competition in decisions involving subjective preferences. Affectively valenced pictures and monetary gambles were used as stimuli in binary decision tasks in Experiment 1 and 2, respectively. The results of Experiment 1 provide evidence that the LRP is capable of measuring preparatory motor activity underlying the dynamic accumulation of subjective preference in the premotor cortex. Neural signatures indicated there was more response competition when participants chose between more similar stimuli (affective valence) as indicated by the neural signatures. When choosing among gambles in Experiment 2, we again observed increased response competition when participants chose between more similar stimuli (risk). Experiment 3 served to reinforce the findings of Experiment 2 using a similar experimental setup with gamble stimuli counterbalanced based on a different metric for risk.
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