Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences

Abstract

In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

Adaptive Choice Biases in Mice and Humans

“…Animals choosing the ‘correct arm’ displaying the S D were allowed to rest on the elevated platform for 40 s, but they could rest only for 10 s after choosing the ‘wrong arm’. The arm projecting the S D was selected pseudo-randomly on each trial, but it could not repeat over consecutive trials ( Herrera and Trevino, 2015 ).…”

“…To estimate the contributions of previous visual choices, side-choices, and their outcomes (success or failure) on the production of current side-choices, we carried out multiple logistic regression model (MLRM) analyses, as described before ( Trevino, 2014 ; Herrera and Trevino, 2015 ). For mice’s analyses, we also included the position of the S D and the chosen platform as potential predictors of current choice behavior.…”

Noise Improves Visual Motion Discrimination via a Stochastic Resonance-Like Phenomenon

“…The dots had limited lifetimes because when they reached the edges of the projection area, they were randomly repositioned along the dimension (x- or y-axis) that was exceeded (yet keeping their trajectories constant). We changed the direction of coherent movement pseudo-randomly on every trial (Treviño, 2014; Herrera and Treviño, 2015). We controlled the strength of motion in the RDM task by changing the relative proportion of dots moving coherently from 0 to 50%.…”

“…Indeed, many decision theories suggest that animals make their choices by accumulating sufficient stimulus information until they emit their responses (Smith and Ratcliff, 2004; Gold and Shadlen, 2007; Treviño et al, 2013). The build-up of such discriminative information is sensitive to relevant parameters such as the intensity and quality of the stimulus, prior response probabilities, and reward probabilities (Smith and Ratcliff, 2004; Treviño et al, 2013; Treviño, 2014; Herrera and Treviño, 2015). However, additional, relevant, and not yet explored variables could also interact and influence behavioral output in the RDM task.…”

Associative Learning Through Acquired Salience

“…In this task, the animals learned to associate that swimming toward a conditioned stimulus (CS + ) and reaching a transparent submerged platform was rewarded with escape from water, whereas swimming toward a non-conditioned stimulus (CS − ) was not. For every trial, we displayed the CS + in a different arm of the pool according to a Gellerman-like schedule (Treviño et al, 2011 ; Herrera and Trevino, 2015 ). Choices were considered as made, once the animals crossed an imaginary line that outlined a decision area with visual access to both CS + and CS − images (Treviño et al, 2012 ).…”