Behavioral and Physiological Evidence Challenges the Automatic Acquisition of Evaluations

Abstract

Dual-learning theories of evaluations posit that evaluations can be automatically (i.e., efficiently, unconsciously, uncontrollably, and involuntarily) acquired. They also often assume the existence of evaluative-learning processes that are impervious to verbal information. In this article, we explain that recent research challenges both assertions for three categories of measures: explicit evaluative measures, implicit evaluative measures, and physiological measures of fear. In doing so, we also question the widespread assumption that implicit (i.e., typically behavioral and physiological) compared with explicit (i.e., self-reported) evaluative measures are indicative of the way evaluations are acquired. In the second part of the article, we discuss the practical implications of these recent findings.

Keywords

attitudes automaticity dual-process models evaluative conditioning fear conditioning physiology

An influential view pervading psychological research (e.g., in social, consumer, health, and clinical psychology) is that evaluations can be acquired through an associative/affective-learning mode that automatically registers mere stimuli co-occurrences encountered in the environment. In attitude research, associative learning is thought to allow for the unconscious, efficient, involuntary, and uncontrollable formation of evaluations and to be insensitive to the relational meaning of stimuli co-occurrences (e.g., Gawronski & Bodenhausen, 2014). Likewise, influential theories of fear learning posit that fear can be automatically acquired (i.e., uncontrollably and without consciousness, effort, or intention), resulting in nonconscious and unqualified associative representations that automatically elicit fear when activated (e.g., LeDoux & Pine, 2016; Öhman & Mineka, 2001).

This automatic-learning view implies a high susceptibility of individuals to social and environmental influences. For instance, people may fall prey to a negative political advertisement if the information it contains creates, uncontrollably or unconsciously, a negative evaluation of the targeted candidate. It also often assumes that it is difficult to change automatic evaluative responses on the basis of mere verbal information. For instance, a fear of dogs may be difficult to change using verbal instruction if the fear response reflects the automatic activation of an inaccessible or unconscious mental association between a dog and having been bitten.

These questions have been mostly investigated using conditioning procedures. In attitude research, a neutral stimulus (the conditioned stimulus, or CS; e.g., a neutral face) is paired with a positive or negative stimulus (the unconditioned stimulus, or US: e.g., a pleasant or unpleasant sound or picture). An evaluative-conditioning effect is said to occur when the evaluation of the CS changes after the CS–US pairing. In fear-conditioning research, typically aversive USs (e.g., an electric shock, a loud noise) are used, and subjective, behavioral, and physiological responses related to fear and arousal (e.g., distress ratings, avoidance responses, skin conductance responses) are measured (e.g., Lipp, 2006). Evaluative and fear-conditioning procedures involve a simple associative procedure (i.e., pairing CSs with USs) that is considered ideally suited to the investigation of associative/affective learning.

Testing the Automatic Learning of Evaluations Using Explicit, Implicit, and Physiological Measures

The automatic learning of evaluations has been tested using explicit, implicit, and physiological measures. Explicit measures are self-reported evaluations, such as good/bad judgments or direct scale ratings about an attitude object. Implicit measures are less clearly defined (see Corneille & Hütter, 2020) but generally rely on indirect behavioral tasks that reduce participants’ control and deliberation during measurement. The Implicit Association Test is considered the gold standard of implicit evaluative measures. In this task, respondents may sort faster positive stimuli alongside Category A (e.g., thin people) and negative stimuli alongside Category B (e.g., obese people). The extent to which they do so (or the opposite) indicates their implicit preference for one category over the other. Measurement outcomes of this sort are thought to reflect biased mental associations, commonly referred to as implicit biases or unconscious biases. Physiological measures record participants’ physiological responses to stimuli. Skin conductance responses and potentiation of the startle reflex are examples of such measures, and they capture a mix of arousal and evaluation (Lonsdorf et al., 2017). Whereas explicit and implicit evaluative measures are frequently used in social-psychological, social-cognition, and consumer research, physiological measures are more common in clinical and neuroscience research.

Dual-learning theories assume that less controllable and deliberate measures of evaluations (i.e., implicit and physiological measures) provide a better window into associative/affective learning (e.g., Gawronski & Bodenhausen, 2014; LeDoux & Pine, 2016). This is because these measures reduce the influence of deliberate and controlled postlearning processes on task performance.

Growing Evidence Challenges the Automatic Acquisition of Evaluations on All Categories of Measures

Research on evaluative conditioning has largely failed to obtain conclusive evidence for automatic evaluative learning (for a comprehensive review, see Corneille & Stahl, 2019). No evaluative-learning effect is observed in procedures preventing a conscious encoding of the stimuli, such as when using short-timed, parafoveal, or visually suppressed presentations. Evaluative learning is also resource demanding. It is fully disrupted when participants are engaged in a concurrent task during the stimuli presentation. Finally, research indicates that evaluative learning is sensitive to participants’ processing goals. For instance, evaluative-learning effects are largely weakened when participants are distracted from processing the affective quality of the information.

Of importance, too, evidence that may be considered supportive of automatic evaluative learning is unrelated to implicit compared with explicit measurement. The mere-exposure effect, often considered the hallmark of “preferences that need no inferences,” is typically demonstrated on self-reported evaluations (e.g., “Which of these two stimuli do you prefer?”). Likewise, preliminary evidence for uncontrollable evaluative learning (e.g., participants presumably unable not to start liking a stimulus paired with positive information) has been best observed on explicit liking measures (Hütter & Sweldens, 2018). Hence, contrary to a widespread dual-process view, findings have shown that whether an evaluation is measured on an implicit task or on a self-reported evaluation is unrelated to how it was acquired in the first place (Corneille & Hütter, 2020).

In addition to positing the automaticity of evaluative learning, dual-learning models of evaluations assume that associative knowledge captures raw associations between events, unqualified by the relational meaning of these associations. For instance, people may harbor negative associations about a pain-relieving medication because of its co-occurrence with pain (i.e., the raw contingency) despite the medication relieving the pain (i.e., the relational implication of the medication). Preliminary evidence supporting the possibility of such unqualified representations or processes, however, has again been found on explicit measures (e.g., Kukken, Hütter, & Holland, 2020), and these effects are highly sensitive to task structure when measured with implicit tasks (e.g., Bading, Stahl, & Rothermund, 2020).

As it appears, differences in performance on explicit tasks compared with implicit tasks cannot be univocally interpreted in terms of dissociations in learning modes. Postlearning processes may be responsible for these differences. For instance, a smaller amount of information is likely retrieved from memory when one completes a speeded task (e.g., an Implicit Association Test) than a nonspeeded task (e.g., an evaluative rating). Hence, divergences in outcomes on implicit tasks compared with explicit tasks may reflect divergences in retrieval despite originating in a unitary learning process that is neither unconscious nor immune to verbal information. As a matter of fact, implicit attitudes can be created in a snap on the basis of mere verbal information (e.g., De Houwer, 2009). And this is also true when it comes to changing evaluations—for instance, merely telling participants that Gandhi prevented his wife from taking a medical treatment that could have saved her life resulted in a quick reversal of scores on implicit measures (Van Dessel, Ye, & De Houwer, 2019). This runs counter to a prevalent view that sees implicit biases as the result of unconsciously and slowly acquired associations.

So far, we have discussed behavioral measures. As explained above, physiological measures may provide a more sensitive test for the existence of a distinct learning mode grounded in more affective systems. Here, too, however, recent research questions automatic learning and supports the sensitivity of physiological indicators of evaluations to verbal instructions (e.g., Mertens, Boddez, Sevenster, Engelhard, & De Houwer, 2018). For instance, a recent meta-analysis concluded in a weak fear-acquisition effect, plausibly driven by publication bias and methodological artifacts, when using subliminal stimuli (see Mertens & Engelhard, 2020). And similar to explicit and implicit evaluative measures, fear responses can also be modulated by verbal instruction (Atlas, 2019; Mertens et al., 2018). For example, conditioned physiological responses can be rapidly abolished when participants are told that no more USs will be delivered (Luck & Lipp, 2016). It is telling that these effects have also been demonstrated using the startle reflex (Luck & Lipp, 2015; Mertens & De Houwer, 2016), a basic defensive reflex evident within 20 ms to 120 ms (Blumenthal et al., 2005) that is considered an index of amygdala activity (Hamm & Weike, 2005).

Implications for Theory and Practice

Implications of these findings for psychological theories are straightforward. There is less evidence for and more evidence against the automatic learning of evaluations than one may think. There is also more evidence than is commonly assumed for the role of verbal information in the acquisition and change of evaluations. Alternative single-learning models should, therefore, be considered a more parsimonious alternative to dual-learning models. Examples of such models are propositional models (De Houwer, 2009), goal-directed models (e.g., Boddez, Moors, Mertens, & De Houwer, 2020), and retrieval-based models (e.g., Stahl & Aust, 2018). Perhaps even more important are the practical implications of these recent findings, which we now briefly discuss in the contexts of social, consumer, and health and clinical psychology.

Social psychology

An influential social-psychological view that found its way to the general public, and that is currently inspiring large-scale social interventions, is that mental associations automatically formed about social groups (termed unconscious bias or implicit bias) influence people’s judgment and behavior. Drawing on automatic evaluative learning may facilitate people’s acceptance of their prejudice. Unfortunately, this may also normalize prejudice (Eberhardt & Banks, 2019) and undermine accountability for social discrimination (Daumeyer, Onyeador, Brown, & Richeson, 2019). That is, if mental associations about social groups build up unconsciously and uncontrollably, one may consider that people are not so much responsible for holding them.

As discussed here, however, the evidence is scarce that evaluations may be acquired automatically. Likewise, it is unclear whether implicit measures assess unconscious evaluations (e.g., Gawronski, 2019). As provocative as this may seem, even assuming implicit tasks do assess unconscious and automatically acquired associations, we do not even know whether they have any behavioral impact at all. Because outcomes on implicit measures can be neither coherently nor distinctly induced, their causal role remains unestablished (for a recent discussion, see Corneille & Hütter, 2020).

Again, this conclusion runs against a popular view holding that “unconscious bias infiltrates every arena of life” and “can be just as devastating as the harm done by explicit racism, sexism or homophobia” (Eberhardt & Banks, 2019, paragraphs 5 and 4, respectively). Admittedly, unconscious bias may be understood here in terms of automatically enacted behaviors as opposed to automatically acquired and unconsciously held mental associations presumably underlying these behaviors (e.g., Gawronski, Ledgerwood & Eastwick, 2020). However, this alternative understanding is uncommon, and it would be highly problematic if the unconscious-bias construct were applied both to the cause and its consequences.

Similar conclusions apply to personality research that has theorized discrepancies in so-called implicit and explicit self-esteem. If implicit measures of the self are not diagnostic of distinct learning processes and representations (see also Schimmack, 2019), one is left to wonder how discrepancies in these measures should be interpreted as well as how and why implicit self-esteem should be changed at all.

Problematic theorization on the implicit construct is widespread in social-psychological research, and this can be consequential (for a comprehensive discussion, see Corneille & Hütter, 2020). During the COVID-19 outbreak, the Society for Personality and Social Psychology (SPSP) featured research inviting people to indulge in comfort food to feed “primitive and implicit feelings of being cared for and loved” (SPSP, 2020). This surprising recommendation (literally, eat highly caloric food to please your unconscious inner self) may contribute to another epidemic: the obesity one.

Consumer psychology

Consumers may be upset about the use of subliminal conditioning (e.g., when briefly pairing Al Gore with “rats” in a political advertisement). In all likelihood, however, subliminal conditioning is largely ineffective for changing people’s evaluations (see Corneille & Stahl, 2019). Ironically, people generally feel at ease with blatant social-influence techniques (e.g., a presidential candidate featured against the American flag) that are much more influential. This is not to say that automatic processes, including unconscious ones, play no role in consumer or social behavior. They certainly do. Yet as we have already pointed out, many of these effects may occur at a postlearning stage.

Consistent with associative/affective-learning views, companies have relied on implicit measures to assess unconscious preferences in their consumers. The company Pizza Hut, for instance, has used eye-tracking technology to assess unconscious pizza-topping preferences in consumers. There is, however, no indication that eye tracking is relevant to the study of unconsciously acquired or unconsciously held pizza-topping preferences (not to speak of whether eye tracking provides a valid measure of preferences at all). And even if it were, there is no evidence either that this measure of preference would allow for tastier experiences. It is largely assumed, when using implicit measures, that testers know more about respondents than respondents know about themselves. This, however, is a philosophically and empirically tricky assumption. And if it is incorrect, it may have ethically questionable consequences (e.g., feeding consumers with pizza topping they do not like, persuading individuals that they hold conflicting conscious and unconscious evaluations about themselves and about others).

Health and clinical psychology

That fear responding is acquired and may be changed on the basis of mere verbal instructions could be inspirational for new clinical interventions. For instance, in therapy, patients can be asked to formulate their expectations (e.g., if I walk in the park, a dog will attack me) and asked how these expectations may be tested (e.g., take a walk in the park during a busy afternoon). This approach can help identify patients’ idiosyncratic expectations and allow for more targeted exposure therapy. Interventions of this sort have been successfully developed in cognitive-behavioral therapies (Hofmann, 2008). Surprisingly enough, research on these types of interventions remains surprisingly scarce in fundamental research on fear conditioning (see Carpenter, Pinaire, & Hofmann, 2019). A broader appreciation for the role of consciously accessible representations could lead to a more widespread application of such cognitive interventions to challenge dysfunctional beliefs about contingencies in the world.

Clarification and Limitations

We want to emphasize that we fully endorse the view that automatic processes influence people’s judgments and behaviors. Clearly, for instance, people may show efficient behavioral and physiological responses when exposed to disliked stimuli. Likewise, it would be unreasonable to assume that people are constantly aware of the determinants of their perceptions, judgments, and behaviors. Instead, we are pointing to a growing body of empirical evidence challenging the view that these responses are automatically learned and are insensitive to verbal information. We also question the view that some measures (e.g., implicit and physiological measures) would best indicate an associative/affective-learning mode or qualitatively different representations. Although our analysis was mainly based on conditioning procedures (for the reasons we have explained), similar conclusions have been reached in related paradigms, such as approach-avoidance and the mere-exposure effect (for an in-depth discussion, see Corneille & Stahl, 2019).

Our analysis did not include studies with brain-imaging techniques and brain-lesioned populations. With regard to studies on brain lesions and process-specific impairments, some studies have shown that region-specific lesions (e.g., amygdala damage) result in process-specific impairments (e.g., successful acquisition of fear conditioning with skin conductance responses but no acquisition of declarative knowledge), whereas the reverse is true for damage in another region (i.e., hippocampal damage and unsuccessful fear conditioning but the successful acquisition of declarative knowledge; Bechara et al., 1995).

Such findings may be seen as providing strong evidence for dual-learning theories of evaluations. However, other researchers have pointed out that double dissociations of this sort do not necessarily need to be interpreted as evidence for modularity and independent processes (e.g., Plaut, 1995). Furthermore, conflicting findings have been reported in the literature. For instance, Coppens, Spruyt, Vandenbulcke, Van Paesschen, and Vansteenwegen (2009) reported that patients with unilateral amygdala damage can in fact acquire conditioned fear responses after acquiring explicit knowledge of CS–US contingencies.

With regard to brain-imaging studies, we believe that brain activation should not be considered a direct measure of attitude or fear. Instead, brain-imaging data reflect a neural-implementation level of analysis and can be logically consistent with different models of how evaluations are acquired, retrieved, and expressed. Additionally, it can be noted that associative mental representations are not necessarily neuroanatomically, neurochemically, or neurobiologically more plausible than nonassociative (e.g., propositional) representations (e.g., Langille & Gallistel, 2020). Given their different levels of analysis, however, psychological and neurocognitive research can fruitfully complement each other by advancing psychological and neurobiological models of how evaluations and fears are acquired and can be changed (e.g., Amodio & Ratner, 2011).

Conclusion

A growing body of research challenges the existence of an associative/affective formation of attitudes and fears when this learning mode is defined as automatic and impervious to verbal information. As discussed in this article, the acquisition of evaluations and fear is much less automatic than often assumed, and both can be modulated using verbal instruction, even when using implicit and physiological measures. Acknowledging this recent evidence more broadly is important for both theory and practice. The unconscious and resource-free learning of attitudes and fears should be questioned. In addition, the potential of verbal instructions for changing “deep-rooted” evaluations offers a promising avenue for social and clinical interventions.

Footnotes

Transparency

Action Editor: Robert L. Goldstone

Editor: Robert L. Goldstone

ORCID iD

Olivier Corneille

References

Amodio

D. M.

Ratner

K. G.

(2011). A memory systems model of implicit social cognition. Current Directions in Psychological Science, 20, 143–148.

Atlas

L. Y.

(2019). How instructions shape aversive learning: Higher order knowledge, reversal learning, and the role of the amygdala. Current Opinion in Behavioral Sciences, 26, 121–129.

Bading

Stahl

Rothermund

(2020). Why a standard IAT effect cannot provide evidence for association formation: The role of similarity construction. Cognition and Emotion, 34, 128–143. doi:10.1080/02699931.2019.1604322

Bechara

Tranel

Damasio

Adolphs

Rockland

Damasio

A. R.

(1995). Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans. Science, 269, 1115–1118.

Blumenthal

T. D.

Cuthbert

B. N.

Filion

D. L.

Hackley

Lipp

O. V.

Van Boxtel

(2005). Committee report: Guidelines for human startle eyeblink electromyographic studies. Psychophysiology, 42, 1–15.

Boddez

Moors

Mertens

De Houwer

(2020). Tackling fear: Beyond associative memory activation as the only determinant of fear responding. Neuroscience & Biobehavioral Reviews, 112, 410–419.

Carpenter

J. K.

Pinaire

Hofmann

S. G.

(2019). From extinction learning to anxiety treatment: Mind the gap. Brain Sciences, 9(7), Article 164. doi:10.3390/brainsci9070164

Coppens

Spruyt

Vandenbulcke

Van Paesschen

Vansteenwegen

(2009). Classically conditioned fear responses are preserved following unilateral temporal lobectomy in humans when concurrent US-expectancy ratings are used. Neuropsychologia, 47, 2496–2503.

Corneille

Hütter

(2020). Implicit? What do you mean? A comprehensive review of the implicitness construct in attitude research. Personality and Social Psychology Review, 24, 212–232.

10.

Corneille

Stahl

(2019). Associative attitude learning: A closer look at evidence and how it relates to attitude models. Personality and Social Psychology Review, 23, 161–189.

11.

Daumeyer

N. M.

Onyeador

I. N.

Brown

Richeson

J. A.

(2019). Consequences of attributing discrimination to implicit vs. explicit bias. Journal of Experimental Social Psychology, 84, Article 103812. doi:10.1016/j.jesp.2019.04.010

12.

De Houwer

(2009). The propositional approach to associative learning as an alternative for association formation models. Learning & Behavior, 37, 1–20.

13.

Eberhardt

J. L.

Banks

(2019, July 12). Implicit bias puts lives in jeopardy. Can mandatory training reduce the risk? Los Angeles Times. Retrieved from https://www.latimes.com/opinion/op-ed/la-oe-eberhardt-banks-implicit-bias-training-20190712-story.html

14.

Gawronski

(2019). Six lessons for a cogent science of implicit bias and its criticism. Perspectives on Psychological Science, 14, 574–595.

15.

Gawronski

Bodenhausen

G. V.

(2014). The associative-propositional evaluation model: Operating principles and operating conditions of evaluation. In Sherman

J. W.

Gawronski

Trope

(Eds.), Dual-process theories of the social mind (pp. 188–203). New York, NY: Guilford Press.

16.

Gawronski

Ledgerwood

Eastwick

P. W.

(2020). Implicit bias and antidiscrimination policy. Policy Insights from the Behavioral and Brain Sciences, 7, 99–106.

17.

Hamm

A. O.

Weike

A. I.

(2005). The neuropsychology of fear learning and fear regulation. International Journal of Psychophysiology, 57, 5–14.

18.

Hofmann

S. G.

(2008). Cognitive processes during fear acquisition and extinction in animals and humans: Implications for exposure therapy of anxiety disorders. Clinical Psychology Review, 28, 199–210.

19.

Hütter

Sweldens

(2018). Dissociating controllable and uncontrollable effects of affective stimuli on attitudes and consumption. Journal of Consumer Research, 45, 320–349.

20.

Kukken

Hütter

Holland

R. W.

(2020). Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of CS-US pairings and their meaning. Cognition and Emotion, 34, 170–187.

21.

Langille

J. J.

Gallistel

C. R.

(2020). Locating the engram: Should we look for plastic synapses or information-storing molecules? Neurobiology of Learning and Memory, 169, Article 107164. doi:10.1016/j.nlm.2020.107164

22.

LeDoux

J. E.

Pine

D. S.

(2016). Using neuroscience to help understand fear and anxiety: A two-system framework. American Journal of Psychiatry, 173, 1083–1093.

23.

Lipp

O. V.

(2006). Human fear learning: Contemporary procedures and measurement. In Craske

M. G.

Hermans

Vansteenwegen

(Eds.), Fear and learning: From basic processes to clinical implications (pp. 37–51). Washington, DC: American Psychological Association.

24.

Lonsdorf

T. B.

Menz

M. M.

Andreatta

Fullana

M. A.

Golkar

Haaker

Drexler

S. M.

(2017). Don’t fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear. Neuroscience & Biobehavioral Reviews, 77, 247–285.

25.

Luck

C. C.

Lipp

O. V.

(2015). A potential pathway to the relapse of fear? Conditioned negative stimulus evaluation (but not physiological responding) resists instructed extinction. Behaviour Research and Therapy, 66, 18–31.

26.

Luck

C. C.

Lipp

O. V.

(2016). Instructed extinction in human fear conditioning: History, recent developments, and future directions. Australian Journal of Psychology, 68, 209–227.

27.

Mertens

Boddez

Sevenster

Engelhard

I. M.

De Houwer

(2018). A review on the effects of verbal instructions in human fear conditioning: Empirical findings, theoretical considerations, and future directions. Biological Psychology, 137, 49–64.

28.

Mertens

De Houwer

(2016). Potentiation of the startle reflex is in line with contingency reversal instructions rather than the conditioning history. Biological Psychology, 113, 91–99.

29.

Mertens

Engelhard

I. M.

(2020). A systematic review and meta-analysis of the evidence for unaware fear conditioning. Neuroscience & Biobehavioral Reviews, 108, 254–266.

30.

Öhman

Mineka

(2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108, 483–522.

31.

Plaut

D. C.

(1995). Double dissociation without modularity: Evidence from connectionist neuropsychology. Journal of Clinical and Experimental Neuropsychology, 17, 291–321.

32.

Schimmack

(2019). The implicit association test: A method in search of a construct. Perspectives on Psychological Science. Advance online publication. doi:10.1177/1745691619863798

33.

Society for Personality and Social Psychology [@SPSPnews]. (2020, March 25). Preparing and eating [comfort] foods activates a primitive and implicit feeling of being cared for and loved, ow.ly/GJSK50yVtqK via @UBNewsSource [Tweet]. Twitter. Retrieved from https://twitter.com/SPSPnews/status/1242837564156936194?s=20

34.

Stahl

Aust

(2018). Evaluative conditioning as memory-based judgment. Social Psychological Bulletin, 13(3). doi:10.5964/spb.v13i3.28589

35.

Van Dessel

De Houwer

(2019). Changing deep-rooted implicit evaluation in the blink of an eye: Negative verbal information shifts automatic liking of Gandhi. Social Psychological and Personality Science, 10, 266–273.

Behavioral and Physiological Evidence Challenges the Automatic Acquisition of Evaluations

Abstract

Keywords

Testing the Automatic Learning of Evaluations Using Explicit, Implicit, and Physiological Measures

Growing Evidence Challenges the Automatic Acquisition of Evaluations on All Categories of Measures

Implications for Theory and Practice

Social psychology

Consumer psychology

Health and clinical psychology

Clarification and Limitations

Conclusion

Recommended Reading

Footnotes

Transparency

ORCID iD

References