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Mental Chronometry Mental chronometry Zhuanghua Shi (Strongway) 1 2 https://www.youtube.com/watch?v=fwb4aNkcofI Airport Check 3 Zhuanghua Shi, LMU, Munich 4 Zhuanghua Shi, LMU, Munich Bistable image 5 Mamassian & Goutcher, 2005, JOV Naming the color of the following words Red Green Color GSN Yellow Blue Green 6 Zhuanghua Shi, LMU, Munich Mental processes and Reaction time ◻ Mental processes requires some time ◻ Speed of process correlates with cognitive and motoric functions ◻ We can infer inner mental processes and mechanisms by investigating response times (RT) Mental Chronometry Stimuli Mental Responses processes 7 Zhuanghua Shi, LMU, Munich Mental Chronometry Mental chronometry is the use of response time to infer mental processes. The way for this is the manipulation of the tasks and/or of variables determining the behavior of participants in the tasks. Mental chronometry is one of the core paradigms of experimental and cognitive psychology. 8 Mental processes and Reaction time ■ It is generally assumed that mental processes is constructed with multiple modules. Motor Perception Cognition response ■ By observing different reaction times under different conditions, processing time could be observed. 9 Zhuanghua Shi, LMU, Munich Mental processes and reaction time ■ Assumptions and paradigm ◻ the temporal sequencing of information processing in the human brain. ◻ Manipulation of the tasks/stimuli → observe the time course of mental operation Task 1: Detection Response simple detection Task 2: discrimination task Detection Discrimination Response 10 Zhuanghua Shi, LMU, Munich Main goals ■ To determine components and structure of mental processes (i.e., cognitive modules) Reaction time STIMULUS RESPONSE ◻ Number of subcomponents ◻ Processing time ◻ Serial, parallel or cascade 11 Zhuanghua Shi, LMU, Munich History ■ 1822 Francis Galton ◻ People who reacted faster are more intelligent than others ■ 1850 Hermann von Helmholtz ◻ Simple reaction time ◻ Neural transmission time ~ 30 m /s 12 Zhuanghua Shi, LMU, Munich History ■ 1868 Franciscus Donders: Subtraction method ◻ Duration of subcomponent can be measured by subtracting two tasks which only differ that component ■ 1885 J. Merkel discovered the response time is longer when a stimulus belongs to a larger set of stimuli ■ 1951 Hick further developed Hick’s law ■ 1964 E. Roth demonstrated correlation between IQ and RT 13 Zhuanghua Shi, LMU, Munich History ■ 1969 Robert Sternberg devised a memory-scanning task, and developed the additive factor method for dividing RTs in successive stages ■ 1979 ~ Modern methods ◻ Cascade model ◻ Diffusion model 14 Zhuanghua Shi, LMU, Munich Donders’ subtraction method ■ Donders (1868) The idea occurred to me to interpose into the process of the physiological time some new components of mental action. If I investigated how much this would lengthen the physiological time, this would, I judged, reveal the time required for the interposed term. (Donders, 1969, p418) ◻Assumption of ‘pure insertion’: ■ Task A has all the stages of RT Task B lacks an extra process, then A ■ Extra process can be measured by: RTA-RTB RTB 15 Zhuanghua Shi, LMU, Munich An example ■ Comparison of different tasks RT(A) ◻ Simple Detection SD MR RT(B) ◻ Choice Reaction SD DIS RS MR SD DIS MR RT(C) ◻ Go/No-Go Response selection = RT(B) – RT(C) • SD: Stimulus detection; • DIS: stimulus discrimination • RS: response selection; Stimulus discrimination = RT(B) – RT(C) • MR: motor response 16 Zhuanghua Shi, LMU, Munich Example: letter matching (Posner) ■ Several tasks associated with recognition of a pair of letters ◻ Physical match task ■ e.g. AA →same, AK→different ◻ Name match task ■ e.g. Aa → Same, Ak → different ◻ Rule match task (vowel/Consonant) ■ e.g. AE → same, AB → different ■ Using the subtraction method the cognitive processes associated with each of these tasks can be approximate determined. 17 Zhuanghua Shi, LMU, Munich Posner’s letter matching studies Physical match AA, ee 549 ms Name match Aa, Ee 623 ms Rule match AE, CD 801 ms ■ Name match: 64 ms (623 -549 ms) ■ Rule match: 178 ms (801 - 623 ms) 18 Zhuanghua Shi, LMU, Munich Problems with the method of subtraction ■ Any potential problems? ■ Transitivity problem ■ Do individually isolated durations sum up to the duration of the conditions in which they all take place? C1 C2 + P1 P2 C1 C2 P1 P2 19 Zhuanghua Shi, LMU, Munich Problems with the method of subtraction ■ Pure insertion ◻ Assumption: Insertion/removal of processing stages does not influence other processing stages (i.e. they are independent) ◻ Sub-modules should be independent and serial ■ Külpe (1893) – criticism: ◻ insertion of a new process → changes of the whole task 20 Zhuanghua Shi, LMU, Munich Sternberg’s additive-factor method ■ If two factors affect two different stages, then their effects on the overall RTs should be additive ones. F G H 21 Zhuanghua Shi, LMU, Munich Sternberg’s additive-factor method ■ If one modulates F, changing its latency from RTa1 to RTa2 and one modulates G, changing its latency from RTb1 to RTb2, then the two changes can be described by: ■ Applying both manipulations: 22 Zhuanghua Shi, LMU, Munich Sternberg’s additive-factor method ■ When two factors show an interaction effect on the RT, two factors affect at least one common processing stage F G H RT G1 G1 G1 G2 G2 G2 H1 H2 H1 H2 H1 H2 23 Zhuanghua Shi, LMU, Munich Example: Sternberg Memory Scanning ■ Memory scanning: to identify if a probe item is in a memory list or not (Sternberg, 1969) 24 Zhuanghua Shi, LMU, Munich Example: Sternberg task ■ Stimulus quality ■ set size ◻ are two additive factors 25 Zhuanghua Shi, LMU, Munich What if we observe no interactions? ■ Manipulations affect independent processes ■ or the statistics is under power? 26 Example: AFM in cognitive neuroscience ■ Dehaene (1996) ◻ The organization of brain activations in number comparison: Event-related potentials and the additive-factors method. J Cogn Neurocsci 1: 47–68. ◻ investigated a simple task of number comparison: A number is present on the monitor and subject has to compare if the number is above or below five. ◻ Factors ■ Input: Arabic digits / spell numbers (4 / four) ■ Comparison: Near 5/ far from 5 ■ Response: dominant / non-dominant hand 27 Zhuanghua Shi, LMU, Munich Cont. ■ Four stages processes encoding comparison Response Checking error selection Arabic digits/ Non-dominant / Error/Correct Close/Far Spelled numbers Dominant hand trials ◻ According to AFM, a variable that affects overall reaction time by varying the time to complete one stage will be additive with the effects of factors that affect other stages. 28 Zhuanghua Shi, LMU, Munich An example of AFM EEG and fMRI studies showed these four factors processed in different regions Arabic / spelled numbers Close/far Non-dominant/Dominant hand 29 Zhuanghua Shi, LMU, Munich Does AFM make sense? ■ Partially ■ No problem with ‘pure insertion’ ◻ Manipulation of the duration of a processing stages ■ Comparison between one and the same type of tasks ◻ Criticism of Külpe does not hold 30 Zhuanghua Shi, LMU, Munich Problems with AFM ■ Basic assumptions ◻ Factors can affect certain processing stages, while leave other stages unaffected ◻ Stages should not temporally overlap ◻ Discrete serial assumption ■ Problems with the reversed inference ◻ Additive effects of two factors does not necessarily mean that two independent stages ■ Independence is sufficient, but not necessary, to lead additive effect ■ If p then q → if q then p? 31 Zhuanghua Shi, LMU, Munich Cascaded processes model ■ McClelland (1979) ◻ A series of processes cascading activation from an input level to an output level. Thus it allows a given processing level to start transmitting output (activation) before it has finished processing. time 32 Zhuanghua Shi, LMU, Munich How is response time determined in cascade model? ■ Accumulation of response unit activates in time ■ All processing stages influence this activation more or less simultaneously time 33 Zhuanghua Shi, LMU, Munich Accumulation model and diffusion model ■ Accumulation models ◻ Decision information accumulates over time ◻ When the accumulated information reach a boundary (e.g. threshold), a response is made. (Smith, Ratcliff, 2004) 34 Zhuanghua Shi, LMU, Munich Accumulation model and diffusion model ■ Those models are inspired by neural activations (Smith, Ratcliff, 2004) 35 Zhuanghua Shi, LMU, Munich Stochastic accumulation models ■ General concepts ◻ Sensory evidence of a stimulus accumulates over time ◻ Multiple competed information accrue in parallel ◻ A decision is made according accumulated evidence ■ Simple response: signal detection ■ Choice responses: choice among several possible outcomes ■ Models usually concern three major aspects: ◻ How is decision information being accumulated? ◻ When to stop? ◻ What is the basis for making a decision? 36 Zhuanghua Shi, LMU, Munich Diffusion model ■ Diffusion model is a continuous version of accumulator model. ◻ Simple Response Model for simple and Go/NoGo RT. The time-dependent information function u(t) is perturbed by white noise W(t) and accumulated. A response is emitted when the accumulated decision stage activation X(t) exceeds a criterion. (smith, 2000) 37 Zhuanghua Shi, LMU, Munich Diffusion model ◻ Two-choice Response Smith, 2000 38 Zhuanghua Shi, LMU, Munich Beyond reaction time: Speed accuracy trade-off (SAT) ■ RTs typically co-vary with error rates ■ Speed-accuracy trade-off function ◻ Fixed accuracy, measuring RTs (fair comparison across conditions) 39 Zhuanghua Shi, LMU, Munich Next week ■ Hands-on RT analyses ◻ Import RT data from a behavioral study ◻ Summarize RT data ◻ Visualize results ■ Requirement ◻ Please make sure your R and Rstudio work ◻ tidyverse package is installed 40.
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