Summary of article (Cavanagh)

Eye Tracking and Pupillometry are Indicators of Dissociable Latent Decision Processes

(Cavanagh, Wiecki, Kochar, & Frank, 2014)

In this article, the authors argue that by using eye tracking and pupil dilation, it is possible to predict dissociable biases during decision making. They give examples of where the Drift Diffusion Model (DDM) has successfully been used in value-based decisions. Visual attention can predict decision outcomes through measurement of gaze dwell time suggesting a causal influence in value comparison. This has led to a model of attention DDM (aDDM) “in which gaze dwell time influences the speed of integrated evidence (drift rate), acting to amplify the inherent value of the fixated relative to nonfixated item”(Cavanagh et al., 2014, p. 1476). In the past however, explicit self-reported ratings of stimulus value were relied on. Because of the problem of using self-reported ratings, the authors used controlled and implicit measures of value to obtain a range of positive and negative values. They found that both gaze and value influence drift rate as independent (as opposed to modulatory) values. They also found that pupil dilation reflects a need to increase the decision threshold in a difficult choice scenario. The authors suggest that pupil dilation  is an observable “downstream” measure reflecting the need for control of decision thresholds. To test this, the research used Bayesian parameter estimation of the DDM to test for independent and interactive influences of eye gaze dwell time and value on drift rate. They then tested if pupil dilation predicts an increase in decision threshold during decision conflict. In their conclusion, they support the hypothesis that eye gaze and pupil dilation reflect dissociated biases in latent decision parameters of evidence accumulation (drift rate) and response caution (decision threshold).

MY NOTES:

  1. Good definition of DDM (p. 1476) “provides an algorithmic account of how evidence accumulation and response caution contribute to a binary decision process through the separate latent parameters of drift rate and decision threshold, respectively”
  2. Pupil dilation increases caused by anticipation, fear, cognitive load, arousal, difficulty, anticipation, risk, novelty, surprise and conflict.
  3. EEG shows that perigenual anterior cingulate cortex (ACC) activity specifically implicated in conflict-induced decision threshold adjustment.
  4. Note the method section – how the experiment was carried out. (p. 1477)
  5. NB DISTANCE BETWEEN BOUNDARIES=DECISION THRESHOLD
  6. Hierarchical Bayesian Parameter Estimation of the DDM http://ski.clps.brown.edu/hddm_docs/ (uses Python)
  7. A recent paper by Roger Ratcliff quantitatively compared DMAT, fast-dm, and EZ, and concluded: “We found that the hierarchical diffusion method [as implemented by HDDM] performed very well, and is the method of choice when the number of observations is small.” Find the paper here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517692/

Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143(4), 1476–1488.