Comparing person and people perception: Multiple group members do not increase stereotype priming

A characteristic feature of daily life is encountering people in groups. Surprisingly, however, at least during the initial stages of processing, research has focused almost exclusively on the construal of single individuals. As such, it remains unclear whether person and people (i.e., group) perception yield comparable or divergent outcomes. Addressing this issue, here we explored a core social-cognitive topic—stereotype activation—by presenting both single and multiple facial primes in a sequential-priming task. In addition, the processes underlying task performance were probed using a drift diffusion model analysis. Based on prior work, it was hypothesised that multiple (vs. single) primes would increase stereotype-based responding. Across two experiments, a consistent pattern of results emerged. First, stereotype priming was insensitive to the number of primes that were presented and occurred only at a short prime-target stimulus onset asynchrony (i.e., 250 ms). Second, priming was underpinned by a bias towards congruent (vs. incongruent) prime-target responses. Collectively these findings advance understanding of the emergence and origin of stereotype priming during person and people perception.


Drift Diffusion Modeling
Data were submitted to a DDM analysis (Ratcliff et al., 2016;Voss, Nagler et al., 2013). This approach is highly sensitive as the DDM simultaneously models the latencies of correct and incorrect judgments, in combination with overall response accuracy, to estimate the latent processes associated with task performance. The model assumes that, during binary decision-making, noisy information is sequentially sampled until sufficient evidence is acquired to make a response (Ratcliff, 1978;Voss et al., 2015). The benefit of this analytic approach resides in the ability of the DDM to yield parameters that index the underlying stimulus and/or response biases that underpin task performance (Ratcliff et al., 2016;Voss, Nagler et al., 2013;C. N. White & Poldrack, 2014).
Drift rate (v) estimates the speed of information gathering (i.e., larger drift rate = faster evidence sampling uptake), thus is interpreted as a measure of the efficiency of stimulus processing during decision-making (i.e., stimulus bias). Boundary separation (a) estimates the distance between the two decision thresholds (e.g., feminine vs. masculine), hence indicates how much evidence is required before a response is made (i.e., larger [smaller] values indicate more conservative [liberal] responding). The starting point (z) defines the position between the decision thresholds at which evidence accumulation begins.
If z is not centered between the thresholds (z ≠ .50), this denotes an a priori bias in favor of the response that is closer to the starting point (i.e., response bias). In other words, less evidence is required to reach the preferred (vs. non-preferred) threshold. Finally, the duration of all non-decisional processes is given by the additional parameter t0, which is taken to indicate differences in stimulus encoding and response execution.
Previous research using a DDM analysis has traced priming effects to the operation of both stimulus and response biases (Falbén et al., 2019, Voss, Rothermund et al., 2013, although initial evidence suggests that stereotype-based priming originates in a response bias (Tsamadi et al., 2020). In the current context, this gives rise to several possibilities. If priming was driven by a stimulus bias (i.e., spreading activation), then drift rates (v) should be larger for stereotype-consistent compared to stereotype-inconsistent targets (Voss, Rothermund et al., 2013). Alternatively, if a response bias (i.e., shift in the starting point of evidence accumulation, z) underpins stereotype-based priming, less evidence should be required to generate stereotype-consistent than stereotype-inconsistent responses (Falbén et al., 2019, Tsamadi et al., 2020. Finally, stereotype-based priming may be supported by the operation of these stimulus and response biases in combination. To identify the processes underpinning stereotype-based priming, data were submitted to a hierarchical drift diffusion model (HDDM) analysis (Wiecki et al., 2013 (Lerche et al., 2017;Wiecki et al., 2013).
To explore whether task performance was underpinned by a stimulus and/or response bias, models across both experiments were response coded, such that the upper threshold corresponded to a feminine response and the lower threshold to a masculine response (Falbén et al., 2019;Tsamadi et al., 2020). In Experiment 1, five models were estimated for comparison (see Table S5 Table S5, model 1 yielded the best fit (i.e., lowest Deviance Information Criterion value, DIC). The DIC was adopted as it is routinely used for hierarchical Bayesian model comparison (Spiegelhalter et al., 1998). As diffusion models were fit hierarchically rather than individually for each participant, a single value was calculated for each model that reflected the overall fit to the data at the participant-and group-level. Lower DIC values favor models with the highest likelihood and least number of parameters. The means and the upper (97.5q) and lower (2.5q) quantiles of the best fitting model parameters are presented in Table S6. Note. a = threshold separation, v = drift rate, z = starting point, sv = inter-trial variability in drift rate, st = inter-trial variability in non-decision time, sz = inter-trial variability in starting point. Note. a = threshold separation, v = drift rate, z = starting point, sv = inter-trial variability in drift rate, st = inter-trial variability in non-decision time, sz = inter-trial variability in starting point.