Blog

New paper accepted combining the DDM with a Deep Neural Network

We have a new paper at Computational Brain & Behavior that describes a joint deep neural network and evidence accumulation modeling approach to human decision-making with naturalistic images. Evidence accumulation models (EAM) have proven to be an invaluable tool in probing the dynamical properties of decisions over recent decades. However, much of this literature has studied decisions utilizing simple stimuli where the experimenter has perfect knowledge and control over stimulus properties. Here we develop and test a new method for studying decisions involving naturalistic stimuli (medical images in this case) where the experimenter has neither perfect knowledge nor control of the stimuli properties. The central challenge in studying such decisions is to extract useful representations of images that can be associated with accumulation or drift rates in EAMs. Here we couple a deep convolutional neural network (CNN) with the diffusion decision model (DDM) to study how expert pathologists and novices make decisions involving the classification of digital images of blood cells as either normal (Non-Blast) or cancerous (Blast). In our approach, the CNN is the basis of a function that translates each image into a drift rate for use in the DDM. Results of fitting the joint CNN-DDM model to choice and response time data demonstrates that 1) both novices and experts demonstrated substantial speed accuracy tradeoffs, 2) both were susceptible to biases introduced by the presentation of pre-stimulus probabilistic cues, and 3) experts were more adept at extracting useful information from images than novices. These results demonstrate that this is a fruitful approach to studying decisions involving complex stimuli that will open new avenues for studying questions not possible with existing methods. Furthermore, this approach is technically feasible and has the potential to be translated into other domains of decision making research.

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Now Accepting Applications for Research Analyst Position

The Computational Decision-making Lab at Vanderbilt University invites applications for a full-time Research Analyst position to begin in Fall 2019. We will begin reviewing applications immediately and continue until the position is filled. We are looking for applicants interested in applying a computational approach to studying human judgment and decision-making.

The Research Analyst will contribute to all aspects of our lab’s research, including (1) programming experiments and collecting data for laboratory and online studies; (2) analyzing data using practices geared toward fully reproducible science (e.g. OSF); and (3) general lab support and management. The ideal candidate will be interested in ultimately pursuing graduate study in psychology, neuroscience, data science, or a related field.

Qualifications:

  • A Bachelor’s degree in Cognitive Science, Computer Science, Psychology, Neuroscience, or a related field
  • Experience developing and implement behavioral experiments in the lab and online (such as with PsychoPy, Qualtrics, jsPsych)
  • Experience interacting with human subjects
  • Experience managing large online experiments (e.g., with Amazon Mechanical Turk)
  • Excellent organizational and interpersonal skills

The ideal candidate will be able to commit to the position for at least one year. Renewal for a second year is contingent on availability of funds, satisfactory performance, acceptable progress in carrying out the assigned duties, and mutual agreement.

Application Instructions:
Interested applicants should apply online at https://vanderuniv.taleo.net/careersection/.vu_cs/jobdetail.ftl?job=1901397&tz=GMT-05%3A00&tzname=America%2FChicago

The application can also be accessed at https://www.vanderbilt.edu/work-at-vanderbilt/ by searching for position number 190397. Candidates will need to create an account in the Work at Vanderbilt system in order to apply.

New paper exploring automatic biases in intertemporal choice

In a new paper accepted at Psychonomic Bulletin & Review, we examine different types of biases and how they influence intertemporal choices. Dual process theories of intertemporal decision making propose that decision makers automatically favor immediate rewards. In this paper, we use a drift diffusion model to implement these theories, and empirically investigate the role of their proposed automatic biases. Our model permits automatic biases in the response process, in the form of a shifted starting point, as well as automatic biases in the evaluation process, in the form of an additive drift rate intercept. We fit our model to individual-level choice and response time data, and find that automatic biases (as measured though the starting point and drift rate intercept in our model) are prevalent in intertemporal choice, but that the type, magnitude, and direction of these biases vary greatly across individuals. Our results pose new challenges for theories of intertemporal choice behavior.