Steven S. Hsiao

Neural coding and the basic law of psychophysics.

There have been three main ideas about the basic law of psychophysics. In 1860, Fechner used Weber’s law to infer that the subjective sense of intensity is related to the physical intensity of a stimulus by a logarithmic function (the Weber-Fechner law). A hundred years later, Stevens refuted Fechner’s law by showing that direct reports of subjective intensity are related to the physical intensity of stimuli by a power law. MacKay soon showed, however, that the logarithmic and power laws are indistinguishable without examining the underlying neural mechanisms. Mountcastle and his colleagues did so, and, on the basis of transducer functions obeying power laws, inferred that subjective intensity must be related linearly to the neural coding measure on which it is based. In this review, we discuss these issues and we review a series of studies aimed at the neural mechanisms of a very complex form of subjective experience—the experience of roughness produced by a textured surface. The results, which are independent of any assumptions about the form of the psychophysical law, support the idea that the basic law of psychophysics is linearity between subjective experience and the neural activity on which it is based.

Human vs. robotic tactile sensing: Detecting lumps in soft tissue

Humans can localize lumps in soft tissue using the distributed tactile feedback and processing afforded by the fingers and brain. This task becomes extremely difficult when the fingers are not in direct contact with the tissue, such as in laparoscopic or robot-assisted procedures. Tactile sensors have been proposed to characterize and detect lumps in robot-assisted palpation. In this work, we compare the performance of a capacitive tactile sensor with that of the human finger. We evaluate the response of the sensor as it pertains to robot-assisted palpation and compare the sensor performance to that of human subjects performing an equivalent task on the same set of artificial tissue models. Furthermore, we investigate the effects of various tissue parameters (lump size, lump depth, and surrounding tissue stiffness) on the performance of both the human finger and the tactile sensor. Using signal detection theory for determining tactile sensor lump detection thresholds, the tactile sensor outperforms the human finger in a palpation task.

Roughness coding in the somatosensory system.

Roughness perception is coded in the somatosensory system by neurons in the type I slowly adapting (SAI) system. When the fingers scan a surface, an isomorphic representation of the surface is encoded in the discharge patterns of SAI afferents. Central neurons in area 3b of primary somatosensory (SI) cortex spatially filter the peripheral image to compute local spatial variation. The outputs from these neurons converge onto neurons in area 1 and onto neurons in secondary somatosensory (SII) cortex which we believe is the critical processing pathway underlying roughness perception.

Feeling Better Separate Pathways for Targeted Enhancement of Spatial and Temporal Touch

People perceive spatial form and temporal frequency through touch. Although distinct somatosensory neurons represent spatial and temporal information, these neural populations are intermixed throughout the somatosensory system. Here, we show that spatial and temporal touch can be dissociated and separately enhanced via cortical pathways that are normally associated with vision and audition. In Experiments 1 and 2, we found that anodal transcranial direct current stimulation (tDCS) applied over visual cortex, but not auditory cortex, enhances tactile perception of spatial orientation. In Experiments 3 and 4, we found that anodal tDCS over auditory cortex, but not visual cortex, enhances tactile perception of temporal frequency. This double dissociation reveals separate cortical pathways that selectively support spatial and temporal channels. These results bolster the emerging view that sensory areas process multiple modalities and suggest that supramodal domains may be more fundamental to cortical organization.

Neural coding of passive lump detection in compliant artificial tissue

Here, we investigate the neural mechanisms of detecting lumps embedded in artificial compliant tissues. We performed a combined psychophysical study of humans performing a passive lump detection task with a neurophysiological study in nonhuman primates (Macaca mulatta) where we recorded the responses of peripheral mechanoreceptive afferents to lumps embedded at various depths in intermediates (rubbers) of varying compliance. The psychophysical results reveal that human lump detection is greatly degraded by both lump depth and decreased compliance of the intermediate. The neurophysiology results reveal that only the slowly adapting type 1 (SA1) afferents provide a clear spatial representation of lumps at all depths and that the representation is affected by lump size, depth, and compliance of the intermediate. The rapidly adapting afferents are considerably less sensitive to the lump. We defined eight neural response measures that we hypothesized could explain the psychophysical behavior, including peak firing rate, spatial spread of neural activity, and additional parameters derived from these measures. We find that peak firing rate encodes the depth of the lump, and the neural spatial spread of the SA1 response encodes for lump size but not lump shape. We also find that the perception of lump size may be affected by the compliance of the intermediate. The results show that lump detection is based on a spatial population code of the SA1 afferents, which is distorted by the depth of the lump and compliance of the tissue.

A neural network model of transformations in the somatosensory system

Neural responses simulated using a feed-forward, linear neural network were compared to responses recorded from neurons of the primary somatosensory cortex responding to stimulation of the finger pad with embossed letters. Based on the similarity between simulated and observed responses, it is hypothesized that ascending paths of the somatosensory system might have a general structure similar to the organization of this model network and that early cortical responses could be partial transforms leading to a nonisomorphic higher level representation of spatial form.<<ETX>>