Over the last forty years, Members of Congress (MCs) have grown increasingly polarized in their legislative behavior, while representing electorates that are much more moderate in their policy views. This lack of anchoring by median preferences highlights a central puzzle in American politics: How do polarized candidates run and win elections based on legislative records that are increasingly 'out of step' with their districts and states?

Existing research points to two potential electoral sources for this representation disconnect. A predominant view is that this polarization process is the result of a changing balance of electoral forces that favor the demands of partisan and ideological voters over those expressed by centrists. The growing importance of primary elections, campaign cash, and clarified party brands, for example, may all create incentives for candidates to tack to the extremes as a precursor to successfully running in the general election. Alternatively, this dissertation argues that polarization is being driven, at least in part, through legislative 'shirking' by policy-motivated MCs who aim to pass their preferred policies while securing electoral insulation through communication effort in the campaign.

To address these competing accounts, this dissertation examines over forty years of campaign advertising to examine how candidates discuss their legislative records during elections and whether these campaign communications influence the way voters decide. In doing so, the project collects and analyzes 12,692 television commercials from House and Senate races between 1968 and 2008 in the Congressional Ads Project (CAP), the largest dataset ever assembled on campaign advertising in U.S. elections. The CAP dataset offers the first-ever glimpse into the political communication strategies developed by House and Senate candidates over multiple decades, including measures of the issues, positions, character appeals, partisanship, and other information candidates present to voters in the campaign. In addition to these data, the CAP data includes the transcribed positions taken by candidates across a number of issues in each of their ads. Finally, the project also examines additional survey, election, and campaign data, including ads from the 2008 election linked to the vote choices and attitudes of voters in order to evaluate position taking in more recent campaigns.

In analyzing this new dataset, this dissertation finds that candidates are increasingly discussing issues in their campaign advertisements. This increase is especially stark relative to the decline evident in candidate efforts to communicate their characteristics, seniority, leadership or other personal qualifications for office. Additionally, in discussing issues, candidates are also increasingly portraying themselves as moderates on policy, while characterizing their opponents as extremists through a process of issue distancing. In this process, candidates use issue-based strategies to confuse voters over which of the two competing candidates is most extreme by tacking to the center in elections, potentially providing an electoral boost to advantaged candidates and incumbents on non-policy grounds.

Further, this dissertation develops and implements a research design that exploits the disjuncture between media markets and electoral jurisdictions to identify the causal effects of position taking in campaign advertising. Due to the way markets are designed, candidates cannot efficiently target all voters in their districts and states, thus some ad messages are 'wasted' on certain voters. The design uses this inefficiency to draw comparisons across otherwise similar voters exposed to different kinds of issue positions. In doing so, this dissertation finds consistent evidence that distancing in the campaign helps candidates win votes, and can help mitigate the fallout from their polarized records.

Overall, this project provides additional support for the elite-driven account of a representational disconnect in American politics, suggesting fundamental limits to the ability of voters to hold their representatives accountable in contemporary elections. Moreover, the process of issue distancing may be an additional mechanism that can help sustain polarization in Congress in spite of the growing dissatisfaction of voters, and concerns over the well-functioning of America's majoritarian and divided powers system colliding with strong and (ir)responsible parties.

As we sample the world via shifts in gaze, the visual system filters out irrelevant information to prioritize the most relevant visual input. However, there is debate regarding why one source of information is selected over another for attention. The literature has suggested that the physical salience of image features is a dominant guidance factor in scene perception. However, cognitive relevance theory suggests that it is actually scene meaning (our knowledge of the world) that guides attention. Because meaning and image salience are correlated but have been represented differently in the literature, however, it has previously been impossible to test whether meaning or image salience uniquely predict attention when they are represented in the same format. To test their unique contributions to attention, Chapters 2 and 3 tested whether attention, as operationalized by fixation densities, was more related to meaning maps, which capture the spatial distribution of semantic densities in real-world scenes, or to saliency maps, which capture the spatial distribution of physically conspicuous features in scenes. Chapter 2 used a task in which viewers were instructed to count bright patches in scenes or rate the overall brightness of scenes while their eye movements were recorded. This resulted in image salience being task-relevant and meaning being task-irrelevant. Despite its task-irrelevance, meaning predicted fixation densities uniquely whereas image salience did not. A caveat of Chapter 2, however, is that the task required that eye movements be directed to scene-dependent information, thereby conflating whether the task was truly meaning-independent. To remedy this, Chapter 3 employed a free viewing task that did not require participants to attend to meaning or salience. Here, it was found that even during free viewing, meaning continued to explain the overall and unique patterns of attention significantly better than image salience. Together, these findings suggest that the visual system selects meaningful information for attentional selection, as consistent with cognitive relevance theory. Finally, prior work has combined spatial constraint (knowledge of where objects are located in scenes) and image salience to predict where fixations are directed during visual search. Given that meaning uniquely predicts attention beyond image salience, however, Chapter 4 therefore tested whether combining spatial constraint and meaning also predicts eye movements during visual search. Here, meaning was represented as meaning maps and spatial constraint was represented as surface maps that represented the likely locations of target objects as continuous probabilities. The results showed that combining spatial constraint and meaning predicted eye movements better than spatial constraint or meaning alone. This suggested that the visual system selects meaningful regions that appear on surfaces related to visual search targets for fixation. These findings collectively demonstrate that the human visual system prioritizes scene regions that contain meaningful content based upon our knowledge of the world for attention. This has implications for cognitive relevance theory which describes how humans orient attention in the real world and may help inform technologies that reduce distractions.