The strength-based mirror effect in subjective strength ratings: The evidence for differentiation can be produced without differentiation

Abstract

Criss (Cognitive Psychology 59:297-319, 2009) reported that subjective ratings of memory strength showed a mirror effect pattern in which strengthening the studied words increased ratings for targets and decreased ratings for lures. She interpreted the effect on lure items as evidence for differentiation, a process whereby lures produce a poorer match to strong than to weak memory traces. However, she also noted that participants might use different mappings between memory evidence and levels of the rating scale when they expected strong versus weak targets; that is, the effect might be produced by decision processes rather than differentiation. We report two experiments designed to distinguish these accounts. Some participants studied pure lists of weak or strong items (presented once or five times, respectively), while others studied mixed lists of half weak and half strong items. The participants from both groups had pure-strength tests: Only strong or only weak items were tested, and the participants were informed of which it would be before the test. The results showed that strength ratings for lures were lower when strong versus weak targets were tested, regardless of whether the study list was pure or mixed. In the mixed-study condition, the effect was produced even after identical study lists, and thus the same degree of differentiation in the studied traces. Therefore, our results suggest that the strength-rating mirror effect is produced by changes in decision processes.

Neural activity reveals interactions between episodic and semantic memory systems during retrieval.

“…Experimentally manipulating strength of encoding (e.g., by increasing study time; Ratcliff, Clark, & Shiffrin, 1990; Stretch & Wixted, 1998) not only increases hit rates, but also decreases the likelihood of false alarms to lures (i.e., a strength-based mirror effect; Glanzer & Adams, 1985). Whereas some accounts for this effect attribute it to response biases (Starns, White, & Ratcliff, 2012; Stretch & Wixted, 1998), it has been successfully modeled by assuming that the match signal aggregates evidence across the full set of targets, leading to a poorer match for lures when they are compared to more strongly encoded targets (Criss, 2010; Criss & McClelland, 2006; Kiliç, Criss, Malmberg, & Shiffrin, 2017; Shiffrin, Ratcliff, & Clark, 1990; Shiffrin & Steyvers, 1997). Our results provide converging evidence that the structure of encoded material can have profound effects that are specific to processes involved in probing memory (i.e., retrieval, rather than encoding, processes).…”

Validating the unequal-variance assumption in recognition memory using response time distributions instead of ROC functions: A diffusion model analysis

“…Researchers have also attempted to test the unequal-variance assumption by directly calculating the variability of memory-strength ratings on scales that have many levels (e.g., 20 or 100 different rating options; Mickes, Wixted, & Wais, 2007; also see Criss, 2009, and Starns, White, & Ratcliff, 2012). As expected, studies of this sort show that ratings are more variable for targets than for lures.…”

Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

“…Typically, studies examined the effects of a particular manipulation on a subset of parameters only, and typically the manipulation consisted of two levels (e.g., fast vs. accurate SAT setting or easy vs. difficult condition) rather than the more detailed five levels used in the present study. Nevertheless, even with these caveats, previous studies found a number of deviations from the selective influence assumption such as speed emphasis leading to decreased non-decision times [40][41][42][43] , decreased drift rate variability 44 , and decreased drift rates 42,[44][45][46][47][48] . Moreover, easier stimuli were found to decrease the non-decision time, increase the starting point of the accumulation, and increase the decision boundary 41 .…”