Response Frequency

Abstract: It has been observed that animals and people are often responsive or \"sympathetic\" to the emotional states of others. Of course, emotional reactions of one individual do not always result in sympathetic responses by another, so the general problem is to determine some of the conditions affecting the development of sympathetic responses. Conditioning theory provides a straightforward explanation for the development of sympathetic responses (Allport, 1924). According to the theory a particular unconditioned stimulus elicits the emotional response; for example, electric shock elicits a pain or fear response. Now if 5 experiences the pain reactions of another S to a shock and is himself shocked, on subsequent trials he should show fear at the pain reactions of the other S. The present experiment is a test of this hypothesis, using the depression in the rale of bar pressing for food reinforcement as the measure of degree of fear (Estes & Skinner, 1941).

Abstract: Steady-state stimulation is a useful paradigm in many physiologic and clinical situations, for ERG, Pattern-ERG and VEP. One of the advantages is the easy evaluation of the response via Fourier analysis. However, the question whether a given response is statistically significant or not has received little attention so far, although it is especially relevant in high noise, low amplitude recordings, as often occur in pathologic conditions. A given response is statistically significant if it is unlikely that its value is due to noise fluctuations. Thus appropriate estimates of noise and response are required. We have analytically derived formulas for the statistical significance of a given signal-to-noise-ratio s, based on two different estimates of noise: (1) Noise estimate by a 'no stimulus' recording, or by a '+/-average'. The former needs an additional recording, the latter can simultaneously be calculated as the standard average. (2) Noise is estimated as the average of the two neighboring spectral lines (one below, and one above the response frequency). Analytical solutions were obtained for both noise estimates that can easily be evaluated in all appropriate recordings. Noise estimate (1) performs much poorer than noise estimate (2), as can be seen from the following landmark values: Typical significance levels of 5%, 1%, and 0.1% require s values of 4.36, 9.95, and 31.6 (1), and 2.82, 4.55, and 8.40 (2). The noise estimate based on the neighboring frequencies can be easily applied after recording, provided that the noise spectrum is reasonably smooth around the response and frequency-overspill was avoided. It allows a quantitative assessment of low responses in physiological threshold analyses and pathological conditions, e.g., 'submicrovolt flicker-ERG'.