Probabilistic learning and inference in schizophrenia

Abstract

Patients with schizophrenia make decisions on the basis of less evidence when required to collect information to make an inference, a behavior often called jumping to conclusions. The underlying basis for this behavior remains controversial. We examined the cognitive processes underpinning this finding by testing subjects on the beads task, which has been used previously to elicit jumping to conclusions behavior, and a stochastic sequence learning task, with a similar decision theoretic structure. During the sequence learning task, subjects had to learn a sequence of button presses, while receiving a noisy feedback on their choices. We fit a Bayesian decision making model to the sequence task and compared model parameters to the choice behavior in the beads task in both patients and healthy subjects. We found that patients did show a jumping to conclusions style; and those who picked early in the beads task tended to learn less from positive feedback in the sequence task. This favours the likelihood of patients selecting early because they have a low threshold for making decisions, and that they make choices on the basis of relatively little evidence.

Fig. 1

Task. A. Top shows sequence of images presented in a single trial. Bottom shows sequence of trial events. Each cue, button press, feedback series is presented 4 times in a single trial. M1 – movement 1, M2 – movement 2, etc. B. Sequence of sets, blocks and trials. Each set is composed of 6 sequence blocks and each block is composed of a single sequence, executed until the learning criterion is met (t1, t2, … refers to trial 1, trial 2, etc.). Subjects progress through a single sequence block by learning and executing the sequence correctly 6 times.

Fig. 2

Behavioral and modeling results. A. Fraction of correct trials as a function of the number of correct trials per block. Zero correct trials will always have zero correct by definition. B. Parameter estimates for data fit to entire block of trials for patients and controls showing how much was learned from positive and negative feedback. C. Parameter estimates for data fit to entire block of trials with discounting of old evidence. This analysis emphasizes response to feedback at the end of the block. Note, the overall increase in the parameter values reflects the discount parameter, so they cannot be directly compared to the results in (B). D. Number of draws to decision in the urn task for controls and patients. E. Correlation between average number of draws to decision in patients and learning from positive feedback. Green line shows least squares fit.

Fig. 3

Bayesian belief estimates for the beads task. A. Estimates based on an ideal observer for the indicated sequences. B. Belief estimates of an ideal observer, and two possible hypotheses for why patients jump to conclusions: either they make their decision based on a lowered threshold, or they believe more strongly than they should on the basis of limited feedback. Dashed lines indicate the threshold; solid lines indicate belief estimates for the RRRRRR sequence. Red lines indicate hypotheses for patient performance; blue lines indicate possible control values.