Browsing by Subject "Value"

The first essay of my dissertation introduces a new method for eliciting market beliefs about the expected outcomes of a merger negotiation after announcement. During a merger negotiation, the market prices of the firms involved

reflect beliefs about their values both in the merged and

standalone states, as well as the likelihood of either outcome.

These beliefs determine stock price reactions to news of a possible

merger, but those prices alone do not contain sufficient information

to identify the latent beliefs that they reflect. I develop a new

method which, by using additional data in the form of option prices,

is able to identify these beliefs. This method allows for a clear

decomposition of a negotiating firm's expected value change into two

parts: the value of the transaction to the firm, and new information

about its standalone value. Previous research into estimating

merger synergies has struggled to obtain an appropriate alternative

against which to measure the realized outcome. The market's beliefs

about state-contingent firm values give an estimate of both. Through

a direct comparison of the estimates of a firm's value in both the

merged and standalone states, I obtain a strong, practical measure

of the expected value-creating potential of a merger before its

consummation.

The second essay applies the state-contingent payoff estimation method developed previously to addressing questions about the size effect in mergers. A growing body of evidence indicates that large acquisitions destroy value. However, we do not yet know why. Several theories have been advanced, but their effects are difficult to observe in isolation. It has thus been impossible to tell whether negative post-announcement acquirer returns are caused by market expectations of value-destroying acquisitions or revealed bad news about standalone value. This paper resolves this issue by decomposing expectations about merger outcomes into expected value change from completing the acquisition and revision of beliefs about standalone firm value. The data show that deal size is correlated with value destruction, while acquirer size is correlated with release of unfavorable information. Deal size correlates with value destruction, acquirer size with bad news about the firm. Furthermore, the results suggest that overpayment is a prerequisite for large acquisitions. These findings reduce the set of possible theoretical explanations for the size effect.

Few aspects of human cognition are more personal than the choices we make. Our decisions — from the mundane to the impossibly complex — continually shape the courses of our lives. In recent years, researchers have applied the tools of neuroscience to understand the mechanisms that underlie decision making, as part of the new discipline of decision neuroscience. A primary goal of this emerging field has been to identify the processes that underlie specific decision variables, including the value of rewards, the uncertainty associated with particular outcomes, and the consequences of social interactions. Here, across three independent studies, I focus on the neural circuitry supporting social valuation — which shapes our social interactions and interpersonal choices. In the first study (Chapter 2), I demonstrate that social valuation relies on the posterior ventromedial prefrontal cortex (pVMPFC). Extending these findings, I next show that idiosyncratic responses within pVMPFC predict individual differences in complex social decision scenarios (Chapter 3). In addition, I also demonstrate that decisions involving other people (e.g., donations to a charitable organization) produce increased activation in brain regions associated with social cognition, particularly the temporal-parietal junction (TPJ). Finally, in my last study (Chapter 4), I employ functional connectivity analyses and show that social cognition regions — including the TPJ — exhibit increased connectivity with pVMPFC during social valuation, an effect that depends upon individual differences in preferences for social stimuli. Collectively, these results demonstrate that the computation of social value relies on distributed neural circuitry, including both value regions and social cognition regions. Future research on social valuation and interpersonal choice must build upon this emerging theme by linking neural circuits and behavior.

An animal's ability to make adaptive choices is key to its fitness. Thus, the process of determining options, making a decision, evaluating outcomes, and learning from those outcomes to adjust future behavior is a central function of our nervous system. Determining the neural mechanisms of these cognitive processes is a crucial goal. One brain region, the posterior cingulate cortex (CGp), a central hub within the default mode network, is prominently dysregulated in Alzheimer's Disease and schizophrenia. Despite its clinical importance, the posterior cingulate cortex remains an enigmatic nexus of attention, memory, and motivation, all pointing to a role in decision-making. This dissertation is concerned with the role of this brain region in the learning and valuation processes involved in making adaptive choices. Specifically, I used rhesus macaques (Macaca mulatta) to examine the neural activity in posterior cingulate associated with specific learning and valuation -related variables. In the first experiment, I showed that posterior cingulate neurons track decision salience--the degree to which an option differs from a standard--but not the subjective value of a decision. To do this, I recorded the spiking activity of CGp neurons in monkeys choosing between options varying in reward-related risk, delay to reward, and social outcomes, each of which varied in level of decision salience. Firing rates were higher when monkeys chose the risky option, consistent with their risk-seeking preferences, but were also higher when monkeys chose the delayed and social options, contradicting their preferences. Thus, across decision contexts, neuronal activity was uncorrelated with how much monkeys valued a given option, as inferred from choice. Instead, neuronal activity signaled the deviation of the chosen option from the standard, independently of how it differed. The observed decision salience signals suggest a role for CGp in the flexible allocation of neural resources to motivationally significant information, akin to the role of attention in selective processing of sensory inputs. This pointed to a role for CGp in learning rather than subjective value signaling, and the second set of experiments aimed to test the role of CGp in associative learning. I recorded from single CGp neurons in monkeys performing a simple conditional motor association task while varying stimulus familiarity and motivation. CGp neurons responded phasically following commission of errors, and this error signal was modulated by motivation and stimulus novelty. Moreover, slow variations in firing rates tracked variations in learning rate over the course of sessions. Silencing these signals with muscimol impaired learning in low motivational states but spared learning in high motivational states, and spared recall of familiar associations as well. These findings endorse a role for CGp in performance and environment monitoring to regulate learning rate. Collectively, these experiments reshape our understanding of the role of posterior cingulate cortex in cognition, integrate default mode and value-based theories of CGp function, and provide a potential foundation for a circuit-level explication of Alzheimer's Disease and schizophrenia.

Over the past decade there has been an explosion of interest in exploring how attention and reward value can interact with one another during cognition and behavior. This interdisciplinary work has already provided many critical findings that have revolutionized what have traditionally been isolated fields of study. However, there are still many unexplored aspects by which attention and value can interact with one another. Here I advance upon this interdisciplinary work by investigating several aspects of the dynamic interplay between attention and value. In the first three studies I look at how reward can influence attention and assess its neural impact using EEG. I first detail the neural mechanisms underlying reward-driven salience, a phenomenon that describes how reward-associated items receive higher priority during attentional orienting. These findings provide evidence that value-driven salience generates a unique increase in the strength of attentional orienting. In the second study I investigate whether associating distractors with rewards can lead to larger impairments in sustained spatial attention. Results indicate that sustained spatial attention can be resistant to a distractor’s reward-history, highlighting an important boundary condition for reward-related distraction. The objective of the third study was to investigate the neural processes underlying reward expectation and outcome processing, with a focus on how these reward expectations influence attention and attentional orienting. A core finding from this experiment is that outcome valence modulates the strength of attentional orienting, while uncertain outcomes lead to elongated attentional processing. In the fourth and final study I turn to investigating how attention can influence decision making. A burgeoning body of work has shown that attention can be highly predictive of choice, but it has not yet determined how inattention can influence decision processes. To this end I investigated if and how attentional distractors influence nutritional decision making using a combination of behavioral and eye-tracking measures. The results indicate that distractors can interrupt the decision process but that they do not reset it. Collectively, these studies demonstrate the diverse ways in which attention and reward can interact with one another, and how studying these interactions can help us better understand a number of real-world behaviors and circumstances.