The role of cross-situational stimulus generalization in the formation of trust towards face: A perspective based on direct and observational learning

doi:10.3724/SP.J.1041.2023.01099

Abstract

Abstract:

Based on associative learning theory, three experiments were conducted to investigate the role of cross-situational (fairness-trust) stimulus generalization in formation of facial trust. From perspective of direct interaction and observational learning, respectively, Experiment 1a and Experiment 1b show that compared with medium unfair condition, as the perceptual similarity between the morphed trustee’s face and the face of the fair (unfair) allocator in the previous interaction increases, the degree of trust (distrust) towards the trustee gradually increases. In addition, this effect is asymmetrical, participants preferentially avoided more unfair morphs in comparison with fair morphs. This suggests an asymmetric overgeneralization toward individuals perceived to be morally aversive. Using drift-diffusion modeling (DDM), we found that drift rate v under unfair conditions was significantly smaller than that under medium or fair conditions, and most of them are in range of less than 0. This suggests that individuals are more likely to accumulate evidence of distrust when making trust decisions about unfamiliar faces that are similar to the allocator who was unfair in previous interactions. In Experiment 2, under an unintentional situation, the above-mentioned cross-situational generalization effect disappeared. These results indicated that individuals use associative learning mechanisms to generalize stimulus value acquired in different situations to new interactive situations, and then guide subsequent trust decisions.

Key words: trust formation, associative learning, stimulus generalization, behavioral intention, drift diffusion model

YUAN Bo, WANG Xiaoping, YIN Jun, LI Weiqiang. (2023). The role of cross-situational stimulus generalization in the formation of trust towards face: A perspective based on direct and observational learning. Acta Psychologica Sinica, 55(7), 1099-1114.

Figures/Tables 9

Figure 1. Schematic diagram of the construction process of face stimulation. (A) The original faces and 6 pre-rated faces were morphed in 11% increments. To avoid too similar with each other, only the faces with two increments along the same continuum were selected. (B) The original face that was eventually used in the experiment and the corresponding morphed face.

Figure 2. Flow charts of stimulus association and stimulus generalization stages in Experiment 1a.

Figure 3. Schematic diagram of drift diffusion model when individuals make trust choice.

Figure 4. (A) Regression analysis of face similarity and ratio of selected morphed faces under different face association types; (B) Posterior probability distribution of fair/unfair face association × face similarity regression coefficient and corresponding confidence interval CIs.

Figure 5. Probability density distribution of four parameters of DDM under different face connection types. (A) represents the table drift rate v; (B) represents the boundary height α; (C) represents the boundary starting point deviation z; (D) represents non-decision time τ.

Figure 6. (A) Regression analysis of face similarity and ratio of selected deformable faces under the same face association type; (B) Posterior probability distribution of fair/unfair face association × face similarity regression coefficient and correspo nding confidence interval CIs.

Figure 7. Probability density distribution of four parameters of DDM under different face association types. (A) represents the table drift rate v; (B) represents the boundary height α; (C) represents the boundary starting point deviation z; (D) represents non-decision time τ.

Figure 8. (A) Regression analysis of face similarity and ratio of selected morphed faces under different face association types; (B) Posterior probability distribution of fair/unfair face association × face similarity regression coefficient and corresponding confidence interval CIs.

Figure 9. Probability density distribution of four parameters of DDM under different face connection types. (A) represents the table drift rate v; (B) represents the boundary height α; (C) represents the boundary starting point deviation z; (D) represents non-decision time τ.

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