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However, Walker also wrote an obscure earlier account in a letter of 2 September , the day on which the Parliamentarian Army surrendered. This account was published anonymously in the Royalist newspaper Mercurius Aulicus on 7 September Its true author was unknown until an early draft of the document, also held by the National Army Museum, was transcribed and clearly established Walker as its creator.

This means that a fascinating comparison with his later account can now be made. The origins of the battle lie in the failure of the Royalist invasion of south-east England in Spring This would culminate in an attempt to conquer the Royalist stronghold in Cornwall. In a series of battles and manoeuvres the Royalists tightened the noose around the Parliamentarians and forced them to fall back into increasingly untenable positions.

Finally, towards the end of the month, the Parliamentarians lost their forage area and small supply ports to the south and west of the town. Their situation was now desperate and the Parliamentarians elected to attempt a daring breakout by their horse, while their foot would enact a fighting retreat towards Fowey in the vain hope that they could be evacuated by the navy.

The escape of the Parliamentarian horse in the early hours of 31 August was a striking success. Essex and a few other commanders escaped by sea, leaving 6, men to surrender. The Royalists captured a huge stock of arms. Paradoxically, it also helped sow the seeds of their ultimate defeat. Charles came away with the conviction that he was his own best general. But his hubris masked his defects as a commander, in particular his vacillation and lack of personal authority.

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By contrast, defeat at Lostwithiel, combined with other setbacks that year, galvanized the Parliamentarians. While they show a broad correlation, they disagree on some crucial points of detail. Most significantly, they differ over the escape in the early hours of 31 August of the Parliamentarian horse, commanded by Sir William Balfour.

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The issue is further obscured by Parliamentarian sources. Unless a further source comes to light it will be impossible to resolve this issue. Another area of disagreement is over the handling of the prisoners of war taken at the end of the battle. There are, in fact, three versions of this episode.

An Eyewitness History of World War II

His early draft is far more revealing about the grim fate that awaited the prisoners. Although an understanding of the relationship between response bias and expected value is important, expected value in this case has little to do with the diagnostic accuracy of an eyewitness report. But it does nonetheless bear on decisions about which lineup procedure should be employed. In other words, the approach affords insight into and quantification of the sensory and cognitive processes that are believed to underlie memory-based classification decisions see Box , such as eyewitness identifications.

Despite these merits, as a general statistical procedure for evaluation of binary classification performance and as a tool for evaluation of eyewitness performance, the ROC approach has some well-documented quantitative shortcomings. For example, ROC analysis depends on the ability to manipulate response bias or to estimate it from some other variable, and in the case of eyewitness identification that ability has been the subject of some debate.

This proxy relationship is inherently noisy within individuals, and the noisy relationship is exacerbated by the fact that the eyewitness identification ROC is population-based; individual data points are obtained from different people who may scale their confidence reports differently. Brewer and G. See Appendix C. An additional prerequisite for the use of ECL as a measure of response bias is that an orderly relationship exists between confidence and accuracy—that witnesses expressing greater confidence are more likely to be accurate in their identifications.

Although this hypothesis conforms to intuition, 48 the existence of a significant confidence—accuracy relationship has been challenged repeatedly over the years.

Another technical concern raised by the use of ROC analysis to evaluate eyewitness identification performance is that it relies on a partial , rather than full, area under the ROC curve measure see Box as an index of discriminability that is separate from response bias. This is necessitated by the fact that the highest false alarm rates in eyewitness identification data are commonly well below 1.

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Both hit rates and false alarm rates declined steeply—implying an increasingly conservative response bias—as confidence levels increased. Diagnosticity ratios increased monotonically with increasing confidence. Roediger III, J. Wixted, and K. Nadel and W. Deffenbacher and E. Wells, T. Ferguson, and R.

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Wells and D. Wells and E. Cutler and S. Bothwell, K.

nekynkeco.cf Deffenbacher, and J. Busey et al. Juslin, N. Olsson, and A.

Because the standard error of the partial area under the curve measure depends upon the degree of truncation, accuracy of this discriminability measure can easily vary across conditions and across studies, making the interpretation difficult. While ROC analysis has many recognized merits for the evaluation of binary classification, the residual concerns associated with its typical use for evaluating eyewitness performance merit consideration of other statistical approaches to this problem. As noted above, many methods have been proposed—and adopted in specific applications—for evaluation of binary classification performance.

Moreover, because they have not been vetted, the committee is not in a position to endorse any specific statistical tool, the committee nevertheless encourages a general exploration of these alternatives. Nonetheless, some of these methods may provide greater insight into the factors that affect eyewitness identification performance and may, in turn, suggest ways of improving performance. To illustrate this opportunity by example, we consider the following possibilities. It has been argued that a basic weakness of the existing ROC approach to binary classification performance results from the fact that, in principle.

The costs of classification errors may be similar across some lineup comparisons and across some conditions of other systems variables, and for others they may be different. But for the most part they are not precisely known, and this is thus a topic that deserves greater attention given the growing use of ROC-based evaluation of eyewitness identification performance. This field has a long and rich history, and candidate methods are summarized in several texts on statistical classification and machine learning, such as Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning and A.

But without a precise understanding of these relative decision costs, the area under the ROC curve measure can be incoherent, in that it depends as much on the classification conditions as it does on the sensitivity of the classifier. For a given response bias, PPV is related to the diagnosticity ratio, in that, given equal prevalence of the culprit in two conditions e.

As discussed above, the diagnosticity ratio is a critical piece of information in efforts to evaluate eyewitness performance. While NPV is commonly used to evaluate the accuracy of human classification decisions, such as in medical diagnosis, and is a source of information that may similarly be of additional value in efforts to evaluate lineup procedures, it has been largely neglected in the field of eyewitness identification. Hoffmann, S. Bennett, and C. Contrary to that intuition, however, evidence from studies of analogous binary classification problems reveals that these two predictive probabilities can vary with respect to one another in complex ways.

In practice, NPV-related measures quantified as negative likelihood ratios can be subjected to ROC analysis to account for the effects of response bias in the same manner as PPV-related measures quantified as positive likelihood ratios, i. Consideration of NPV and its relationship to PPV, by this and other means, may provide additional insight into the ways in which estimator and system variables such as lineup procedures influence eyewitness identification performance. In sum, a formal understanding of the task facing an eyewitness, in conjunction with an appreciation of causal models of human recognition memory, has led to a potentially more comprehensive method—ROC analysis—for evaluating eyewitness identification performance.

Despite these advances, it is important that practitioners in this field broadly explore the large and rich field of statistical tools for evaluation of binary classifiers.

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  • While the committee recognizes that these tools are uninvestigated for this application and may possess their own share of unforeseen problems or disadvantages, a move in this direction may be of great value for improving the validity of eyewitness identification. The nature of law enforcement interactions with the eyewitness before, during, and after the identification plays a role in the accuracy of eyewitness identifications and in the confidence expressed in the accuracy of those identifications by witnesses.


    Clark, T. Marshall, and R. Furthermore, some types of law enforcement communication with a witness, after the witness has made an identification e.