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Judgment distributions are often skewed and we know little about why. This paper explains the phenomenon of skewed judgment distributions by introducing the augmented quincunx (AQ) model of sequential and probabilistic cue categorization by neurons of judges. In the process of developing inferences about true values, when neurons categorize cues better than chance, and when the particular true value is extreme compared to what is typical and anchored upon, then populations of judges form skewed judgment distributions with high probability. Moreover, the collective error made by these people can be inferred from how skewed their judgment distributions are, and in what direction they tilt. This implies not just that judgment distributions are shaped by cues, but that judgment distributions are cues themselves for the wisdom of crowds. The AQ model also predicts that judgment variance correlates positively with collective error, thereby challenging what is commonly believed about how diversity and collective intelligence relate. Data from 3053 judgment surveys about US macroeconomic variables obtained from the Federal Reserve Bank of Philadelphia and the Wall Street Journal provide strong support, and implications are discussed with reference to three central ideas on collective intelligence, these being Galton's conjecture on the distribution of judgments, Muth's rational expectations hypothesis, and Page's diversity prediction theorem.

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Data was obtained from the Federal Reserve Bank of Philadelphia's Surveys of Professional Forecasters, and the Wall Street Journal's Economic Forecasting Survey. Judgments from the Federal Reserve Bank of Philadelphia about nominal GDP and housing starts concern annual percentage growth in seasonally adjusted values, while judgments about unemployment concern seasonally adjusted workforce percentages. Judgments from the Wall Street Journal also concern annual percentage growth, except for unemployment, which concerns percentages of the workforce. All judgments concern the US macroeconomic system.Surveys of Expectations.

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Evidence suggests that average individual error is greater when true values are more extreme.Testing H4: Positive association between and .

Evidence strongly suggests that greater judgment variance is associated with greater collective error squared.Testing H6: Positive association between CE2 and var(eT).

Evidence strongly suggests that greater judgment variance is associated with greater average individual error.Testing H5: Positive association between and var(eT).

Evidence suggests that atypically large true values are associated with judgment distributions that have greater negative skew. The reverse holds for atypically small true values.Testing H1: Negative association between and .

Evidence strongly suggests that atypically large true values are associated with positive collective error, or alternatively, that collectives underestimate atypically large true values. The reverse occurs for atypically small true values.Testing H2: Positive association between and .

Evidence suggests that judgment variance is greater when the true value is more extreme. These patterns are, unlike the patterns presented in Table 2 - Table 7, inconsistent with predictions of the AQ model under the applied assumptions. The discussion section includes an explanation of why these patterns may occur using AQ model logic.Testing H7: No association between var(eT) and .

Evidence mildly suggests that negatively skewed judgment distributions are associated with positive collective error, or alternatively, that negative skew of the judgment distribution signals underestimation by the collective. The reverse occurs for positively skewed distributions.Testing H3: Negative association between and .