Distinguishing Evidence Accumulation from Response Bias in Categorical Decision-Making Vincent P. Ferrera 1,2, Jack Grinband 1,2, Quan Xiao 1,2, Joy Hirsch 2, Roger Ratcliff 3 (1) Mahoney Center for Brain and Behavior Research, (2) Center for Neurobiology and Behavior Columbia University, (3) Department of Psychology, Ohio State University Introduction Categorical decision-making is a critical aspect of sensory-motor behavior. Yet little is known about how sensory representations are transformed into categorical or “decision-based” representations. To investigate this process, subjects performed a speed categorization task with a randomly varying category boundary separating “slow” and “fast”. We used a diffusion model to fit each subject’s performance (accuracy and reaction times). The model can help determine if subjects reacted to the shifting category boundary by biasing their responses, or, alternatively, by adjusting their internal decision criterion. References 1. Grinband J, Hirsch J, Ferrera VP. A neural representation of categorization uncertainty in the human brain. Neuron ;49(5): Ratcliff R, Van Zandt T, McKoon G. Connectionist and diffusion models of reaction time. Psychol Rev (2): Speed Categorization Task Human and monkey subjects performed a task in which they judged the speed of a moving random-dot pattern as fast or slow relative to a variable criterion speed. The criterion speed changed randomly from trial to trial and was indicated by a cue presented at the beginning of each trial. The stimulus probabilities were adjusted so that for each criterion the correct response was just as likely to be “fast” as “slow.” Humans responded with a button press, while monkeys used a touch panel. Auditory feedback was provided at the end of each trial to indicate correct or incorrect responses. Task Performance Trials were sorted according to stimulus speed and cue color. For each condition, performance was quantified as the percentage of trials on which the subject categorized the stimulus as “fast.” Reaction time was calculated as the interval between motion stimulus onset and the subject’s response. Discussion For the variable-criterion speed categorization task, the model attributed the subjects’ performance to variation in sensory evidence rather than response bias. Drift rate was found to vary almost linearly as a function of the relative distance of the stimulus from the category boundary. The same stimulus speed can give rise to different drift rates depending on the criterion. This indicates that the drift rate is not a function of stimulus properties per se, but reflects the relationship between the stimulus and the category boundary. We have previously found (1) that categorization tasks such as that presented here are relatively insensitive to the effects of stimulus probability. Stimulus probability effects can be quite large in other tasks and there is some evidence that these effects are better modeled as changes in response bias rather than changes in drift rate (2). These results suggest that perceptual categorization is based on a representation that encodes the relationship of the stimulus to the category boundary. They also suggest that the process of categorization can be separated from response selection at the behavioral level. These studies provide a basis for identifying neural activity related to categorical decision-making using single cell recordings or functional imaging. Diffusion Model Diffusion models treat a decision process as the accumulation of evidence toward a threshold. These models attribute differences in performance across task conditions to changes in either the rate of evidence accumulation (drift rate) or a bias toward one or the other response. # Figure 3. (Top) Accumulated evidence as a function of time for single trial. (Bottom) Diffusion model fits shape of psychometric function and reaction time distribution. Figure 4. Drift rates determined by fitting diffusion model to data in Fig. 2. Figure 5. Response biases determined by fitting diffusion model. X-axis is bias for trials with the slower criterion speed, y-axis is bias for the faster criterion. Acknowledgements Supported by NIH-MH59244 (VPF), R37-MH44640 (RR), T32-EY013933, T32-MH15174 Figure 1. (Top) Illustration of trial events during task. (Bottom) The full range of stimulus speeds was divided into “slow” and “fast” categories by one of two criterion speeds. Figure 2. (Left) Human psychometric functions averaged across observers (n=8). (Middle) Human reaction times (Right) Monkey psychometric functions. Drift Rate Model parameters for drift rate and response bias were estimated by fitting the proportion of “fast” responses and reaction time distributions for correct and error trials for each condition (stimulus speed x cue). The fitting process determines, for each conditions, the starting point of the diffusion process (bias) and drift rate. Variations in response bias tend to produce large changes in the leading edge of the reaction time distribution, whereas variations in drift rate have a much smaller effect on the leading edge. Bias The bias in the starting point of the diffusion process was estimated for each cue (speed criterion). The bias was small and was in the same direction for both criteria.