The impact of emotional valence on generalization gradients

Abstract

Generalization enables individuals to respond to novel stimuli based on previous experiences. The degree to which organisms respond is determined by their physical resemblance to the original conditioned stimulus (CS+), with a stronger response elicited by more similar stimuli, resulting in similarity-based generalization gradients. Recent research showed that cognitive or conceptual dimensions also result in gradients similar to those observed with manipulations of physical dimensions. Such findings suggest that attributes beyond physical similarity play a role in shaping generalization gradients. However, despite its adaptive relevance for survival, there is no study exploring the effectiveness of affective dimensions in shaping generalization gradients. In two experiments (135 Spanish and 150 English participants, respectively), we used an online predictive learning task, in which different stimuli (words and Gabor patches) were paired with the presence - or absence - of a fictitious shock. After training, we assessed whether valence (i.e., hedonic experience) conveyed by words shape generalization gradients. In Experiment 1, the outcome expectancy decreased monotonically with variations in valence of Spanish words, mirroring the gradient obtained with the physical dimension (line orientation). In Experiment 2, conducted with English words, a similar gradient was observed when non-trained (i.e., generalization) words varied along the valence dimension, but not when words were of neutral valence. The consistency of these findings across two different languages strengthens the reliability and validity of the affective dimension as a determinant of generalization gradients. Furthermore, our data highlight the importance of considering the role of affective features in generalization responses, advancing the interplay between emotion, language, and learning.

Keywords: Emotion; Generalization gradients; Language; Predictive learning; Valence.

Fig. 1

(a) Gabor stimuli, (b) word stimuli. Note. Figure 1a represents Gabor stimuli in which the CS+ was 0° and the CS- was 90° (counterbalanced). GS refers to generalization stimuli, and the numbers symbolize the distance with the CS+. Figure 1b represents the four subsets of lists used in the experiment and the correspondence with Gabor stimuli. Bold words are the trained words. In these examples, words of valence around 2 were used as CS+ and words with valence around 8 were used as CS- (counterbalanced)

Fig. 2

Generalization gradient. Note. Panel A represents the overall outcome expectancy for both stimuli dimensions. The CS+ was on the left part and the CS- on the right part of the figure. GS refers to generalization stimuli that were not presented during training. Numbers of GS refer to the proximity in terms of valence and orientation to the CS+ of each GS. GS1 was the closest to the CS+ and GS5 the furthest away. Error bars are the 95% confidence intervals (CIs). Panel B represents the violin plots of the slope considering the five GS. Black lines represent the medians and red lines the quartiles

Fig. 3

Expectancy test, Experiment 2. Note. Overall outcome expectancy for both stimuli dimensions. The CS+ is on the left part and the CS- on the right part of Fig. 3A. GS refers to generalization stimuli that were not presented during training. Number of GS refers to the proximity in terms of valence to the CS+ of each GS. GS1 was the closest to the CS+ and GS5 the furthest away. Error bars are the 95% confidence intervals (CIs). Panel B represents the violin plots of the slopes considering the five GS. Black lines represent the medians and red lines the quartiles