Predicting favorable and unfavorable consequences of perceptual learning: worsening and the peak shift

Modelling generalisation gradients as augmented Gaussian functions

Studying generalisation of associative learning requires analysis of response gradients measured over a continuous stimulus dimension. In human studies, there is often a high degree of individual variation in the gradients, making it difficult to draw conclusions about group-level trends with traditional statistical methods. Here, we demonstrate a novel method of analysing generalisation gradients based on hierarchical Bayesian curve-fitting. This method involves fitting an augmented (asymmetrical) Gaussian function to individual gradients and estimating its parameters in a hierarchical Bayesian framework. We show how the posteriors can be used to characterise group differences in generalisation and how classic generalisation phenomena such as peak shift and area shift can be measured and inferred. Estimation of descriptive parameters can provide a detailed and informative way of analysing human generalisation gradients.

Auditory detection learning is accompanied by plasticity in the auditory evoked potential

Auditory detection can improve with practice. These improvements are often assumed to arise from selective attention processes, but longer-term plasticity as a result of training may also play a role. Here, listeners were trained to detect either an 861-Hz or 1058-Hz tone (counterbalanced across participants) presented in noise at SNRs varying from -10 to -24 dB. On the following day, they were tasked with detecting 861-Hz and 1058-Hz tones at an SNR of -21 dB. In between blocks of this active task, EEG was recorded during passive presentation of trained and untrained frequency tones in quiet. Detection accuracy and confidence ratings were higher for trials at listeners' trained, than untrained-frequency (i.e., learning occurred). During passive exposure to sounds, the P2 component of the auditory evoked potential (∼150 - 200 ms post tone onset) was larger in amplitude for the trained compared to the untrained frequency. An analysis of global field power similarly yielded a stronger response for trained tones in the P2 time window. These effects were obtained during passive exposure, suggesting that training induced improvements in detection are not solely related to changes in selective attention. Rather, there may be an important role for changes in the long-term neural representations of sounds.

Machine Learning in Psychometrics and Psychological Research

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions.

Benefits of fading in perceptual learning are driven by more than dimensional attention

Individuals learn to classify percepts effectively when the task is initially easy and then gradually increases in difficulty. Some suggest that this is because easy-to-discriminate events help learners focus attention on discrimination-relevant dimensions. Here, we tested whether such attentional-spotlighting accounts are sufficient to explain easy-to-hard effects in auditory perceptual learning. In two experiments, participants were trained to discriminate periodic, frequency-modulated (FM) tones in two separate frequency ranges (300–600 Hz or 3000–6000 Hz). In one frequency range, sounds gradually increased in similarity as training progressed. In the other, stimulus similarity was constant throughout training. After training, participants showed better performance in their progressively trained frequency range, even though the discrimination-relevant dimension across ranges was the same. Learning theories that posit experience-dependent changes in stimulus representations and/or the strengthening of associations with differential responses, predict the observed specificity of easy-to-hard effects, whereas attentional-spotlighting theories do not. Calibrating the difficulty and temporal sequencing of training experiences to support more incremental representation-based learning can enhance the effectiveness of practice beyond any benefits gained from explicitly highlighting relevant dimensions.