Evaluating an internet-delivered fear conditioning and extinction protocol using response times and affective ratings.

Pavlovian fear conditioning is widely used to study mechanisms of fear learning, but high-throughput studies are hampered by the labor-intensive nature of examining participants in the lab. To circumvent this bottle-neck, fear conditioning tasks have been developed for remote delivery. Previous studies have examined remotely delivered fear conditioning protocols using expectancy ratings and affective ratings. Here we replicate and extend these findings using an internet-delivered version of the Screaming Lady paradigm, evaluating the effects on negative affective ratings and response time to an auditory probe during stimulus presentations. In a sample of 80 adults, we observed clear evidence of both fear acquisition and extinction using affective ratings. Response times were faster when probed early, but not later, during presentation of stimuli paired with an aversive scream. The response time findings are at odds with previous lab-based studies showing slower responses to threat-predicting cues. The findings underscore the feasibility of employing remotely delivered fear conditioning paradigms with affective ratings as outcome, and highlights the need for further research examining optimal parameters for concurrent response time measures or alternate modes of non-verbal estimation of conditioned responses in Pavlovian conditioning protocols.

Colexification Networks Encode Affective Meaning

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.

The Dynamics of Affect Across the Wake-Sleep Cycle: From Waking Mind-Wandering to Night-Time Dreaming

Affective experiences occur across the wake-sleep cycle—from active wakefulness to resting wakefulness (i.e., mind-wandering or daydreaming) to sleep (i.e., dreaming). Yet, we know little about the dynamics of affective experiences across these states. Here, we investigated the within-person fluctuations in the prevalence and valence of affect experienced during mind-wandering and night-time dreaming. We compared the affective ratings of 328 mind-wandering and 529 dream episodes from 32 healthy adults. In a sub-sample, we additionally analysed the affective ratings of 548 waking episodes from 15 participants. Results showed that mind-wandering was more positively valenced than dreaming, and that both mind-wandering and dreaming were more negatively valenced than active wakefulness. We also compared participants’ self-ratings of affect with external ratings of affective experiences described in verbal reports regarding the same episodes. With self-ratings all the episodes were predominated by positive affect. However, the affective valence of verbal reports changed from positively valenced waking reports to affectively balanced mind-wandering reports to negatively valenced dream reports. Together, the findings show that (1) the positivity bias (i.e., more positive than negative affect) characteristic to waking experiences decreases across the wake-sleep continuum, and (2) conclusions regarding the affective nature of subjective experiences depend on whether self-ratings of affect or the verbal reports describing these experiences are analysed. These findings contribute to our understanding of the nature and possible function of affective experiences across different states of consciousness and call for more integration between the fields of emotion research, mind-wandering research, and dream research.