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SummaryThe intelligence community uses “structured analytic techniques” to help analysts think critically and avoid cognitive bias. However, little evidence exists of how techniques are applied and whether they are effective. We examined the use of the analysis of competing hypotheses (ACH)—a technique designed to reduce “confirmation bias.” Fifty intelligence analysts were randomly assigned to use ACH or not when completing a hypothesis testing task that had probabilistic ground truth. Data on analysts' judgement processes and conclusions were collected using written protocols that were then coded for statistical analyses. We found that ACH‐trained analysts did not follow all of the steps of ACH. There was mixed evidence for ACH's ability to reduce confirmation bias, and we observed that ACH may increase judgement inconsistency and error. It may be prudent for the intelligence community to consider the conditions under which ACH would prove useful and to explore alternatives.

A mathematically based fed-batch bioprocess demonstrated the suitability of using a relatively cheap and renewable substrate (butyric acid) for Pseudomonas putida CA-3 high cell density cultivation. Butyric acid fine-tuned addition is critical to extend the fermentation run and avoid oxygen consumption while maximising the biomass volumetric productivity. A conservative submaximal growth rate (μ of 0.25 h(-1)) achieved 71.3 g L(-1) of biomass after 42 h of fed-batch growth. When a more ambitious feed rate was supplied in order to match a μ of 0.35 h(-1), the volumetric productivity was increased to 2.0 g L(-1) h(-1), corresponding to a run of 25 h and 50 g L(-1) of biomass. Both results represent the highest biomass and the best biomass volumetric productivity with butyrate as a sole carbon source. However, medium chain length polyhydroxyalkanoate (mcl-PHA) accumulation with butyrate grown cells is low (4 %). To achieve a higher mcl-PHA volumetric productivity, decanoate was supplied to butyrate grown cells. This strategy resulted in a PHA volumetric productivity of 4.57 g L(-1) h(-1) in the PHA production phase and 1.63 g L(-1) h(-1)over the lifetime of the fermentation, with a maximum mcl-PHA accumulation of 65 % of the cell dry weight.

According to the scales of justice, the judge, in an unbiased way and directed by law, attends to all of the available information in a case, weighs it according to its significance, and integrates it to make a decision. By contrast, research suggests that judicial decision-making departs from the cognitive balancing act depicted by the scales of justice. Nevertheless, the research is often dismissed as irrelevant, and the judiciary, legal policymakers and the public remain largely unconvinced that the status quo needs improving. One potential rebuttal to the scientific findings is that they lack validity because researchers did not study judges making decisions on real cases. Another potential argument is that researchers have not pinpointed the psychological processes of any specific judge because they analyzed data over judges and/or used statistical models lacking in psychological plausibility. We review these 2 grounds for appeal against the scientific research on judicial decision-making, and note that it appears researchers' choices of data collection methods and analytic techniques may, indeed, be inappropriate for understanding the phenomena. We offer 2 remedies from the sphere of decision-making research: collecting data on judicial decision-making using representative design, and analyzing judicial decision data using more psychologically plausible models. Used together, we believe these solutions can help researchers better understand and improve legal decision-making. What is the significance of this article for the general public?We propose that researchers studying judicial decision-making ought to examine decisions made on real(istic) cases using a representative experimental design, and they should analyze individual judge or bench decision data using psychologically plausible models. This will make the research more relevant to the judiciary and thus make it more difficult for judges and legal policy-makers to ignore the findings.

Cognitive continuum theory points to the middle-ground between the intuitive and analytic modes of cognition, called quasirationality. In the context of sentencing, we discuss how legal models prescribe the use of different modes of cognition. These models aim to help judges perform the cognitive balancing act required between factors indicating a more or less severe penalty for an offender. We compare sentencing in three common law jurisdictions (i.e., Australia, the US, and England and Wales). Each places a different emphasis on the use of intuition and analysis; but all are quasirational. We conclude that the most appropriate mode of cognition will likely be that which corresponds best with properties of the sentencing task. Finally, we discuss the implications of this cognition-task correspondence approach for researchers and legal policy-makers.