Understanding Speed–Accuracy Processing Dynamics in Aphasia Using Response Time Modeling

Abstract

People with aphasia demonstrate language impairments evident in both performance accuracy and processing speed, but the direct relationship between accuracy and speed requires further consideration. This article describes two recent attempts to make quantitative progress in this domain using response time modeling: the diffusion model (Ratcliff, 1978) applied to two-choice tasks and a multinomial ex-Gaussian model applied to picture naming. The diffusion model may be used to characterize core linguistic processing efficiency and speed–accuracy tradeoffs independently, and research suggests that maladaptive speed–accuracy tradeoffs lead to performance impairments in at least some people with aphasia. The multinomial ex-Gaussian response time model of picture naming provides a simple and straightforward way to estimate the optimal response time cutoffs for individual people with aphasia (i.e., the cutoff where additional time is unlikely to lead to a correct response). While response time modeling applied to aphasia research is at an early stage of development, both the diffusion model and multinomial ex-Gaussian response time model of picture naming show promise and should be further developed in future work. This article also provides preliminary recommendations for clinicians regarding how to conceptualize, identify, and potentially address maladaptive speed–accuracy tradeoffs for people with aphasia.

Keywords

speed–accuracy tradeoff - response time modeling - aphasia - anomia

Publikationsverlauf

Artikel online veröffentlicht:
14. Juli 2021

• References

• 1 Ratcliff R. A theory of memory retrieval. Psychol Rev 1978; 85 (02) 59-108
• 2 Cavanaugh R, Haley KL. Subjective communication difficulties in very mild aphasia. Am J Speech Lang Pathol 2020; 29: 437-448
• 3 DeDe G, Salis C. Temporal and episodic analyses of the story of Cinderella in latent aphasia. Am J Speech Lang Pathol 2020; 29: 449-462
• 4 Fromm D, Forbes M, Holland A, Dalton SG, Richardson J, MacWhinney B. Discourse characteristics in aphasia beyond the Western Aphasia Battery Cutoff. Am J Speech Lang Pathol 2017; 26 (03) 762-768
• 5 Neto B, Santos ME. Language after aphasia: only a matter of speed processing?. Aphasiology 2012; 26 (11) 1352-1361
• 6 Conroy P, Sotiropoulou Drosopoulou C, Humphreys GF, Halai AD, Lambon Ralph MAJB. Time for a quick word? The striking benefits of training speed and accuracy of word retrieval in post-stroke aphasia. Brain 2018; 141 (06) 1815-1827
• 7 Wickelgren WA. Speed-accuracy tradeoff and information processing dynamics. Acta Psychol (Amst) 1977; 41 (01) 67-85
• 8 Ceccarini F, Guerra S, Peressotti A. et al. Speed-accuracy trade-off in plants. Psychon Bull Rev 2020; 27 (05) 966-973
• 9 Latty T, Beekman M. Speed-accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum. Proc Biol Sci 2011; 278 (1705): 539-545
• 10 Campanella F, Skrap M, Vallesi A. Speed-accuracy strategy regulations in prefrontal tumor patients. Neuropsychologia 2016; 82: 1-10
• 11 Starns JJ, Ratcliff R. The effects of aging on the speed-accuracy compromise: Boundary optimality in the diffusion model. Psychol Aging 2010; 25 (02) 377-390
• 12 Touron DR, Swaim ET, Hertzog C. Moderation of older adults' retrieval reluctance through task instructions and monetary incentives. J Gerontol B Psychol Sci Soc Sci 2007; 62 (03) 149-155
• 13 Wagenmakers EJ, Ratcliff R, Gomez P, McKoon G. A diffusion model account of criterion shifts in the lexical decision task. J Mem Lang 2008; 58 (01) 140-159
• 14 Kagan A, Simmons-Mackie N, Rowland A. et al. Counting what counts: a framework for capturing real-life outcomes of aphasia intervention. Aphasiology 2008; 22 (03) 258-280
• 15 Roach A, Schwartz MF, Martin N, Grewal RS, Brecher A. The Philadelphia naming test: scoring and rationale. Clin Aphasiol 1996; 24: 121-133
• 16 Evans WS, Hula WD, Starns JJ. Speed-accuracy trade-offs and adaptation deficits in aphasia: finding the “sweet spot” between overly cautious and incautious responding. Am J Speech Lang Pathol 2019; 28 (1S): 259-277
• 17 Conroy P, Scowcroft J. Decreasing cues for a dynamic list of noun and verb naming targets: a case-series aphasia therapy study. Neuropsychol Rehabil 2012; 22 (02) 295-318
• 18 Conroy P, Sage K, Lambon Ralph MA. The effects of decreasing and increasing cue therapy on improving naming speed and accuracy for verbs and nouns in aphasia. Aphasiology 2009; 23 (06) 707-730
• 19 Abel S, Schultz A, Radermacher I, Willmes K, Huber W. Decreasing and increasing cues in naming therapy for aphasia. Aphasiology 2005; 19 (09) 831-848
• 20 Minkina I, Rosenberg S, Kalinyak-Fliszar M, Martin N. Short-term memory and aphasia: from theory to treatment. Semin Speech Lang 2017; 38 (01) 17-28
• 21 Martin N, Minkina I, Kohen FP, Kalinyak-Fliszar M. Assessment of linguistic and verbal short-term memory components of language abilities in aphasia. J Neurolinguist 2018; 48: 199-225
• 22 Martin N, Dell GS. Maintenance versus transmission deficits: the effect of delay on naming performance in aphasia. Front Hum Neurosci 2019; 13 (406) 406
• 23 Schnur TT, Schwartz MF, Brecher A, Hodgson C. Semantic interference during blocked-cyclic naming: evidence from aphasia. J Mem Lang 2006; 54 (02) 199-227
• 24 Brogan E, Ciccone N, Godecke E. An exploration of aphasia therapy dosage in the first six months of stroke recovery. Neuropsychol Rehabil 2020; ; (epub ahead of print)
• 25 Evans WS, Hula WD, Quique Y, Starns JJ. How much time do people with aphasia need to respond during picture naming? Estimating optimal response time cutoffs using a multinomial ex-gaussian approach. J Speech Lang Hear Res 2020; 63 (02) 599-614
• 26 Ratcliff R, McKoon G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput 2008; 20 (04) 873-922
• 27 Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cognit Psychol 2008; 57 (03) 153-178
• 28 Smith PL, Van Zandt T. Time-dependent Poisson counter models of response latency in simple judgment. Br J Math Stat Psychol 2000; 53 (Pt 2): 293-315
• 29 Gomez P, Ratcliff R, Perea M. A model of the go/no-go task. J Exp Psychol Gen 2007; 136 (03) 389-413
• 30 White CN, Ratcliff R, Starns JJ. Diffusion models of the flanker task: discrete versus gradual attentional selection. Cognit Psychol 2011; 63 (04) 210-238
• 31 Paulon G, Llanos F, Chandrasekaran B, Sarkar A. Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning in adults. J Am Stat Assoc 2020; ; (epub ahead of print)
• 32 Voss A, Voss J, Lerche V. Assessing cognitive processes with diffusion model analyses: a tutorial based on fast-dm-30. Front Psychol 2015; 6 (336) 336
• 33 Wabersich D, Vandekerckhove J. The RWiener Package: an R Package providing distribution functions for the Wiener Diffusion Model. R J 2014; 6 (01) 49-56
• 34 Ratcliff R, Thapar A, Gomez P, McKoon G. A diffusion model analysis of the effects of aging in the lexical-decision task. Psychol Aging 2004; 19 (02) 278-289
• 35 Ratcliff R, Gomez P, McKoon G. A diffusion model account of the lexical decision task. Psychol Rev 2004; 111 (01) 159-182
• 36 Pirrone A, Dickinson A, Gomez R, Stafford T, Milne E. Understanding perceptual judgment in autism spectrum disorder using the drift diffusion model. Neuropsychology 2017; 31 (02) 173-180
• 37 White CN, Ratcliff R, Vasey MW, McKoon G. Using diffusion models to understand clinical disorders. J Math Psychol 2010; 54 (01) 39-52
• 38 White CN, Ratcliff R, Vasey MW, McKoon G. Anxiety enhances threat processing without competition among multiple inputs: a diffusion model analysis. Emotion 2010; 10 (05) 662-677
• 39 Huang-Pollock C, Ratcliff R, McKoon G. et al. A diffusion model analysis of sustained attention in children with attention deficit hyperactivity disorder. Neuropsychology 2020; 34 (06) 641-653
• 40 Todorova L, Neville DA, Piai V. Lexical-semantic and executive deficits revealed by computational modelling: a drift diffusion model perspective. Neuropsychologia 2020; 146: 107560
• 41 Anders R, Riès S, Van Maanen L, Alario FX. Lesions to the left lateral prefrontal cortex impair decision threshold adjustment for lexical selection. Cogn Neuropsychol 2017; 34 (1-2): 1-20
• 42 Ratcliff R, Perea M, Colangelo A, Buchanan L. A diffusion model account of normal and impaired readers. Brain Cogn 2004; 55 (02) 374-382
• 43 Starns JJ, Ratcliff R. Age-related differences in diffusion model boundary optimality with both trial-limited and time-limited tasks. Psychon Bull Rev 2012; 19 (01) 139-145
• 44 Salthouse TA. Theoretical Perspectives on Cognitive Aging. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc; 1991
• 45 Bogacz R, Brown E, Moehlis J, Holmes P, Cohen JD. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol Rev 2006; 113 (04) 700-765
• 46 Fillingham JK, Sage K, Lambon Ralph MA. The treatment of anomia using errorless learning. Neuropsychol Rehabil 2006; 16 (02) 129-154
• 47 Fillingham J, Sage K, Lambon Ralph M. Further explorations and an overview of errorless and errorful therapy for aphasic word-finding difficulties: the number of naming attempts during therapy affects outcome. Aphasiology 2005; 19 (07) 597-614
• 48 Fillingham JK, Sage K, Ralph MA. Treatment of anomia using errorless versus errorful learning: are frontal executive skills and feedback important?. Int J Lang Commun Disord 2005; 40 (04) 505-523
• 49 Fillingham JK, Hodgson C, Sage K, Lambon Ralph MA. The application of errorless learning to aphasic disorders: a review of theory and practice. Neuropsychol Rehabil 2003; 13 (03) 337-363
• 50 Ratcliff R, Tuerlinckx F. Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychon Bull Rev 2002; 9 (03) 438-481
• 51 Foygel D, Dell GS. Models of impaired lexical access in speech production. J Mem Lang 2000; 43 (02) 182-216
• 52 Fergadiotis G, Kellough S, Hula WD. Item response theory modeling of the Philadelphia Naming Test. J Speech Lang Hear Res 2015; 58 (03) 865-877
• 53 Walker GM, Hickok G, Fridriksson J. A cognitive psychometric model for assessment of picture naming abilities in aphasia. Psychol Assess 2018; 30 (06) 809-826
• 54 Heck DW, Erdfelder E. Extending multinomial processing tree models to measure the relative speed of cognitive processes. Psychon Bull Rev 2016; 23 (05) 1440-1465
• 55 Starns JJ. Adding a speed-accuracy trade-off to discrete-state models: a comment on Heck and Erdfelder (2016). Psychon Bull Rev 2018; 25 (06) 2406-2416
• 56 Van Zandt T. How to fit a response time distribution. Psychon Bull Rev 2000; 7 (03) 424-465
• 57 Swinburn K, Porter G, Howard D. Comprehensive Aphasia Test. Psychology Press; 2004
• 58 Ratcliff R, Thapar A, McKoon G. Individual differences, aging, and IQ in two-choice tasks. Cognit Psychol 2010; 60 (03) 127-157
• 59 Evans WS, Cavanaugh R, Quique Y. et al. BEARS- Balancing Effort, Accuracy, and Response Speed in semantic feature verification anomia treatment. Paper Presented at the Academy of Aphasia Conference 2020. Online conference.
• 60 Hambridge T. “Yikes! I had the yips for aphasia.” Stroke Survivor Strategies Web site. Published 2017. Updated May 22, 2017. Accessed 2020 at: http://stroke-survivor-strategies.blogspot.com/2017/05/yikes-i-had-yips-for-aphasia-video.html
• 61 Cahana-Amitay D, Albert ML, Pyun SB. et al. Language as a stressor in aphasia. Aphasiology 2011; 25 (02) 593-614