Eric C. Nolan

Bilateral Thalamic Lesions Affect Recollection-and Familiarity-Based Recognition Memory Judgments

The contribution of the thalamus to different forms of explicit memory is poorly understood. In the current study, explicit memory performance was examined in a 40-year-old male (RG) with bilateral anterior and medial thalamic lesions. Standardized tests indicated that the patient exhibited more severe recall than recognition deficits and his performance was generally worse for verbal compared to nonverbal memory. Recognition memory tests using the remember-know (R/K) procedure and the confidence-based receiver operating characteristic (ROC) procedure were used to examine recollection- and familiarity-based recognition. These tests revealed that RG had deficits in recollection and smaller, but consistent deficits in familiarity. The results are in agreement with models indicating that the anteromedial thalamus is important for both recollection- and familiarity-based recognition memory.

The Optimal Model: Linear Programming

This is not a book on Linear Programming (LP). This is a book on decision making. It is a book on behavior, specifically resource-allocation behavior. But just as people’s decision making under choice cannot be studied in the absence of an understanding of Bayesian math, neither can people’s decisions about the allocation of resources be understood without an understanding of LP. LP is the mathematical model used in Operations Research and Management Science to find the optimal solution to resource-allocation problems when certain variables are known. This chapter will provide the reader with a very fundamental introduction to LP but it is far beyond the scope of this book (and the ability of the authors) to provide a comprehensive tutorial on all aspects of LP. Hundreds of books have been written on the topic since Dantzig’s 1963 Linear Programming and Extensions. But if the reader wishes to explore the topic in depth, one very authoritative place to start would be Linear Programming (Dantzig & Thapa, 1997).

RAB with Various Levels of Information

The first four chapters in this book introduced Linear Programming as an optimal model for calculating the optimal solution to a resource-allocation problem when certain values are known. In this chapter and the seven chapters that follow, we will view resource-allocation behavior under various conditions, structures, and environments and we will examine both normative and cognitive models for understanding how people make resource-allocation decisions. We will begin in this chapter with an examination of how people allocate resources under conditions of Certainty, Risk, and Uncertainty. We will see that participants can learn to perform a resource-allocation task with surprising success, that participants perform best under Certainty and worst under Uncertainty, that participants tend to allocate more resources early in a time period, and that participants prefer to hold some resources in reserve in case of unanticipated needs. This chapter will draw heavily on the 1993 article entitled, Resource-Allocation Behavior Under Certainty, Risk, and Uncertainty, by Langholtz, Gettys, and Foote with sections reproduced here with permission.

Conclusions and Future Areas to be Mapped

In the 11 preceding chapters we have examined how people make resource-allocation decisions. We started with some basic questions and tried to expand outwards from the beginning point in a logical and methodical manner. It was clear from the initial research that people could function surprisingly well as intuitive resource allocators, solving problems without having had any background in Linear Programming. Most participants could initially obtain approximately 80% to 85% of the optimal LP solution, and with practice, most could improve to 95% or more. This finding remained generally consistent and stable throughout all the studies we conducted under a variety of conditions.

Cognitive Strategies for RAB

In Chapters 5 through 9, we examined how people perform when called upon to make resource-allocation decisions in a variety of situations. We examined resource-allocation behavior under conditions of Certainty, Risk, and Uncertainty; in Harsh and Benign Environments; when Gains and Losses are possible; with objective functions of varying slopes; in both technical and commonplace tasks; and in two and three dimensions. But until now we have not addressed in any detail how individuals process the information necessary to make these resource-allocation decisions. It is our goal in this chapter to discover some of the cognitive strategies people use in approaching resource-allocation decisions. Here we will discuss how participants solve a 7-day meal-scheduling task similar to the one used in Chapter 9, but we will conduct a Verbal Protocol Analysis, a research methodology that allows a more detailed analysis of the cognitive processes involved in making such decisions. The contents of this chapter will be based on Ball, Langholtz, Auble, and Sopchak (1998), parts of which are reprinted here with permission. We will see in the research that is discussed, that a few participants attempted to first solve the problem and determine the maximum meals possible in a week before scheduling this solution across the seven days (solve-and-schedule strategy), but the majority of participants simply consumed meals on a day-to-day basis while checking resource availability each day to allow for full resource consumption (consume-and-check strategy).

RAB in Commonplace but Complex Tasks

In Chapters 5 through 8, we examined people’s behavior in several specific resource-allocation situations. In Chapter 5, we examined resource-allocation behavior under conditions of Certainty, Risk, and Uncertainty, where fluctuations in resources were possible, finding that participants performed best under Certainty and worst under Uncertainty. We saw that people were capable of intuitively solving simple technical two-dimensional resource-allocation problems, initially finding solutions that were 80–90% of the optimal solution that could be obtained with LP. With practice many participants’ scores improved to 95% of the optimal LP solution. Chapter 5 also discussed the equal-scheduling tendency where participants would tend to schedule equal use of the two alternatives provided in the scenario of the problem, even when the optimal solution called for unequal scheduling. Participants also showed a tendency not to take precautions against losses but instead to react after a loss had occurred.

RAB when the Objective Function Changes

In Chapters 5 through 7, we examined how people behave in resource-allocation situations under various conditions. In Chapter 5 we saw how participants perform under conditions of Certainty, Risk, and Uncertainty. In Chapter 6 we saw how they would perform in Harsh and Benign Environments. And in Chapter 7 we saw how they perform when Gains and Losses are possible. In the remaining chapters of this book we will examine how they perform in both professional resource-allocation tasks and tasks of everyday life, in tasks that involve social issues, how they solve resource-allocation problems in three dimensions, and we will examine some of the cognitive strategies people use when making resource-allocation decisions. In all these chapters we manipulate the availability of resources to construct resource-allocation scenarios and we examine how people solve these resource-allocation problems to achieve the maximum number of meals, helicopter hours, boat hours, and humanitarian projects, as examples of goals to be achieved in the allocation of resources and as calculated using Linear Programming to determine the optimum solution.

Distributive Justice in Resource-Allocation

In Chapters 5 through 10, we examined how people allocate their resources in varying types of resource-allocation tasks. In Chapters 5 through 7, we examined resource-allocation behavior in job- and technical-oriented tasks. In these chapters, U.S. Coast Guard personnel were the participants instructed to solve resource-allocation problems involving the scheduling of helicopters or boats with varying fuel and personnel requirements. The tasks presented in Chapters 5–7 are only a few examples of the resource-allocation tasks that people undertake as part of their occupation.

RAB in Harsh and Benign Environments

In Chapters 1 through 4, we explored resource-allocation problems and how the optimal solution to resource-allocation problems can be determined by using Linear Programming, either mathematically with the Simplex Method, or visually using the Graphical Method. In Chapter 5, we introduced the concept of resource-allocation behavior and examined how people might be expected to perform when solving simple resource-allocation problems under conditions of Certainty, Risk, and Uncertainty.

RAB when Gains and Losses are Possible

In Chapters 1 through 4 we introduced the concept of resource-allocation and in Chapter 5 we saw how people performed in simple resource-allocation situations. We found that participants could learn to perform simple resource-allocation tasks surprisingly well, often exceeding 90% of the optimal LP solution after a few learning trials. Our data showed that participants performed best under Certainty, worst under Uncertainty, and that performance improved with learning. One interpretation of these results, and those of previous researchers (Gingrich & Soli, 1984; Busemeyer, Swenson, & Lazarte, 1986), is that people are capable resource-allocators, with an efficiency and accuracy almost as good as the optimal LP model.