Biomedical subjects

Frank Jäkel

Publications and source records attributed to Frank Jäkel.

3 recordsLinked to original sources

PubMed2006

Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers.

H. R. Blackwell (1952) investigated the influence of different psychophysical methods and procedures on detection thresholds. He found that the temporal two-interval forced-choice method (2-IFC) combined with feedback, blocked constant stimulus presentation with few different stimulus intensities, and highly trained observers resulted in the "best" threshold estimates. This recommendation is in current practice in many psychophysical laboratories and has entered the psychophysicists' "folk wisdom" of how to run proper psychophysical experiments. However, Blackwell's recommendations explicitly require experienced observers, whereas many psychophysical studies, particularly with children or within a clinical setting, are performed with naïve observers. In a series of psychophysical experiments, we find a striking and consistent discrepancy between naïve observers' behavior and that reported for experienced observers by Blackwell: Naïve observers show the "best" threshold estimates for the spatial four-alternative forced-choice method (4-AFC) and the worst for the commonly employed temporal 2-IFC. We repeated our study with a highly experienced psychophysical observer, and he replicated Blackwell's findings exactly, thus suggesting that it is indeed the difference in psychophysical experience that causes the discrepancy between our findings and those of Blackwell. In addition, we explore the efficiency of different methods and show 4-AFC to be more than 3.5 times more efficient than 2-IFC under realistic conditions. While we have found that 4-AFC consistently gives lower thresholds than 2-IFC in detection tasks, we have found the opposite for discrimination tasks. This discrepancy suggests that there are large extrasensory influences on thresholds--sensory memory for IFC methods and spatial attention for spatial forced-choice methods--that are critical but, alas, not part of theoretical approaches to psychophysics such as signal detection theory.

Attention↗

PubMed2006

Multimodal similarity and categorization of novel, three-dimensional objects.

Similarity has been proposed as a fundamental principle underlying mental object representations and capable of supporting cognitive-level tasks such as categorization. However, much of the research has considered connections between similarity and categorization for tasks performed using a single perceptual modality. Considering similarity and categorization within a multimodal context opens up a number of important questions: Are the similarities between objects the same when they are perceived using different modalities or using more than one modality at a time? Is similarity still able to explain categorization performance when objects are experienced multimodally? In this study, we addressed these questions by having subjects explore novel, 3D objects which varied parametrically in shape and texture using vision alone, touch alone, or touch and vision together. Subjects then performed a pair-wise similarity rating task and a free sorting categorization task. Multidimensional scaling (MDS) analysis of similarity data revealed that a single underlying perceptual map whose dimensions corresponded to shape and texture could explain visual, haptic, and bimodal similarity ratings. However, the relative dimension weights varied according to modality: shape dominated texture when objects were seen, whereas shape and texture were roughly equally important in the haptic and bimodal conditions. Some evidence was found for a multimodal connection between similarity and categorization: the probability of category membership increased with similarity while the probability of a category boundary being placed between two stimuli decreased with similarity. In addition, dimension weights varied according to modality in the same way for both tasks. The study also demonstrates the usefulness of 3D printing technology and MDS techniques in the study of visuohaptic object processing.

Concept Formation↗

PubMed2005

Bayesian inference for psychometric functions.

In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

Bayes Theorem↗