No abstract

Political candidates often believe they must focus their campaign efforts on a small number of swing voters open for ideological change. Based on the wisdom of opinion polls, this might seem like a good idea. But do most voters really hold their political attitudes so firmly that they are unreceptive to persuasion? We tested this premise during the most recent general election in Sweden, in which a left- and a right-wing coalition were locked in a close race. We asked our participants to state their voter intention, and presented them with a political survey of wedge issues between the two coalitions. Using a sleight-of-hand we then altered their replies to place them in the opposite political camp, and invited them to reason about their attitudes on the manipulated issues. Finally, we summarized their survey score, and asked for their voter intention again. The results showed that no more than 22% of the manipulated replies were detected, and that a full 92% of the participants accepted and endorsed our altered political survey score. Furthermore, the final voter intention question indicated that as many as 48% (±9.2%) were willing to consider a left-right coalition shift. This can be contrasted with the established polls tracking the Swedish election, which registered maximally 10% voters open for a swing. Our results indicate that political attitudes and partisan divisions can be far more flexible than what is assumed by the polls, and that people can reason about the factual issues of the campaign with considerable openness to change.

We present an open-source software platform that transforms emotional cues expressed by speech signals using audio effects like pitch shifting, inflection, vibrato, and filtering. The emotional transformations can be applied to any audio file, but can also run in real time, using live input from a microphone, with less than 20-ms latency. We anticipate that this tool will be useful for the study of emotions in psychology and neuroscience, because it enables a high level of control over the acoustical and emotional content of experimental stimuli in a variety of laboratory situations, including real-time social situations. We present here results of a series of validation experiments aiming to position the tool against several methodological requirements: that transformed emotions be recognized at above-chance levels, valid in several languages (French, English, Swedish, and Japanese) and with a naturalness comparable to natural speech.

Speech is usually assumed to start with a clearly defined preverbal message, which provides a benchmark for self-monitoring and a robust sense of agency for one's utterances. However, an alternative hypothesis states that speakers often have no detailed preview of what they are about to say, and that they instead use auditory feedback to infer the meaning of their words. In the experiment reported here, participants performed a Stroop color-naming task while we covertly manipulated their auditory feedback in real time so that they said one thing but heard themselves saying something else. Under ideal timing conditions, two thirds of these semantic exchanges went undetected by the participants, and in 85% of all nondetected exchanges, the inserted words were experienced as self-produced. These findings indicate that the sense of agency for speech has a strong inferential component, and that auditory feedback of one's own voice acts as a pathway for semantic monitoring, potentially overriding other feedback loops.

What would it be like if we said one thing, and heard ourselves saying something else? Would we notice something was wrong? Or would we believe we said the thing we heard? Is feedback of our own speech only used to detect errors, or does it also help to specify the meaning of what we say? Comparator models of self-monitoring favor the first alternative, and hold that our sense of agency is given by the comparison between intentions and outcomes, while inferential models argue that agency is a more fluent construct, dependent on contextual inferences about the most likely cause of an action. In this paper, we present a theory about the use of feedback during speech. Specifically, we discuss inferential models of speech production that question the standard comparator assumption that the meaning of our utterances is fully specified before articulation. We then argue that auditory feedback provides speakers with a channel for high-level, semantic “self-comprehension”. In support of this we discuss results using a method we recently developed called Real-time Speech Exchange (RSE). In our first study using RSE (Lind et al., in press) participants were fitted with headsets and performed a computerized Stroop task. We surreptitiously recorded words they said, and later in the test we played them back at the exact same time that the participants uttered something else, while blocking the actual feedback of their voice. Thus, participants said one thing, but heard themselves saying something else. The results showed that when timing conditions were ideal, more than two thirds of the manipulations went undetected. Crucially, in a large proportion of the non-detected manipulated trials, the inserted words were experienced as self-produced by the participants. This indicates that our sense of agency for speech has a strong inferential component, and that auditory feedback of our own voice acts as a pathway for semantic monitoring. We believe RSE holds great promise as a tool for investigating the role of auditory feedback during speech, and we suggest a number of future studies to serve this purpose.