What do we know about implicit false-belief tracking?

Abstract

Visualizing acoustic features of speech has proven helpful in speech therapy; however, it is as yet unclear how to create intuitive and fitting visualizations. To better understand the mappings from speech sound aspects to visual space, a large web-based experiment (n=249) was performed to evaluate spatial parameters that may optimally represent pitch and loudness of speech. To this end, five novel animated visualizations were developed and presented in pairwise comparisons, together with a static visualization. Pitch and loudness of speech were each mapped onto either the vertical (y-axis) or the size (z-axis) dimension, or combined (with size indicating loudness and vertical position indicating pitch height) and visualized as an animation along the horizontal dimension (x-axis) over time. The results indicated that firstly, there is a general preference towards the use of the y-axis for both pitch and loudness, with pitch ranking higher than loudness in terms of fit. Secondly, the data suggest that representing both pitch and loudness combined in a single visualization is preferred over visualization in only one dimension. Finally, the z-axis, although not preferred, was evaluated as corresponding better to loudness than to pitch. This relation between sound and visual space has not been reported previously for speech sounds, and elaborates earlier findings on musical material. In addition to elucidating more general mappings between auditory and visual modalities, the findings provide us with a method of visualizing speech that may be helpful in clinical applications such as computerized speech therapy, or other feedback-based learning paradigms.

Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive.

“…This trade-off function has been a central aspect of models of decision-making in psychology, neuroscience, and behavioural ecology (e.g., Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006; Chittka, Skorupski, & Raine, 2009). However for many decisions, such as food choice, decision-makers should optimize value, not accuracy, and decision-making processes should take this fact into account (Pirrone, Stafford, & Marshall, 2014; Teodorescu, Moran, & Usher, 2015). Both the cost of a decision – in time taken and risk of error – and benefit of a decision – in reward – may frequently depend on the value of options.…”

“…At the same time, of two sources of the same food we would say that the bigger has an higher value. It seems reasonable to assume a correlation in many ecological scenarios between stimulus magnitude (or salience) and fitness value; for example, a brighter fruit may be riper and thus more nutritionally beneficial (Schaefer, McGraw, & Catoni, 2008), or a high intensity cue may indicate a more dangerous situation (Teodorescu et al, 2015). Prominent computational models of choice work by integrating difference (Ratcliff & McKoon, 2008) or ratio (Brown & Heathcote, 2008) in evidence between alternatives, thus disregarding information related to the absolute value of the alternatives under consideration (Pirrone et al, 2014; Teodorescu et al, 2015).…”

“…It seems reasonable to assume a correlation in many ecological scenarios between stimulus magnitude (or salience) and fitness value; for example, a brighter fruit may be riper and thus more nutritionally beneficial (Schaefer, McGraw, & Catoni, 2008), or a high intensity cue may indicate a more dangerous situation (Teodorescu et al, 2015). Prominent computational models of choice work by integrating difference (Ratcliff & McKoon, 2008) or ratio (Brown & Heathcote, 2008) in evidence between alternatives, thus disregarding information related to the absolute value of the alternatives under consideration (Pirrone et al, 2014; Teodorescu et al, 2015). Such systems may also exhibit decision deadlock between equal alternatives, which can be solved by adding urgency signals, asymmetry of inhibition or collapsing thresholds (Ditterich, 2006; Thura, Beauregard-Racine, Fradet, & Cisek, 2012), however, these additions are motivated by avoiding long reaction times in low evidence trials, without explicit reference to implementing ecologically-relevant option magnitude sensitivity.…”

“…A first demonstration of value sensitivity in human decision making comes from Teodorescu et al (2015), and some preliminary results about magnitude sensitivity are also present in Teodorescu and Usher (2013). In Teodorescu et al (2015) subjects were required to choose the brighter of two grey patches presented on the screen.…”

“…In Teodorescu et al (2015) subjects were required to choose the brighter of two grey patches presented on the screen. Compared to a baseline condition, the authors increased the overall value of the alternatives while holding the ratio or the difference between the mean luminances of the two grey patches constant.…”