Effects of Stimulus Exploration Length and Time on the Integration of Information in Haptic Softness Discrimination

In haptic perception, information is often sampled serially (e.g., a stimulus is repeatedly indented to estimate its softness), requiring that sensory information is retained and integrated over time. Hence, integration of sequential information is likely affected by memory. Particularly, when two sequentially explored stimuli are compared, integration of information on the second stimulus might be determined by the fading representation of the first stimulus. We investigated how the exploration length of the first stimulus and a temporal delay affect contributions of sequentially gathered estimates of the second stimulus in haptic softness discrimination. Participants subsequently explored two silicon rubber stimuli by indenting the first stimulus one or five times and the second stimulus always three times. In an additional experiment, we introduced a 5-s delay after the first stimulus was indented five times. We show that the longer the first stimulus is explored, the more estimates of the second stimulus' softness contribute to the discrimination of the two stimuli, independent of the delay. This suggests that the exploration length of the first stimulus influences the strength of its representation, persisting at least for 5 s, and determines how much information about the second stimulus is exploited for the comparison

Top-down modulation of shape and roughness discrimination in active touch by covert attention

Due to limitations in perceptual processing, information relevant to momentary task goals is selected from the vast amount of available sensory information by top-down mechanisms (e.g. attention) that can increase perceptual performance. We investigated how covert attention affects perception of 3D objects in active touch. In our experiment, participants simultaneously explored the shape and roughness of two objects in sequence, and were told afterwards to compare the two objects with regard to one of the two features. To direct the focus of covert attention to the different features we manipulated the expectation of a shape or roughness judgment by varying the frequency of trials for each task (20%, 50%, 80%), then we measured discrimination thresholds. We found higher discrimination thresholds for both shape and roughness perception when the task was unexpected, compared to the conditions in which the task was expected (or both tasks were expected equally). Our results suggest that active touch perception is modulated by expectations about the task. This implies that despite fundamental differences, active and passive touch are affected by feature selective covert attention in a similar way

Integration of serial sensory information in haptic perception of softness.

Redundant estimates of an environmental property derived simultaneously from different senses or cues are typically integrated according to the maximum likelihood estimation model (MLE): Sensory estimates are weighted according to their reliabilities, maximizing the percept’s reliability. Mechanisms underlying the integration of sequentially derived estimates from one sense are less clear. Here we investigate the integration of serially sampled redundant information in softness perception. We developed a method to manipulate haptically perceived softness of silicone rubber stimuli during bare-finger exploration. We then manipulated softness estimates derived from single movement segments (indentations) in a multisegmented exploration to assess their contributions to the overall percept. Participants explored two stimuli in sequence, using 2–5 indentations, and reported which stimulus felt softer. Estimates of the first stimulus’s softness contributed to the judgments similarly, whereas for the second stimulus estimates from later compared to earlier indentations contributed less. In line with unequal weighting, the percept’s reliability increased with increasing exploration length less than was predicted by the MLE model. This pattern of results is well explained by assuming that the representation of the first stimulus fades when the second stimulus is explored, which fits with a neurophysiological model of perceptual decisions (Deco, Rolls, & Romo, 2010)

Unequal - but fair? Weights in the serial integration of haptic texture information

<p>The sense of touch is characterized by its sequential nature. In texture perception, enhanced spatio-temporal extension of exploration leads to better discrimination performance due to combination of repetitive information. We have previously shown that the gains from additional exploration are smaller than the Maximum Likelihood Estimation (MLE) model of an ideal observer would assume. Here we test if this suboptimal integration can be explained by unequal weighting of information. Participants stroke 2 to 5 times across a virtual grating and judged the ridge period in a 2IFC task. We presented slightly discrepant period information in one of the strokes in the standard grating. Results show linearly decreasing weights of this information with spatio-temporal distance (number of intervening strokes) to the comparison grating. For each exploration extension (number of strokes) the stroke with the highest number of intervening strokes to the comparison was completely disregarded. The results are consistent with the notion that memory limitations are responsible for the unequal weights. This study raises the question if models of optimal integration should include memory decay as an additional source of variance and thus not expect equal weights.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2014). Unequal - but fair? Weights in the serial integration of haptic texture information. <em>Haptics: Neuroscience, Devices, Modeling, and Applications</em> (pp. 386-392). Springer: Heidelberg.</p> <p> </p> <p>The Zip file contains all data relative to the publication. The data of each participant is contained in a separate file.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p