Item-specific processing reduces false recognition in older and younger adults: Separating encoding and retrieval using signal detection and the diffusion model

Huff, Mark J.; Aschenbrenner, Andrew J.

doi:10.3758/s13421-018-0837-1

“…Initial recall testing has been found to encourage organizational/relational processing that mitigates the effects of distinctive item-specific processing on a subsequent recognition test ( Burns, 1993 ; Zaromb and Roediger, 2010 ). Our findings are more consistent with those of Huff and Aschenbrenner (2018) , who found a false recognition reduction for categorically related lures, indicating that distinctive encoding tasks can be effective with other types of related lures.…”

Section: Discussionsupporting

confidence: 90%

“… Huff and Aschenbrenner (2018) studied how distinctive item-specific encoding instructions influenced correct and false recognition for categorized word lists rather than DRM lists. Their recognition task included categorically related critical lures.…”

Section: Introductionmentioning

confidence: 99%

Reducing False Recognition in the Deese-Roediger/McDermott Paradigm: Related Lures Reveal How Distinctive Encoding Improves Encoding and Monitoring Processes

Huff

¹

,

Bodner

²

,

Gretz

³

2020

Self Cite

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In the Deese-Roediger/McDermott (DRM) paradigm, distinctive encoding of list items typically reduces false recognition of critical lures relative to a read-only control. This reduction can be due to enhanced item-specific processing, reduced relational processing, and/or increased test-based monitoring. However, it is unclear whether distinctive encoding reduces false recognition in a selective or global manner. To examine this question, participants studied DRM lists using a distinctive item-specific anagram generation task and then completed a recognition test which included both DRM critical lures and either strongly related lures (Experiment 1) or weakly related lures (Experiment 2). Compared to a read-control group, the generate groups showed increased correct recognition and decreased false recognition of all lure types. We then estimated the separate contributions of encoding and retrieval processes using signal-detection indices. Generation improved correct recognition by both increasing encoding of memory information for list words and by increasing memory monitoring at test. Generation reduced false recognition by reducing the encoding of memory information and by increasing memory monitoring at test. The reduction in false recognition was equivalent for critical lures and related lures, indicating that generation globally reduces the encoding of related non-presented items at study (not just critical lures), while globally increasing list-theme-based monitoring at test.

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“…Finally, the non-decision time t 0 is a composite of the time needed for all non-decisional processes such as sensory processing, encoding, and response execution. Because the diffusion model provides a process-based account of decision making that allows the measurement and mathematical separation of different processes involved in decision making, it has become increasingly popular in individual differences and aging research (e.g., Dirk et al 2017;Dully et al 2018;Frischkorn and Schubert 2018;Huff and Aschenbrenner 2018;Nunez et al 2015;Ratcliff et al 2003;Schubert et al 2015Schubert et al , 2016Schubert et al , 2019Schmiedek et al 2007;Schmitz and Wilhelm 2016;Schmitz et al 2018;Spaniol et al 2008;Yap et al 2012;Ratcliff et al 2004;Ratcliff et al 2010;Ratcliff et al 2011). The drift rate parameter in particular has been consistently associated with intelligence (Ratcliff et al 2010(Ratcliff et al , 2011Schmiedek et al 2007;Schmitz and Wilhelm 2016;Schmitz et al 2018;Schubert et al 2015;Schubert et al 2019), suggesting that smarter individuals benefit from a higher rate of evidence accumulation.…”

Section: Diffusion Modelingmentioning

confidence: 99%

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Schubert

¹

,

Hagemann

²

,

Löffler

³

et al. 2019

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Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined.

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“…Finally, the non-decision time t 0 is a composite of the time needed for all non-decisional processes such as sensory processing, encoding, and response execution. Because the diffusion model provides a process-based account of decision making that allows the measurement and mathematical separation of different processes involved in decision making, it has become increasingly popular in individual differences and aging research [e.g., [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. The drift rate parameter in particular has been consistently associated with intelligence [38][39][40][42][43][44][45], suggesting that smarter individuals benefit from a higher rate of evidence accumulation.…”

Section: Diffusion Modelingmentioning

confidence: 99%

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Schubert¹,

Hagemann²,

Löffler³

et al. 2019

Preprint

View full text Add to dashboard Cite

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined. Data and analysis code are available at https://osf.io/hy5fw/.

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Item-specific processing reduces false recognition in older and younger adults: Separating encoding and retrieval using signal detection and the diffusion model

Cited by 17 publications

References 85 publications

Reducing False Recognition in the Deese-Roediger/McDermott Paradigm: Related Lures Reveal How Distinctive Encoding Improves Encoding and Monitoring Processes

Reducing False Recognition in the Deese-Roediger/McDermott Paradigm: Related Lures Reveal How Distinctive Encoding Improves Encoding and Monitoring Processes

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

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