Thin slices ratings (i.e., ratings based on first impressions) have yielded intriguingly accurate results in various domains. Among other, researcher have applied the thin slices technique to assess instructional quality, showing that teacher-student interactions can be reliably inferred by just very short snippets of classroom instruction. The accuracy of thin slices ratings is often explained by dual process theories of social cognition, whereby System 1 refers to an intuitive and fast way of processing, while System 2 denotes a more reflective and analytical way of processing. System 1 is considered the cognitive foundation of thin slices ratings. The central aim of the present study was to understand the underlying cognitive processes shaping the impression formation of thin slices raters of teaching quality. Therefore, an unconventional and innovative research design was required to gain insights into the cognitive "black box" of thin slices raters by examining their verbal data. In an exploratory mixed method research design, we set up Cognitive Laboratories with two different rating situations. In a thin slices rating situation, participants rated instructional quality based on short classroom videos (30 seconds). Participants in a long-video rating situation rated instructional quality based on longer classroom videos (10 minutes). We collected, coded and statistically analyzed participants' verbal reports regarding their rating processes. The findings suggest that thin slices ratings evolve primarily based on typical processes of System 1 and not on those of System 2. For instance, thin slices ratings are associative and tend to be rather negative than positive. Moreover, an initially formed impression tends to remain stable and is resistant to alteration. Ratings of instructional quality based on longer videos rely on both cognitive systems, with System 2 possibly modifying an initial judgment. Thus, our study does not only explain the cognitive processes underlying the thin slices ratings, but additionally provides valuable insights into the processes occurring in conventional rating settings.