Top

Gepubliceerd in:

14-03-2024 | Research

Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards

Auteurs: Daniel H. Weissman, James R. Schmidt

Gepubliceerd in: Psychological Research | Uitgave 4/2024

Abstract

Contingency learning can involve learning that the identity of one stimulus in a sequence predicts the identity of the next stimulus. It remains unclear, however, whether such learning speeds responses to the next stimulus only by reducing the threshold for triggering the expected response after stimulus onset or also by preparing the expected response before stimulus onset. To distinguish between these competing accounts, we manipulated the probabilities with which each of two prime arrows (Left and Right) were followed by each of two probe arrows (Up and Down) in a prime-probe task while using force-sensitive keyboards to monitor sub-threshold finger force. Consistent with the response preparation account, two experiments revealed greater force just before probe onset on the response key corresponding to the direction in which the probe was more (versus less) likely to point (e.g., Up vs. Down). Furthermore, mirroring sequential contingency effects in behavior, this pre-probe force effect vanished after a single low-probability trial. These findings favor the response preparation account over the threshold only account. They also suggest the possibility that contingency learning in our tasks indexes trial-by-trial expectations regarding the utility of the prime for predicting the upcoming probe.

vorige artikel The role of motor inhibition in implicit negation processing: two Go/No-Go behavioral studies

volgende artikel Instructed speed and accuracy affect binding

In these studies, contingency learning effects appear when the prime and probe appear simultaneously. Such effects, however, are stronger when the prime appears before the probe (Schmidt & De Houwer, 2016b).

The finding may alternatively be explained by the fact that the relative frequencies with which high- and medium-contingency trials appeared (50% vs. 25%) differed more than the relative frequencies with which medium- and low-frequency trials appeared (25% vs. 17%).

We mistakenly reported this partial-eta-squared value as 0.41 in our pre-registration.

To be consistent with our prior studies of the prime-probe task, we used the same key-hand mapping for all participants, rather than mapping the left and right arrow-direction keys to the left hand and the up and down arrow-direction keys to the right hand in half the participants and using the opposite key-hand mapping in the other half. Using a constant mapping does not lead to a design confound because we use the same mapping in all conditions.

We identified outliers using both correct and error RTs in the analysis of the mean ER data, not only correct RTs as in the analysis of the mean RT data. Thus, these analyses produced slightly different percentages of outliers.

In our pre-registration, we stated that we would analyze force in trials with correct responses to both the prime and the probe. An anonymous reviewer pointed out a valid concern with this approach, however. Excluding trials with an incorrect response to the probe could bias the results to show greater pre-probe force on the high-contingency response key than on the low-contingency response key. Indeed, randomly increasing pre-probe force on the high-contingency response key (e.g., due to guessing) could facilitate a correct probe response such that the trial is included in the analysis. In contrast, randomly increasing pre-probe force on the low-contingency response key could facilitate an incorrect probe response such that the trial is not included. To prevent such a bias, we analyzed pre-probe force regardless of whether the subsequent probe response was correct. We note that this change to our pre-registered analysis plan did not change any of the inferences that we describe below.

The authors thank Alexander Weigard at the University of Michigan for bringing this possibility to their attention.

Arunkumar, M., Rothermund, K., Kunde, W., & Giesen, C. G. (2022). Being in the know: The role of awareness and retrieval of transient stimulus-response bindings in selective contingency learning. Journal of Cognition, 5(1), 36. https://doi.org/10.5334/joc.227CrossRefPubMedPubMedCentral

Bruhn, P. (2013). Emergence of spontaneous anticipatory hand movements in a probabilistic environment. Advances in Cognitive Psychology, 9(2), 62–73. https://doi.org/10.2478/v10053-008-0132-yCrossRefPubMedPubMedCentral

Bruhn, P., Huette, S., & Spivey, M. (2014). Degree of certainty modulates anticipatory processes in real time. Journal of Experimental Psychology. Human Perception and Performance, 40(2), 525–538. https://doi.org/10.1037/a0034365CrossRefPubMed

Bugg, J. M. (2014). Conflict-triggered top-down control: Default mode, last resort, or no such thing? Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(2), 567–587. https://doi.org/10.1037/a0035032CrossRefPubMed

Corbett, E. A., Martinez-Rodriguez, L. A., Judd, C., O’Connell, R. G., & Kelly, S. P. (2023). Multiphasic value biases in fast-paced decisions. eLife, 12, e67711. https://doi.org/10.7554/eLife.67711CrossRefPubMedPubMedCentral

Cox, J. A., & Aimola Davies, A. M. (2022). Age differences in visual statistical learning: Investigating the effects of selective attention and stimulus category. Psychology and Aging, 37(6), 698–714. https://doi.org/10.1037/pag0000697CrossRefPubMed

de Lange, F. P., Rahnev, D. A., Donner, T. H., & Lau, H. (2013). Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. The Journal of Neuroscience, 33(4), 1400–1410. https://doi.org/10.1523/JNEUROSCI.1094-12.2013CrossRefPubMedPubMedCentral

Dunovan, K. E., Tremel, J. J., & Wheeler, M. E. (2014). Prior probability and feature predictability interactively bias perceptual decisions. Neuropsychologia, 61, 210–221. https://doi.org/10.1016/j.neuropsychologia.2014.06.024CrossRefPubMedPubMedCentral

Dunovan, K., & Wheeler, M. E. (2018). Computational and neural signatures of pre and post-sensory expectation bias in inferior temporal cortex. Scientific Reports, 8(1), 13256. https://doi.org/10.1038/s41598-018-31678-xCrossRefPubMedPubMedCentral

Eimer, M., Goschke, T., Schlaghecken, F., & Stürmer, B. (1996). Explicit and implicit learning of event sequences: Evidence from event-related brain potentials. Journal of Experimental Psychology. Learning, Memory, and Cognition, 22(4), 970–987. https://doi.org/10.1037//0278-7393.22.4.970CrossRefPubMed

Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.CrossRefPubMed

Fiser, J., & Aslin, R. N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology. Learning, Memory, and Cognition, 28(3), 458–467. https://doi.org/10.1037//0278-7393.28.3.458CrossRefPubMed

Forrin, N. D., & MacLeod, C. M. (2018). Contingency proportion systematically influences contingency learning. Attention, Perception and Psychophysics, 80(1), 155–165. https://doi.org/10.3758/s13414-017-1424-4CrossRefPubMed

Giesen, C. G., Schmidt, J. R., & Rothermund, K. (2020). The law of recency: An episodic stimulus-response retrieval account of habit acquisition. Frontiers in Psychology, 10, 2927. https://doi.org/10.3389/fpsyg.2019.02927CrossRefPubMedPubMedCentral

Godijn, R., & Theeuwes, J. (2003). Parallel allocation of attention prior to the execution of saccade sequences. Journal of Experimental Psychology. Human Perception and Performance, 29(5), 882–896. https://doi.org/10.1037/0096-1523.29.5.882CrossRefPubMed

Gratton, G., Coles, M. G. H., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation and responses. Journal of Experimental Psychology. General, 4, 480–506.CrossRef

Huang, C., Donk, M., & Theeuwes, J. (2022). Proactive enhancement and suppression elicited by statistical regularities in visual search. Journal of Experimental Psychology. Human Perception and Performance, 48(5), 443–457. https://doi.org/10.1037/xhp0001002CrossRefPubMed

JASP Team. (2023). JASP (Version 0.18.0.0) [Computer software]. https://jasp-stats.org/.

Kazak, A. E. (2018). Editorial: Journal article reporting standards. American Psychologist, 73(1), 1–2. https://doi.org/10.1037/amp0000263CrossRefPubMed

Kelly, S. P., Corbett, E. A., & O’Connell, R. G. (2021). Neurocomputational mechanisms of prior-informed perceptual decision-making in humans. Nature Human Behaviour, 5(4), 467–481. https://doi.org/10.1038/s41562-020-00967-9CrossRefPubMed

Ko, Y. T., Alsford, T., & Miller, J. (2012). Inhibitory effects on response force in the stop-signal paradigm. Journal of Experimental Psychology. Human Perception and Performance, 38(2), 465–477. https://doi.org/10.1037/a0027034CrossRefPubMed

Lin, O.Y.-H., & MacLeod, C. M. (2018). The acquisition of simple associations as observed in color-word contingency learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 44(1), 99–106. https://doi.org/10.1037/xlm0000436CrossRefPubMed

Logan, G. D., & Zbrodoff, J. N. (1979). When it helps to be misled: Facilitative effects of increasing the frequency of conflicting stimuli in a Stroop-like task. Memory and Cognition, 7(3), 166–174.CrossRef

Luo, J., Yang, M., & Wang, L. (2022). Learned irrelevant stimulus-response associations and proportion congruency effect: A diffusion model account. Journal of Experimental Psychology. Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0001158CrossRefPubMed

MacLeod, C. M. (2019). Learning simple associations. Canadian Psychology, 60(1), 3.CrossRef

Mattes, S., Leuthold, H., & Ulrich, R. (2002). Stimulus-response compatibility in intensity-force relations. The Quarterly Journal of Experimental Psychology. a, Human Experimental Psychology, 55(4), 1175–1191. https://doi.org/10.1080/02724980244000152CrossRefPubMed

Miller, J., & Alderton, M. (2006). Backward response-level crosstalk in the psychological refractory period paradigm. Journal of Experimental Psychology. Human Perception and Performance, 32(1), 149–165. https://doi.org/10.1037/0096-1523.32.1.149CrossRefPubMed

Miller, J., & Rouast, N. M. (2016). Dissociations of spatial congruence effects across response measures: An examination of delta plots. Psychological Research Psychologische Forschung, 80(5), 805–820. https://doi.org/10.1007/s00426-015-0694-5CrossRefPubMed

Moeller, B., & Frings, C. (2019). From simple to complex actions: Response-response bindings as a new approach to action sequences. Journal of Experimental Psychology: General, 148(1), 174–183. https://doi.org/10.1037/xge0000483CrossRefPubMed

Mulder, M. J., Boekel, W., Ratcliff, R., & Forstmann, B. U. (2014). Cortico-subthalamic connection predicts individual differences in value-driven choice bias. Brain Structure and Function, 219(4), 1239–1249. https://doi.org/10.1007/s00429-013-0561-3CrossRefPubMed

Mulder, M. J., Wagenmakers, E.-J., Ratcliff, R., Boekel, W., & Forstmann, B. U. (2012). Bias in the brain: A diffusion model analysis of prior probability and potential payoff. The Journal of Neuroscience, 32(7), 2335–2343. https://doi.org/10.1523/JNEUROSCI.4156-11.2012CrossRefPubMedPubMedCentral

Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Hochenberger, R., Sogo, H., Kastman, E., & Lindelov, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195–203. https://doi.org/10.3758/s13428-018-01193-yCrossRefPubMedPubMedCentral

Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420CrossRefPubMedPubMedCentral

Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111(2), 333–367. https://doi.org/10.1037/0033-295X.111.2.333CrossRefPubMedPubMedCentral

Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6(6), 855–863. https://doi.org/10.1016/S0022-5371(67)80149-XCrossRef

Rousseeuw, P. J., & Crouz, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical Association, 88(424), 1273–1283.CrossRef

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science (new York, N.y.), 274(5294), 1926–1928. https://doi.org/10.1126/science.274.5294.1926CrossRefPubMed

Schmidt, J. R. (2013). Questioning conflict adaptation: Proportion congruent and Gratton effects reconsidered. Psychonomic Bulletin and Review. https://doi.org/10.3758/s13423-012-0373-0CrossRefPubMed

Schmidt, J. R. (2018). Best not to bet on the horserace: A comment on Forrin and MacLeod (2017) and a relevant stimulus-response compatibility view of colour-word contingency learning asymmetries. Memory and Cognition, 46, 326–335.CrossRefPubMed

Schmidt, J. R. (2019). Evidence against conflict monitoring and adaptation: An updated review. Psychonomic Bulletin and Review, 26(3), 753–771. https://doi.org/10.3758/s13423-018-1520-zCrossRefPubMed

Schmidt, J. R., & Besner, D. (2008). The Stroop effect: Why proportion congruent has nothing to do with congruency and everything to do with contingency. Journal of Experimental Psychology. Learning, Memory, and Cognition, 34(3), 514–523. https://doi.org/10.1037/0278-7393.34.3.514CrossRefPubMed

Schmidt, J. R., Crump, M. J. C., Cheesman, J., & Besner, D. (2007). Contingency learning without awareness: Evidence for implicit control. Consciousness and Cognition, 16(2), 421–435. https://doi.org/10.1016/j.concog.2006.06.010CrossRefPubMed

Schmidt, J. R., & De Houwer, J. (2016a). Contingency learning tracks with stimulus-response proportion. Experimental Psychology, 63(2), 79–88. https://doi.org/10.1027/1618-3169/a000313CrossRefPubMed

Schmidt, J. R., & De Houwer, J. (2016b). Time course of colour-word contingency learning: Practice curves, pre-exposure benefits, unlearning, and relearning. Learning and Motivation, 56, 15–30. https://doi.org/10.1016/j.lmot.2016.09.002CrossRef

Schmidt, J. R., & De Houwer, J. (2019). Cue competition and incidental learning: No blocking or overshadowing in the colour-word contingency learning procedure without instructions to learn. Collabra: Psychology. https://doi.org/10.1525/collabra.236CrossRef

Schmidt, J. R., De Houwer, J., & Rothermund, K. (2016). The parallel episodic processing (PEP) model 2.0: A single computational model of stimulus-response binding, contingency learning, power curves, and mixing costs. Cognitive Psychology, 91, 82–108. https://doi.org/10.1016/j.cogpsych.2016.10.004CrossRefPubMed

Schmidt, J. R., Giesen, C. G., & Rothermund, K. (2020). Contingency learning as binding? Testing an exemplar view of the colour-word contingency learning effect. Quarterly Journal of Experimental Psychology (2006), 73(5), 739–761. https://doi.org/10.1177/1747021820906397CrossRefPubMed

Schmidt, J. R., Houwer, J. D., & Besner, D. (2010). Contingency learning and unlearning in the blink of an eye: A resource dependent process. Consciousness and Cognition, 19(1), 235–250. https://doi.org/10.1016/j.concog.2009.12.016CrossRefPubMed

Spinelli, G., & Lupker, S. J. (2023). Robust evidence for proactive conflict adaptation in the proportion-congruent paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 49(5), 675–700. https://doi.org/10.1037/xlm0001144CrossRefPubMed

Urai, A. E., & Donner, T. H. (2022). Persistent activity in human parietal cortex mediates perceptual choice repetition bias. Nature Communications, 13(1), 6015. https://doi.org/10.1038/s41467-022-33237-5CrossRefPubMedPubMedCentral

Weissman, D. H. (2019). Let your fingers do the walking: Finger force distinguishes competing accounts of the congruency sequence effect. Psychonomic Bulletin & Review, 26(5), 1619–1626.CrossRef

Weissman, D. H., Egner, T., Hawks, Z., & Link, J. (2015). The congruency sequence effect emerges when the distracter precedes the target. Acta Psychologica, 156, 8–21. https://doi.org/10.1016/j.actpsy.2015.01.003CrossRefPubMed

White, C. N., & Poldrack, R. A. (2014). Decomposing bias in different types of simple decisions. Journal of Experimental Psychology. Learning, Memory, and Cognition, 40(2), 385–398. https://doi.org/10.1037/a0034851CrossRefPubMed

Titel: Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards
Auteurs: Daniel H. Weissman
James R. Schmidt
Publicatiedatum: 14-03-2024
Uitgeverij: Springer Berlin Heidelberg
Gepubliceerd in: Psychological Research / Uitgave 4/2024
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI: https://doi.org/10.1007/s00426-024-01940-1

Andere artikelen Uitgave 4/2024

Conflicts between priming and episodic retrieval: a question of fluency?

Open Access
Research

Social excluder’s face reduces gaze-triggered attention orienting

Research

Uncertainty salience reduces the accessibility of episodic future thoughts

Research

Instructed speed and accuracy affect binding

Open Access
Research

The illusory certainty: Information repetition and impressions of truth enhance subjective confidence in validity judgments independently of the factual truth

Open Access
Research

Emotional cues reduce Pavlovian interference in feedback-based go and nogo learning

Open Access
Research