The contribution of hypersalience to the "jumping to conclusions" bias associated with delusions in schizophrenia

Abstract

Background: Previous schizophrenia research involving the "beads task" has suggested an association between delusions and 2 reasoning biases: (1) "jumping to conclusions" (JTC), whereby early, resolute decisions are formed on the basis of little evidence and (2) over-adjustment of probability estimates following a single instance of disconfirmatory evidence. In the current study, we used a novel JTC-style paradigm to provide new information about a cognitive operation common to these 2 reasoning biases.

Methods: Using a task that required participants to rate the likelihood that a fisherman was catching a series of black or white fish from Lake A and not Lake B, and vice versa, we compared the responses of 4 groups (healthy, bipolar, nondelusional schizophrenia and delusional schizophrenia) when we manipulated 2 elements of the Bayesian formula: incoming data and prior odds.

Results: Regardless of our manipulations of the Bayesian formula, the delusional schizophrenia group gave significantly higher likelihood ratings for the lake that best matched the colour of the presented fish, but the ratings for the nonmatching lake did not differ from the other groups.

Limitations: The limitations of this study include a small sample size for the group of severely delusional patients and a preponderance of men in the schizophrenia sample.

Conclusion: Delusions in schizophrenia are associated with hypersalience of evidence-hypothesis matches but normal salience of nonmatches. When the colour of the incoming data is uniform (fish of only one colour), this manifests as JTC early in a series, and when the colour of incoming data varies (both black and white fish), this manifests as an overadjustment midseries. This account can provide a unifying explanation for delusion-associated performance patterns previously observed in the beads task in schizophrenia.

Fig. 1

Sample screen shot of the fishing task. Differences in ratios of black to white fish in each lake for each series were represented graphically. The fisherman was updated for each catch to display the colour of the current fish catch. Following each catch, participants were instructed to make separate ratings using the 2 sliding scales of the likelihood that the fish were being caught exclusively from Lake A and the likelihood that they were being caught exclusively from Lake B.

Fig. 2

Average matching lake (top) and nonmatching lake (bottom) ratings for series 2, 3 and 4. A “match” is a situation in which the ratio of fish in one lake makes it the best choice with regards to the colour of the current fish catch. Error bars represent the standard error of the mean.

Fig. 3

Ratings for series 1 for Lake A and B, plotted as a function of catches. Counterbalancing was accounted for by designating Lake A as the lake best supported by the first fish catch. Error bars represent the standard error of the mean.

Fig. 4

Ratings for series 5 for Lake A and B, plotted as a function of catches. Counterbalancing was accounted for by designating Lake A as the lake best supported by the majority of the fish catches. Error bars represent the standard error of the mean.

Fig. 5

The average ratings for series 1–5 plotted as a function of group and whether the presented fish matched the lake being rated. Error bars represent the standard error of the mean. **p < 0.01 for adjacent bars.

Fig. 6

Ratings for matching lakes compared with the optimal Bayesian reasoning pattern expected if the information presented over the whole series of catches is taken into account, and the opposite, non-Bayesian reasoning pattern in which individual catches are rated as independent events. Probabilities were computed as the probability of the focal hypothesis (i.e., the lake best supported by the fish catches or “matching lake”) being true and not the alternate hypothesis.